Tuning White Light for Color Rendition and Why it Matters

Transcription

1 Tuning White Light for Color Rendition and Why it Matters ETC Workshop July 14, 2016 Kevin W. Houser, PhD, PE, FIES, LC Professor of Architectural Engineering The Pennsylvania State University Editor-in-Chief LEUKOS, the journal of IES

2 Cue 1 & 2 Which do you prefer? 1 2 R a (CRI)= 50 R 9 = -80 CCT = 3501 K D uv = R a (CRI)= 75 R 9 = 20 CCT = 3501 K D uv =

3 Today s Outline Brief overview of CIE CRI Introduction to TM IES Method for Evaluating Light Source Color Rendition Demonstration Results from recent experiment

4 Today s Outline Brief overview of CIE CRI Introduction to TM IES Method for Evaluating Light Source Color Rendition Demonstration Results from recent experiment

5 CIE CRI (R a ) Test Source Reference Illuminant (approximately) SAME CCT For further reading see CIE , or Houser K, Mossman M, Smet K, Whitehead L Tutorial: Color Rendering and Its Applications in Lighting. LEUKOS.

6 CIE CRI (R a ) Approximation of Color Samples for R a Color Samples for R 9 R 14 TCS 01 TCS 02 TCS 03 TCS 04 TCS 09 TCS 10 TCS 11 TCS 12 TCS 05 TCS 06 TCS 07 TCS 08 TCS 13 TCS 14

7 CIE CRI (R a ) R Y GY G BG PB P RP (Illustration Only) +20 GY +10 G Y V* R -10 BG -20 PB P RP U*

8 CIE CRI (R a ) R Y GY G BG PB P RP (Illustration Only) +20 GY +10 G Y V* R -10 BG -20 PB P RP U*

9 CIE Method for Color Rendering Color Fidelity The accurate rendition of color so that they appear as they would under familiar (reference) illuminants CIE CRI (R a )

10 CRI = 95, Original Image Original Image courtesy of Randy Burkett Lighting Design

11 CRI = 80, Desaturated Image Original Image courtesy of Randy Burkett Lighting Design

12 CRI = 80, Saturated Image (Red Enhanced) Original Image courtesy of Randy Burkett Lighting Design

13 Original Baseline Original image courtesy of Randy Burkett Lighting Design

14 CRI = 80 - Hue Shift

15 CRI = 80 + Hue Shift

16 CRI = 80 Saturated

17 CRI = 80 Desaturated

18 Limitations of Considering Only Fidelity Positive Hue Shift Constant CIE CRI Decrease Saturation Perfect Fidelity CRI = 80 CRI = 80 Increase Saturation Negative Hue Shift

19 Limitations of Considering Only Fidelity Positive Hue Shift Decrease Saturation Constant CRI One measure is not enough! Perfect Fidelity CRI = 80 CRI = 80 Increase Saturation Negative Hue Shift

20 CRI (R a ): A measure of average color fidelity. But what about. chroma changes? hue shifts? color discrimination? color preference?

21 One index is not enough. But how many are needed? And what should they be? Attributes of Color Rendition include: Color Fidelity Color Discrimination Color Preference Tend to be related to saturation, which can be quantified with gamut Sidebar for Further Reading: The more than 25 indices of color rendition that appear in the scientific literature tend to cluster into two categories, those based on comparison to a reference illuminant (i.e., to quantify fidelity), and those related to gamut area (i.e., to quantify increase or decrease in saturation).* * Houser KW, Wei M, David A, Krames MR, Shen XS. Review of Measures for Light-Source Color Rendition and Considerations for a Two-Measure System for Characterizing Color Rendition. Optics Express. 2013; 21(8);

22 Today s Outline Brief overview of CIE CRI Introduction to TM IES Method for Evaluating Light Source Color Rendition Demonstration Results from recent experiment

23 Two primary motivations for developing the IES Method: 1. The need for an improved measure of color fidelity 2. The need to provide supplementary information about color rendering ability of any given light source

24 IES Method for Color Rendition High Level Average Values Fidelity Index (R f ) Gamut Index (R g ) Core Calculation Engine Modern Color Science New Color Samples Graphical Representations Color Vector Graphic Color Distortion Graphic Detailed Values Skin Fidelity (R f,skin ) Fidelity by Hue (R f# ) Chroma Shift by Hue (R c# ) Fidelity by Sample (R f,ces# )

25 IES Method for Color Rendition Color Fidelity Color Gamut Graphics The accurate rendition of color so that they appear as they would under familiar (reference) illuminants Fidelity Index (R f ) (0-100) The average level of saturation relative to familiar (reference) illuminants. Gamut Index (R g ) ~ when R f > 60 Visual description of hue and saturation changes. Color Vector Graphic

