1. Advances in colour-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. 2 /26 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. INDEX

3. 3 /26 Introduction

4. 4/26  R  G  B  X  Y  Z CIELAB CIEDE2000 CMC CIE94 OSA-GP OSA-GPe CAM02 ¿Wich metric must we use? Introduction DIN99

5. 5/26 Division 1: Vision and Colour TC1-27Colour appearance for reflection/VDU comparison TC1-36Fundamental chromaticity diagram TC1-37Supplementary system of photometry TC1-41Extension of V(l) beyond 830nm TC1-42 Colour appearance in peripheral vision TC1-44Practical daylight sources for colorimetry TC1-54Age-related change of visual response TC1-55Uniform colour space for industrial colour difference evaluation TC1-56Improved color matching functions TC1-57Standards in colorimetry TC1-58Visual performance in the mesopic range TC1-60Contrast sensitivity function TC1-61Categorical colour identification TC1-63Validity of the range of CIEDE2000 TC1-64Terminology for vision, colour, and appearance TC1-66Indoor daylight illuminant TC1-67The effect of ation TC1-72Measurement odynamic and stereo visual images on human health TC1-68Effect of stimulus size on colour appearance TC1-69Colour rendition by white light sources TC1-70Metameric sample for indoor daylight evaluation TC1-71Tristimulus integrf appearance network: MApNet TC1-73Real colour gamuts TC1-74Methods for Re-Defining CIE D-Illuminants Introduction

6. 7/26 Testing colour-differences Formulas. STRESS index

7. 5.57.9 5.42.6 5.65.1 3.92.0 4.12.4  E* ab  E 00 From Test Targets 8.0, Prof. Bob Chung. Rochester Institute of Technology, NY, USA 8/26 Introduction

8. PF/3 = 0 (Luo et al.,1999). Perfect Agreement: 9/26 log 10  )  1 V AB = 0 CV = 0 Testing colour-differences formulas PERFORMANCE FACTOR PF/3

9. 0 ≤ STRESS ≤ 100 Proposal of STRESS index (Kruskal’s STRESS) (STandardized REsidual Sum of Squares) F < F C A is significantly better than B F > 1/F C A is significantly poorer than B F C ≤ F <1 A is insignificantly better than B 1 < F ≤ 1/ F C A is insignificantly poorer than B F = 1 A is equal to B Assuming the same set of ∆V i (i=1…N) data P.A. García, R. Huertas, M. Melgosa, G. Cui. JOSA A, 24 (7), 1823-1829, 2007 10/26 Perfect Agreement STRESS = 0 Testing colour-differences formulas

10. COM Weighted (11273 color pairs) STRESS (%)for the three last CIE recommended formulas 11/26 Testing 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.

11. STRESS (%) increase for reduced models & COM Weighted 12/26 Testing colour-differences formulas

12. 14/26 Colour-differences in OSA-UCS space

13. 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 (L OSA, C OSA, H OSA ). The format is analogous to the CIE94 one. 15/26 Colour-differences in OSA-UCS space Similar STRESS% than CIEDE2000, but simpler and physiologically based

14. Note that G E 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 Colour-differences in OSA-UCS space

15. CIELAB DIN99d GP, Euc CAM02-SCD 17/26 Colour-differences in OSA-UCS space

16. 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. 18/26 Testing colour-differences formulas

17. 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 19/26 Testing colour-differences formulas

18. 21/26 Testing colour-differences databases. Fuzzy Metric method.

19. 22/26 EVEV FM give us an idea if pair i agrees with its near neighbors 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

20. 23/26 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.

21. 26/26 Thank you for your attention