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
1. Introduction
2. Testing colour-differences formulas. STRESS
3. Colour-differences in OSA-UCS space
4. Testing colour-differences databases. Fuzzy method.
5. Checking Recent Colour-Difference Formulas with a
Dataset of Just Noticeable Colour-Difference.
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Introduction
Introduction
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R  G  B
XYZ
CMC
OSA-GPe
OSA-GP
CIE94
CIELAB
CAM02
CIEDE2000
DIN99
¿Wich metric must we use?
Introduction
5/26
Division 1: Vision and Colour
TC1-27
TC1-36
TC1-37
TC1-41
TC1-42
TC1-44
TC1-54
TC1-55
TC1-56
TC1-57
TC1-58
TC1-60
TC1-61
TC1-63
TC1-64
TC1-66
TC1-67
TC1-72
TC1-68
TC1-69
TC1-70
TC1-71
TC1-73
TC1-74
Colour appearance for reflection/VDU comparison
Fundamental chromaticity diagram
Supplementary system of photometry
Extension of V(l) beyond 830nm
Colour appearance in peripheral vision
Practical daylight sources for colorimetry
Age-related change of visual response
Uniform colour space for industrial colour difference evaluation
Improved color matching functions
Standards in colorimetry
Visual performance in the mesopic range
Contrast sensitivity function
Categorical colour identification
Validity of the range of CIEDE2000
Terminology for vision, colour, and appearance
Indoor daylight illuminant
The effect of ation
Measurement odynamic and stereo visual images on human health
Effect of stimulus size on colour appearance
Colour rendition by white light sources
Metameric sample for indoor daylight evaluation
Tristimulus integrf appearance network: MApNet
Real colour gamuts
Methods for Re-Defining CIE D-Illuminants
7/26
Testing colour-differences
Formulas. STRESS index
Introduction
8/26
E*ab
E00
5.5
7.9
5.4
2.6
5.6
5.1
3.9
2.0
4.1
2.4
From Test Targets 8.0, Prof. Bob Chung. Rochester Institute of Technology, NY, USA
Testing colour-differences formulas
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PERFORMANCE FACTOR PF/3 (Luo et al. ,1999).
Perfect Agreement:
2

 


E

E
1 
i
i


l
o
g

l
o
g

l
o
g

 
 
1
0
1
0
1
0
N

V

V
i

1


 i i


N

E
F

V
1 
i
i
V


A
B
N
E

V
i

1 
iF
i
N
2
N
F

i
Vi
V
Ei
i1
N
i
VAB = 0
N
2
 
E
i1

Ef

V
1N
i
i
C
V

1
0
0 
2
N
i

1

E
log10) 1
f 
E V
i 1
N
i
i
V
i 1
CV = 0
2
i
1
0
0
1

V

C
V




A
B


P
F
/
3

3
PF/3 = 0
Testing colour-differences formulas
10/26
Proposal of STRESS index (Kruskal’s STRESS)
(STandardized REsidual Sum of Squares)
   Vi  F Ei 2 

STRESS  100 
2


 Vi


2
V
S
T
R
E
S
S
A
F
A
2
V
S
T
R
E
S
S
B
B
Assuming the same set
of ∆Vi (i=1…N) data

E
V


E

F
i
0 ≤ STRESS ≤ 100
i
2
i
Perfect Agreement
STRESS = 0
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
P.A. García, R. Huertas, M. Melgosa, G. Cui. JOSA A, 24 (7), 1823-1829, 2007
Testing colour-differences formulas
11/26
STRESS (%)for the three last CIE recommended formulas
COM Weighted (11273 color pairs)
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.
Testing colour-differences formulas
12/26
STRESS (%) increase for reduced models & COM Weighted
14/26
Colour-differences in
OSA-UCS space
Colour-differences in OSA-UCS space
15/26
The GP (Granada-Parma) formulas
R. 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.

1
0
L

1
0
C

1
0
H












O
S
A
O
S
A
O
S
A

E









G
P
S
S
S
 L
 C
 H
2
2
2
 
S 1.2350.0581
 0C 
S 1.3920.0171
 0C 
SL 2.4990.00710L
O
SA
C
O
SA
C
O
SA
Similar STRESS% than CIEDE2000, but simpler and physiologically based
Colour-differences in OSA-UCS space
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
E

L


G


J




G
P
,
E 
2
O
S
A
,
E
2
E
2
E
 1   0.015

LE  
10LOSA 
ln 1
 0.015  2.890

 1   0.050

CE  
10COSA 
ln 1
 0.050   1.256

 J 
h  arctan 
G
GE CE cos(h)
JE  CE sin(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
Colour-differences in OSA-UCS space
CIELAB
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DIN99d
CAM02-SCD
GP, Euc
Testing colour-differences formulas
18/26
STRESS (%)
30
20
10
0
CIEDE2000
DIN99d
DE(GP,Euc)
CAM02-SCD
Fórmulas de Diferencia de Color
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.
Testing colour-differences formulas
19/26
60
COM Unweighted Data
(3813 color pairs)
STRESS (%)
50
TC 1- 63
CIELAB
OSAGP
CAMUCS
CAMSCD
DIN99
CIE00
CIE94
40
30
>9
,0
0,
51,
0
1,
01,
5
1,
52,
0
2,
02,
5
2,
53,
0
3,
03,
5
3,
54,
0
4,
04,
5
4,
55,
0
5,
06,
0
6,
07,
0
7,
09,
0
00,
5
20
CIELAB Range
• 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).
21/26
Testing colour-differences
databases.
Fuzzy Metric method.
Testing colour-differences databases. Fuzzy Metric method
22/26
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)
Vi
Ri 
Ei

F
M
(,
R
R
,

)



RR
i
i
i
i
i
E
V
 N (S)R
S
SS
, jSi
j
R
i
Si
1
j
Sj
SS
, jSi
j
i
( )
(R
R
)
NS
j
 N (S)
Si
1
i
S
S
,S
S


i
j
j
S
i
1
j
i
( )
NS
S
S
,S
S
j
j
i
(unreliable) 0  FM  1 ( perfect reliability)
FM give us an idea if pair i agrees with its near neighbors
2
j
S
i
1
j
i
Testing 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 with
highest FM seem to match with cases of best linear correlation.
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Thank you for your attention