A Self-Adaptive Hybrid Genetic Algorithm for Color Clustering

Color palettes are inherent to color quantized images and represent the range of possible colors in such images. When converting full true color images to palletized counterparts, the color palette should be chosen so as to minimize the resulting distortion compared to the original. In this paper, we show that in contrast to previous approaches on color quantization, which rely on either heuristics or clustering techniques, a generic optimization algorithm such as a self-adaptive hybrid genetic algorithm can be employed to generate a palette of high quality. Experiments on a set of standard test images using a novel self-adaptive hybrid genetic algorithm show that this approach is capable of outperforming several conventional color quantization algorithms and provide superior image quality.