26 b' Fidelity Index: R f a' Reference Source Test Source Quantifies average similarity in appearance of test and reference sources Analogous to CIE R a, but more accurate Scores of 0 to 100 Scale similar to CIE R a, but high scores harder to achieve Equal weight to all directions of shift Should not be expected to correlate with any single object color [Flattened to 2D]

27 b' b' Relative Gamut Index: R g a' Reference Source Test Source a' Reference Source Test Source 15

28 b' Relative Gamut Index: R g R g = 100 A t A r R g > 100: Average increase in saturation R g < 100: Average decrease in saturation a' Reference Source Test Source 15

29 Theoretical Example Original Desaturated Red-Enhanced CRI = 95 CRI = 80 CRI = 80 R f = 93 R f = 78 R f = 78 R g = 100 R g = 90 R g = 110 Original Image courtesy of Randy Burkett Lighting Design

30 Theoretical Example Average values can hide important information! Original Desaturated Red-Enhanced CRI = 95 CRI = 80 CRI = 80 This is limitation of CIE R a, and IES R f and R g R f = 93 R f = 78 R f = 78 R g = 100 R g = 90 R g = 110 Image courtesy of Randy Burkett Lighting Design

31 Color Vector Graphic Gamut is not a dimension of perception. It is best interpreted with reference to a complementary graphic. Color Vector Graphic R f = 81 R g = 101 CCT = 2496 K R a = 88 (Source No. 286)

32 b' Color Vector Graphic COLOR VECTOR GRAPHIC CES CHROMATICITY COMPARISON a' Reference Source Test Source 15

33 b' Color Vector Graphic CES CHROMATICITY COMPARISON a' Reference Source Test Source 15

34 b' Color Vector Graphic CES CHROMATICITY COMPARISON Increased Saturation Decreased Saturation Hue Shift a' Reference Source Test Source 15

35 Theoretical Example Original Desaturated Red-Enhanced CRI = 95 CRI = 80 CRI = 80 R f = 93 R f = 78 R f = 78 R g = 100 R g = 90 R g = 110 Original Image courtesy of Randy Burkett Lighting Design

36 Today s Outline Brief overview of CIE CRI Introduction to TM IES Method for Evaluating Light Source Color Rendition Demonstration Results from recent experiment

37 Demonstration Live demonstration of SPDs realized using the 7-channel ETC D22, illustrating variations in IES R f, R g, and CIE R a and R 9. The below observations can be partially understood by examination of the color vector graphics on the slides that follow. What to look for? Source 7 Source 1 Very close to reference conditions Despite a CIE R a of 50 (and IES R f of 64), many would not find this source objectionable because of the manner in which it enhances gamut. 1 vs 2 Despite a 25 point difference in CIE R a, the higher CIE R a source is clearly less desirable 2 vs 3 Despite similar CIE R a and R 9, color rendering is very different. The IES R f and R g measures, plus the graphics, provide more appropriate information. 3 vs 4 Both have similar IES R f and R g, but render objects differently. This illustrates the importance of the color vector graphic. 4 vs 5 Same IES R f, but source 5 has an IES R g that is greater than source 4 by 15 points. Note that source 5 appears more desirable than source 4, despite the fact that CIE R a is 11 points lower. Red rendition is very different, even though CIE R 9 is similar for both. 5 vs 6 IES R f is approximately 80 for both, but source 6 has IES R g of 87 (desaturating), versus IES R g of 115 (saturating) for source 5. Note that CIE R a is higher for the desaturating source (which makes object appear less desirable), yielding a result that is different than many would expect.

38 Cue 11 7

39 Cue 11 7

40 Cue 1 1 2

41 Cue 1 1 2

42 Cue 3 Comparable CIE R a and R 9 3 2

43 Cue 3 2 3

44 Cue 5 34

45 Cue 5 3 4

46 Cue 7 Case 4 is eleven points higher in CRI (83 vs. 72) 5 4

47 Cue 7 4 5

48 Cue 9 Case 6 is eight points higher in CRI (80 vs. 72) 5 6

49 Cue 9 5 6

50 Cue Cue Hunt effect compensation As above, left/right reversed Cue K K K K Non-metameric

51 Today s Outline Brief overview of CIE CRI Introduction to TM IES Method for Evaluating Light Source Color Rendition Demonstration Results from recent experiment

52 Human Judgements of Color Rendition Vary with Average Fidelity, Average Gamut, and Gamut Shape Michael Royer, Pacific Northwest National Laboratory Andrea Wilkerson, Pacific Northwest National Laboratory Minchen Wei, Hong Kong Polytechnic University Kevin Houser, Penn State University Robert Davis, Pacific Northwest National Laboratory Funding Royer, Wilkerson, and Davis supported by U.S. Department of Energy Laboratory Directed Research and Development (LDRD) award Houser subcontracted by Pacific Northwest National Laboratory. Wei supported by Penn State, with later stages supported by Hong Kong Polytechnic.

