Psychophysical evaluation of gamut mapping techniques using simple rendered images and artificial gamut boundaries

• Published in: IEEE transactions on image processing Vol. 6; no. 7; pp. 977 - 989
• Main Authors: Montag, E.D., Fairchild, M.D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.1997
• Subjects: Cathode ray tubes; Image coding; Image segmentation; Printers; Psychology; Rendering (computer graphics); Skin; Statistical analysis; Testing
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/83.597273

Using a paired comparison paradigm, various gamut mapping algorithms were evaluated using simple rendered images and artificial gamut boundaries. The test images consisted of simple rendered spheres floating in front of a gray background. Using CIELAB as our device-independent color space, cut-off values for lightness and chroma, based on the statistics of the images, were chosen to reduce the gamuts for the test images. The gamut mapping algorithms consisted of combinations of clipping and mapping the original gamut in linear piecewise segments. Complete color space compression in RGB and CIELAB was also tested. Each of the colored originals (R,G,B,C,M,Y, and Skin) were mapped separately in lightness and chroma. In addition, each algorithm was implemented with saturation (C/sup *//L/sup */) allowed to vary or retain the same values as in the original image. Pairs of test images with reduced color gamuts were presented to twenty subjects along with the original image. For each pair the subjects chose the test image that better reproduced the original. Rank orders and interval scales of algorithm performance with confidence limits were then derived. Clipping all out-of-gamut colors was the best method for mapping chroma. For lightness mapping at low lightness levels and high lightness levels particular gamut mapping algorithms consistently produced images chosen as most like the original. The choice of device-independent color space may also influence which gamut mapping algorithms are best.