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

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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

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Introduction

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RGBXYZ

CIELAB

CIEDE2000

CMC

CIE94

OSA-GP

OSA-GPe

CAM02

¿Wich metric must we use?

Introduction

DIN99

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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

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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:

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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

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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

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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

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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

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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

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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

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Testing colour-differences databases.

Fuzzy Metric method.

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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.

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Thank you for your attention