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March 31, 2016 ENERGY STAR Webinar Evaluating Color Rendering With TM-30 Dr. Michael Royer, PNNL
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March 31, 2016
ENERGY STAR Webinar
Evaluating Color Rendering With TM-30
Dr. Michael Royer, PNNL
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1. How do I know how the colors in a space will appear?
2. Will a given appearance be liked (or perceived as natural, saturated, etc.)?
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Tools
Fidelity Index (Rf) Gamut Index (Rg)
High Level Average Values
TM-30 Calculation Engine Modern Color Science New Color Samples
Color Vector Graphic Color Distortion Graphic
Graphical Representations
Skin Fidelity (Rf,skin) Fidelity by Hue (Rf,h#) Chroma Shift by Hue (Rcs,h#) Fidelity by Sample (Rf,CES#)
Specific Sample Fidelity R9
CRI Calculation Engine Outdated Color Science Limited Color Samples
Ra (CRI) Average Fidelity
Detailed Values
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Color Fidelity
Fidelity Index (Rf)
TM-30 Method for Color Rendition
The accurate rendition of color so that they appear as they would under familiar (reference) illuminants
(0-100)
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Perfect Fidelity
Increase Saturation
Decrease Saturation
Positive Hue Shift
Negative Hue Shift
CRI = 80 CRI = 80
Constant Fidelity (CRI)
(Also possible to change lightness, not shown)
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Color Fidelity
Fidelity Index (Rf)
TM-30 Method for Color Rendition
The accurate rendition of color so that they appear as they would under familiar (reference) illuminants
(0-100)
Color Gamut
The average level of saturation relative to familiar (reference) illuminants.
Gamut Index (Rg) ~60-140 when Rf > 60
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60
70
80
90
100
110
120
130
140
50 60 70 80 90 100
Gam
ut In
dex,
Rg
Fidelity Index, Rf
Reduced Fidelity
Incre
ased
Satu
ratio
n De
creas
ed S
atura
tion Reference
Illuminant
• Evaluate tradeoffs between fidelity and saturation.
• When disparate fidelity and gamut measures are used together, the tradeoffs are less apparent.
• But average values don’t tell the whole story…
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Color Fidelity
Fidelity Index (Rf)
TM-30 Method for Color Rendition
The accurate rendition of color so that they appear as they would under familiar (reference) illuminants
(0-100)
Color Gamut
The average level of saturation relative to familiar (reference) illuminants.
Gamut Index (Rg) ~60-140 when Rf > 60
Gamut Shape
Changes over different hues
Color Vector Graphic, Hue Bin Chroma Shift
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Increased Saturation
Rf = 75 | Rg = 100 | CCT = 3500 K Rf = 75 | Rg = 100 | CCT = 3500 K
Decreased Saturation
Hue Shift
10 10
76 72 64
74
85 82 75 72 75
68 72 71
83 87 84 81
0
20
40
60
80
100
Fide
lity
Inde
x by
Hue
, Rcs
,hj
12% 11% 5%
-3% -6%
3%
10% 16% 14%
10% 4%
-1% -4% -5%
2%
9%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Chro
ma
Chan
ge b
y Hu
e, R
cs,h
j
74 73 63
72 80 80 79
85 83 74 70 72
83 78
74 73
0
20
40
60
80
100
Fide
lity
Inde
x by
Hue
, Rcs
,hj
-14% -11%
-3%
5% 11% 11%
4%
-2%
-8% -13%
-3%
7% 9% 11%
2%
-5%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Chro
ma
Chan
ge b
y Hu
e, R
cs,h
j
Same red fidelity, shift in opposite directions.
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CIE CRI (1965/1974) IES TM-30-15 (2015)
CIE 1964 U*V*W* CAM02-UCS (CIE CAM02)
Ref Illuminant Step Function Ref Illuminant Continuous (Uses same reference sources, but blended between 4500 K and 5500 K)
8 color samples 99 color samples Medium chroma/lightness Spectral sensitivity varies
Uniform color space coverage Spectral sensitivity neutral Variety of real objects Munsell samples only
No lower limit for scores and inconsistent scales
0 to 100 scale (fidelity)
Fidelity Metric Only Fidelity, Gamut, Graphical, Detailed/Hues
[1, 2, 3, 4, 5]
[6, 7, 8, 9, 10]
[11, 12, 13, 14]
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Which source is best?
