Controlling Multicolor LED Luminaires

Public Disclosure

Ian Ashdown, P. Eng., FIES

Senior Scientist, Lighting Analysts Inc.

[Please send all comments to allthingslighting@gmail.com.]

First, a disclaimer: this is not a commercial announcement!

San Jose, CA, USA: March 31, 2016. LED Engin, Inc. announces the world’s first 7-color, high power LED to be produced on a single emitter. The compact LZ704MU00 emitter enables the design of stage or architectural lighting that produces sophisticated effects over the full color spectrum. Its RGBW die are complemented by phosphor-converted (PC) amber, cyan and violet to provide richer, wide-ranging color effects. PC amber delivers the same saturation as regular amber but with 5 times the flux at temperature, cyan fills the spectrum gap between blue and green, and violet enables black or crisp white lighting effects. Typical stage and studio applications include moving heads with zoom optics and round wash lights. In architectural lighting, the emitters enhance the performance of everything from static lights to linear wash lights…

FIG. 1 – LED Engin seven-color LED package (www.ledengin.com).

FIG. 1 – LED Engin seven-color LED package (www.ledengin.com).

The reason for this lengthy quote from LED Engin’s press release is that it succinctly introduces the background to this blog article. Multicolor LED luminaires are nothing new – the theatrical lighting industry saw them introduced nearly a decade ago, with products such as the ETC Selador Desire series of seven-color LED luminaires (FIG. 2).

FIG. 2 – ETC Selador Desire seven-color LED luminaire (www.etcconnect.com).

FIG. 2 – ETC Selador Desire seven-color LED luminaire (www.etcconnect.com).

What is new and significant about the LED Engin product announcement is that having seven different color LED dice in a single package enables manufacturers to design potentially more-compact luminaires that do not exhibit multicolor shadows at close range.

What is equally significant, however, is that there is a problem with multicolor LED luminaires, a serious problem that has been basically overlooked for the past decade. This blog article offers a potential solution.

A Matter of Control

To understand the problem, first consider color-changing LED modules with red, green, and blue (RGB) LEDs. Generating a specific color is a simple matter of choosing the appropriate ratios of the red, green, and blue LED intensities. For example, if we want to generated 4150 K white light, we might choose the intensity ratios shown in FIG. 3.

FIG. 3 – Three-color 4150K white light source spectral power distribution.

FIG. 3 – Three-color 4150 K white light source spectral power distribution.

As any theatrical lighting designer will quickly point out, however, there are two problems with RGB LED modules and luminaires. First, their color gamuts are fairly limited. In particular, they are typically unable to produce saturated blue, violet, and cyan colors (e.g., FIG. 4).

FIG. 4 – Typical RGB LED module color gamut.

FIG. 4 – Typical RGB LED module color gamut.

Second, the lack of spectral radiant flux between approximately 550 nm and 600 nm, generally perceived as yellow light, results in yellow objects illuminated by RGB light sources appearing dull and lifeless in comparison to an equivalent white light source. Given a choice, most theatrical lighting designers would probably choose a conventional quartz-halogen luminaire (with a color temperature of 3200 K) and a Rosco 202 Half CT Blue polyester color filter to raise its color temperature to approximately 4150 K. For professional stage lighting, color quality is paramount.

The disadvantage, of course, is that even with motorized color filter wheels, quartz halogen luminaires offer limited dynamic color opportunities. If the designer needs to change colors during or between scenes, multiple luminaires are generally required. This is why multicolor LED luminaires are so attractive – with six or seven colors, they provide larger color gamuts (e.g., FIG. 5), smooth transitions between colors are possible, and they do not suffer from the color rendering issues of RGB luminaires.

FIG. 5 – Six-color LED module color gamut.

FIG. 5 – Six-color LED module color gamut.

The problem is that there are now six or seven LED intensities to control if you need to generate a specified color. Unlike RGB LED modules, there are basically an infinite number of LED intensity combinations to choose from.

As an example, suppose we are given a six-color LED module with the following colors:

  • Blue (440 nm)
  • Cyan (495 nm)
  • Green (525 nm)
  • Amber (595 nm)
  • Red (630 nm)
  • White (3000K)

We need to generate 4150 K white light, for which we find through laborious trial and error with a colorimeter are the two solutions shown in Figures 6A and 6B – two very different solutions with radically different spectral power distributions (SPDs), and with relative LED intensity ratios shown in Table 1. (The luminous efficacies and quantities of LEDs per color vary; the intensities are in relation to their full power settings – see FIG. 7.)

Figure 6A – Six-color 4150K white light SPD (first example).

FIG. 6A – Six-color 4150K white light SPD (first example).

FIG. 6B – Six-color 4150K white light SPD (second example).

FIG. 6B – Six-color 4150K white light SPD (second example).

Color Example 1 Example 2
Red 1.00 1.00
Amber 1.00 0.18
Green 0.85 0.51
Cyan 0.18 0.00
Blue 0.20 0.10
White 1.00 1.00
Intensity 2712 1471
CRI Ra 62 27

Table 1 – Multicolor LED relative intensities comparison

The important point here is that while both solutions may look the same visually, the second example generates only 54 percent as much luminous flux (i.e., lumens) as the first example. (This makes sense, of course, as the human eye is more sensitive to amber light than it is to red light.)

Another point is that the first example not only generates more luminous flux, but it also has much better color rendering properties due to the greater amount of amber light. (A CRI Ra value of 62 versus 27.)

This is the problem with multicolor LED luminaires – how do you choose the “best” solution from an infinite number of relative intensity ratios for a given color? It depends, of course, on whether “best” means maximum luminous intensity or color rendering properties, such as the CIE Ra and R9 color rendering metrics for white light. (There are no established color rendering metrics for chromatic illumination, but they conceivably could be developed.)

The situation is exacerbated if you need to transition between two colors while maintaining constant intensity. Multicolor LED luminaires are clearly capable of doing this, but you need to determine the best solution for all the intermediate colors, preferably fifty or more times a second during the transition to prevent visible flickering.

Manufacturers of multicolor LED luminaires have overlooked this problem from the beginning. The ETC Selador Desire products, for example, present the user with one DMX512 intensity control channel per color, plus a master intensity control channel for all colors. It is a control solution that offers no solution.

To be fair, the manufacturers cannot be faulted for taking this approach. In mathematical terms, the choice of relative intensity ratios is an “overdetermined” problem, where the number of variables (e.g., the number of LED colors) exceeds the number of outputs (e.g., the three broad color bands that the human eye is sensitive to).

This is not to say that the problem is unsolvable – it is. If a million monkeys with typewriters will eventually reproduce all the works of Shakespeare, we can take the same approach (albeit with fewer monkeys) to determining the best solution for a given color.

The “we” in this situation are the luminaire manufacturers and designers. It is basically a product design problem that does not involve the end user. What follows is a reasonably detailed outline of the design of an effective user interface for multicolor LED luminaires.

Solving the Problem

We begin with the acknowledgement that we need to know something about the optical, electrical, and thermal properties of the different color LEDs in order to predict how much light they will generate under various operating conditions. Specifically, we need to know for each color LED:

  • Spectral power distribution
  • Luminous efficacy (lumens per watt)
  • Maximum current (at full intensity)
  • Forward voltage
  • Dynamic resistance
  • LED package thermal resistance
  • LED substrate temperature

We also need to specify the desired color, or “target chromaticity,” expressed in CIE 1931 xy chromaticity coordinates. (Note that the spectral power distribution is dependent on the LED junction temperature.)

Controlling Multicolor LED Luminaires - FIG. 7

FIG. 7 – SSL Designer system parameters.

A proof-of-concept program called SSL Designer™ was developed for this approach, with a screenshot of the above system parameters shown in Figure 7.

Given this information, it is possible to calculate the absolute spectral power distribution for each color LED. These SPDs can then be scaled by the relative intensity ratios (shown in FIG. 8 as channel pulse width modulation duty cycles), following which they are summed and the luminous intensity calculated. (With a bit more work, color rendering metrics such as CIE Ra and R9 for white light applications can also be calculated.)

FIG. 8 – Ten best relative intensity ratio solutions for specified color.

There are ten relative intensity ratio solutions shown in FIG. 8, but these are the best (in the sense of maximum luminous intensity) of tens of thousands of randomly selected solutions that have been evaluated. Of course, it is highly unlikely that a random choice of relative intensity ratios will generate the desired target chromaticity, but some solutions will be closer than others. An evolutionary computation approach called a genetic algorithm is therefore used to intelligently and quickly select and refine those solutions that satisfy the target chromaticity criterion (and optionally one or more color rendering metric criteria), and to order them accordingly as the best solutions. Again in mathematical terms, the genetic algorithm “converges” to the best solutions.

Not shown in Figure 8 is the ability to calculate the best solutions for hundreds of different target chromaticities, which are then stored in memory. When the user later specifies a desired color, the program finds the best solution for the nearest matching color, then uses this to “seed” the initial randomly selected solutions. The genetic algorithm can then converge much more quickly to the best solution.

An RGB Solution

In practice, the user needs to specify both the desired color and intensity. This can be done using CIE xyY values, where xy represent the CIE 1931 chromaticity coordinates (shown as the horizontal and vertical axes in FIG. 5), and Y represents the intensity (expressed as a percentage of maximum intensity). This approach is, however, not as intuitive as the RGB settings most lighting designers are familiar with.

Fortunately, there is a simple solution. Referring to the six-color LED module color gamut shown in FIG. 5, we can completely enclose the six-sided color gamut in a triangle defined by the xy chromaticities of virtual red, green, and blue LEDs (FIG. 9). It does not matter that the red LED chromaticity lies outside the horseshoe-shaped spectral locus and so represents a physically impossible color. What does matter is that the RGB values can be used to represent any color (and intensity) within the six-color LED module color gamut.

FIG. 9 – Virtual RGB LED module color gamut.

FIG. 9 – Virtual RGB LED module color gamut.

The mathematics needed to transform the RGB values to CIE xyY values are straightforward to calculate – a task that is performed by the luminaire’s microcontroller. From the user’s perspective, all that is needed are three DMX512 channels to control the luminaire, which will appear to behave as an RGB LED luminaire with enhanced color rendering capabilities.

A decade ago, this approach would have been impractical due to the computing power requirements. Today, however, it is both practical and economical to embed a microcontroller in the luminaire that will perform the necessary calculations in real time (i.e., milliseconds).

Public Service

The SSL Designer software developed to demonstrate this novel approach is a proof-of-concept program that simulates a multicolor LED luminaire on a desktop computer. Following standard industry practice, it would make sense to apply for a patent on the invention and license the technology to theatrical luminaire manufacturers.

Most countries have differing intellectual property laws, but they all agree that if an invention is publicly disclosed prior to filing a patent application, it is ineligible for patent protection. This blog article therefore represents an intentional and deliberate public disclosure of the invention. Specifically, this invention has been released into the public domain. No patent application has been filed, and so lighting manufacturers and designers are both free and encouraged to implement the above royalty-free approach in their multicolor LED luminaire products.

