A Self-Adaptive Hybrid Genetic Algorithm for Color Clustering

Abstract

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.

Fuzzy ART for Relatively Fast Unsupervised Image Color Quantization

“…The RMSE yielded between the original and color quantized image, as shown in [Fig. 2], is competitive with that of other methods in the literature [6], [7]. One unfortunate disadvantage of using FA for CQ is one cannot directly specify the number of colors that results in the color quantized image.…”

“…In order to evaluate the quality of the color quantized image, the root mean squared error (RMSE) between the original color image and the color quantized image is used. The RMSE is also used in both [6] and [7] where they have benchmarked their algorithms on the Lenna and Mandrill images as well. The MSE for the red color component is calculated as follows:…”

A Hybrid Genetic Algorithm for Partitioning of Data Model in Distribution Management Systems

“…Furthermore, parallel GA [14,15] was developed to speed up the algorithm. If the accuracy of a simple GA is not satisfied, then variants of hybrid GA are recommended [16][17][18][19]. GA finds a solution close to the optimum and some other algorithm (often KL/FM) is used for local refinement [13].…”