color spaces and planckian loci: understanding all those ... · correlated color temperature (cct)...
TRANSCRIPT
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Color Spaces and Planckian Loci: Understanding all those Crazy Color Metrics
Naomi J. Miller, FIALD, FIES Michael Royer, PhD
Pacific Northwest National Laboratory Portland, OR
Locust
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What is COLOR? for color scientists?
for building occupants?
for designers/specifiers?
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COLOR… • is one of the key attributes of
lighting quality • is rooted in human perception
COLOR METRICS… • allow for communication of color
attributes • attempt to characterize human
perception, but aren’t always perfect
• have changed and improved over time
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Halogen 99 CRI , 2917 K, Duv 0.000
Compact Fluorescent 82 CRI, 2731 K, Duv 0.003
LED 84 CRI, 2881K , Duv 0.000
Metrics aren’t perfect!
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Quantifying Color The CIE System of Colorimetry
Chromaticity Diagrams & Chromaticity Coordinates
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Color Matching Functions (Transformed)
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y
x
CIE 1931 (x, y) Chromaticity Diagram
x = X / (X + Y + Z) y = Y / (X + Y + Z)
• x-y coordinates • x + y + z =1 • Third dimension is
lightness
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Spectrum LocusPlanckian locus
CIE 1931 (x, y) Chromaticity Diagram
• Colors of the spectrum appear around upper edge (in nm)
• Bottom edge displays non-spectral colors; “purple line”
• Colored background is theoretical only; cannot be displayed accurately
• Black body locus (also called Planckian locus)
• Use for light sources, not determining absolute appearance of objects!
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• It’s not perceptually uniform!!!
CIE 1931 (x, y) Chromaticity Diagram
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u
u = 4x / (-2x + 12y + 3) v = 6y / (-2x + 12y + 3)
CIE 1960 (u, v) Chromaticity Diagram (“UCS”)
• Simply linear transformation of CIE 1931 x-y
• u-v coordinates • Intended to be more
uniform (although not perfect)
• Used for calculating CCT and Duv
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u’ = 4x / (-2x + 12y + 3) v’ = 9y / (-2x + 12y + 3)
CIE 1976 (u’, v’) Chromaticity Diagram
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u'
• Further transformation of CIE 1960 UCS (mulitple v by 1.5)
• u’-v’ coordinates • Is the most uniform
available (still does not apply to objects)
• Used for calculating Δu’v’
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Object Color Appearance
• The dimension of lightness exists for objects
• Color solids (e.g., Munsell) • Three-dimensional color spaces
• CIE Lab • CIE Luv
• Color difference formula (ΔE*ab, ΔE*oo)
• Chromatic adaptation • Color Appearance Models
(CAMs) • CIECAM02
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…….but…..
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…….but…..
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…….but…..
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…….but…..
Bottom line: color science can’t always describe human perception. 16
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Color Appearance Correlated Color Temperature (CCT)
Duv Δu’v’
MacAdam Ellipses Binning
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Correlated Color Temperature (CCT) 20
00 K
3000
K
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K
5000
K
6000
K
Approximate Illustration!
What about adaptation? Our eye-brain system is very good at making these look the same!
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575 580 570
585
Correlated Color Temperature (CCT)
CIE 1960 (u, v) Chromaticity Diagram
• Iso-CCT lines are perpendicular to Planckian locus in CIE 1960 UCS
• Two sources that appear very different can have the same CCT!
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585
+ Duv
- Duv
CCT + Duv
CIE 1960 (u, v) Chromaticity Diagram
• Duv adds a second dimension to better convey appearance
• Iso-CCT lines shown are ± 0.02 Duv
• Typical limits for white light are -0.006 to 0.006 (but depends on CCT)
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• Both LED output and phosphor performance degrade over time • Phosphor performance can vary for different types, integration
approaches, and/or manufacturers • Faster phosphor degradation than blue die degradation can lead to color
shift over time
Color Maintenance
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Describing Color Maintenance
• Listing two CCTs from different points in time does not describe color difference
• Listing two Duvs From different points in time does not describe color difference
• Use Δu’v’
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v'
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Target Chromaticity
Test Samples
1-Step Ellipse
2-Step Ellipse
3-Step Ellipse
4-Step Ellipse
MacAdam Ellipses
Note: A & B are 1 step from the Target, but 2 steps from each other! 26
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Established Tolerances
Linear Fluorescent: 4-step CFL: 7-step LED: Quadrangle (Duv)
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(radius)
(radius)
• x-step MacAdam ellipses are approximately equal to circles with a radius 0.00x on (u’v’) chromaticity diagram.
