color tolerancingthe case for instrumental · 2001. 11. 6. · wyszecki robertson ganz macadam....
TRANSCRIPT
ROY S. BERNS
Munsell Color Science Laboratory Chester F. Carlson Center for Imaging Science
Rochester Institute of Technology54 Lomb Memorial Drive
Rochester, New York [email protected]
ROY S. BERNS
Munsell Color Science Laboratory Chester F. Carlson Center for Imaging Science
Rochester Institute of Technology54 Lomb Memorial Drive
Rochester, New York [email protected]
The Case for InstrumentalColor Tolerancing
The Case for InstrumentalColor Tolerancing
This talk was presented at a meeting of the Detroit Color Council on June 10, 1997 in Dearborn, Michigan. The theme for this meeting was color measurement and control of automotive materials. This presentation was supported by the Detroit Color Council.
This talk was presented at a meeting of the Detroit Color Council on June 10, 1997 in Dearborn, Michigan. The theme for this meeting was color measurement and control of automotive materials. This presentation was supported by the Detroit Color Council.
Outline of PresentationOutline of Presentation
• Introduction• Data Bases• Visualization of Systematic Trends• Model Development• Model Optimization• Conclusions
Two Competing Philosophies:Two Competing Philosophies:
Visual Instrumental
A Case for Instrumental QAA Case for Instrumental QA
PrerequisitesStatistical sampling of batch
Precise and accurate instrument
Presentation OutlineLimitations of CIELAB
Review of CMC and CIE94
Observer uncertainty
Recommendations
1973 Meeting of Colorimetry Committee1973 Meeting of Colorimetry Committee
City University, London
WyszeckiRobertson
Ganz
MacAdam
CIELABCIELAB"Pending the development of an improved coordinate system..." (CIE Pub. 15.2)
65432100
2
4
6
8
∆∆∆∆ E*ab
∆∆∆∆ V
875 color-difference pairs judged by 50 observers (43,750 judgements)
Systematic Limitations in CIELABSystematic Limitations in CIELAB
Color difference pairs with the same visual difference had ∆∆∆∆E*
ab values that depended on
the C*ab
of the standard
McDonald, 1974
∆E = ∆L2 + ∆C1+ 0.02C
2
+ ∆H2
1/2
C*ab
Sc
1.0
Sc
Society of Dyers and Colourists, 1984: CMCSociety of Dyers and Colourists, 1984: CMC
∆ECMC(l:c) = CF∆L*
lSL
2
+ ∆Cab*
cSC
2
+ ∆Hab*
SH
2
1/2
1008060402000.4
0.6
0.8
1.0
1.2
1.4
1.6
CIELAB L*
CM
C S
l
10050000
1
2
3
4
CIELAB C*
CM
C S
c
300200100001.0
1.2
1.4
1.6
1.8
2.0
CIELAB hue angleC
MC
Sh
CIELAB C* = 50
Wound thread
CIE Division 1 Meeting: Buenos Aires, 1989CIE Division 1 Meeting: Buenos Aires, 1989
TC 1-27 Color differences - soft copy & hard copy
TC 1-28 Parametric factors
TC 1-29 Industrial color difference equation
TC 1-27 Color differences - soft copy & hard copy
TC 1-28 Parametric factors
TC 1-29 Industrial color difference equation
TC 1-29 PhilosophyTC 1-29 PhilosophyContinuous improvementApplicable to all industriesStatistical validityTimely results
k = parametric factor / functions = corrective function
∆ETC1−29* = ∆L*
kLSL
2
+ ∆Cab*
kCSC
2
+ ∆Hab*
kHSH
2
1/2
Statistically Valid DataStatistically Valid Data
Requirements: Multiple observers, precision estimates, surface colors
• WittPainted samples, hairline separation, 20%Y background, 22-24 observers
• Luo-RiggWool serge, hairline separation, 14.2%Y background, 20 observers
Goal to combine 13 experiments each of about 20 observers
• RIT-DupontPainted samples, hairline separation, 10.9% background, 50 observers
Visualization of Systematic Trends: Lightness R 2=0.05Visualization of Systematic Trends: Lightness R 2=0.05
1008060402000
1
2
3