In the spirit of published patents, there are numerous implementation details that have been glossed over in this article. However, the details that have been presented are “sufficient for one skilled in the art” to implement the invention “without undue experimentation” (35 U.S.C. 112(a), www.uspto.gov). Anyone curious about the implementation details of SSL Designer is welcome to contact the author with questions.

Multicolor LED luminaires have been commercially available for nearly ten years. It is time for the luminaire manufacturers and designers to make them truly useful.

 

Sports Lighting Regulations

Play Ball and Play Fair!

Ian Ashdown, P. Eng., FIES

Senior Scientist, Lighting Analysts Inc.

[ Please send all comments to allthingslighting@gmail.com ]

This blog article has a somewhat frustrating history. About a year ago, I was asked to volunteer my time to write a primer of light and color as it relates to sports lighting regulations. I was told the name of the organization I was volunteering my time for, but I did not pay much attention – it seemed like a good cause.

I should have perhaps paid more attention before agreeing to volunteer – the Green Sports Alliance is not the poorest of socially responsible organizations.

Upon completing the primer, I was told that it was far too technical for its intended audience. Hopefully, you as my readers will disagree.

Sports Lighting Requirements

Sports lighting has specific requirements that may not be familiar to many lighting designers. The Illuminating Engineering Society publishes detailed recommendations related to sports lighting (IES 2009, 2010a, 2015), while various professional sports organizations have their own specific requirements (for example, FIFA 2007, FIH 2011, NCAA 2010a and 2010b, and Lewis and Brill 2013).

Illuminance

In sports lighting, there are two forms of illuminance measurements that are of interest: horizontal illuminance and vertical illuminance.

Horizontal illuminance is typically measured on a horizontally oriented imaginary surface one meter (~3 feet) above the field surface. Multiple measurements are usually measured (or calculated during the lighting design phase) on a grid. The National Football League, for example (Lewis and Brill 2013), specifies a grid spacing of 5 meters (~16 feet).

Vertical illuminance is measured on a vertically oriented imaginary surface. Unlike horizontal illuminance, both the position and orientation of the vertical surface must be specified. To understand why, consider a vertical surface illuminated by a single light source (FIG. 1).

FIG. 5 – Illuminance of surface depends on angle of illumination

FIG. 1 – Illuminance of surface depends on angle of illumination

As the angle of illumination decreases, the lumens per square meter decrease as well, until at grazing angles the surface is barely illuminated at. This can clearly be seen with a sphere illuminated by a single light source (FIG. 2).

FIG. 6 – Sphere illuminated by a single distant light source

FIG. 2 – Sphere illuminated by a single distant light source

In practice, there will be multiple luminaires illuminating the field, each of which will contribute to the illumination of a vertical surface – such as a player’s face. It is therefore important to ensure that the vertical illuminance is within minimum and maximum limits so that the players’ faces and team numbers can always be seen.

With this in mind, the “falloff” in illuminance with distance from a single luminaire must also be kept in mind. As shown in FIG. 3, a light source S illuminates two imaginary surfaces, the first one at distance d from the light source, and the second at twice the distance. Both surfaces receive the same amount of light (lumens) from S, but the area of the second surface is four times that of the first. Consequently, its illuminance (lumens per square meter) is only one-quarter that of the first surface.

FIG. 7 – Inverse Square Law

FIG. 3 – Inverse Square Law

Generalizing this to any distance, it is easy to see that the illuminance from a single luminaire will decrease, or “fall off,” according to the square of the distance. This is the basis of the inverse square law used by lighting designers.

Finally, “TV illuminance” is occasionally used for television broadcasting purposes (IES 2015). It is the illuminance measured at a position on the playing field when the illuminance meter is aimed directly at a specified camera position.In practice, of course, multiple luminaires are used to (more or less) evenly illuminate a playing field.

Uniformity

Uniformity of illumination is important for sports. It enables both the players and the spectators to easily follow the action, and it provides consistent lighting for the television cameras and photographers. Sports field lighting for internationally televised events must meet exacting standards, while more leeway is generally allowed for other events.

There are three measures (or more properly metrics) used to specify the desired uniformity of horizontal and vertical illuminance on the playing field. The simplest metric is the maximum-to-minimum ratio, commonly referred to as the uniformity ratio. Using NFL requirements as an example, horizontal illuminance is designated Eh, and so the uniformity ratio is expressed as Ehmax/Ehmin. Using a measurement grid for the playing field with 5-meter spacing, this ratio for all measurement values must be 1.4:1 or less.

Again using the NFL requirements, vertical illuminance is designated Ev, and the uniformity ratio Evmax/Evmin must also be 1.4:1 or less.

The NFL requirements go further in specifying that: 1) the ratio of the average horizontal illuminance Ehavg to average vertical illuminance Evavg as seen from camera #1 (that is, with each vertical surface facing the camera) must be between 1.0 and 2.0, with a target value of 1.5; 2) the ratio of vertical illuminances at any point on the field between the four imaginary vertical surfaces facing the four sides of the field shall be between 0.6 and 0.9; and 3) the average vertical illuminance Evavg facing towards camera #1 shall not be less that Evavg for the other three orthogonal (that is, right-angle) orientations. In other words, it can get complicated.

The second uniformity metric is the coefficient of variation, designated CV. Without delving into the mathematics of this statistical value, it can be likened to the point spread in sports betting. (If you must know the details, the equation is:

Sports Lighting Primer - EQN. 1

with details left to the interested reader – see [IES 2009, 2015].) It is basically a measure of how “smooth” the lighting distribution is across the playing field.

The third metric is the uniformity gradient, designated UG. It is defined as the ratio between illuminance values between adjacent measuring points on a square grid. Whereas CV describes the average non-uniformity for the entire field, UG describes the maximum nom-uniformity. It is particularly important in sports with fast-moving balls and the like, as changes in illuminance can make it more difficult to judge their speed.

Visual Glare

Visual glare occurs when the luminance of the luminaires within the observer’s field of view (either a player or spectator) is sufficiently greater than the average luminance to which the observer’s eye have adapted. It may cause visual discomfort (in response to which we tend to squint), or it may impair the vision of objects and details (such as past-moving balls and the like).

As a psychophysiological phenomenon, glare is both literally and figuratively “in the eye of the beholder.” All lighting researchers can do is present subjects in a laboratory with a lighting setup and ask them to rate the glare on a subjective scale. While it cannot be directly measured in the field, a glare rating metric, designated GR, can be calculated (typically at the design phase) in accordance with CIE 112-1994, Glare Evaluation System for Use with Outdoor Sports and Area Lighting (CIE 1994).

Central to these calculations are five parameters:

  1. The luminances of the luminaires as seen by the observer;
  2. The angular extent of the luminaires in the observer’s field of view;
  3. The position of the luminaires in the observer’s field of view relative to the line of sight;
  4. The number of luminaires in the observer’s field of view; and
  5. The average luminance of the observer’s entire field of view.

It is important to note that the GR metric depends on where the observer is positioned relative to the luminaires, and the line of sight direction. Consequently, any GR requirements must specify these parameters. The NFL requirements, for example, require that GR be less than 40 for all main cameras (Lewis and Brill 2013).

Color

Many sports organizations specify the allowable correlated color temperature, designated CCT, for sports field lighting. For example:

Organization CCT
FIFA ≥ 4000K
FIH > 4000K
NCAA > 3600K
NFL 5600K (alternatively 5000K to 7000K)

where the symbol ‘K’ represents kelvins (where one kelvin is equal to one degree Celsius).

To put these numbers into context, quartz halogen and warm white LED lamps typically have CCTs of approximately 3000K, metal halide lamps typically have CCTs of 4000K, and daylight LED lamps typically have CCTs of 5000K.

FIG. 8 – Light source correlated color temperatures

FIG. 4 – Light source correlated color temperatures

Our eyes adapt quite well to light sources with different CCTs, ranging from 2700K for 100-watt incandescent lamps to 10000K for the blue sky. Even though the light itself may look colored (FIG. 8), objects seen under these light sources appear to have approximately the same colors, with whites looking white.

The same is not true with television and digital cameras, however, which must be adjusted (color-balanced) to display the colors we expect to see. This is why it is important that all the luminaires in a sports lighting installation have approximately the same CCT. If they do not, the television cameras will display annoying color shifts as they pan across the field.

Many sports organizations also specify the minimum allowable color rendering index, designated CRI, for sports lighting. For example:

Organization CRI Ra
FIFA ≥ 65
FIH > 65
NCAA > 65
NFL ≥ 90

where the CRI Ra metric is a measure of the average color shift of various colors viewed under the light source when compared to viewing the colors under an incandescent or daylight source with the same CCT. A detailed explanation of color rendering is beyond the scope of this introductory chapter, but the topic is fully explained in CIE 13.3-1995, Method of Measuring and Specifying Colour Rendering Properties of Light Sources (CIE 1995).

In general, a minimum CRI of 65 is merely adequate, and is representative of what could be achieved with high-wattage metal halide lamps. With today’s solid-state lighting, a minimum CRI of 80 or greater is common, and CRIs of 90 and above are preferred.

It must also be emphasized that Ra metric represents the average color shift. Solid-state lighting products may also specify a CRI R9 metric, which represents the color shift specifically for red colors. A high R9 value is desirable, especially where team outfits feature saturated red colors.

In terms of television broadcast cameras, a more appropriate color rendering metric is the Television Lighting Consistency Index TLCI-2012 (EBU 2014). Like the CRI Ra metric, this is a measure of the average color shift of various colors viewed under the light source; the difference is that the observer is a color television camera rather than a human.

Spectrally Enhanced Lighting

There is some interest in the topic of spectrally enhanced lighting for sports field applications. For some visually demanding tasks, the recommended illuminance values can be reduced through the use of light sources with high blue content. A full discussion is presented in IES TM-24-13, An Optional Method for Adjusting the Recommended Illuminance for Visually Demanding Tasks Within IES Illuminance Categories P through Y Based on Light Source Spectrum (IES 2013).

It could be argued TM-24-13 can be applied to sports lighting, as it defines (p. 3) “visually demanding tasks” as “… tasks that are based on the ability to discern visual detail to ensure speed and/or accuracy.” In this situation, “visual detail” could be interpreted as a fast-moving ball or hockey puck.

Furthering the argument, TM-24-13 applies to illuminance categories P through Y, which the IES Lighting Handbook, 10th Edition (IES 2010a) defines in Table 4.1, Recommended Illuminance Targets, as interior and exterior lighting installations where the illuminance targets are in excess of 300 lux. Categories P (average 300 lux) through W (average 3000 lux) specifically include “some sports situations” (without defining them).

There are several problems, however. The first is that most sports organizations specify minimum horizontal and vertical illuminances without taking spectrally enhanced lighting into account. Any sports lighting that reduced these values based on TM-24-13 would not be in compliance with these specifications.

The second problem is that the recommended illuminance targets for sports lighting involving television broadcasting are based on the minimum illuminance requirements of the television cameras. These are of course independent of the human visual system, and so the reduced illuminance values calculated in accordance with TM-24-13 do not apply.

The third problem is the most crucial: the Illuminating Engineering Society issued a lengthy position statement (included in TM-24-13) that unequivocally states (in boldface type), “TM-24 should not be used for the development of energy policy or energy efficiency programs purposes for any lighting applications, as this goes against current IES recommendations.”

