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Color Rendering of Light Sources


A symbolic photograph of the types of stimuli used for visual experimentation of color rendering.

Figure 1
A Color Quality Scale (CQS) is being developed at NIST with close contacts with the lighting industry and the CIE to address the problems of the CIE Color Rendering Index (CRI) for solid-state light sources and to meet the new needs in the lighting industry and consumers for communicating color quality of lighting products, The CQS evaluates several aspects of the quality of the color of objects illuminated by a light source. This metric involves several facets of color quality, including color rendering, chromatic discrimination, and observer preferences. The method for calculating the CQS is derived from modifications to the method used in the CRI. Though simulations support for the appropriateness and usefulness of the proposed metric, visual tests are being planned. The results of the vision experiments will be used to improve upon and eventually verify the CQS, which is to be proposed as a new international standard.

Color Rendering vs. Color Quality

The quality of object color under artificial lighting is an important aspect of the value of the light sources, particularly to consumers. Assessing and quantifying this dimension of lamps is complicated.

The attribute of color rendering of light sources is often interpreted as indicating object color quality. However, color rendering is actually defined as the “effect of an illuminant on the color appearance of objects by conscious or subconscious comparison with their color appearance under a reference illuminant” [Ref. 1]. Color rendering only refers to color fidelity, the accurate representation of object colors compared to those same objects under a reference source, and does not include other aspects of color quality, such as chromatic discrimination and color preference. The CIE color rendering index (CRI) [Ref. 2] is the only internationally-accepted metric for assessing the color rendering performance of light sources.

Color Rendering Index (CRI)

In the calculation of the CRI, the color appearance of 14 reflective samples is simulated when illuminated by a reference source and the test source. The reference source is a Planckian radiator (if below 5000 K) or a CIE Daylight source (if at or above 5000 K), matched to the correlated color temperature (CCT) of the test source. After accounting for chromatic adaptation with a Von Kries correction, the difference in color appearance ΔEi for each sample between the test and reference light sources is computed in CIE 1964 W*U*V* uniform color space. The special color rendering index (R) is calculated for each reflective sample by:


$R_i = 100-4.6 \Delta E_i$ (Eq. 1)


The general color rendering index (R) is simply the average of R for the first eight samples, all of which have low to moderate chromatic saturation:

$R_a = \frac{1}{8} \sum_{i=1}^8 R_i$ (Eq. 2)


A perfect score of 100 represents no color differences in any of the eight samples under the test and reference sources.

Eight colored squares, appearing peach, tan, green, blue, lavender, and pink.

Figure 2. The eight color samples used in the calculation of Ra.


Shortcomings of the CRI

The CRI has a number of problems, particularly when applied to LEDs or when used as an indicator of color quality. The uniform color space used to calculate color differences is outdated and no longer recommended for use. The red region of this color space is particularly non-uniform. Instead, the CIE currently recommends CIE 1976 L*a*b* (CIELAB) and CIE 1976 L*u*v* (CIELUV) [Ref. 3] for calculating color differences. Additionally, the chromatic adaptation transform is considered inadequate. The Von Kries chromatic adaptation correction used in the CRI has been shown to perform poorer than other available models, such as the CMCCAT2000 (the Colour Measurement Committee’s chromatic adaptation transform) and the CIE CAT02 (the CIE’s chromatic adaptation transform) [Ref. 4].

The CRI method specifies that the CCT of the reference source be matched to that of the test source, which assumes complete chromatic adaptation to the chromaticity of the light source. This assumption fails at extreme CCTs, however. For example, a 2000 K (very reddish) blackbody source achieves Ra = 100, as does a daylight spectrum of 20,000 K (very bluish). However, neither of these sources renders colors well.

None of the eight reflective samples used in the computation of Ra are highly saturated. This is problematic, especially for the peaked spectra of white LEDs. Color rendering of saturated colors can be very poor even when the Ra value is good. Further, by optimization of lamps’ spectra to the CRI, Ra values can be made very high while actual color rendering is much poorer. This problem exists because too few samples are used in the calculation of Ra, and they are of too low chromatic saturation.

