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Advances in colour-differences evaluation. Luis Gómez-Robledo, Rafael Huertas, Manuel Melgosa, Enrique Hita, Pedro A. García, Samuel Morillas, Claudio Oleari, Guihua Cui. CIENCIA Y TECNOLOGÍA DEL COLOR. 26 Y 27 DE NOVIEMBRE DE 2009 .UNIVERSIDAD PÚBLICA DE NAVARRA. PAMPLONA. 2 /26. INDEX. - PowerPoint PPT Presentation
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Advances incolour-differences evaluation
CIENCIA Y TECNOLOGÍA DEL COLOR. 26 Y 27 DE NOVIEMBRE DE 2009 .UNIVERSIDAD PÚBLICA DE NAVARRA. PAMPLONA
Luis Gómez-Robledo, Rafael Huertas, Manuel Melgosa, Enrique Hita,Pedro A. García, Samuel Morillas, Claudio Oleari, Guihua Cui
2 /26
1. Introduction2.Testing colour-differences formulas. STRESS3.Colour-differences in OSA-UCS space4.Testing colour-differences databases. Fuzzy
method.5.Checking Recent Colour-Difference Formulas with a
Dataset of Just Noticeable Colour-Difference.
INDEX
3 /26
Introduction
4/26
RGBXYZ
CIELAB
CIEDE2000
CMC
CIE94
OSA-GP
OSA-GPe
CAM02
¿Wich metric must we use?
Introduction
DIN99
5/26
Division 1: Vision and Colour
TC1-27 Colour appearance for reflection/VDU comparisonTC1-36 Fundamental chromaticity diagramTC1-37 Supplementary system of photometryTC1-41 Extension of V(l) beyond 830nmTC1-42 Colour appearance in peripheral visionTC1-44 Practical daylight sources for colorimetryTC1-54 Age-related change of visual responseTC1-55 Uniform colour space for industrial colour difference evaluationTC1-56 Improved color matching functionsTC1-57 Standards in colorimetryTC1-58 Visual performance in the mesopic rangeTC1-60 Contrast sensitivity functionTC1-61 Categorical colour identificationTC1-63 Validity of the range of CIEDE2000TC1-64 Terminology for vision, colour, and appearanceTC1-66 Indoor daylight illuminantTC1-67 The effect of ationTC1-72 Measurement odynamic and stereo visual images on human healthTC1-68 Effect of stimulus size on colour appearanceTC1-69 Colour rendition by white light sourcesTC1-70 Metameric sample for indoor daylight evaluationTC1-71 Tristimulus integrf appearance network: MApNetTC1-73 Real colour gamutsTC1-74 Methods for Re-Defining CIE D-Illuminants
Introduction
7/26
Testing colour-differences Formulas. STRESS index
5.5 7.9
5.4 2.6
5.6 5.1
3.9 2.0
4.1 2.4
E*ab E00
From Test Targets 8.0, Prof. Bob Chung. Rochester Institute of Technology, NY, USA
8/26Introduction
100 1/ 3
3ABV CV
PF PF/3 = 0
2
10 10 101
1log log log
Ni i
i i i
E E
N V V
2
1
1 Ni i
ABi i i
E F VV
N E F V
2
21
1100
Ni i
i
E f VCV
N E
(Luo et al. ,1999).Perfect Agreement:
9/26
log10) 1
VAB = 0
CV = 0
Testing colour-differences formulas
PERFORMANCE FACTOR PF/3
1
1
N
i iiN
i ii
E VF
V E
1
2
1
N
i ii
N
ii
E Vf
V
22
100 i i
i
V F ESTRESS
V
0 ≤ STRESS ≤ 100
Proposal of STRESS index (Kruskal’s STRESS) (STandardized REsidual Sum of Squares)
F < FC A is significantly better than B
F > 1/FC A is significantly poorer than B
FC ≤ F <1 A is insignificantly better than B
1 < F ≤ 1/ FC A is insignificantly poorer than B
F = 1 A is equal to B
Assuming the same set of ∆Vi (i=1…N) data
P.A. García, R. Huertas, M. Melgosa, G. Cui. JOSA A, 24 (7), 1823-1829, 2007
10/26
2
i i
i
E VF
E
2
2A A
B B
V STRESSF
V STRESS
Perfect AgreementSTRESS = 0
Testing colour-differences formulas
COM Weighted (11273 color pairs)
STRESS (%)for the three last CIE recommended formulas
11/26Testing colour-differences formulas
For COM Weighted each one of corrections proposed by CIEDE2000 or CIE94 were found statistically significant at 95% confidence level.