Light Pollution

Outdoor lighting illuminates not only objects on the ground, but the overhead sky as well. The International Dark-Sky Association reminds us that this unintentional light pollution threatens professional and amateur astronomy, disrupts nocturnal ecosystems, affects circadian rhythms of both humans and animals, and wastes over two billion dollars of electrical energy per year in the United States alone.

It might seem obvious that sports field lighting is a major contributor to light pollution, but this is true only in a local sense. According to a US Department of Energy study (DOE 2010), stadium lighting contributes a maximum of 6 percent (compared to 48 percent for roadway lighting and 34 percent for parking lot lighting) on a national scale. (This further assumes that the stadium lighting is always on at night.)

Outdoor Lighting Percent Lumens
Roadway 48
Parking 34
Building exteriors 10
Stadiums 6
Billboards 1
Traffic signals 1

On a local scale, however, light pollution from stadiums and sports fields can be a concern, particularly for surrounding residential neighborhoods. This includes not only light that is reflected from the ground and illuminates the sky overhead, but also light trespass and glare from improperly shielded luminaires.

IES TM-15-11, Luminaire Classification System for Outdoor Luminaires (IES 2011a) and the Joint IDA-IES Model Lighting Ordinance (MLO) with User’s Guide (IES 2011b) provide detailed information on designing outdoor lighting systems that minimize unintended light pollution.

References

CIE. 1994. CIE 112-1994, Glare Evaluation System for Use within Outdoor Sports and Area Lighting. Vienna, Austria: Commission International de l’Eclairage.

CIE. 1995. CIE 13.3-1995, Method of Measuring and Specifying Colour Rendering Properties of Light Sources. Vienna, Austria: Commission International de l’Eclairage.

DOE. 2010. 2010 U.S. Lighting Market Characterization, U.S. Department of Energy Building Technologies Program.

EBU. 2014. Tech 3355, Method for the Assessment of the Colorimetric Properties of Luminaires: The Television Lighting Consistency Index (TLCI-2012) and the Television Luminaire Matching Factor (TLMF-2013. Geneva, Switzerland: European Broadcast Union.

FIFA. 2007. Football Stadiums: Technical Recommendations and Requirements, 4th Edition. Zurich, Switzerland: Fédération Internationale de Football Association.

FIH. 2011. Guide to the Artificial Lighting of Hockey Pitches, 6th Edition. Lausanne, Switzerland: International Hockey Federation.

IES. 2009. IES RP-6-09, Recommended Practice for Sports and Recreational Area Lighting. New York, NY: Illuminating Engineering Society.

IES. 2010a. IES Lighting Handbook, 10th Edition. New York, NY: Illuminating Engineering Society.

IES. 2011a. IES TM-15-11, Luminaire Classification System for Outdoor Luminaires. New York, NY: Illuminating Engineering Society.

IES. 2011b. Joint IDA-IES Model Lighting Ordinance (MLO) with User’s Guide. New York, NY: Illuminating Engineering Society.

IES. 2013. IES TM-24-13, An Optional Method for Adjusting the Recommended Illuminance for Visually Demanding Tasks Within IES Illuminance Categories P through Y Based on Light Source Spectrum. New York, NY: Illuminating Engineering Society.

IES. 2015. IES RP-6-15, Sports and Recreational Area Lighting. New York, NY: Illuminating Engineering Society.

Lewis, D., and S. Brill. 2013. Broadcast Lighting: NFL Stadium Lighting. The Design Lighting Group Inc.

NCAA. 2010a. NCAA Basketball Championships Best Lighting Practices. National Collegiate Athletic Association.

NCAA. 2010b. NCAA Best Lighting Practices. National Collegiate Athletic Association.

Appendix A

A.1.         What is Light?

A primer on sports lighting must answer the obvious question: what is light? The Oxford English Dictionary, the pre-eminent dictionary of the English language, describes light rather loosely as, “the natural agent that stimulates the sense of sight.” More technically, light is electromagnetic radiation.

What we see as visible light is only a tiny fraction of the electromagnetic spectrum, extending from very low-frequency radio waves through microwaves, infrared, visible light, and ultraviolet to x-rays and ultra-energetic gamma rays. Our eyes respond to visible light; detecting the rest of the electromagnetic spectrum requires an arsenal of scientific instruments ranging from radio receivers to scintillation counters.

Our interest however is solely in visible light – it is what we see when we look at the world.

A.2.         Quantifying Light

We can think of light as massless subatomic particles called photons. They are emitted by light sources such as metal halide lamps and light-emitting diodes (LEDs), and travel through space until they encounter physical objects. They may then be reflected, refracted, scattered, or absorbed. Some of those photons will intersect our eyes, enabling us to see (FIG. A1).

FIG. 1 - Photons emitted by light source S

FIG. A1 – Photons emitted by light source S

The number of photons emitted by a typical light source per second is unimaginably large (think of the number ten followed by 30 to 40 zeroes), and so we express this quantity in lumens, where one lumen is approximately the number of photons emitted per second by a wax candle[1]. A typical light source will emit tens of thousands of lumens.

A.3.         Measuring Light

Photons emitted by light sources travel outwards in random directions. When these photons encounter a surface, they illuminate the surface (FIG. A2). From the perspective of the surface, it does not matter where the light comes from; it can be a single light source, multiple sources, or even the entire sky.

FIG. 2 - Light illuminating a surface A

FIG. A2 – Light illuminating a surface A

We can use a device called a photometer to measure the number of photons arriving at (incident upon) the surface per second. Of course, this number will depend on the surface area of the photometer’s sensor, and so we express the illuminance of the surface in terms of lumens per square meter, or lux. (Lumens per square foot are referred to as a foot-candle – please do not ask why.)

Note that the illuminated surface can be real or imaginary. We can, for example, imagine a “surface” positioned one meter above a physical surface, such as a playing field. The light will of course pass through this imaginary surface, but we can still measure its illuminance with a photometer (which is also called an “illuminance meter” by lighting designers or an “incident light meter” by photographers).

Illuminance is one of the two fundamental units of measurement for lighting designers. While we can measure illuminance with a photometer, we cannot see illuminance. For this, we need another fundamental unit of measurement.

Imagine looking at a computer display. The display consists of an array of a million or so pixels. We see each pixel because some of the photons it is emitting intersect our eye. We can therefore think of these photons as a ray of light, where all of the photons are traveling in the same direction. The more photons per second there are in the ray, the brighter the pixel appears to our eye. This is the luminance of the ray, sometimes referred to as “photometric brightness.”

FIG. 3 – Light ray from a computer display pixel as seen by observer

FIG. A3 – Light ray from a computer display pixel as seen by observer

Textbooks on lighting design typically define luminance as the property of a real or imaginary surface, which leads to the very confusing unit of measurement, “lumens per square meter per steradian,” or lm/m2-sr. It is much easier, however (and just as accurate), to think of luminance as a property of the light ray itself. (The light we see coming from the blue sky, for example, has luminance, but it does not have a real or imaginary surface.)

We can easily measure the luminance of a ray by using a telescope to focus a narrow beam of light onto a photometer sensor (FIG. A4). This is a luminance meter; it measures what we see.

FIG. 4 – Luminance meter

FIG. A4 – Luminance meter

[1] A century ago, national standards for measuring light relied on precisely specified wax candles made from spermaceti (whale oil).

Filtered LEDs and Light Pollution

An Astronomical Problem

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send all comments to allthingslighting@gmail.com ]

UPDATE 2016/03/03 – Revised Figure 6.

The problem is astronomical – the blue light emitted by LED roadway luminaires has been shown to contribute to light pollution, especially when cool white LEDs are used. Blue light is preferentially scattered by air molecules, and so the higher the correlated color temperature (CCT), the greater the light pollution problem becomes. It is for this reason that the International Dark Sky Association requires a maximum CCT of 3000K for its Fixture Seal of Approval outdoor lighting certification program.

Sometimes, however, even warm white LED street lighting is not enough. For cities that are in close proximity to astronomical observatories, such as Flagstaff, AZ and the nearby US Naval Observatory Flagstaff Station, any amount of blue light is bad news.

Until recently, low-pressure sodium (LPS) street lighting has been the preferred choice. LPS luminaires are ideal light sources in that their monochromatic radiation (590 nm) is easily filtered out for astronomical observations. However, the large physical size of the lamps makes it difficult to control the luminous intensity distributions. For this and other reasons, municipalities are looking at “filtered LED” (FLED) street lighting as an option.

The reasoning is simple: combine a white light LED with a yellow filter and you can eliminate the blue peak that plagues astronomical observations. Figure 1, for example, shows the spectral power distributions (SPDs) of 2700K and 5000K white light LEDs with their characteristic blue peaks, while Figure 2 shows the SPDs of the same LEDs combined with yellow filters. The blue peaks have not been alleviated; they have been completely eliminated.

FIG. 1 – White light LED spectral power distributions.

FIG. 1 – White light LED spectral power distributions.

FIG. 2 – Filtered white light LED spectral power distributions.

FIG. 2 – Filtered white light LED spectral power distributions.

So, FLEDs are good for astronomical purposes, but what about lighting design?

Luminous Efficacy

At first glance, you might assume that filtering out the blue light will significantly reduce luminous efficacy. Perhaps surprisingly, this is not the case. Based on the SPDs shown in Figure 1 and Figure 2, the loss of luminous efficacy is less than ten percent for both warm white and cool white LEDs,

As a practical example, the SPDs shown in the above figures were taken from the photometric laboratory test reports of two commercial products from CW Energy Solutions. The salient data for these products are:

  WW-CW8-450 CW-CW7-350
Luminaire efficacy (lumens / watt) 106 122
CIE 1931 chromaticity x = 0.5223 y = 0.4072 x = 0.4719 y = 0.5176
CRI Ra 55.1 38.8
CRI R9 -56.5 -81.9

Table 1 – CW Energy Solution filtered LED roadway luminaire product specifications

To be clear, this is not an endorsement of these commercial products. This information is being provided for educational purposes only.

Chromaticity

We can plot the chromaticity xy coordinates shown in Table 1 on a CIE 1931 chromaticity diagram (FIG. 3), but what do the actual colors look like? Unfortunately, most such diagrams reproduce the actual colors of the CIE 1931 color space very poorly. (Worse, it is impossible to display most saturated colors using the RGB color gamut of video displays.)

FIG. 3 – CIE 1931 xy chromaticity diagram. (Source: Wikipedia)

FIG. 3 – CIE 1931 xy chromaticity diagram. (Source: Wikipedia)

To answer this question, we can convert the xy chromaticity coordinates into CIE XYZ tristimulus values, and from there, assuming a video display with a 6500K white point, into RGB values for display. The chromaticity coordinates listed in Table 1 then appears much like these colors on a calibrated video display:

FIG. 4A - WW-CW8-450 light source color   

FIG. 4A – WW-CW8-450 light source color

FIG. 4B - CW-CW7-350 light source color

FIG. 4B – CW-CW7-350 light source color

These are clearly not the sort of “white light” luminaires we would normally use for retail or residential lighting … but wait, there is more to this than meets the eye.