53 Relate judgements of color quality to TM-30 measures R g Color Vector Graphic analyses and plots courtesy of Tony Esposito, PhD R f Candidate, Penn State University. Based on simulations using 11-channel LED Cube. Goals Hypotheses Methods Results Discussion Conclusions

54 Relate judgements of color quality to TM-30 measures R g Color Vector Graphic analyses and plots courtesy of Tony Esposito, PhD R f Candidate, Penn State University. Based on simulations using 11-channel LED Cube. Goals Hypotheses Methods Results Discussion Conclusions

55 a priori hypotheses 1. As Rf increases, color would be judged as more normal. 2. As Rg increases, color would be judged as more saturated. 3. Higher levels of Rg would be more preferred than lower levels of Rg. 4. Higher levels of red saturation would be preferred. Goals Hypotheses Methods Results Discussion Conclusions

56 Apparatus and Test Space Goals Hypotheses Methods Results Discussion Conclusions

57 Independent Variables: Rf, Rg, and Gamut Shape R g R f Goals Hypotheses Methods Results Discussion Conclusions

58 Independent Variables: Rf, Rg, and Gamut Shape R g R f Goals Hypotheses Methods Results Discussion Conclusions

59 Independent Variables: Rf, Rg, and Gamut Shape R g R f Goals Hypotheses Methods Results Discussion Conclusions

60 26

61 10

62 1

63 Participants 28 participants (12 male, 16 female) 19 to 65 years of age (mean male = 44, mean female = 38) Ishihara 24 plate test revealed one redgreen deficient male, not excluded. Goals Hypotheses Methods Results Discussion Conclusions

64 Dependent Measures a priori hypotheses 1. As Rf increases, color would be judged as more normal. 2. As Rg increases, color would be judged as more saturated. 3. Higher levels of Rg would be more preferred than lower levels of Rg. 4. Higher levels of red saturation would be preferred. Normal Saturated Like Shifted Dull Dislike Goals Hypotheses Methods Results Discussion Conclusions

65 Procedures Pre-Experimental Preparation Informed Consent Ishihara Color Screening White Lab Coat Experimental Trials 3 practice trials (2 announced) 26 experimental trials (walk and look within room, await cue from experimenter, complete survey, step out, exchange survey forms, repeat). Goals Hypotheses Methods Results Discussion Conclusions

66 Preference varied systematically. Higher levels of Rg were generally preferred to lower levels of Rg. p = Like 130 Model r 2 = 0.68 a priori hypotheses IES TM-30 R g Higher levels of Rg would be more preferred than lower levels of Rg. 4. Higher levels of red saturation would be preferred Dislike IES TM-30 R f p = Goals Hypotheses Methods Results Discussion Conclusions

67 Preference varied systematically. Higher levels red saturation were preferred (These aren t necessarily the most preferred sources possible, just the most preferred sources from this experiment). Goals Hypotheses Methods Results Discussion Conclusions

68 Same fidelity and gamut, but different gamut shape, can lead to significantly different preference. p = Like Model r 2 = Dislike IES TM-30 R g IES TM-30 R f p = Goals Hypotheses Methods Results Discussion Conclusions

69 Same fidelity and gamut, but different gamut shape, can lead to significantly different preference. Goals Hypotheses Methods Results Discussion Conclusions

70 Preference increased with red-saturation, with limits. Mean Preference Rating 8 7 Dislike y = x x x R² = Like 1-30% -20% -10% 0% 10% 20% 30% Hue Bin 16 Chroma Shift (R cs,h16 ) Goals Hypotheses Methods Results Discussion Conclusions

71 Participant Preference Rating Post-hoc modeling of preference 7 6 Less Liked More Liked R² = TM-30 Model Predicted Preference Rating Best Model for Preference: Like-Dislike = (R f ) (R cs,h163 ) (R cs,h16 ) Goals Hypotheses Methods Results Discussion Conclusions

72 What about existing light sources? 50% 40% 30% 20% 10% Experimental Preferred Zone* R cs,h16 0% -10% -20% -30% -40% -50% Goals Hypotheses Methods Results Discussion Conclusions