CIEDE2000 (but not CIE94) significantly improves CMC.
STRESS (%) increase for reduced models & COM Weighted
12/26Testing colour-differences formulas
14/26
Colour-differences in OSA-UCS space
The GP (Granada-Parma) formulasR. Huertas et al. JOSA A 23, 2077-2084 (2006) C. Oleari et al. JOSA A 26, 121-134 (2009)
See references for definitions of (LOSA, COSA, HOSA ). The format is analogous to the CIE94 one.
2.499 0.007 10
1.235 0.058 10
1.392 0.017 10
L OSA
C OSA
C OSA
S L
S C
S C
2 2 210 10 10OSA OSA OSA
GPL C H
L C HE
S S S
15/26Colour-differences in OSA-UCS space
Similar STRESS% than CIEDE2000, but simpler and physiologically based
1 0.015ln 1 10
0.015 2.890
1 0.050ln 1 10
0.050 1.256
arctan
cos( )
sin( )
E OSA
E OSA
E E
E E
L L
C C
Jh
G
G C h
J C h
• Note that GE axis is green-red, just opposite to CIELAB a* axis.
• Compression is used in the chroma equation (very important), and also in lightness (less important).
Similar STRESS% than CIEDE2000, but simpler and physiologically based
16/26
2 2 2, ,GP E OSA E E EE L G J
Colour-differences in OSA-UCS space
CIELAB DIN99d
GP, EucCAM02-SCD
17/26Colour-differences in OSA-UCS space
STRESS results are very close to those of CIEDE2000, and new formulas are both simpler (Euclidean) and increasingly based on physiology.
Anyway a ~25% STRESS is an “unsatisfactory state of affairs” (R. Kuehni, CR&A, 2008), and new reliable experimental data are required.
CIEDE2000 DIN99d DE(GP,Euc) CAM02-SCD0
10
20
30
ST
RE
SS
(%
)
Fórmulas de Diferencia de Color
18/26Testing colour-differences formulas
20
30
40
50
60
ST
RE
SS
(%
)
CIELAB Range
CIELAB OSAGP CAMUCS CAMSCD DIN99 CIE00 CIE94
COM Unweighted Data (3813 color pairs)
• The performance of all formulas strongly deteriorates below 1.0 CIELAB unit.
• CIELAB and CIE94 are worse than the other formulas in most ranges.
• At highest ranges all formulas are slightly worse (except CIELAB and CIE94).
TC 1- 63
19/26Testing colour-differences formulas
21/26
Testing colour-differences databases.
Fuzzy Metric method.
22/26
EV
( , , ) ii i i
i i i
FM R RR R
ii
i
VR
E
FM give us an idea if pair i agrees with its near neighbors
1
,
1,
( )
( )
i
j j i
i
j j i
Sj j
S S S S
i Sj
S S S S
N S R
RN S
21
,
1,
( )( )
( )
i
j j i
i
j j i
Sj j i
S S S S
i Sj
S S S S
N S R R
N S
Fuzzy analysis for detection of inconsistent data in the experimental datasets employed at the development of the CIEDE2000 colour-difference formula (JMO,56:13,1447-1456, 2008)
Testing colour-differences databases. Fuzzy Metric method
( ) 0 1 ( )unreliable FM perfect reliability
23/26Testing colour-differences databases. Fuzzy Metric method
Data with lowest mean FM in corrected COM correspond with cases of low colour difference for which its V is overestimated. On the other hand, data withhighest FM seem to match with cases of best linear correlation.
26/26
Thank you for your attention