Evaluating Light Source Color Rendition using IES TM-30-15

Transcription

1 Evaluating Light Source Color Rendition using IES TM IES Chicago January 26, 2017 Kevin W. Houser, PhD, PE, FIES, LC Professor of Architectural Engineering The Pennsylvania State University Editor-in-Chief LEUKOS, the journal of IES

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

14 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

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

16 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

17 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# )

18 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

19 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]

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

21 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

22 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

23 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

24 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)

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

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

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

28 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

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

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33 2 versus 3 Comparable CIE R a and R 9 3

34 3 versus 4 Comparable IES R f and R g Not Gamut Shape! 4

35 5 versus 4 Case 4 is eleven points higher in CRI (83 vs. 72) 5

36 5 versus 6 Case 6 is eight points higher in CRI (80 vs. 72) 6

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

39 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. Goals Hypotheses Methods Results Discussion Conclusions

40 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

41 Apparatus and Test Space Goals Hypotheses Methods Results Discussion Conclusions

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

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

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

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48 Preference varied systematically. Higher levels of Rg were generally preferred to lower levels of Rg. Like 130 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 Goals Hypotheses Methods Results Discussion Conclusions

49 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

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

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

52 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

53 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

54 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

55 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

56 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

57 IES Journals IES Technical Memorandum (TM) (Includes Excel Calculators): IES Method for Evaluating Light Source Color Rendition Optics Express journal article that provides overview of the IES method: Development of the IES method for evaluating the color rendition of light sources LEUKOS article supporting TM-30 s technical foundations: Smet KAG, David A, Whitehead L Why Color Space and Spectral Uniformity Are Essential for Color Rendering Measures. LEUKOS. 12(1,2): Lighting Research and Technology, Open Letter: Correspondence: In support of the IES method of evaluating light source colour rendition (More than 30 authors) Lighting Research and Technology article showing applicability of TM to human perceptions: Royer MP, Wilkerson A, Wei M, Houser K, Davis R Human perceptions of colour rendition vary with average fidelity, average gamut, and gamut shape. Online before print US DOE LEUKOS article examining reference illuminants Royer MP What is the reference? An examination of alternatives to the reference sources used in IES TM LEUKOS. 13(2): Application webinar co-sponsored by US Department of Energy and Illuminating Engineering Society: Understanding and Applying TM-30-15: IES Method for Evaluating Light Source Color Rendition Technical webinar co-sponsored by US Department of Energy and Illuminating Engineering Society: A Technical Discussion of TM-30-15: Why and How it Advances Color Rendition Metrics DOE Fact Sheet on TM-30 DOE TM-30 FAQs Page:

58 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 58