1 / 21

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.

zanna
Télécharger la présentation

Advances in colour-differences evaluation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related