An experiment was conducted to evaluate how perceptions of a light source’s colour quality depend upon colour rendition and chromaticity. Thirty-four participants each evaluated 50 lighting scenes in a 3.7 m by 5.5 m room filled with objects. The lighting scenes included five chromaticity groups, with 10 systematically-varied colour rendition conditions repeated in each group. Participants, who chromatically adapted to each chromaticity group, were asked to rate each scene on eight point scales for saturated-dull, normal-shifted and like–dislike (preference), as well as choosing whether they found the scenes to be acceptable or unacceptable. The findings suggest that colour rendition perceptions can vary with chromaticity, with an interactive effect of correlated colour temperature and Duv. The same IES TM-30-15 measures, Fidelity Index (IES Rf), Gamut Index (IES Rg) and hue-angle bin 16 (Red) Local Chroma Shift (IES Rcs,h16), could be used to effectively model perceptions within each chromaticity group, and provided suitable performance for the overall set of 50 conditions. The differences in ratings between the chromaticity groups were substantially smaller than the range in ratings for the 10 colour rendition conditions within each group, allowing the same acceptability-based criteria of IES Rf ≥ 75, IES Rg ≥ 98 and −7% ≤ IES Rcs,h16 ≤ 15% to be applied to all chromaticity groups.
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References
1.
DavidAFiniPTHouserKWOhnoYRoyerMPSmetKAGWeiMWhiteheadL. Development of the IES method for evaluating the color rendition of light sources. Optics Express2015; 23: 15888–15906.
2.
Illuminating Engineering Society of North America. IES-TM-30-15 Method for Evaluating Light Source Color Rendition, New York, NY: IESNA, 2015. 26.
3.
RoyerMWilkersonAWeiMHouserKDavisR. Human perceptions of colour rendition vary with average fidelity, average gamut, and gamut shape. Lighting Research and Technology.. First published 10 August 2016. DOI: 1477153516663615.
4.
WeiMHouserKW. Systematic changes in gamut size affect color preference. Leukos2017; 13: 23–32.
5.
Wei M, Houser K, David A, Krames M. Effect of gamut shape on color preference. CIE 2016 Lighting Quality and Energy Efficiency. Vienna: Commission Internationale de l'Eclairage, 2016: pp. 32–41.
6.
WeiMHouserKDavidAKramesM. Colour gamut size and shape influence colour preference. Lighting Research and Technology.. First published 13 August 2016. DOI: 1477153516651472.
7.
XuWWeiMSmetKLinY. The prediction of perceived colour differences by colour fidelity metrics. Lighting Research and Technology.. First published 20 June 2016. DOI: 1477153516653650.
8.
EspositoT. Modeling color rendition and color discrimination with average fidelity, average gamut, and gamut shape, PhD thesis, University Park, PA: Penn State University, 2016.
9.
JeromeCW. The flattery index. Journal of the Illuminating Engineering Society1973; 2: 351–354.
10.
JeromeCW. Flattery vs color rendition. Journal of the Illuminating Engineering Society1972; 1: 208–211.
11.
ThorntonWA. A validation of the color-preference index. Journal of the Illuminating Engineering Society1974; 4: 48–52.
12.
JuddDB. Flattery index for artificial illuminants. IES Transactions1967, pp. 593–598.
13.
ReaMSFreyssinierJP. Color rendering: Beyond pride and prejudice. Color Research and Application2010; 35: 401–409.
14.
Teunissen C, van der Heijden F, Poort S, de Beer E. Characterising user preference for white LED light sources with CIE colour rendering index combined with a relative gamut area index. Lighting Research and Technology2017; 49: 461–480.
15.
HouserKWWeiMDavidAKramesMRShenXS. Review of measures for light-source color rendition and considerations for a two-measure system for characterizing color rendition. Optics Express2013; 21: 10393–10411.
16.
WeiMHouserKWAllenGRBeersWW. Color preference under LEDs with diminished yellow emission. Leukos2014; 10: 119–131.
17.
WeiMHouserKDavidAKramesM. Perceptual responses to LED illumination with colour rendering indices of 85 and 97. Lighting Research and Technology2014; 47: 810–827.