A figure that shows the spectrum of an RGB LED with peaks at 463, 538, and 603 nm. 
Also shown is a CIELAB plot, which shows a marked decrease in chroma for red 
samples illuminated by the test source compared to the reference illuminant. 
In addition to the CIELAB diagram, the simulated appearance of the samples 
under both the reference and test sources is shown.

Figure 3: An example of an RGB LED (spectrum in upper left) that scores reasonably well with the CRI (Ra = 80), but shows large color shifts for saturated red (which appears nearly brown). The upper row of color boxes simulates the color appearance under the reference illuminant and the lower row simulates appearance under the RGB LED.


The eight special color rendering indices are simply averaged to obtain the general color rendering index. This makes it possible for a lamp to score quite well, even when it renders one or two colors very poorly. LEDs are at an increased risk of being affected by this problem, as their peaked spectra are more vulnerable to poor rendering in only certain areas of color space.

Finally, the very definition of color rendering is flawed for use when one is interested in the overall color quality of a light source. Color rendering is a measure of only the fidelity of object colors under the illuminant of interest and any deviations of object color appearance from under a blackbody source is considered bad. Due to this constraint, all shifts in perceived object hue and saturation result in equal decrements in CRI score. In practical application, however, increases in chromatic saturation, observed when certain sources illuminate certain surfaces, is considered desirable. Increases in saturation yield better visual clarity and enhance perceived brightness [Ref. 5]. It is proposed that the absolute focus on color fidelity of the CRI is flawed and a more general metric of color quality be considered.

Further details on the analyses of CRI for solid-state sources are found in references [Ref. 8, Ref. 9, Ref. 10].

Color Quality Scale (CQS)

To solve the problems of the CRI for solid-state light sources mentioned above, and to meet the new needs in the lighting industry and consumers for communicating color quality of all lighting products, a Color Quality Scale (CQS) is being developed at NIST, which evaluates several aspects of the quality of the color of objects illuminated by a light source. The extensive description of the CRI was provided because, rather than inventing an entirely new approach to the metric, much inspiration was taken from the CRI. Borrowing from aspects of the CRI that are successful, the CQS incorporates important modifications to overcome its shortcomings and focuses on a broader definition of color quality. The CQS is being developed with close contacts with the lighting industry and the CIE to be proposed as a future international standard.

The set of reflective samples tested is different from those used in the calculations of the CRI. Fifteen saturated Munsell samples are used in the CQS, with the following hue value/chroma: 7.5 P 4 / 10, 10 PB 4 / 10, 5 PB 4 / 12, 7.5 B 5 / 10, 10 BG 6 / 8, 2.5 BG 6 / 10, 2.5 G 6 / 12, 7.5 GY 7 / 10, 2.5 GY 8 / 10, 5 Y 8.5 / 12, 10 YR 7 / 12, 5 YR 7 / 12, 10 R 6 / 12, 5 R 4 / 14, and 7.5 RP 4 / 12. They were selected to have the highest chroma, span the entire hue circle in approximately even spacing, and be commercially available. Figure 1 shows these samples (bottom row) as well as the eight samples used in the calculation of Ra (top row) when illuminated by a daylight-like source (D65). This representation may be inaccurate due to the properties of the viewing display.

Fifteen colored squares, appearing saturated purple, blue, green, yellow, orange, and red

Figure 4: The 15 samples used by the Color Quality Scale (CQS).

The uniform object color space also differs from that used in the CRI. The 1964 W*U*V* object color space is obsolete, and is very nonuniform: color differences are extremely exaggerated in the red region and suppressed in yellow and blue regions. So, when calculating the CQS, CIE 1976 L*a*b* (“CIELAB”) is used, as it is currently recommended for use by the CIE and is considered to be reasonably uniform.