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Experimental Room
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Experimental Room: Context
Lighting Conditions: 26 Illuminance: 20 fc CCT: 3500 K (on Planckian) Objects: Generic consumer goods, balanced hues Application: Undefined Participants: 19-65, 16 females 12 males Rating Questions: Normal-Shifted, Saturated-Dull, Like-Dislike
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Experimental Conditions
70
80
90
100
110
120
130
60 70 80 90 100
TM-3
0 G
amut
Inde
x, R
g
TM-30 Fidelity Index, Rf
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Experimental Conditions
70
80
90
100
110
120
130
60 70 80 90 100
TM-3
0 G
amut
Inde
x, R
g
TM-30 Fidelity Index, Rf
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We’re going to look at averages (means)…. …but the person to person differences are substantial! Almost every source received ratings across the full range for each question. (Normalness, Saturation, Preference) If you’re a specifier, you get to decide what you like for the given space!
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Preference vs. Fidelity
R² = 0.06
1
2
3
4
5
6
7
8
60 65 70 75 80 85 90 95 100
Mea
n Pr
efer
ence
Rat
ing
Fidelity Index (Rf)
Dislike
Like
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70
80
90
100
110
120
130
60 70 80 90 100
IES
TM-3
0 R g
IES TM-30 Rf
Model r2 = 0.68
p =
0.00
0
p = 0.042
Dislike
Like
5.5
5.0
4.5
4.0
3.5
1
2 3
Preference vs. Fidelity/Gamut
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Same Fidelity, Same Gamut, Significantly Different Rating.
Gamut Shape/Red Rendering
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22
22
21
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R² = 0.81
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2
3
4
5
6
7
8
-30% -20% -10% 0% 10% 20% 30%
Mea
n Pr
efer
ence
Rat
ing
Hue Bin 16 Chroma Shift (Rcs,h16)
Dislike
Like
Preference for Increased Red Saturation…with limits.
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Best Model for Preference: Like-Dislike = 7.396 - 0.0408(Rf) + 103.4(Rcs,h16
3) - 9.949(Rcs,h16)
Preference Model for this Experiment
R² = 0.9355 2
3
4
5
6
7
2 3 4 5 6 7
Part
icip
ant P
refe
renc
e Ra
ting
TM-30 Model Predicted Preference Rating
More Liked
Less Liked
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Normalness = Fidelity + Red Saturation
Summary
Saturation = Red Saturation
Preference = Fidelity + Red Saturation
Rf > 80 0% < Rcs,h1 < 8%
Maximize Rcs,h16, Rcs,h1
Rf > 74 0% < Rcs,h16 < 15% 0% < Rcs,h1 < 15%
(Rg > 100)
Context =
(Rf,h1 > 80)
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60
70
80
90
100
110
120
130
140
50 60 70 80 90 100
IES
TM-3
0 R g
IES TM-30 Rf
Phosphor LED
Color Mixed LED
Hybrid LED
Standard Halogen
Filtered Halogen
Triphosphor Fluorescent, 7XX
Triphosphor Fluorescent, 8XX
Triphosphor Fluorescent, 9XX
Metal Halide
A Look at Existing Sources
Experimental Preferred Zone
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A Look at Existing Sources
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
R cs,
h16
Experimental Preferred Zone
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1. Penalization by CRI
Why so Few Red-Enhancing Sources?
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1. Penalization by CRI
Why so Few Red-Enhancing Sources?
70
80
90
100
110
120
130
140
40 50 60 70 80 90 100
Gam
ut In
dex
Fidelity Index
Rf, Rg (TM-30) Ra, GAI (rescaled)
1
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1. Penalization by CRI
Why so Few Red-Enhancing Sources?
70
80
90
100
110
120
130
140
40 50 60 70 80 90 100
Gam
ut In
dex
Fidelity Index
2
Rf, Rg (TM-30) Ra, GAI (rescaled)
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1. Penalization by CRI
Why so Few Red-Enhancing Sources?
70
80
90
100
110
120
130
140
40 50 60 70 80 90 100
Gam
ut In
dex
Fidelity Index
Rf, Rg (TM-30) Ra, GAI (rescaled)
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CIE
Ra
IES
TM-3
0 R f
1. Penalization by CRI
Why so Few Red-Enhancing Sources?
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Ra 84, R9 = -7, LER 343 Ra 83, R9 = 21, LER 311
Why so Few Red-Enhancing Sources?
2. Efficiency Considerations
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Why so Few Red-Enhancing Sources?