18.
OhnoYFeinGMillerC. Vision experiment on chroma saturation for color quality preference: 28th CIE Session, Manchester, UK / Vienna: Commision Internationale de l'Eclairage, 2015. 2124.
19.
WeiMHouserKDavidAKramesM. Color gamut size and shape influence colour preference. Lighting Research and Technology.. First published 13 August 2016. DOI: 1477153516651472.
20.
LiuATuzikasAZukauskasAVaicekauskasRVittaPShurM. Cultural preferences to color quality of illumination of different artwork objects revealed by a color rendition engine. IEEE Photonics Journal2013; 5: 6801010–6801010.
21.
IslamMSDangolRHyvarinenMBhusalPPuolakkaMHalonenL. User preferences for LED lighting in terms of light spectrum. Lighting Research and Technology2013; 45: 641–665.
22.
SzaboFKeriRSchandaJCsutiPMihalyko-OrbanE. A study of preferred colour rendering of light sources: Home lighting. Lighting Research and Technology2014; 48: 103–125.
23.
Jost-BoissardSAvouacPFontoynontM. Assessing the colour quality of LED sources: Naturalness, attractiveness, colourfulness and colour difference. Lighting Research and Technology2014; 47: 769–794.
24.
DikelEEBurnsGJVeitchJAManciniSNewshamGR. Preferred chromaticity of color-tunable LED lighting. Leukos2013; 10: 101–115.
25.
RoyerMPHouserKWWilkersonAM. Color discrimination capability under highly structured spectra. Color Research and Application2012; 37: 441–449.
26.
ReaMSFreyssinier-NovaJP. Color rendering: A tale of two metrics. Color Research and Application2008; 33: 192–202.
27.
SzabóFCsutiPSchandaJ. Color preference under different illuminants – New approach of light source colour quality: Light and Lighting Conference with Special Emphasis on LEDs and Solid State Lighting, Budapest, Hungary: CIE, 27–29 May 2009. PWDAS-43.
28.
VeitchJAWhiteheadLAMossmanMPilditchTD. Chromaticity-matched but spectrally different light source effects on simple and complex color judgments. Color Research and Application2014; 39: 263–274.
29.
Jost-BoissardSFontoynontMBlanc-GonnetJ. Perceived lighting quality of LED sources for the presentation of fruit and vegetables. Journal of Modern Optics2009; 56: 1420–1432.
30.
LinYWeiMSmetKTsukitaniABodrogiPKhanhT. Colour preference varies with lighting application. Lighting Research and Technology2017; 49: 316–328.
31.
SmetKLinYNagyBVDuque-ChicaGLQuinteroJMChenHSLuoRMSafiMHanselaerP. Cross-cultural variation of memory colors of familiar objects. Optics Express2014; 22: 32308–32328.
32.
SmetKRyckaertWRPointerMRDeconinckGHanselaerP. Memory colours and colour quality evaluation of conventional and solid-state lamps. Optics Express2010; 18: 26229–26244.
33.
Spaulding JM. Evaluation of desirability assessment techniques for tunable solid state lighting applications: Proceedings of the Human Factors and Ergonomics Society Annual Meeting: 2012: pp. 643–647.
34.
ZukauskasAVaicekauskasRVittaPTuzikasAPetrulisAShurM. Color rendition engine. Optics Express2012; 20: 5356–5367.
35.
ZukauskasAVaicekauskasRShurMS. Colour-rendition properties of solid-state lamps. Journal of Physics D: Applied Physics2010; 43: 354006–354006.
36.
ZukauskasAVaicekauskasRShurM. Solid-state lamps with optimized color saturation ability. Optics Express2010; 18: 2287–2295.
37.
DavisWOhnoY. Color quality scale. Optical Engineering2010; 49: 033602–033602.
38.
OhnoYDavisW. Color quality and spectra. Photonics Spectra2008; 42: 71–72.
39.
WeiMHouserKW. What is the cause of apparent preference for sources with chromaticity below the blackbody locus?Leukos2016; 12: 95–99.
40.
SmetKGeertDHanselearP. Chromaticity of unique white in object mode. Optics Express2014; 22: 25830–25841.