One of the major deviations that the CQS takes from the formal definition of color rendering is evident in the saturation factor. The CRI penalizes lamps for shifts in hue, chroma (chromatic saturation), and lightness, in any direction, of the reflective samples under the test source (compared to under the reference source). While a decrease in chroma always has negative effects, an increase in the chroma of objects is considered desirable in many cases. Increases in chroma yield better visual clarity and enhance perceived brightness [Ref. 5]. These are positive effects and are generally preferred, though they cause deviations in color fidelity (compared to reference). In the CQS, lamps are not penalized for increasing object chroma relative to the reference source, though their scores are also not increased. The net result is that a lamp’s score is only penalized for hue shifts, lightness shifts, and reductions in chroma. This is a way to take color preference (and possibly also color discrimination) into account in the CQS. For example, in Figure 5"A" when the chroma increases under the test illuminant (with no change in hue), there is no change in score, 5"B" when the chroma decreases under the test illuminant, the score is decreased, and 5"C" when the chroma increases and the hue shifts, the score is decreased for the hue shift but not decreased for the increase in chroma.

Further details on the CQS are found in reference [Ref. 11].

Three CIELAB plots

Figure 5: Effects of the saturation factor illustrated in CIELAB color space. In 5"A," the chroma of the sample is increased by the test source relative to the reference illuminant, but its hue is unchanged. In 5"B," the chroma of the sample is decreased by the test source relative to the reference illuminant, but its hue is unchanged. In 5"C," the chroma of the sample is increased by the test source relative to the reference illuminant, and its hue is also shifted.


In the CRI, the CCT of the reference source is matched to that of the test source. Therefore the CRI score is perfect (100) for reference sources of any CCT. Actual color rendering, however, is degraded at extremely low or high CCTs. This is a problem with the way the reference source is defined in the current metric, and is one of the most difficult problems to address. The perfect solution to this problem would require thorough understanding of chromatic adaptation. Such investigations have not been conducted yet, but a temporary solution has been developed. Though the CCT of the reference source is also matched to that of the test in the CQS, a multiplication factor is introduced. This CCT factor is determined based on the gamut area in CIELAB space for the 15 samples under the reference source for each CCT, as shown in Table 1. It is assumed that the color rendering performance of the reference source degrades as the gamut area decreases. The multiplication factor, as listed in the table, is the ratio of gamut area of the particular CCT with the gamut area for 6500 K. With this normalization, the multiplication factors at certain CCT ranges (e.g., 4000 K) give values slightly higher than 1, but these are truncated to 1, so that the CQS score will never be higher than 100. The exact effect of CCT on color quality is difficult to quantify, but this method offers at least a temporary solution for sources of extremely low or high CCT.

Table 1: The gamul area of the 15 samples of various CCTs and the CCT multiplication factor used in the CQS.
CCT
(K)
  Gamut  
Area
Multiplication
Factor
1000 2645 0.32
1500 5424 0.65
2000 6902 0.83
2500 7676 0.93
2856 7987 0.97
3000 8075 0.98
3500 8268 1.00
4000 8347 1.00
5000 8341 1.00
6000 8274 1.00
6500 8211 1.00
7000 8151 0.99
8000 8040 0.98
9000 7947 0.97
10000 7868 0.96
15000 7620 0.93
20000 7495 0.91

In the CRI, the color differences (ΔE) for each of the samples is averaged. This makes it possible for a lamp to score quite well, even when it renders one or two samples very poorly. This situation is even more likely with SPDs having narrowband peaks, like LEDs. To ensure that large hue shifts of any sample have notable influence on the CQS, the root-mean-square (RMS) of color shifts of each individual sample is used (rather than arithmetic mean). The RMS color differences of the 15 samples are calculated by:

$\Delta E_{\rm RMS}=\sqrt{\frac{1}{15}\sum_{i=1}^{15}\Delta E_i^2}$ (Eq. 3)

In the CRI, the scaling factor 4.6 is used to convert color differences into color rendering indices. This factor needs to be changed for the CQS since the sample set and color space are different, in order to maintain the consistency with the CRI. For the CQS, a scaling factor of 2.81 is currently chosen so that the average score of the CQS for the CIE standard fluorescent lamp spectra (F1 through F12 [Ref. 6] ) is equal to the average score of the current CRI Ra (=75.1) for these sources. This scaling maintains consistency of the new color quality scale with the current CRI scale for existing lamps. As the CQS formula is further modified, this scaling factor will have to be recalculated.