73 What about existing light sources? IES TM-30 R g Experimental Preferred Zone* Phosphor LED Color Mixed LED Hybrid LED Standard Halogen Filtered Halogen Triphosphor Fluorescent, 7XX Triphosphor Fluorescent, 8XX Triphosphor Fluorescent, 9XX Metal Halide IES TM-30 R f Goals Hypotheses Methods Results Discussion Conclusions

74 Conclusions from this small study TM-30 measures demonstrated excellent correlation with participant evaluations Sources that increased saturation in red were liked (Chroma shift in red of about 2% to 16%) Today s commercially available sources are unlikely to be optimized for preference Goals Hypotheses Methods Results Discussion Conclusions

75 Acknowledgments The slides in this presentation include images, ideas, and contributions from: Randy Burkett, Randy Burkett Lighting Design Tony Esposito, Penn State University Michael Royer, Pacific Northwest National Laboratory The experiment was performed by: Michael Royer, Pacific Northwest National Laboratory (Principal Investigator and Lead Author) Andrea Wilkerson, Pacific Northwest National Laboratory Minchen Wei, Hong Kong Polytechnic Kevin Houser, Penn State University Robert Davis, Pacific Northwest National Laboratory

76 Kevin W. Houser, PhD, PE, FIES Professor of Architectural Engineering The Pennsylvania State University 104 Engineering Unit A University Park, PA USA Phone: (814) khouser@engr.psu.edu Additional TM Collaborators: Resources Dr. Dale Tiller Dr. Xin Hu Dr. Bill Thornton Dr. Steve Fotios Mr. Mike Royer 76

77 Index of Bonus Slides Color Evaluation Samples Fundamentals Color Evaluation Samples More Details Constituency Roles Reference Illuminants IES R f vs. CIE R a Balloting Process and Metric Development Noteworthy History

78 Bonus Slides: Color Evaluation Samples [Basis For and Fundamental Features]

79 Sensitivity to SPD Variations Color Evaluation Samples Color Space Uniformity Wavelength Uniformity CRI (8) 20 b' 0-20 Large Set CES (99) a' l (nm) DE Jab

80 Imagine all possible colors Trim range to common colors All possible colors All observed colors Common colors J' 65,000 a' b'

81 Imagine all possible colors Trim range to common colors Divide volume to select range of colors with equal weighting 5,000 J' J' a' b' a' b'

82 Imagine all possible colors Trim range to common colors Divide volume to select range of colors with equal weighting Find minimum number of samples needed for similar results 99 J' J a' b' a b

83 J' a' b' Wavelength (nm)

84

85 Bonus Slides: Color Evaluation Samples [Additional Details and Attributes, including R 9 ]

86 b' b' Do the 99 CES Contain Saturated Samples? CHROMATICITY COMPARISON (2700 K Planckian) CHROMATICITY COMPARISON (CIE D65) a' CES (TM-30) TCS (CRI Ra) TCS (CRI Ri Special) VS (CQS) a' TCS (CRI Ra) TCS (CRI Ri Special) VS (CQS) CES (TM-30) Yes! The samples cover the area of all 14 CRI samples, as well as the area of the CQS samples. [Note charts only two dimensional representation]

87 Do the 99 CES Contain Saturated Samples? The CES mostly cover the gamut of the most saturated consumer goods. Also remember that spectral features are more important than absolute saturation level.

88 b' Lemon Do the 99 CES Contain Saturated Samples? CHROMATICITY COMPARISON (3500 K Planckian) The CES cover the gamut of typical natural objects Red Onion 2 1 Also remember that spectral features are more important than absolute saturation level Hydrangea a' Natural Objects CES (TM-30)

89 Reflectance What about R 9? R 9 has been useful metric for characterizing deep-red rendering. R 9 has a very strong correlation to the special fidelity index for TM-30 sample CES07. Spectral features are more important than saturation. However, the sensitivity of both metrics is very different! Criteria need to be reevaluated TCS CES R f,ces R 9 R f l R 9

90 Is R 9 Special? R 9 is not some magical sample; it has been useful in the absence of any other way to evaluate reds. It really covers on specific color/spectral feature, and is not necessarily a good indicator of overall red-rendering ability. R f,h1 and/or R f,h2 provide a better average representation of red rendering by considering more types of red samples. All samples below are in hue bins 1 and 2 (R 9 is right on the border): CES15 CES18

91 Bonus Slides: Constituency Roles

92 IES Method Benefits SPECIFIERS New tools to match sources with the right application. Hierarchy of information to meet needs of the user. MANUFACTURERS Better method for optimizing sources and balancing tradeoffs. Enhanced ability to differentiate and market products. RESEARCHERS Reference standard for conducting experiments. New baseline for future improvements. GENERAL More representative averages. More information for a more complete picture. Greater accuracy.

