Abstract
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 K-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 K-means and particle swarm algorithm (PSO) for the color image quantization.
Highlights
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 [1]
Most of the color quantization methods focus on creating an optimal colormap
The test images are quantized into 16 colors
Summary
One of the common image processing techniques, is the process of reducing the number of colors presented in a color image with less distortion [1]. This paper applies DE to solve the color image quantization. It is difficult to set the two parameters For this difficulty, this paper proposes a color image quantization algorithm based on self-adaptive DE (SaDE-CIQ). In SaDE-CIQ, the self-adaptive mechanics in the literature [15, 16] are used to automatically adjust the parameters of DE during the evolution, and K-means is mixed into DE with a little probability for strengthening the local search. By some commonly used color images, the performance of SaDE-CIQ in the color image quantization is compared with that of K-means and PSO.
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