Color Rendering Capabilities

Looking again at Table 1, we see that the CIE General Colour Rendering Index Ra values for these products are frankly abysmal – 55 for the filtered 2700k (warm white) LEDs and 38 for the filtered 5000K (cool white) LEDs. The CIE Special Colour Rendering Index R9 values are even worse, with values of -56.5 and -81.9 respectively.

(As a reminder, a CRI value of 100 means that there is no perceptible color shifts with the eight CRI test color samples viewed under the test and reference lamps. It is quite possible, however, to have negative CRI values for the Special CRI values. Low-pressure sodium lamps, for example, have a CRI Ra values of -17.)

It is also interesting, and indeed useful, for lighting designers to understand why these perceived color shifts occur. Johann von Kries, a physiological psychologist who investigated chromatic adaptation in human color vision, noted in 1905 that we tend to see white objects as “white” regardless of the color temperature of the dominant light source. He postulated that our visual system adjusts the “gain” of the signals received from the red-. green- and blue-sensitive cones[1] in our retinae that are responsible for our color vision (von Kries 1905).

von Kries’ theory was formalized by the polymath Herbert Ives in 1912 as the von Kries transform, a mathematical operation that forms the basis of the calculation method for the CIE Colour Rendering Indices. While this psychophysiological “gain adjustment” works well (but not perfectly) in enabling us to perceive white surfaces under light sources with different CCTs (e.g., from 2800K incandescent lighting to 8000K overcast daylight), it tends to distort our perception of colored surfaces. (By way of analogy, think of adjusting the bass and treble controls on an audio system – particular settings may work for some music, but be unsuitable for other music.)

The beauty of the von Kries transform, however, is that it enables us to mathematically predict the color shifts due to a given test illuminant. Given a set of test colors – the Gretag-Macbeth ColorChecker™ is an obvious choice – we can predict and display what these colors will look like (e.g., Figure 5).

FIG. 5 – Filtered LED color shifts from 6500K daylight.

FIG. 5 – Filtered LED color shifts from 6500K daylight.

True – these color shifts are starkly evident, and would be completely unacceptable for retail and residential lighting. However, we need to remember that the topic of discussion is roadway lighting, specifically where municipalities are considering replacing high-pressure sodium (HPS) lamps with LED modules. With this, we need to look at the SPD of a typical HPS lamp (Figure 6).

FIG. 6 – 2100K high-pressure sodium lamp spectral power distribution.

FIG. 6 – 2100K high-pressure sodium lamp spectral power distribution.

There are three points of interest here. First, the correlated color temperature (CCT) rating of 2100K is nominal – the CIE 1931 xy chromaticity coordinates of this lamp are not particularly close to the blackbody locus, and so by definition the CCT rating is technically meaningless (CIE 2004).

Second, HPS lamps have a CRI Ra value of 24 – worse than filtered LEDs.

Third – and this is the key point – most municipalities have been using HPS street lighting ever since it replaced the mostly unlamented mercury vapor street lighting in the 1980s. After thirty years of use, most residents have known nothing but their orange-yellow glow.

Putting aside the roadway luminaire manufacturers’ arguments that most people prefer “white” light, it is instructive to visualize the color rendering capabilities of filtered LEDs versus HPS lamps (FIG. 7).

FIG. 7 – Filtered LED color shifts from 2100K daylight.

FIG. 7 – Filtered LED color shifts from 2100K daylight.

What is there to say, other than “oh …”? The point is that color rendering under filtered LED illumination is no worse, and arguably somewhat better, than under today’s prevalent HPS roadway illumination. It is not the color of the roadway luminaires that is important; it is the perceived colors of the objects that they illuminate.

The deciding factor for most municipalities will likely be whether residents like, dislike, or are simply neutral regarding the color rendering capabilities of filtered LED roadway lighting. In many cases, a test installation will likely be needed. Before then, however, it is important not to dismiss filtered LEDs simply because they are not “white light.” Furthermore, it is equally important not to compare them with white light LEDs solely on the basis of their CCT, CRI, or chromaticity values.

Conclusions

The purpose of this article is not to promote filtered LEDs as an alternative to low-pressure sodium lamps, or even as a preferred solution to light pollution problems. Rather, it is an attempt to take the various metrics describing the color rendering qualities of filtered LEDs and visualize them.

How lighting designers, roadway luminaire manufacturers, municipal engineers, and community activists choose to use this information is beyond the scope of this article. All that needs to be said is, “a picture is worth a thousand words.”

Acknowledgements

Thanks to Bob Adams of CW Energy Solutions and Tim Robinson of Esterline Corporation for providing the product technical information used in this article.

Thanks to George Livadaras for reporting an error in Figure 6.

References

CIE. 1995. CIE 13.3-1995, Method of Measuring and Specifying Colour Rendering Properties of Light Sources. Vienna, Austria: CIE Central Bureau.

CIE. 2004. CIE 15:2004, Colorimetry, Thirds Edition. Vienna, Austria: CIE Central Bureau.

von Kries, J. 1905. Die Gesichtsempfindungen. Handbuch der Physiologie der Menschen.

[1] These are technically referred to as long-, medium-, and short-wavelength, or LMS, retinal cones.

The Kruithof Curve

A Pleasing Solution

Ian Ashdown, FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to allthingslighting@gmail.com ]

Related Posts

Kruithof Reconsidered

UPDATE 16/04/09 – This metastudy:

Fotois, S. 2106. “A Revised Kruithof Graph Based on Empirical Data,” Leukos. (Published online 08 April 2016, DOI 10.1080/15502724.2016.1159137.)

critically examined 29 studies in which the Kruithof curve was investigated. The author concluded that “… these [studies] do not support Kruithof. For pleasant conditions, these data suggest only avoiding low illuminances and do not favor any CCT.”

After 75 years of misconception and misuse, may this finally mark the end of the Kruithof Curve. If a lighting designer has a personal preference for warm or cool colors, fine – but please, do not try to justify it with scientific mumbo-jumbo.


[UPDATE 15/01/20 – Added Bartleson (1960) reference.]

Lighting designers today will surely recognize the Kruithof curve, in which the color temperature of the light source is related to a range of illuminances that we find “pleasing.” In its modern form, the Kruithof curve has become supposedly irrefutable evidence that the correlated color temperature (CCT) of LED-based lighting should not exceed 4000K for indoor applications.Kruithof - FIG 1FIG. 1 – Kruithof curve, modern version (source: Wikipedia).

In the process, luminaire manufacturers are being lambasted for promoting products with CCTs of 5000K and higher. Worse, some government agencies and non-profit organizations are adopting CCT limits that are presumably based on the Kruithof curve. The DesignLights Consortium, for example, stipulates that luminaires on its Qualified Products List must have CCTs of 5000K or less for most indoor applications.

Unfortunately, the modern version of the Kruithof curve is different from what A. A. Kruithof published 75 years ago. The upper and lower curves are approximately the same, but their interpretation is different from what Kruithof intended. In fact, the Kruithof curve appears to have been basically misinterpreted for the past three-quarters of a century.

The Kruithof curve itself was thoroughly debunked a quarter-century ago with three exhaustive studies involving up to 400 participants (as opposed to two people in Kruithof’s study, including himself). The Kruithof curve was somewhat belatedly removed from the IES Lighting Handbook five years ago (IES 2010).

This article is however not so much about the validity of the Kruithof curve as it is about a careful re-examination of his 1941 paper. While Kruithof has been rightly criticized over the years for not providing experimental details, he wrote enough for us to infer how he arrived at his findings. When you realize that he was working with early prototypes of the first fluorescent lamps, it is in itself an interesting story.

In the Beginning

The year was 1941. Captain America made his first appearance in a comic book, Europe was being torn asunder by World War II … and Philips Research was quietly developing its own fluorescent lamp technology in Eindhoven, the Netherlands. As part of this effort, Philips Technical Review published a paper by A. A. Kruithof titled, “Tubular Luminescence Lamps for General Illumination” (Kruithof 1941).

Kruithof’s paper was primarily about fluorescent lamp technology, which had been commercially released by General Electric in 1938. His experimental meter-long T12 “luminescence lamp” (FIG .2) was designed have a luminous flux output of 1000 lumens.

Kruithof - FIG 2FIG. 2 – Philips “luminescence” lamp (from Kruithof 1941).

Halophosphate-based phosphors were not invented until the following year (McKeag and Ranby 1942), and so these “white” fluorescent lamps used a combination of cadmium borate, willemite (zinc orthosilicate), and magnesium tungstate, which respectively fluoresce red, green, and blue when excited by the ultraviolet radiation emitted by the mercury-argon gas fill.

By themselves, the phosphors resulted in maximum luminous efficacies of approximately 70 lumens per watt for willemite and 35 lumens per watt for cadmium borate and magnesium tungstate. By combining the phosphors in various proportions, it was possible to generate white light with CCTs ranging from 2650K to 10000K.

Kruithof fabricated fluorescent lamps with CCTs of approximately 4200K and 5800K, plus a third lamp type that was so far off the blackbody curve as to be considered colored rather than white. Comparing these to the extant incandescent lamp technology with its typical 15 lumens per watt luminous efficacy, Kruithof was well justified in writing, “These properties give reason to expect that luminescent lamps will be widely used in the future.”

Kruithof also performed an extensive analysis of the color rendering capabilities of his lamps by observing the color shifts of 313 color cards (as opposed to the eight colors used for the CIE General Colour Rendering Index Ra). Using the phosphor and visible mercury line spectra published in his paper, it is possible to estimate CRI values of his lamps as:

Lamp CCT Ra R9
4200K 36 -110
5800K 54 -60

As for the original form of the Kruithof curve (FIG. 3), the paper includes a description of what the author referred to decades later as a “pilot study” of lamp CCT versus illuminance level. To fully understand this study, it is necessary to quote Kruithof (1941) extensively, beginning with:

“In the first place at a given level of illumination it is found that the colour temperature must lie within certain limits if the effect of the illumination is to be pleasing. Roughly, it may be said that a low or a high colour temperature corresponds to a low or a high level of illumination, respectively. We have investigated this relation experimentally somewhat more closely by introducing in a room a variable number of electric lamps whose current (i.e., the temperature of the filaments) could be varied.”

Kruithof - FIG. 3FIG. 3 – Kruithof curve, original version (from Kruithof 1941).

With vague phrases like “pleasing” illumination, “a room,” and a “variable number of electric lights,” we can at best only infer the experimental conditions that were used to develop this curve.

Kruithof continues:

“Below the lowest curve the illumination is ‘dim’ (at low colour temperature) or ‘cold’ (at high colour temperature). Above the highest curve the unnatural colour reproduction was unpleasant.”

There are two items of immediate interest here:

  1. Kruithof used the adjectives “dim” and “cold” rather than the modern interpretation of “appears bluish.”
  2. Kruithof used the adjectives “unnatural” and “unpleasant” rather than “appears reddish.”

It could be argued that the modern interpretation is intuitively valid, but that is not the point. Kruithof measured a specific psychometric parameter that he termed “pleasing” illumination. Recasting the results to support a different hypothesis effectively invalidates the experiment.

