Differential evolution algorithm (DE) is one of the novel stochastic optimization methods. It has a better performance in the problem of the color image quantization, but it is difficult to set the parameters of DE for users. This paper proposes a color image quantization algorithm based on self-adaptive DE. In the proposed algorithm, a self-adaptive mechanic is used to automatically adjust the parameters of DE during the evolution, and a mixed mechanic of DE and -means is applied to strengthen the local search. The numerical experimental results, on a set of commonly used test images, show that the proposed algorithm is a practicable quantization method and is more competitive than -means and particle swarm algorithm (PSO) for the color image quantization. 1. Introduction Color image quantization, one of the common image processing techniques, is the process of reducing the number of colors presented in a color image with less distortion . The main purpose of color quantization is reducing the use of storage media and accelerating image sending time . Color image quantization consists of two essential phases. The first one is to design a colormap with a smaller number of colors (typically 8–256 colors ) than that of a color image. The second one is to map each pixel in the color image to one color in the colormap. Most of the color quantization methods focus on creating an optimal colormap. For being an NP-hard problem, it is not feasible to find the optimal colormap without a prohibitive amount of time . To address this problem, researchers have applied several stochastic optimization methods, such as GA and PSO. In particular, the literature [5–8] has compared the color image quantization algorithm using PSO (PSO-CIQ) and several other well-known color image quantization methods. The experimental results show that PSO-CIQ has higher performance. Differential evolution algorithm (DE) [9–11] is a population-based heuristic search approach. DE has been applied to the classification for gray images [12–14]. In the literature [12–14], DE and PSO show similar performance. However, due to simple operation, litter parameters, and fast convergence, DE is the better choice to use than PSO . However, few researches have been done for using DE to solve the color image quantization. This paper applies DE to solve the color image quantization. However, the performance of DE is decided by two important parameters, the scaling factor and the crossover rate CR. In practice, it is difficult to set the two parameters. For this difficulty, this paper
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F. Alamdar, Z. Bahmani, and S. Haratizadeh, “Color quantization with clustering by F-PSO-GA,” in Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS '10), pp. 233–238, October 2010.
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