Development and Evaluation of Six Gamut-Mapping Algorithms for Pictorial Images

Six techniques for mapping the colors of an image into the gamut of printable colors were compared. Six pictorial scenes were used in two psychophysical experiments, one to test accurate reproduction and one to test preferred reproduction. A new contrast-enhancing algorithm was found to give more favorable reproductions than several gamut-mapping techniques described in the literature. This algorithm performs luminance compression by applying an inverted power function to images in a linear RGB color space: 1 (1 RGB). Remaining out-of-gamut pixels are clipped to the gamut surface in the direction of a central point on the neutral axis. Other algorithms that performed well were those that clip out-of-gamut colors to the surface of the gamut, and do not affect colors within the gamut. These algorithms can sometimes result in undesirable artifacts for certain images, including contouring and loss of shadow detail. However, observers did not object to the loss of shadow detail if the colorfulness of the image was maintained or increased. Also, the results of a matching experiment (original present) and a preference experiment gave quite different results. Clipping algorithms did well in the matching experiments, while contrast boosting algorithms did best in the preference matching. The preferred techniques did well in both experiments.