Luo-Rigg
RIT-Dupont
Witt
L*
Del
ta L
*
• Luo-Rigg: Random
• Witt and RIT-Dupont:
Consistent and different
1008060402000
1
2
3
L*
Fun
ctio
n va
lue
CMC
BFD
RIT-Dupont
Optimized Functions(not normalized)
Visualization of Systematic Trends: Chroma R 2=0.70Visualization of Systematic Trends: Chroma R 2=0.70
1201008060402000
1
2
3
Luo-Rigg
RIT-Dupont
Witt
C*
Del
ta C
*
• Consistent trends
for all data sets
• Main reason
for improvement from:
Mc Donald, 1974
Mc Laren, 1976
JPC79, 1979
CMC, 1984
BFD, 1987
Visualization of Systematic Trends: Hue R 2=0.45Visualization of Systematic Trends: Hue R 2=0.45
1201008060402000.0
0.2
0.4
0.6
0.8
1.0
Luo-RiggRIT-Dupont
Witt
C*
Del
ta H
* • Upward Trend
• Weaker Than Chroma Trend
Visualization of Systematic Trends: Hue Angle R 2=0.01Visualization of Systematic Trends: Hue Angle R 2=0.01
40030020010000.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Luo-RiggRIT-Dupont
Witt
h
Del
ta
Hue
/(1+
0.01
5C*s
td)
Corrected for C* position
• Random Dispersion
• No Consistent Trend
TC 1-29 ConclusionsTC 1-29 Conclusions
• SL other than unity would fit parametric
factors• S
L other than unity would not be universal
across industries• S
C and S
H functions should be linear due
to data dispersion• S
H function should not have a hue angle
dependency
CIE94 EquationCIE94 Equation
CIE 116-1995
∆E94* =
∆L*
kLSL
2
+ ∆Cab*
kCSC
2
+ ∆Hab*
kHSH
2
1/2
SL = 1
SC = 1 + 0.045Cab*
SH = 1 + 0.015Cab*
1008060402000.0
1.0
CIELAB L*
Fu
nct
ion
va
lue
CMC
CIE94
-15 -5 5 15-5 5 15
-15
-5
5
15
∆a*∆
L*
CMC vs. CIE94 Lightness ToleranceCMC vs. CIE94 Lightness Tolerance
CMC
Who's right?Who's right?
Optimizing TolerancesOptimizing Tolerances
22 observers making "pass/fail" decisions for 32 samples distributed about a standard
- 1 5 - 5 5 1 5- 1 5
- 5
5
1 5
∆ a *
- 1 5 - 5 5 1 5
- 5
5
- 1 5 - 5 5 1 5- 1 5
- 1 5 - 5 5 1 5∆ a *
- 1 5 - 5 5 1 5
1 5
Optimized Ellipsoids for Four Observers, All Rejecting 18 Samples!
Optimized Ellipsoids for Four Observers, All Rejecting 18 Samples!
Who's right?Who's right?
Number of samples that were unacceptable
Num
ber
of o
bser
vers
0
1
2
3
4
13
14
15
16
17
18
19
20
21
22
23
24
25
Optimized Ellipsoids for Three ObserversOptimized Ellipsoids for Three Observers13, 19, and 25 samples rejected
∆b*
∆a*
∆L*
∆a*
∆L*
∆b*
Who's right?Who's right?
Where Are We?Where Are We?
CMC CIE94Optimize visual/instrumental data"Tomorrow is another day"
We Understand the Primary Limitation of CIELABWe Understand the Primary Limitation of CIELAB
Instrumental Color Tolerancing Can be Accomplished
1201008060402000
1
2
3
Luo-Rigg
RIT-Dupont
Witt
C*
Del
ta C
*
1008060402000
1
2
3
4
5
6
C *Fu
ncti
on v
alu
e CIE94
CMC
ab
Choose an Appropriate EquationChoose an Appropriate Equation
Be Conservative!
CIE94 unless you KNOW betterCIE94 unless you KNOW better
Only Adjust the Commercial FactorOnly Adjust the Commercial Factor
One observer is never representative of a population.
- 1 5 - 5 5 1 5
- 5
5
- 1 5 - 5 5 1 5- 1 5
- 1 5 - 5 5 1 5∆ a *
- 1 5 - 5 5 1 5
1 5
Finally,Finally,
empower yourself through understanding
Thanks DCC for sponsoring this meeting.
Thanks DCC for sponsoring this meeting.
And...And...
Support research that leads to improved equation development.
Munsell Color Science Laboratory Industrial Color Difference
Consortium
Munsell Color Science Laboratory Industrial Color Difference
Consortium
For example, join the:
Current Members:Current Members:
3MBayerDatacolorDetroit Color CouncilDupontDystarGretag/MacbethInter-Society Color CouncilPPGSociety of Plastics EngineersXerox