Predicting the performance of a spatial gamut mapping algorithm

Abstract

Gamut mapping algorithms are currently being developed to take advantage of the spatial information in an image to improve the utilization of the destination gamut. These algorithms try to preserve the spatial information between neighboring pixels in the image, such as edges and gradients, without sacrificing global contrast. Experiments have shown that such algorithms can result in significantly improved reproduction of some images compared with non-spatial methods. However, due to the spatial processing of images, they introduce unwanted artifacts when used on certain types of images. In this paper we perform basic image analysis to predict whether a spatial algorithm is likely to perform better or worse than a good, non-spatial algorithm. Our approach starts by detecting the relative amount of areas in the image that are made up of uniformly colored pixels, as well as the amount of areas that contain details in out-of-gamut areas. A weighted difference is computed from these numbers, and we show that the result has a high correlation with the observed performance of the spatial algorithm in a previously conducted psychophysical experiment.

A new device color gamut boundary description algorithm based on irregular segmentation

“…5 The color gamut can be constructed by gamut boundary descriptors. The construction of a gamut boundary is also the first step in the process of performing color gamut mapping, 6 color gamut extension, 7 spatial color gamut mapping, 8,9 dependent primary control system, 10,11 and so forth. With the visualized color gamut, it can be easier to analyze the reproduction effect of a certain printing material.…”