Perceptual Evaluation of Colour Gamut Mapping Algorithms Fabienne Dugay The Norwegian Color Research Laboratory Faculty of Computer Science and Media Technology Gjøvik University College, Gjøvik, Norway Master’s thesis presentation, 7 th June 2007

2 Outline Introduction Colour Gamuts Goal Gamut mapping algorithms Gamut mapping algorithms (GMAs) Experimental setup Psychophysical evaluation Images, Media, Viewing conditions Results & Analysis Conclusion and perspectives

3 Introduction Gamut = range of reproducible colours of a device or range of colours in a image Printers have smaller gamut than monitor How to reproduce those out-of-gamut colours ? Gamut mapping algorithms (GMAs): ensure a good correspondence of overall colour appearance between the original and the reproduction

4 Goal Evaluate the performance of selected GMAs on real images Influence of the test images Influence of the observers Influence of the experiments

5 Gamut mapping algorithms Non-spatial GMAs The image is treated globally Gamut compression or gamut clipping Spatial GMAs Depend on the neighbourhood pixels Balance both colour accuracy and preservation of details

6 Experimental methods No metrics have been proved to be efficient for evaluating the performance of GMAs Psychophysical tests with a panel of observers 20 observers (11 “experts” & 9 “non-experts”) Asked about the accuracy of the reproductions The raw data from the experiments are treated statistically to obtain z-scores

7 Experimental methods 20 test images with various characteristics Original: sRGB image on calibrated monitor

8 Experimental methods Reproductions on a inkjet printer with plain paper

9 Experimental methods 5 GMAs: HPminDE: Hue preserving minimum delta E clipping SGCK: lightness and chroma compression, hue preserving Zolliker: recovers local contrast, preserves lightness and saturation Kolås: hue and edge preserving spatial GMA Gatta: preserves hue and local relationships

10 Experimental methods Viewing conditions follow the CIE guidelines: Simulated D50 lights for the prints D65 white point for the monitor Viewed in a neutral grey room with lights at their minimum intensity Original and reproduction images have the same size and a white border Neutral grey background

11 Experimental methods Two psychophysical experiments With printed reproductions Ranking (rank the 5 reproductions from the most to the least accurate to the original displayed on the monitor) With simulated printed reproductions on screen Pair comparison (choose the most accurate reproduction in a pair)

12 Results Results from the ranking experiment

13 Analysis HPminDE: not an accurate GMA Kolås, SGCK and Gatta not significantly different A spatial and non-spatial GMAs seen as accurate

14 Results Results from the ranking experiment, for each image and GMA

15 Analysis Dependant on the test images

16 Analysis But strong correlation between the % of out-of-gamut colours and the number of distinguishable GMAs Strong correlation between the % of out-of-gamut colours and the perceived difficulty to rank the reproductions Gamut mapping especially important when dealing with small gamut devices

17 Results Dependant on the observers

18 Analysis Different results between the two groups Stronger consensus among the experts All GMAs have tight scores for the non-experts Experts look at the best rendering of details Non-experts look more at the saturation

19 Results Dependant on the experiments

20 Analysis Globally comparable results Some other parameters: Random of the scenes Accuracy or preference? Other media/printers LCD/CRT monitors

21 Conclusion and perspectives None GMA is significantly better than all the others HPminDE (clipping) is not perceived as an accurate GMA The choice of a efficient GMA may depend on the image, the media, the target customer and an universal GMA seems inexistent Meta-analysis to join the results of the different GMA evaluations?

22 Thank you for you attention Any questions?