How New Methods for Evaluating Color Rendering Will Affect You · The CIE Color Rendering Index...
Transcript of How New Methods for Evaluating Color Rendering Will Affect You · The CIE Color Rendering Index...
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May 5–7, 2015
Lightfair
How New Methods for Evaluating Color Rendering will Affect You
Michael Royer, PNNL
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Color Fidelity: A Partial Picture
Reference Image
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The CIE Color Rendering Index (CRI)
What CRI conveys:
• Average color fidelity/color shift (but with limitations) – Outdated color science
– Few samples
– Only pastel Munsell samples
– Can be selectively optimized
What CRI doesn’t convey:
• Direction/type of color shifts
• Increases or decreases in chroma
• Information about specific hue regions
• Human preference
• Color discrimination potential
• Difference in color for any specific object
• How one source will make things look compared to another
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One Index is Not Enough. But how many are needed?
And what should they be?
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A Path Forward: IES Color Metrics Task Group
• Technical Memorandum (i.e., calculation procedure), TM-30
• Currently in final stage of balloting
• It draws from a variety of other color perception research
• Based objective/mathematical approach
• Two-metric system (fidelity [Rf] and gamut [Rg]) for quantifying average color rendition
• 99 color evaluation samples (CES) with color space and wavelength uniformity – Excellent correlation to much larger sets of color samples
• Improved color science
• Many additional tools (e.g., color distortion icon) for better understanding rendition of specific hues.
TM-30 IS NOT YET A FINAL DOCUMENT!
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The CIE Color Rendering Index (CRI)
What CRI conveys:
• Average color fidelity/color shift (but with limitations) – Outdate color science
– Few samples
– Only Pastel Munsell Samples
– Can be selectively optimized
What CRI doesn’t convey:
• Direction/type of color shifts
• Increases or decreases in chroma
• Information about specific hue regions
• Human preference
• Color discrimination potential
• Difference in color for any specific object
• How one source will make things look compared to another
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Summary of Notable Features
Calculation Engine
• 99 color evaluation samples – Real objects
– Color space uniformity
– Wavelength uniformity
• Calculations in CAM02-UCS – CIECAM02 chromatic
adaptation
– Most uniform color space
– Stability over CCT
• Mixed reference (4500 K to 5500 K) eliminates discontinuity
Outputs
• Average Color Fidelity: Rf
• Average Color Gamut: Rg
• Rf Versus Rg Plot
• Color Distortion Icon / Color Saturation Icon
• Color Fidelity by Hue Angle (16 Groups)
• Chroma Change by Hue Angle (16 Groups)
• Skin Tone Color Fidelity
• Color Fidelity by Sample
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A Two-Metric System for Average Characterization
0 < Rf ≤ 100
When Rf > 60:
60 < Rg ≤ 140
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Hue shift
Rf Versus Rg Plot
Increasing
saturation
Decreasing
saturation
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What does it all mean…
For Specifiers?
For Manufacturers?
For Researchers?
For Energy Efficiency Programs/Utilities?
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Specifiers
1. Learn about the new metrics – Presentations, Journal Articles, TM-30 (Pending), DG-01 (Pending)
– What they do and don’t characterize; limitations
2. Examine values for existing sources, understand your preferences in applications where you commonly specify
3. Use additional information for better specifications
• Case study: Museums
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Comparing Rf and Rg
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70
80
90
100
110
120
130
140
50 60 70 80 90 100
Gam
ut
Ind
ex, R
g
Fidelity Index, Rf
HalogenNeodymium IncandescentF40T12WW/RS/EWF32T8TL830CDM 830Hybrid LEDRAGB LEDPhosphor LED
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Color Distortion Icon
Rf = 83 | Rg = 112 Rf = 83 | Rg = 99
Increased Saturation
Decreased Saturation
Hue Shift
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Manufacturers
1. Learn about the new metrics – Presentations, Journal Articles, TM-30 (Pending), DG-01 (Pending)
– What they do and don’t characterize; limitations
2. How do your products measure up against others?
3. Does your product have distinguishing features that are marketable?
4. Use tools for future optimizations – Balance between efficiency and color rendition
• Case study: Neodymium Lamps
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Communicating Performance
(Live Calculation Tool Demo)
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Beyond Averages
Rf = 82
Rg = 95
LER = 342
Rf = 82
Rg = 95
LER = 318
0
0.5
1
1.5
2
2.5
380 430 480 530 580 630 680 730 780
Rad
ian
t P
ow
er
(no
rmal
ize
d t
o Y
=10
0)
l (nm)
0
0.5
1
1.5
2
2.5
380 430 480 530 580 630 680 730 780
Rad
ian
t P
ow
er
(no
rmal
ize
d t
o Y
=10
0)
l (nm)
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Beyond Averages
50
55
60
65
70
75
80
85
90
95
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Rf b
y H
ue
Hue Bin
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Ch
rom
a Sh
ift
By
Hu
e
Hue Bin
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55
60
65
70
75
80
85
90
95
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Rf b
y H
ue
Hue Bin
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Ch
rom
a Sh
ift
By
Hu
e
Hue Bin
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EE Programs/Regulators
1. Learn about the new metrics – Presentations, Journal Articles, TM-30 (Pending), DG-01 (Pending)
– What they do and don’t characterize; limitations
2. How do existing thresholds for color rendering relate?
3. Should all sources that previously qualified still do so? Should the numerical value stay the same?
• Case study: Triphosphor Fluorescent
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Comparing Rf and Ra
~16 point spread in
Rf scores at Ra =
~80 70
75
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85
90
95
100
70 75 80 85 90 95 100
Rf
CRI Ra
FilamentDaylight ModelsNarrowband FluorescentBroadband FluorescentHIDHybrid LEDColor Mixed LEDPhosphor LED
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Researchers
1. Learn about the new metrics – Presentations, Journal Articles, TM-30 (Pending), DG-01 (Pending)
– What they do and don’t characterize; limitations
2. New information can improve characterization (but averages shouldn’t always be used to establish correlations)
3. What new investigations are warranted?
• Case study: Preference by Application
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WHY ADOPT?
• With two average numbers and other visualization tools, TM-30 provides a more complete characterization of color rendition than a fidelity metric alone (e.g., CRI).
• With a greater number of samples, the values are harder to selectively optimize and should provide a better representation of average color rendering.
• The method can help manufacturers optimize spectral designs, as well as accurately weigh tradeoffs between color rendition and other characteristics.
• The color rendition characteristics of products can more easily be differentiated (and marketed).
• The method can help specifiers and purchasers to select products that are more appropriate for their needs.
• Thresholds, design guidance, and preconceptions will need to be re-evaluated.
TM-30 IS NOT YET A FINAL DOCUMENT!