93 RESEARCHERS SPECIFIERS CODES AND PROGRAMS MANUFACTURERS IES Method Implementation Evaluate Sources Philosophical Changes Help Develop Criteria Pull Provide Data Engineer New Sources Push Help Develop Criteria Continue Improving Science Implement New Criteria

94 Metrics, Criteria, Standards, Guides Metrics/Measures R f (IES TM-30-15) R g (IES TM-30-15) CRI R a (CIE ) CRI R 9 (CIE ) CCT u'v' D uv Criteria K 5000 K Standards ANSI C ANSI/IES RP-1-12 ISO (CIE S 008) Design Guidance IES DG-1

95 Bonus Slides: Reference Illuminants

96 Reference Illuminants IES TM-30, like CIE CRI, uses reference illuminants: blackbody radiation and daylight phases. Daylight is universally available and by definition it produces the true color of natural objects. Some dispute blackbody radiators as a reference for low CCT They say standard of convenience but not so consider HPS Blackbody light enables people to judge objects daylight color, because both spectra are quite smooth. Sources can deviate from the blackbody curve and score well, because of the chromatic adaptation calculation of CIECAM02.

97 Reference Illuminants CIE CCT 5000 K CIE D Series (Model of Daylight) CCT < 5000 K Planckian Radiation (Think Incandescent) IES CCT 5500 K 5500 K > CCT > 4500 K CCT 4500 K CIE D Series (Model of Daylight) Proportional blend of D Series and Planckian Planckian Radiation (Think Incandescent) 5000K 5500K 6000K 5000K 4500K 4000K

98 Reference Illuminants K: Blackbody (Planckian) K: 50%-50% Mix K: D Series

99 Mixed Reference: Minimal Effect on Fidelity Values Fidelity Index R f CCT (K)

100 Mixed Reference: Minimal Effect on Fidelity Values Fidelity Index R f The effect for any one source is very small, but the change is conceptually important, especially given the increasing availability of color-tunable luminaires. CCT (K)

101 Bonus Slides: IES Balloting Process and Metric Development

102 Color Metrics Task Force Membership Manufacturing Aurelien David Paul Fini Kees Teunissen Randy Burkett Specification Research/ Government Kevin Houser Yoshi Ohno Michael Royer (Chair) Minchen Wei (Advisory)

103 IES Balloting Process August 2015 November 2014 Color Metrics Task Group Color Committee Technical Review Council Board of Directors At least 2/3 majority approval required at each step. Any non-editorial revision require recirculation ballot. Must attempt to resolve any disapproval vote.

104 Metric Development Development and Issuance Use and Evaluation (Revision) Industry Consensus Standard Obsolescence

105 Bonus Slides: IES R f versus CIE R a

106 IES R f versus CIE R a R f Filament Daylight Models Narrowband Fluorescent Broadband Fluorescent HID Hybrid LED Color Mixed LED Phosphor LED ~16 point spread in R f scores at R a = ~80 CRI R a

107 IES R f versus CIE R a TM-30 R f point spread (error) in fidelity score at CRI of CIE R a 5,000 Real and Modelled* SPDs *All modelled SPDs composed of combinations of Gaussian primaries; chromaticity on Planckian locus between 2700 K and 7000 K

108 Bonus Slides: Noteworthy History

109 Timeline of Color Rendering Metrics Committees 1965 CIE E1.3.2 recommends the CIE General Color Rendering Index (R a ). Research dates to Major revision of CRI (CIE ). Limitations noted Latest revision of CRI (CIE ). No major changes CIE TC1-33: Color Rendering [No Agreement Reached; Closed 1999] This committee was not successful in its purposes mainly due to the disagreement between those who advocated including the advances of science and those who recommended that industry did not want change. 1 CIE TC1-62: Color Rendering of White LED Light Sources [Published CIE 177:2007, recommends a new metric be developed] The Committee recommends the development of a new colour rendering index This index shall not replace the current CIE colour rendering index immediately. The usage of the new index or indices should provide information supplementary to the current CIE CRI, and replacement of CRI will be considered after successful integration of the new index CIE 177:2007.

110 Timeline of Color Rendering Metrics Committees 2006 CIE TC1-69: Color Rendition by White Light Sources Goal of developing single number replacement for CRI, with a focus on psychophysical research. [No Agreement Reached] 2012 CIE TC1-90: Color Fidelity Index [Ongoing] 2012 CIE TC1-91: New Methods for Evaluating the Colour Quality of White-Light Sources [Ongoing] 2013 IES Color Metrics Task Group [Developed TM-30-15]