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Advances in colour-differences evaluation

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.

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Advances in colour-differences evaluation

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  1. Advances incolour-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. 2 /26 INDEX Introduction Testing colour-differences formulas. STRESS Colour-differences in OSA-UCS space Testing colour-differences databases. Fuzzy method. Checking Recent Colour-Difference Formulas with a Dataset of Just Noticeable Colour-Difference.

  3. 3 /26 Introduction

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

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

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

  7. Introduction 8/26 From Test Targets 8.0, Prof. Bob Chung. Rochester Institute of Technology, NY, USA

  8. Testing colour-differences formulas 9/26 PERFORMANCE FACTOR PF/3 (Luo et al. ,1999). Perfect Agreement: log10(g)= 1 VAB= 0 CV = 0 PF/3 = 0

  9. Testing colour-differences formulas 10/26 Proposal of STRESS index (Kruskal’s STRESS) (STandardized REsidual Sum of Squares) 0 ≤ STRESS ≤ 100 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 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. 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.

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

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

  13. 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. Similar STRESS% than CIEDE2000, but simpler and physiologically based

  14. Colour-differences in OSA-UCS space 16/26 • 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

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

  16. Testing colour-differences formulas 18/26 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.

  17. Testing colour-differences formulas 19/26 TC 1- 63 • 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).

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

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

  20. Testing colour-differences databases. Fuzzy Metric method 23/26 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

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