In terms of the illumination referenced in FIG. 1 appearing reddish or blush, it must be noted here that no academic studies published to date support this hypothesis. The modern form of the Kruithof curve appears to be an interpretation with no scientific evidence to support it.

Continuing:

“These obviously vague limits within which the illumination is considered ‘pleasing’ could in our experiments be determined at least with an accuracy of 20 or 30 percent.”

This is a blazing red flag that something may be seriously wrong with this study – how can you measure something as subjective as “pleasing” with an accuracy of 20 to 30 percent in terms of illuminance? What Kruithof said – without providing any evidence – is that we can apparently tell the difference between say 100 lux and 125 lux of illumination. This is not a side-by-side comparison of two illuminated surfaces, but by simply walking into a room and deciding whether or not the colors are “unnatural.”

Much has been written in the following years that Kruithof did not provide any significant details of his experimental apparatus or protocols, and so it is difficult to accept the Kruithof curve as being valid. However, the rest of his paper is reasonably detailed and informative, indicating that Kruithof was a careful researcher. He must therefore have had some reason for claiming an accuracy of 20 to 30 percent.

Continuing with Kruithof, the extensive caption he wrote for FIG. 3 reads in part:

“The left-hand part of the limiting curves, up to a colour temperature of 2850 ºK, is recorded by allowing electric lamps with variable (decreased) current to burn in a room, and varying the number of lamps. The illumination intensity on a table 80 cm high was here measured. In the right-hand part the lowest level which does not give the impression of coldness was determined by experiments with daylight itself and with the daylight luminescence lamps to be described below.”

This is all frustratingly vague, but there is a key point: “… varying the number of lamps.”

Continuing:

“The shape of the upper curve has been extrapolated in this region with the help of the fact that in direct sunlight (colour temperature 5000 ºK) even with the highest illumination intensities occurring (104 or 105 lux) the colour rendering is never found ‘unnatural’.”

This is a crucial quote in that Kruithof decided that direct sunlight was not – and by definition could not – be “unnatural.” This brings us back to the question of the upper curve for color temperatures below 2850K (FIG. 3), where dimmed incandescent lamps were used.

Looking deeper into the question of “unnatural color reproduction,” Kruithof stated that he used a selection of color cards from the Ostwald Color Atlas, a contemporary color classification scheme of the Munsell color system we use today (FIG. 4). With respect to this, he wrote:

“While the colour rendering can be judged by comparison when luminescence is used in combination with other light and the designation of the colour impression obtained and the saturation of the colour obtained must agree, when only luminescence lamps are used no comparison is possible. In judging the colour rendering therefore in this case one must have recourse to ‘colour memory’ which is chiefly confined only to the designation of colours.”

Kruithof - FIG. 4FIG. 4 – Ostwald color atlas (source: Wikipedia).

This is even more puzzling in that our ability to recall colors is mediocre at best. In general, we tend to remember colors as being more saturated than they really were (Bartleson 1960). It therefore makes even less sense that an accuracy (or more properly repeatability) of 20 to 30 percent could be perceived.

It does make sense, however, if Kruithof switched lamps on and off to vary the illuminance while maintaining constant color temperature. This would be a form of flicker photometry. We are mostly insensitive to absolute illuminances, but we are highly sensitive to changes in illuminance. Switching illumination levels would reveal even subtle changes in the perceived chromaticities of the color cards.

… which brings us to the first of two color appearance effects, the Bezold-Brücke hue shift effect. This effect was first reported in the 1870s, but it was not studied extensively until the 1930s (Purdy 1931). Even then, the study was published in the American Journal of Psychology. A lamp research engineer like Kruithof could be excused for not being aware of the paper and its implications.

The Bezold-Brücke effect results in perceived color hues changing with changes in luminance. As shown in FIG. 5, the wavelength shifts required to maintain constant perceived hue for monochromatic colors can be huge, particularly for red and cyan, with a ten-fold increase in luminance. The effect on Kruithof’s color cards would have been generally less noticeable, but may have been still evident with, for example, saturated red colors.

Kruithof - FIG. 5FIG. 5 – Bezold-Brücke effect for monochromatic light (from Wyszecki & Stiles 1982, Fig. 2(5.9))

The second color appearance effect would not have been known to Kruithof because it was not reported until the 1950s. The Hunt effect (Hunt 1950, 1952, 1953) results in the perceived chroma (i.e., colorfulness) of illuminated objects increasing with increased illuminance. This effect is important enough to have been built into the CIECAM02 color appearance model that is widely used for color management for displays, printers, and other imaging devices.

For a given CCT, Kruithof presumably began with a low illumination level and then increased the illuminance until color appearance of the color cards more or less matched his color memory of them. The changes in perceived color would presumably be due to both the Bezold-Brücke and Hunt effects.

As however he continued increasing the illuminance, the continuation of these effects would result in the colors no longer matching his color memory. This would result in, to use Kruithof’s own words, “unnatural color reproduction.”

If Kruithof had reduced the daylight illuminance using, for example, neutral density filters, he would likely have observed the same behavior. However, even if he had done so, the time taken to move the filters into position would likely have masked the color differences. He also would have had the conundrum of having to call natural daylight “unnatural” and “unpleasant.”

In Kruithof’s defense, he may have been one of the first researchers to observe the Bezold-Brücke and Hunt effects using white light illumination with constant CCT. Certainly color shifts with changes in CCT were well-known at the time and modeled by the von Kries chromatic adaptation model. Color shifts with constant CCT were, however, a different matter.

Kruithof was certainly aware of the color shifts that his fluorescent lamps produced, as shown by FIG. 6 and FIG. 7, where the circled areas labelled ‘2’ represent “clearly appreciable” color shifts.

Kruithof - FIG. 6FIG.6 – Color shifts between daylight and 5800K fluorescent lamp (from Kruithof 1941).

Kruithof - FIG. 7.FIG. 7– Color shifts between electric light and 4200K fluorescent lamp (from Kruithof 1941).

Interestingly, Kruithof wrote:

“Most colours are somewhat less saturated in luminescence light than in daylight.”

which is exactly what you would expect from the Hunt effect. Nevertheless, Kruithof had little choice but to describe the 5800K lamp as generating “pleasing” illumination above some threshold.

It must be emphasized, however, that this is only a hypothesis – it would need a carefully designed large-scale experiment to determine whether in fact the Bezold-Brücke and/or Hunt effects can satisfactorily explain Kruithof’s results. Even then, it will be impossible to know with any certainty because Kruithof described his experiments so frugally. On the other hand, they at least offer a plausible explanation of his claim to 20 to 30 percent repeatability.

Time Marches On

If Kruithof considered his work to be a mere “pilot study,” the follow-up studies have been anything but. Bodman (1967) for example performed studies wherein he varied the illuminance in a conference room illuminated by fluorescent lamps. Remarkably, more than 400 subjects took part in these studies. Like Kruithof, he found that people had a preferred illuminance range over which 90 percent of the subjects found the lighting to be “good.” However, As can be seen from FIG. 8, the preferences appear to be influenced more by the lamp spectral power distributions than by their CCTs. At the time that these studies were conducted, “deluxe” fluorescent lamps had CRI values of 90 or so, but warm white fluorescent lamps with their halophosphate phosphors had CRI values as low as 50.

Kruithof - FIG. 8FIG. 8 – Preferred illuminance range versus fluorescent lamp type (from Bodman 1961)

What are interesting are the terms that Bodman’s subjects used to describe the lighting (FIG. 9). The warm white fluorescent lighting (CCT < 3000K) was described as “excessive” and “artificial” (terms which may have influenced by the low CRI values) at high illuminances, but for white (CCT » 4000K) and daylight (CCT > 6000K), the terms used were “pleasant” and “lively.” This is consistent with what you might expect from an increase in colorfulness as provided by the Hunt effect.

Average Illuminance (lux) Color of light
Warm white White Daylight
< 700 Not unpleasant Dim Cool
700 – 3000 Pleasant Pleasant Neutral
> 3000 Excessive, artificial Pleasant, lively Pleasant

FIG. 9 – Subjective impressions of illumination levels (Bodman 1961).

Boyce and Cuttle (1990) performed similar experiments using fluorescent lamps with CCTs ranging from 2700K to 6300K, and with average illuminances ranging from 30 to 600 lux, to illuminate a small office space. All of the fluorescent lamps had CRI values ranging from 82 to 85.

Doing a detailed statistical analysis of 410 questionnaires completed by 15 subjects who spent 20 minutes becoming visually adapted to the room, the authors found that the lamp CCT had no statistically significant influence on the subjective assessments. Instead, the major factor in both color discrimination tests and subjective assessments was illuminance.

Interestingly, none of the subjects who were unfamiliar with lighting design used the terms “warm” or “cool” to describe the lighting.

Davis and Ginthner (1990) also performed similar experiments with 40 test subjects. Using 2750K and 5000K fluorescents lamps with CRI values of 90 and illuminance levels of 270, 600, and 1350 lux, they confirmed the findings of Boyce and Cuttle (1990) that the subjective ratings of preference were influenced by illuminance only. They also found that low light levels were rated as less colorful than high light levels for the same CCT, which again suggests the Hunt effect.

Finally, Viénot et al. (2008) performed experiments designed to investigate the validity of the Kruithof curve using LED modules rather than fluorescent lamps. To ensure high CRI with variable color temperature, they used LED clusters with independently-controlled blue, cyan, green, amber, orange, red, cool white, and warm white LEDs. These provided CRI values from 91 to 96 over an illuminance range of 150, 300, and 600 lux and a CCT range of 2700K, 4000K, and 6500K.

Unlike the previous studies, however,the  Viénot et al. (2008) experimental setup was not a room but a light booth measuring some 41 x 35 x 38 cm (16 x 14 x 15 inches) that had an 80-degree field of view with a dark surround. It is debatable whether the results of the 20 subjects can be applied to offices spaces, but the authors concluded that:

“In one sense, we have validated Kruithof’s statement that high CCT at low illuminance is unpleasant. Nevertheless, we cannot conclude that low CCT should be confined to low illuminance to arouse pleasant sensations.”

and:

“When the colour rendering index is very high and the light spectrum is under control, there is no indication that high colour temperature is judged more pleasant than low colour temperature at higher illuminance levels.”

Summary

There have been many more studies related to the Kruithof curve, including Cockram et al. (1970), Denk et al. (2014), Dikel et al. (2014), Fotois et al. (2013), Hu et al. (2006), Ishi and Kakitsuba (2003), Juslén (2006), Küller et al. (2006), Logadóttir and Christoffersen (2008), Mills et al. (2007), Naoyuki and Tomimatsu (2005), Navvab (2001), Park et al. (2010), Pinto et al. 2008), Weintraub (2000), and Zhai (2014).

None of these, however, are as focused or comprehensive as those of Bodman (1967), Boyce and Cuttle (1990), and Davis and Ginthner (1990). The common conclusion of these authors is that while dim lighting at any CCT is seen as unpleasant, there is no observational evidence in support of Kruithof’s upper curve (FIG. 3).

What this discussion has shown, however, is that Kruithof may not have meant “unpleasant” in the sense of poor lighting quality, but rather in the sense of optimal color reproduction.

