1 / 22

Perceptual Evaluation of Colour Gamut Mapping Algorithms

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 fabiennedugay@wanadoo.fr http://www.colorlab.no

kael
Download Presentation

Perceptual Evaluation of Colour Gamut Mapping Algorithms

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. 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 fabiennedugay@wanadoo.frhttp://www.colorlab.no Master’s thesis presentation, 7th 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 • Viewingconditions 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?

More Related