Color quantization with genetic algorithms

The need for quantization of color images arises because of limitations of image display and hardcopy, data storage and data transmission devices. Many of the present algorithms for color quantization find non-optimal solutions, giving rise to visible shifts in color and false contours when the number of quantization colors is small. This paper describes a new approach to finding the optimal solutions of the color image quantization problem using a genetic algorithm. The nature and the difficulty of the problem and its formulation are discussed. Then genetic algorithms (GAs) are presented and the representation of the problem with this method is explained. The effect of the parameters such as mutation and crossover probabilities and population size on the quality of solutions is studied. Solutions obtained with genetic algorithms are compared with those of heuristics and the K-means clustering algorithm and the superior quality of the results of the GA is shown.

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