It must remembered that Kruithof was working with incandescent lamps with CCTs varying from 1800K to 2850K, 4000K and 5800K fluorescent lamps that probably had CRI values of less than 60, and “natural” daylight with unknown CCT. He likely would have never seen chromaticity shifts with changes in illuminance at constant CCT prior to his experiments, and probably (and quite reasonably) saw them as unnatural and hence unpleasant. This despite the fact that he observed exactly this when comparing his 5800K lamp with daylight, and explicitly commented on the fact.

Conclusion

The conclusion is straightforward, and indeed was established more than a quarter-century ago with three major studies: the Kruithof curve is essentially meaningless. There is no upper boundary to “pleasant” illumination at any CCT, and the best that can be said about the lower boundary is the obvious: dim lighting can be unpleasant, regardless of the CCT.

At the same time, however, Kruithof deserves credit for having been the first to investigate the topic. His failure to describe his experiments in more detail is regrettable, but perhaps understandable. Done as a pilot study, the brief discussion in his paper is basically a progress report with preliminary findings.

It has basically been through our continued misunderstanding of his term “pleasing” that the Kruithof curve continues to persist in lighting design practice. If Kruithof were able to comment on this today, he would likely have only two words to say (in Dutch):

“Stop daarmee!” (English translation: “Stop that!”)

References

Bartleson, C. J. 1960. “Memory Colors of Familiar Objects,” Journal of the Optical Society of America 50(1):73-77.

Bodman, H. W. 1967. “Quality of Interior Lighting Based on Luminance,” Transactions of the Illuminating Engineering Society of Great Britain 32(1):22.

Boyce, P. R., and C. Cuttle. 1990. “Effect of Correlated Colour Temperature on the Perception of Interiors and Colour Discrimination,” Lighting Research and Technology 22(1):19-36.

Cockram, A. H., J. B. Collins, and F. J. Langdon. 1970. “A Study of User Preferences for Fluorescent Lamp Colours for Daytime and Night-Time Lighting,” Lighting Research & Technology 2(4):249-256.

Davis, R. G., and D. N. Ginthner. 1990. “Correlated Color Temperature, Illuminance Level, and the Kruithof Curve,” Journal of the Illuminating Engineering Society 19(1):27-38.

Denk, E., P. Jimenez, and B. Schulz. 2014. “The Impact of Light Source Technology and Colour Temperature on the Well-being, Mental State and Concentration of Shop Assistants,” Lighting Research & Technology (in press).

Dikel, E. E., G. J. Burns, J. A. Veitch, S. Mancini, and G. R. Newsham. 2014. “Preferred Chromaticity of Color-Tunable LED Lighting,” Leukos 10(2):101­-115.

Fotois, S., S. Atli, C. Cheal, K. Houser, and A. Logadóttir. 2013. “Lamp Spectrum and Spatial Brightness at Photopic Levels: A Basis for Developing a Metric,” Lighting Research and Technology 0:1-23.

Hu, X., K. W. Houser, and D. K. Tiller. 2006. “Higher Colour Temperature Lamps May Not Appear Brighter,” Leukos 3(1):69-81.

Hunt, R. W. G. 1950. “The Effects of Daylight and Tungsten Light-Adaptation on Color Perception,” Journal of the Optical Society of America 40(6):362-371.

Hunt, R. W. G. 1952. “Light and Dark Adaptation and the Perception of Color,” Journal of the Optical Society of America 42(3):190-199.

Hunt, R. W. G. 1953. “The Perception of Color in 1º Fields for Different States of Adaptation,” Journal of the Optical Society of America 43(6):479-484.

IES. 2010. IES Lighting Handbook, Tenth Edition. New York, NY: Illuminating Engineering Society.

Ishi, M., and N. Kakitsuba. 2003. “Preferred Color Temperatures at 200 lx during Exposure to Cool or Warm Environments for Middle-Aged Female Subjects,” Journal of the Human-Environmental System 6(2):93-100.

Juslén, H. 2006. Influence of the Colour Temperature of Preferred Lighting Level in an Industrial Work Area Devoid of Daylight,” Ingineria Illuminatului 18(8):25-36.

Kruithof, A. A. 1941. “Tubular Luminescence Lamps for General Illumination,” Philips Technical Review Vol. VI, No. 3, pp. 65-73.

Küller, R., S. Ballal, T. Laike, B. Mikellidesa, and G. Tonello. 2006. “The Impact of Light and Colour on Psychological Mood: A Cross-Cultural Study of Indoor Work Environments,” Ergonomics 49(14):1496-1507.

Logadóttir, A., and J. Christoffersen. 2008. “Individual Dynamic Lighting Control in a Daylit Space,” Proc. Eleventh International Conference on Indoor Air Quality and Climate (Indoor Air 2008). Technical University of Denmark.

McKeag, A. H., and P. W. Ranby. 1942. Great Britain Patent 578,192. Improvements in Luminescent Materials.

Mills, P. R., S. C. Tomkins, and L. J. M. Schlangen. 2007. “The Effect of High Correlated Colour Temperature Office Lighting on Employee Wellbeing and Work Performance,” Journal of Circadian Rhythms 5(2):1-9.

Naoyuki, O., and N. Tomimatsu. 2005. “Preference on Illuminance and Colour Temperature of Interior for Various Behavior Settings,” Proc. 2005 Annual Conference of the Illuminating Engineering Institute of Japan (in Japanese).

Navvab, M. 2001. “A Comparison of Visual Performance under High and Low Colour Temperature Fluorescent Lamps,” Journal of the Illuminating Engineering Society 30(2):170-175.

Park, B.-C., J.H. Chang, Y.-S. Kim, J.-W. Jeong, and A.-S. Choi. 2010. “A Study of the Subjective Response for Corrected Colour Temperature Conditions of a Specific Space,” Indoor and Built Environment 19:623-637.

Pinto, D. P., J. M. M. Linhares, and S. M. C. Nascimento. 2008. “Correlated Color Temperature Preferred by Observers for Illumination of Artistic Paintings,” Journal of the Optical Society of America 25(3):623-630.

Purdy, D. M. 1931. “Spectral Hue as a Function of Intensity,” American Journal of Psychology 43:541-559.

Wyszecki, G., and W. Stiles. 1982. Color Science: Concepts and Methods, Quantitative Data and Formulae, Second Edition. New York, NY: John Wiley & Sons.

Viénot, F., M.-L. Durand, and E. Mahler. 2009. “Kruithof’s Rule Revisited Using LED Illumination,” Journal of Modern Optics 56(13):1433-1446.

Weintraub, S. 2000. “The Color of White: Is there a ‘Preferred’ Color Temperature for the Exhibition of Works of Art?”, Western Association for Art Conservation Newsletter 21(3).

Zhai, Q.-Y., M.-R. Luo, and X.-Y. Liu. 2014. “The Impact of Illuminance and Colour Temperature on Viewing Fine Art Paintings under LED Lighting,” Lighting Research & Technology 2014 (in press).

 

In the Blood

Lighting Design for Medical Diagnosis

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to allthingslighting@gmail.com. ]

We most often think of lighting design in terms of lumens, color temperature, and CRI, but there are occasional situations where a deeper analysis is required. One such situation is as close as your doctor’s office: the examination room.

An examination room is typically windowless and illuminated only by linear fluorescent lamps. In examining the patient’s skin for anything from bruises to lesions, the doctor relies on experience to assess skin color. Anything that influences this perceived color should be a concern, for it could potentially lead to a misdiagnosis.

As lighting designers, our usual criterion for selecting light sources is the CIE General Colour Rendering Index (CRI) metric (CIE 1995). Anyone who remembers the first generation of white light LED products will recognize that CIE Special Colour Rendering Index R9 is also important, as it determines the red content needed for acceptable skin tones.

This however may not be enough for applications involving medical diagnosis. It is an interesting exercise to ask what determines our skin color and whether different illuminants might lead to unexpected changes in perceived color.

Skin Color

Skin color shows large variations across continental populations, but it is mostly due to the concentration of two biopolymers: eumelanin and pheomelanin (e.g., Parra 2007). Eumelanin is dark brown to black in color, while pheomelanin is yellow to reddish brown. Our skin produces melanin in response to exposure to ultraviolet radiation, causing the skin to visibly tan. (Melanin is a highly effective natural sunscreen.)

By itself, melanin is not particularly interesting from a lighting design perspective. As shown in FIG. 1, it has a smooth spectral absorption spectrum that increases monotonically with decreasing wavelength. Changes in the spectral power distribution of the light source will have little effect on perceived color as long as the light sources have the same correlated color temperature (CCT) and similar CRI values.

Melanin

FIG. 1 – Melanin absorption spectrum (Kollias [1995]).

A secondary but still important determinant of skin color is the hemoglobin in our blood. It is responsible for example for the reddish color of sunburnt skin, as well as the inflammation that accompanies many skin infections. Its presence becomes more noticeable in fair-skinned individuals.

There are two types of hemoglobin in our blood – oxygenated (designated HbO2) and deoxygenated (designated Hb). Unlike melanin, they have rather complex spectral absorption spectra, as shown in in FIG. 2.

Hemoglobin Spectra

FIG. 2 – Hemoglobin absorption spectra (Prahl [1999]).

The contributions of both melanin and hemoglobin are evident in FIG. 3, which shows the variation in spectral reflectance of the inner upper arms (chosen to avoid suntan issues) of subjects from several different continental populations. Those with dark skin have smooth spectral reflectance distributions characteristic of melanin, while those with fair skin have complex spectral reflectance distributions due to the contribution of hemoglobin.

Skin reflectanceFIG. 3 – Skin spectral reflectance (Parra [2007]).

Hypothesis

What is interesting about FIG. 3 is that Caucasian skin exhibits a pronounced dip in reflectance at 430 nm, presumably due to the absorption spectrum of both oxygenated and deoxygenated hemoglobin. This corresponds almost exactly with the narrowband emission of blue light from linear fluorescent lamps, which peaks at 435 nm as shown in FIGs. 4A – 4C.

Halophosphate fluorescent lamp spectra (CIE 2004)

FIG. 4A – Halophosphate fluorescent lamp spectra (CIE 2004).

Broadband fluorescent lamp spectra (CIE 2004)

FIG. 4B – Broadband fluorescent lamp spectra (CIE 2004).

Triphosphor fluorescent lamp spectra (CIE 2004)

FIG. 4C – Triphosphor fluorescent lamp spectra (CIE 2004).

By comparison, FIG. 5 shows the spectral power distribution of a typical white light phosphor-coated LED (in this case a Philips Luxeon K LED array). The phosphor pump LED has a peak wavelength of 450 nm.

PhilipsLumileds_LUXEON_K_chart

FIG. 5 – White light phosphor-coated LED spectra (Philips Lumileds 2014).

The hypothesis is this: even if an LED-based replacement lamp for a linear fluorescent lamp has the same CCT and similar CRI, the LED peak wavelength is not centered on the absorption peak of hemoglobin. As shown in FIG. 3, Caucasian skin reflectance at 450 nm can be some 25 percent greater than at 430 nm. Certainly increasing the amount of blue light from an RGB luminaire by 25 percent changes the color of the emitted light. The question is whether this will change the perceived skin color for medical diagnosis.