41.
SmetKDeconinckGHanselaerP. Chromaticity of unique white in illumination mode. Optics Express2015; 23: 12488–12495.
42.
Ohno Y, Fein M. Vision experiment on acceptable and preferred white light chromaticity for lighting: CIE 2014 “Lighting Quality and Energy Efficiency”. Kuala Lumpur, Malaysia: 23–26 April 2014.
43.
ReaMSFreyssinierJP. White lighting. Color Research and Application2013; 38: 82–92.
44.
OhnoYOhS. Vision experiment II on white light chromaticity for lighting. CIE x042:20162016, pp. 175–184.
45.
WangYWeiM. Preference among light sources with different Duv but similar colour rendition: A pilot study. Lighting Research and Technology.. First published 14 June 2017. DOI: 1477153517712552.
46.
OhnoY. Practical use and calculation of CCT and Duv. Leukos2013; 10: 47–55.
47.
Vick K, Allen GR. Quantifying consumer lighting preference. 14th International Symposium on the Science and Technology of Lighting (LS14). Como, Italy: 2014.
48.
Khanh T, Bodrogi P, Vinh Q, Stojanovic D. Colour preference, naturalness, vividness and colour quality metrics, Part 1: Experiments in a room. Lighting Research and Technology. First published 7 April 2016. DOI: 1477153516643359.
49.
Khanh T, Bodrogi P. Colour preference, naturalness, vividness and colour quality metrics, Part 3: Experiments with makeup products and analysis of the complete warm white dataset. Lighting Research and Technology. First published 16 September 2016. DOI: 1477153516669558.
50.
Khanh T, Bodrogi P, Vinh Q, Stojanovic D. Colour preference, naturalness, vividness and colour quality metrics, Part 2: Experiments in a viewing booth and analysis of the combined dataset. Lighting Research and Technology. First published 7 April 2016. DOI: 1477153516643570.
51.
BodrogiPLinYXiaoXStojanovicDKhanhT. Intercultural observer preference for perceived illumination chromaticity for different coloured object scenes. Lighting Research and Technology2017; 49: 305–315.
52.
National Electral Manufacturers Association. ANSI C78.377-2015 American National Standard for Electric Lamps – Specifications for the Chromaticity of Solid State Lighting (SSL) Products, Rosslyn, VA: NEMA, 2015.
53.
RoyerMWeiM. The role of presented objects in deriving color preference criteria from psychophysical studies. Leukos2017; 13: 143–157.
54.
FairchildMDReniffL. Time course of chromatic adaptation for color-appearance judgments. Journal of the Optical Society of America A: Optics, Image Science and Vision1995; 12: 824–833.
55.
ShevellSK. The time course of chromatic adaptation. Color Research and Application2001; 26: S170–173.
56.
RinnerOGegenfurtnerKR. Time course of chromatic adaptation for color appearance and discrimination. Vision Research2000; 40: 1813–1826.
57.
TregillusKWebsterMA. Dynamics of color contrast adaptation. Journal of the Optical Society of America A: Optics, Image Science and Vision2014; 31: A314–321.
58.
SmetKRyckaertWRPointerMRDeconinckGHanselaerP. Correlation between color quality metric predictions and visual appreciation of light sources. Optics Express2011; 19: 8151–8166.
59.
HashimotoKYanoTShimizuMNayataniY. New method for specifying color-rendering properties of light sources based on feeling of contrast. Color Research and Application2007; 32: 361–371.
60.
WindischRHeidelGBinderUBergenekK. Impact of spectral features of common LED lighting systems on TM-30 color indices. Optics Express2017; 25: 1824–1830.
61.
RoyerMP. What is the reference? An examination of alternatives to the reference sources used in IES TM-30-15. Leukos2016; 12: 3–5.
62.
FairchildMD. Color Appearance Models, 3rd Edition.. Chichester, UK: Wiley, 2013.
63.
DiLauraDLHouserKWMistrickRGSteffyGR. The Lighting Handbook Reference and Application, 10th Edition, New York, NY: The Illuminating Engineering Socity of North America, 2011.
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