2. Efficiency Considerations
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F32T8/735
Projected Rank: 22 of 26
F32T8/835
Projected Rank: 16 of 26
Blue-Pump Phosphor LED (81 CRI)
Projected Rank: 18 of 26
(Developed for CRI Ra):
Ra 74, LER 348 Ra 85, LER 343 Ra 83, LER 309
Common Commercially Available Sources
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LED (Patent Application) Neodymium Incandescent LED (Available Product)
Ra 80, LER 272 Ra 77, LER 136 Ra 87, LER 295
Enhanced Sources (Developed for CRI Ra and/or Gamut Area)
(Might be perfect for a different application!)
(Note different CCT)
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Case Studies
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Additional Resources IES Technical Memorandum (TM) 30-15 (Includes Excel Calculators): IES Method for Evaluating Light Source Color Rendition http://bit.ly/1IWZxVu Optics Express journal article that provides overview of the IES method: Development of the IES method for evaluating the color rendition of light sources http://bit.ly/1J32ftZ 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 http://1.usa.gov/1YEkbBZ 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 http://1.usa.gov/1Mn15LG LEUKOS journal article supporting TM-30’s technical foundations: Smet KAG, David A, Whitehead L. 2015. Why Color Space and Spectral Uniformity Are Essential for Color Rendering Measures. LEUKOS. 12(1,2):39-50. http://dx.doi.org/10.1080/15502724.2015.1091356 LEUKOS editorial discussing next steps: Royer MP. 2015. IES TM-30-15 Is Approved—Now What? Moving Forward with New Color Rendition Measures. LEUKOS. 12(1,2):3-5. http://dx.doi.org/10.1080/15502724.2015.1092752 Lighting Research and Technology, Open Letter: Correspondence: In support of the IES method of evaluating light source colour rendition (More than 30 authors) http://dx.doi.org/10.1177/1477153515617392 DOE Fact Sheet on TM-30 http://energy.gov/eere/ssl/downloads/evaluating-color-rendition-using-ies-tm-30-15 DOE TM-30 FAQs Page: http://energy.gov/eere/ssl/tm-30-frequently-asked-questions
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1. David A, Fini P, Houser K, Ohno Y, Royer M, Smet K, Wei M, Whitehead L. 2015. Development of the IES method for evaluating the color rendition of light sources. Opt Expr 23(12):15888.
2. Rea MS, Freyssinier JP. 2010. Color Rendering: Beyond Pride and Prejudice. Color Research and Application 35(6): 401–409.
3. de Beer E, van der Burgt P, van Kemenade J. 2015. Another color rendering metric: do we really need it, can we live without it? Leukos 12(1-2):51–59. DOI: 10.1080/15502724.2014.991793
4. Wei M, Houser KW, David A, Krames MR. 2016. Effect of Gamut Shape on Color Preference. CIE 2016 “Lighting Quality & Energy Efficiency”, Melbourne, Australia, 2016.
5. Ohno Y, Fein M, Miller C. 2015. Vision experiment on chroma saturation for color quality preference. Proceedings of the 28th CIE Session. Manchester, UK. CIE Publication 216:2015 1(1).
6. Luo MR, Cui G, Li C. 2006. Uniform colour spaces based on CIECAM02 color appearance model. Color Research and Application 31(4): 320–330.
7. Smet KAG, David A, Whitehead L. 2015. Why color space uniformity and sample set spectral uniformity are essential for color rendering measures. Leukos 12(1–2):39–50.
8. Sándor N, Schanda J. 2006. Visual colour rendering based on colour difference evaluations. Lighting Res Technol 38(3):225–239. DOI: 10.1191/1365782806lrt168oa
9. Jost-Boissard S, Avouac P, Fontoynont M. 2015. Assessing the colour quality of LED sources: Naturalness, attractiveness, colourfulness and colour difference. Lighting Res Technol 47:769–794. DOI: 10.1177/1477153514555882.
10.Luo MR, Gu HT, Liu XY, Liu HY, Wang BY. 2015. Testing colour rendering indices using visual data under different LED sources. Proceedings of the 28th CIE Session. Manchester, UK. CIE Publication 216:2015 1(1).
11.David A. 2014. Color Fidelity of Light Sources Evaluated over Large Sets of Reflectance Samples. Leukos 10(2):59–75, DOI: 10.1080/15502724.2013.844654
12.Žukauskas A, Vaicekauskas R, Ivanauskas F and others. 2009. Statistical approach to color quality of solid-state lamps. IEEE Quantum Electronics 15(4):1189–1198.
13.van Der Burgt PJM, van Kemenade JTC. 2010. About color rendition of light sources: the balance between simplicity and accuracy. Col Res App 35(2):85–93
14.Li C, Luo MR, Pointer MR, Green P. 2014. Comparison of real colour gamuts using a new reflectance database. Col Res App 39(5):442-451.
References