The issue is complicated by the finite width of the emitted blue light in the blue region of the spectrum. The LED has a full width half maximum (FWHM) value of 35 nm, while the fluorescent lamps appear to have FWHM values of less than 10nm. (The CIE lamp spectra are tabulated in units of 5 nm.)

The best way to test this hypothesis then is to calculate the theoretical perceived skin color (technically its chromaticity) using the lamp spectra and the worst-case skin (Caucasian) spectral reflectance distribution.

Calculation Method

The calculation method is quite simple. We have the fluorescent lamp spectra tabulated at 5 nm, courtesy of Table T.6, “Relative spectral power distributions of illuminants representing typical fluorescent lamps,” from CIE 15:2004, Colorimetry (CIE 2004), over the range of 400 to 700 nm. To ensure comparable CCTs, we choose CIE F6 (4150 K), CIE F9 (4150K), and CIE F11 (4000K).

Philips does not provide tabulated spectral power distribution data for their Luxeon products, but it is easy enough to digitize the data using for example the freeware program Plot Digitizer (http://plotdigitizer.sourceforge.net). Again, the 4000K product is selected.

Finally, the skin reflectance data can be digitized from the PDF file of the paper by Parra (2007).

With this, the CIE 1931 tristimulus coordinates X, Y, Z for each light source can be calculated as the sums:

Tristimulus equations

where s is the skin reflectance at the designated wavelength, E is the relative light source intensity, and x-bar, y-bar, and z-bar are the CIE color matching function values (from CIE 2004). From these, we can calculate the CIE 1931 xy chromaticity values as:

Chromaticity equations

If we choose the CIE F11 triphosphor lamp as our reference illuminant, this gives us:

Light Source CCT x y dx dy JND
F6 4150K 0.395 0.398 -0.009 +0.011 2
F9 4150K 0.393 0.383 -0.011 -0.004 1
F11 4000K 0.404 0.387 +0.000 0.000 0
Luxeon K 4000K 0.398 0.388 -0.006 +0.001 0

where JND represent one MacAdam ellipse (MacAdam 1942), or a “just noticeable difference” in perceived color under laboratory conditions from that of the reference lamp.

The halophosphate F6 fluorescent lamp results provide a useful sanity check. These 1970s-era lamps had very low red content and consequently CRIs in the low 70s. They tended to lend greenish color casts to Caucasian skin, which is shown by the two-MacAdam ellipse color shift towards green.

For modern triphosphor fluorescent lamps, however, we can see that there should be no perceptible color shift for normal skin color if they are replaced with white phosphor-coated LED products. In other words, the hypothesis is disproven.

Disclaimer

This analysis necessarily considers normal Caucasian skin color only, based on the published results of Parra [2007]. It does not consider the abnormal skin colors due to for example hypoxia (low oxygenated blood levels). Such conditions are most often diagnosed using quantitative techniques, such as pulse oximetry (which measures the relative difference in transmittance of Hb and HbO2 at visible and infrared wavelengths.) Research is also being conducted into whether “spectrally-tuned” light sources might increase the contrast of various skin conditions when viewed in visible light – see for example Litorja et al. (2007, 2009, and 2010), amd Murai et al. (2012).

More important, this analysis has not been experimentally verified or peer-reviewed, and must not be taken as medical advice. If the question is asked, all that can be said is that, “theoretical analysis indicates that there should be no difference in the use of fluorescent versus LED-based replacement lamps for medical diagnosis of skin conditions.” If necessary, this analysis should be experimentally confirmed by a qualified physician who will be using the facility being designed.

Conclusion

It is interesting that this analysis has produced what is effectively a negative result. As such, it is unlikely that it would be accepted for publication by a peer-reviewed journal. Regardless, the result (subject to the disclaimer) itself is informative for lighting designers.

Taking a broader view, this analysis highlights the need for an industry standard for the electronic transfer of spectral data, much as IES LM-63-02 and EULUMDAT enable the electronic transfer of photometric data. It is frustrating and error-prone to have to manually digitize spectral data from scans of printed documents and screen captures of PDF files.

Fortunately, this situation is about to change. As of this writing, the IES Board has approved the publication of IES TM-27-14, IES Standard Format for the Electronic Transfer of Spectral Data. This document is scheduled for ANSI, and hopefully IEC, approval as an international standard. Once lamp and LED module manufacturers adopt this data format for their product specifications, studies such as this will become considerably easier to perform.

Acknowledgements

The topic of this blog posting arose from discussions with fellow members of the Human Centric Lighting Committee.

Update 2014/06/25

One of the joys of self-publishing through blogs is that you can post new information as it becomes available.

Cyanosis is the appearance of a blue or purplish discoloration of the skin due to the tissues near the skin surface having low oxygen saturation. I was aware of this medication condition while writing this article, but decided against discussing it on the advice of my family doctor, who had not seen a case of it in 25 years of family and sports medicine practice. If it is seen at all, it is likely in the emergency room, where pulse oximeters are usually available.

Regardless, there is a paper on the topic – “Lighting for Clinical Observation of Cyanosis” – (Midolo and Sergeyeva [2007]) and a government standard that lamps for hospital lighting in Australia and New Zealand must meet (AS/NZS [1997]) – the Cyanosis Observation Index (COI). There is also a now-outdated article on why older-style triphosphor lamps manufactured in the 1990s were not suitable for hospital lighting (LightLab [1997]).

Related to this is an interesting Philips white paper, “The Role of Lighting in Promoting Well-Being and Recovery within Healthcare” (Schlangen 2010). This 32-page publication on human-centric lighting for healthcare provides over 100 useful references to the literature.

References

AS/NZS. 1997. AS/NZS 1680.2.5:1997, Interior Lighting, Part 2.5: Hospital and Medical Tasks. Standards Australia.

CIE. 1995. CIE 13.3-1995, Method of Measuring and Specifying Colour Rendering Properties of Light Sources. Vienna, Austria: Commission Internationale de l’Eclairage.

CIE. 2004. CIE 15:2004, Colorimetry. Vienna, Austria: Commission Internationale de l’Eclairage.

Kollias, N. 1995. “The Spectroscopy of Human Melanin Pigmentation,” in L. Zeise et al., Eds., Melanin: Its Role in Human Photoprotection. Overland Park, KS: Valdenmar Publishing Company.

LightLab. 1997. Why Tri-Phosphor Lamps are Unsuitable for Hospital Lighting, Lab Notes Issue 4. Clontarf, Australia: LightLab International.

Litorja, M., S. W. Brown, M. E. Nadal, D. Allen, and A. Gorbach. 2007. “Development of Surgical Lighting for Enhanced Color Contrast,” Proc. Medical Imaging 2007, SPIE Vol. 61510K.

Litorja, M., S. W. Brown, C. Lin, and Y. Ohno. 2009. “Illuminants as Visualization Tool for Clinical Diagnostics and Surgery,” Proc. Advanced Biomedical and Clinical Diagnostic Systems VII, SPIE Vol. 71691B.

Litorja, M., and B. Ecker. 2010. “Use of a Spectrally Tunable Source to Explore Improvement in Chromatic Contrast for Illumination of Tissues,” Proc. Emerging Digital Micromirror Device Based Systems and Applications II, SPIE Vol. 759607.

MacAdam, D. L. 1942. “Visual Sensitivities to Color Differences in Daylight,” J. Optical Society of America 32(5):247-274.

Midolo, N. A., and L. Sergeyeva. 2007. “Lighting for Clinical Observation of Cyanosis,” The Australian Hospital Engineer 30(2):38-46.

Murai, K., H. Kawahiri, and H. Haneishi. 2012. “Improving Color Appearance of Organ in Surgery by Optimally Designed LED Illuminant,” Proc. World Congress on Medical Physics and Biomedical Engineering,” IFMBE Proceedings 39:1010-1013.

Parra, E. J. 2007. “Human Pigmentation Variation: Evolution, Genetic Basis, and Implications for Public Health,” Yearbook of Physical Anthropology 50:85-105.

Philips. 2010. White Paper: The Role of Lighting in Promoting Well-Being and Recovery within Healthcare. Koninklijke Philips Electronics N.V.

Philips Lumileds. 2014. Luxeon K Datasheet DS102. Philips Lumileds Lighting Company.

Prahl, S. 1999. “Optical Absorption of Hemoglobin,” http://omlc.ogi.edu/spectra/hemoglobin.

Zijlstra, W. G., A. Buursma and O. W. van Assendelft. 2000. “Visible and Near Infrared Absorption Spectra of Human and Animal Haemoglobin,” Utrecht, Netherlands: VSP Publishing.

Thoughts on Color Rendering

Ian Ashdown, P. Eng., FIES

Chief Scientist, Lighting Analysts Inc.

[ Please send comments to allthingslighting@gmail.com. ]

UPDATE 14/10/06 – LightingEurope, the “Voice of the Lighting Industry,” has just published their LightingEurope Position Paper on Color Quality. To summarize:

1. LightingEurope supports to continue the use of the existing Color Fidelity metric CRI including eight reference colors.

2. LightingEurope supports to keep legal minimum requirements on CRI on the current level as defined in the EU Eco-design Regulation.


Lamp with a CRI of 90 or above, good. Lamp with a CRI of less than 80, bad. Is there anything else that lighting designers need to know about the Color Rendering Index (CRI) metric?

To be brutally honest … no. Despite all that has been written on the topic over the past decade, the importance of CRI to everyday lighting design today is minimal at best.

What is (or perhaps was) important is the history of color rendering metrics and the influence they had on fluorescent and LED lamp design. We can mostly ignore the issues of color rendering today precisely because of the CRI metric.

If you want to make senior citizens shudder, ask them what it was like to work in an office in the 1950s with fluorescent lighting. The linear fluorescent lamps of the time used calcium halophosphate phosphors that had a well-deserved reputation for making skin tones appear a sickly gray-green. Brightly-colored fabrics also looked disagreeably different from being viewed under daylight or incandescent lighting conditions.

Figure 1

FIG. 1 – Halophosphate lamp spectrum

Fluorescent lamp manufacturers could address this problem by varying the phosphor composition of their lamps. However, phosphors are expensive, and it is always difficult to convince consumers to pay more for products on the promise that they will “look better.” What was needed was an industry-standard metric.

Beginning in 1948, the Commission Internationale de l’Eclairage (CIE) began the quarter-century process of developing what is now the CIE General Colour Rendering Index, commonly referred to as the CRI metric (CIE 1995). The first version was published in 1965, and it was revised in 1974 to include the psychophysiological effects of chromatic adaptation.

An excellent description of the metric is available from Wikipedia (“color rendering index”), so there is no reason to repeat it here. All that needs to be said is its definition:

Color rendering: effect of an illuminant on the color appearance of objects by conscious or subconscious comparison with their color appearance under a reference illuminant.

and a reminder that the two illuminants (i.e., light sources) must have the same correlated color temperature (CCT).