• Do not use x-step MacAdam ellipses to specify color differences.
• Use ∆u’v’
For Color Difference
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Binning
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Color Rendering Color Rendering Index (CRI, Ra)
R9 Color Quality Scale (CQS)
Others
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Retail Clothing / Furnishings
Homes
Retail (produce)
Color Appearance & Preference
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Face-to-face Communication
Color Appearance & Preference
Hospitality (Hotel/Spa/Restaurant)
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Anywhere food is served Anywhere faces are important
Color Appearance & Preference
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Color Appearance & Preference
An illustration of the differences on camera between daylight and basic white LED lighting of the same color temperature. Note the first two patches on the test chart represent “skin” tones. 34
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Color Appearance & Preference
The picture illustrates the impact on skin tones while using LED lighting. Skin tones should appear natural, otherwise the subject could look ill. 35
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Industrial Color Matching, Interior Design Selections
Food Inspection
Healthcare (jaundice, redness, cyanosis)
Color Fidelity
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Color Rendering Index (CRI) Ra
The basics: • Compares chromaticity of eight (pastel) test color
samples under test illuminant to reference illuminant • It’s intended to be a fidelity metric • Reference is blackbody radiation (< 5000 K) or a
representation of daylight (> 5000 K) at same CCT as test illuminant
• Averages (and scales) differences of each sample to result in single number
• Maximum score of 100 if all samples match exactly • Ra is part of a larger system that includes 14 (now 15)
total samples • Applicable to sources near blackbody locus • Does not predict appearance of specific objects 37
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Color Rendering Index
TCS 01 TCS 02 TCS 03 TCS 04 TCS 05 TCS 06 TCS 07 TCS 08
TCS 09 TCS 10 TCS 11 TCS 12 TCS 13 TCS 14
Approximation of Color Samples for CRI Ra (3000 K Blackbody Radiation)
Color Samples for R9–R14
CRI: The methods behind the metric
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CRI: The methods behind the metric
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• Averaging, references and other methodological issues • Does not convey exact color appearance
• Very saturated could be the same as very unsaturated • Does not work well for very discrete SPDs (i.e., RGB LED)
Limitations of CRI
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R1
R2
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R4
R5
R6
R7
R8
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R10
R11
R12
R13
R14 Ra
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R2
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R4
R5
R6
R7
R8
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R10
R11
R12
R13
R14 Ra
LED, CRI Ra = 82 CFL, CRI Ra = 82
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Different References
Objects will looks different under sources with same CRI at different CCTs (although chromatic adaptation helps)
2700 K (back) versus 6500 K (front)
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Special Color Rendering Index R9
Because color space is skewed at red… R9=0+ is Good; R9=50+ is Very Good; R9=75+ is Excellent [Equivalent R9 CRI = 100 – (100-R9)/4 ]
The basics: • Part of the same system as CRI Ra
• Same calculation method • Saturated red • Red is particularly important for human skin complexion • Often considered a valuable supplement to CRI Ra
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Special Color Rendering Index R9
TCS09
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CRI versus R9
R² = 0.8932 -100
-80
-60
-40
-20
0
20
40
60
80
100
50 60 70 80 90 100
R 9
CRI
CALiPER LEDCIE F SeriesCFLLinear FluorescentLinear (CALiPER LED)
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Other Color Rendering Metrics
Recent • CQS – Color Quality Scale • n-CRI • Philips CRI • MCRI (Memory Color) • Colour Category Index • Darmstadt RCRI • Feeling of Contrast • CRI-CAM02UCS • GAI – Gamut Area Index
…and many others!
Older • CPI – Color Preference Index • CDI – Color Discrimination Index • CRC – Color Rendering Capacity • CSA – Cone Surface Area • Pointer Index • Flattery Index …and many others!
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The Future of Color Rendering Metrics
• Is there a perfect metric for all situations?
• Who needs what information?
• How similar should new metrics be to existing metrics?
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Halogen 99 CRI , 2917 K, Duv 0.000
Compact Fluorescent 82 CRI, 2731 K, Duv 0.003
LED 84 CRI, 2881K , Duv 0.000
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Metrics aren’t perfect!
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Conclusions + Notes
It is important to understand the limitations/intended use of the various color metrics
If color rendering is a critical issue, consider more than just CRI Metrics are a good start, but if you are a designer, you must
evaluate color with your own eyes.
Energy efficiency standards should include minimum targets for color temperature, color rendering, and color maintenance
Standardized photometry should include SPD, CRI, CCT and Duv With an SPD, it should be possible to calculate a large range
of metrics
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