Negative values that the CRI reports for certain lamps are often confusing. CRI and CQS scores lower than 20 or 30 are already very poor and the linearity of the scale below these low scores is not considered important. A scale from 0 to 100 would be better understood by users. A 0-100 scale conversion has been made to avoid the confusion, and has been implemented by using the formula:

$R_{\rm out}=10*\ln [\exp(R_{\rm in}/10)+1]$ (Eq. 4)

where Rin is the input value (which can be a negative number) and Rout is the output value of the conversion. Only values lower than approximately 20 are changed by this conversion, and values above 20 are scarcely affected. As such, all values within the range for usable lamps are unchanged.
Further computational work on the new metric is planned. The chromatic adaptation transform will be updated from Von Kries to the Bradford transform, CMCCAT2000, or the CIE CAT02 [Ref. 4]. Furthermore, the use of ΔE2000 will be investigated as a replacement of ΔE*ab for determining color differences of samples [Ref. 7].

Testing the CQS

Many computational simulations have been performed and, at the level of subjective visual impression, appear to confirm the ideas used in the CQS. For details and examples, see Ref. 8.

A series of thorough and well-controlled vision experiments are necessary to test, improve upon, and validate the computational analyses, however. Experiments testing observers’ chromatic discrimination and absolute hue perception of illuminated objects will be complemented by subjective rankings of naturalistic scenes. Since the CQS is intended to be a metric of overall color quality, the data from several types of experiments will be used to assess and improve its performance. A new vision science laboratory is being built at NIST, with experiments testing CQS taking highest priority.

References

  1. Commission Internationale de l’Eclairage.
    International Lighting Vocabulary, CIE 17.4: item 845-02-59 (1987).

  2. Commission Internationale de l’Eclairage. Method of Measuring and Specifying Colour Rendering Properties of Light Sources, CIE 13.3 (1995).

  3. Commission Internationale de l’Eclairage. Colorimetry, CIE 15 (2004).

  4. Commission Internationale de l’Eclairage. A Review of Chromatic Adaptation Transforms, CIE 160 (2004).

  5. Visual Clarity and Feeling of Contrast, Hashimoto, K. & Nayatani, Y.
    Color Res. Appl., 19(3), 171-185 (1994).

  6. Commission Internationale de l’Eclairage. Colorimetry, 2nd Edition, CIE 15.2 (1986).

  7. Commission Internationale de l’Eclairage. Improvement to industrial colour-difference evaluation. CIE 142 (2001).

  8. Simulation Analysis of White LED Spectra and Color Rendering, Y. Ohno, Proc., CIE Symposium ’04, LED Light Sources: Physical Measurement and Visual and Photobiological Assessment, 7-8 June 2004, Tokyo Japan, 28-32 (2004).

  9. Color Rendering and Luminous Efficacy of White LED Spectra, Proc., Y. Ohno, SPIE Fourth International Conference on Solid State lighting, Denver, CO, August 2004, 5530, 88-98 (2004).

  10. Spectral Design Considerations for Color Rendering of White LED Light Sources, Y. Ohno, Opt. Eng. 44, 111302 (2005).

  11. "Toward an improved color rendering metric," W. Davis and Y. Ohno, in Fifth International Conference on Solid State Lighting, edited by I.T. Ferguson, J.C. Carrano, T. Taguchi, I.E. Ashdown, Proc. SPIE 5941, 59411G (2005).


Return to Vision Science

For technical information or questions, contact:

Wendy Davis
Phone: (301)-975-6963
Fax: (301)-840-8551
Email: wendy.davis@nist.gov
    Yoshi Ohno
Phone: (301)-975-2321
Fax: (301)-840-8551
Email: ohno@nist.gov

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Online: December 2006