This metric worked reasonably well for ranking linear fluorescent lamps from the era. Quartz halogen lamps had CRIs of nearly 100, while warm white fluorescent lamps typically had CRIs between 50 and 60. These lamps were particularly deficient in the red region of the spectrum (see Fig. 1), with warm white lamps having CRI R9 values as low as -111. (No, that is not a misprint; CRI values for specific test colors can be negative.)

Fluorescent lamp manufacturers could increase the red emission by mixing strontium and calcium halophosphates to create so-called “deluxe” phosphors. Lamps using these phosphors could achieve CRIs of approximately 90, but at the cost of roughly a one-third decrease in luminous efficacy (lumens output per electrical watt input).

This situation changed in the 1970s with two important discoveries:

  1. Lamps with improved luminous efficacy and very good color rendering properties could theoretically be achieved with three narrow-band (red, green and blue) lamp spectra (Thornton 1971); and
  2. The development of rare-earth phosphors for Thornton’s “triphosphor” lamps with CRIs of approximately 85 (Verstegen et al. 1974).

Figure 2

FIG. 2 – Rare-earth triphosphor lamp spectrum

By themselves, these two discoveries may or may not have had a significant impact on the manufacture of fluorescent lamps. Looking back, the lighting industry at the time had little to no interest in color rendering issues.

What the lamp manufacturers did have however was a metric to compare products with, and with this the opportunity for an effective marketing campaign. As a result, the otherwise-obscure CRI metric appeared in every manufacturer’s catalogs and sales literature, and it sold lamps.

With CRIs in the range of 85, triphosphor lamps have very good but not excellent color rendering properties. Lamp manufacturers therefore developed so-called “broadband” phosphors with four or five emission bands. With these, CRIs of 90 or so could be achieved.

Figure 3

FIG. 3 – Rare-earth broadband lamp spectrum

Marketing aside, did this really matter? For most commercial applications, the answer was probably no. In 1986, the CIE Guide on Indoor Lighting (CIE 1986) offered this helpful table:

CRI Ra Examples of Usage
> 90 Color matching, art galleries
80 – 90 Homes, restaurants, textile industry
60 – 80 Offices, schools, light industry
40 – 60 Heavy industry
20 – 40 Outdoors

Table 1 – CRI examples of usage

True, this list was likely influenced by the availability of halophosphate fluorescent lamps for indoor use, clear mercury vapor HID lamps for high-bay factory luminaires, and low-pressure sodium (LPS) lamps for roadway and area lighting. Still, it indicated how the CIE viewed its own metric at the time. CRI values were meant to be used as design guides rather than as precise numbers.

More tellingly, van Trigt (1999) presented a scholarly review of the CRI metric, in which he stated that “only a difference of some five points in the index is considered meaningful.”

The problem is that while the CRI metric Ra can be calculated from the measured lamp spectral power distribution (SPD) with arbitrary precision, it is nevertheless based on a mathematical model (the von Kries transformation) of psychophysiological behavior. Given this, it makes sense that the difference between CRI values of for example 88 and 90 is essentially meaningless.

In a sense, the CRI metric has served its purpose in promoting the development and commercialization of rare-earth lamp phosphors. With fluorescent lamp CRIs typically being in the range of 85 to 95 these days, lighting designers and consumers have little need to know anything other than “90 and above good, less than 80 bad.”

But then along came solid-state lighting …

The first commercially-produced SSL luminaire designed expressly for architectural applications was the TIR Systems Lexel, introduced at LightFair in April 2005 (Whitaker 2005). Based on a red-green-blue, high-flux LED die design, it generated white light whose color temperature could be varied from 3000 to 6500 kelvins.

Compared to the cool white LEDs with YAG phosphors and CRIs of approximately 75 available at the time, the white light produced by the Lexel was widely acclaimed by the trade show attendees, particularly for its color rendering properties.

Figure 4

FIG. 4 – LightFair 2005 – TIR Systems Lexel™

What the attendees did not know was that (ahem) the CRI Ra value varied from 25 at 3000K to 40 at 6500K (Speier and Salsbury 2006). By the standards of CIE 29.2, this was barely good enough for outdoor lighting only, along with clear MV and LPS lamps.

This seems odd, particularly when you compare the RGB LED lamp spectrum (FIG. 5) with that of a typical rare-earth triphosphor fluorescent lamp spectrum (FIG. 2). Based on this, you might guess that the CRI should be 85 to 90, not 25 to 40.

Figure 5

FIG. 5 – RGB LED lamp module spectrum

The answer lies in the dominant wavelengths of the emission peaks. Thornton (1974) calculated that the ideal dominant wavelengths (what he called “prime colors”) for triphosphor lamps were 450 nm (blue), 545 nm (green), and 610 nm (red). The dominant wavelengths of the Lexel – and indeed most color-changing RGB LED luminaires on the market today – were 465 nm, 525 nm, and 615 nm. If you were to change the green wavelength from 525 nm to 545 nm, the CRI would be 85 or so.

Unfortunately, the maximum dominant wavelength of reasonably efficient green InGaN LEDs is approximately 530 nm. The white light produced by color-changing RGB LED luminaires looks wonderful, but it is doomed have unacceptably low CRI values.

If anything, this is an example of the abject failure of the CRI metric to predict the color rendering properties of RGB LED luminaires. CIE Technical Committee 1-62 acknowledged this problem (CIE 2007), and recommended the development of a new and improved color rendering metric for all white light sources.

CIE Technical Committee 1-69 was therefore established in 2008 to “investigate new methods for assessing the colour rendition properties of white-light sources used for illumination, including solid-state light sources, with the goal of recommending new assessment procedures.” The committee investigated over a dozen proposals, but could only agree to bitterly disagree on any new metrics. As of this writing, the committee has yet to release its final report.

In response, the CIE recently established two new committees to further study the issue:

TC 1-90: Colour Fidelity Index. To evaluate available indices based on colour fidelity for assessing the colour quality of white- light sources with a goal of recommending a single colour fidelity index for industrial use.

TC 1-91: New Methods for Evaluating the Colour Quality of White-Light Sources. To evaluate available new methods for evaluating the colour quality of white-light sources with a goal of recommending methods for industrial use.

with reports due no earlier than 2015.

Despite numerous calls from the lighting industry for some clarity on color rendering metrics (e.g., Whitaker 2010, Colombo 2013), it is unlikely that the CIE will respond for at least a few more years.

Again however, does any of this really matter? The solid-state lighting industry effectively gave up waiting a number of years ago and began using CIE Special Colour Rendering Index R9 in addition to Ra to quantify the color rendering properties of white light sources (including both semiconductor and organic LEDs) for saturated red colors. While this combination of Ra and R9 is not a perfect solution, it is nonetheless a recognized industry standard, and it generally works (especially for marketing literature).

More to the point however is that the solid-state lighting industry has, like the fluorescent lamp industry before it, mostly outgrown the need for color rendering metrics. As long as the lamp module CRI is 80 or above – which is the case for most commercial products these days – there is little need to worry about CRI except for applications requiring critical color judgment.

With this, it is interesting to look at another industry that relies on the CRI metric: architectural glass. If you think about it, daylight illuminating interior spaces is spectrally filtered by glass windows and curtain walls. What is the CRI of daylight inside the building? If it is less than 80, it is in danger of being banned altogether by the US Environmental Protection Agency in accordance with the minimum CRI requirements of its ENERGY STAR program (EPA 2013).  (Just kidding … I think.)

It may surprise lighting designers to know that there is a European standard (BSI 2011) that specifies the calculation of indoor daylight CRI values, assuming a 6500 K daylight (CIE D65) illuminant. Architectural glass manufacturers publish CRI values for different thicknesses of their glass and window assemblies, and it can be calculated using the International Glazing Database (windowoptics.lbl.gov/data/igdb) and the freeware Optics 6 program from LBNL (windows.lbl.gov/software/Optics/optics.html).

You might think that bronze glass for example would significantly affect the color of indoor daylight, but this is not the case. Taking one manufacturer (Pilkington Glass) as an example, most of their products have CRIs in the mid-90s, with the lowest (Ra of 77) being for their Solar-E Arctic Blue low-emissivity glass with a blue body tint.

More interesting perhaps is that most architectural glass products have similar spectral transmittance spectra (Gombos et al. 2008). They are so similar in fact that the CIE has defined two “indoor daylight illuminants” (ID50 and ID65) that correspond to CIE daylight illuminants D50 and D65 as seen through generic architectural glass (CIE 2009).

As you might expect from architectural glass, the slight greenish tint is due to absorption of red light. This increases the effective color temperature of the incident D50 and D65 daylight to 5100 and 6600 kelvins, respectively.

Figure 6

FIG. 6 – Indoor Daylight ID50 spectrum (5100K)

Figure 7

FIG. 7 – Indoor Daylight ID65 spectrum (6600K)

What makes this interesting for lighting designers is that architects and clients may obsess over the need for “high-CRI lighting” in their buildings. If the design involves both electric lighting and daylighting, one response could be to ask about the CRI of the building glass. If it is less than 90, there may be little point in worrying about the electric lighting.

Lamp with a CRI of 90 or above, good. Lamp with a CRI of less than 80, bad. This, plus the knowledge that “only a difference of some five points in the index is considered meaningful” is likely all you need to know (or talk) about CRI for most lighting design projects.

References

BSI. 2011. BS EN 410:2011, Glass in Building. Determination of Luminous and Solar Characteristics of Glazing. London, United Kingdom: British Standards Institution.

CIE. 1986. Guide on Interior Lighting. CIE 29:2-1986. Vienna, Austria: CIE Central Bureau.

CIE. 1995. Method of Measuring and Specifying Colour Rendering Properties of Light Sources. CIE 13.3-1995. Vienna, Austria: CIE Central Bureau.

CIE. 2007. Colour Rendering of White LED Light Sources, CIE 177:2007. Vienna, Austria: CIE Central Bureau.

CIE. 2009. Indoor Daylight Illuminants. CIE 184:2009. Vienna, Austria: CIE Central Bureau.

Colombo, E. 2013. “Opinion: Choose a Colour Rendering Metric Now,” Lighting Research and Technology 45:520.

EPA. 2013. ENERGY STAR Program Requirements for Integral LED Lamps. Washington, DC: Environmental Protection Agency.

Gombos, K., et al. 2008. “Proposal for an Indoor Daylight Illuminant,” Color Research & Application 34(1):18-25.

Speier, I., and M. Salsbury. 2006. “Color Temperature Tunable White Light LED System,” Proc.  6th International Conference on Solid State Lighting, SPIE Vol. 6337, 63371F.

Thornton, W. A. 1971. “Luminosity and Color Rendering Capability of White Light,” Journal of the Optical Society of America 61:1155-1163.

van Trigt, C. 1999. Color Rendering, a Reassessment. Color Research & Application 24(3):197-206.

Verstegen, J. M. P. J. 1974. “New Class of Phosphors for ‘Deluxe’ Fluorescent Lamps,” Lighting Research and Technology 6:31-32.

Whitaker, T. 2005. “TIR’s LEXEL Platform Provides Cost-Effective LED Lighting,” LEDs Magazine, July, pp. 19-21.

Whitaker, T. 2010. “Search Continues for Replacement for Color Rendering Index,” LEDs Magazine, July/August, p. 59.