Affiliations: Department of Signal Theory and Communications, Universidad de Alcalá Madrid, Madrid, Spain
Correspondence:
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Corresponding author: Dr. Sancho Salcedo Sanz, Department of Signal Theory and Communications, Escuela Politécnica Superior, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, Spain, Salcedo Sanz Sancho. E-mail: [email protected].
Abstract: Color reduction in images is an important problem in image processing, since it is a pre-processing step in applications such as image segmentation or compression. Different methods have been proposed in the literature, several of them involving nature-inspired algorithms such as neural networks. However, not many works involving evolutionary computation techniques have been applied to this problem. This paper proposes a novel evolutionary algorithm to tackle the color reduction of RGB images. The proposed evolutionary algorithm incorporates a procedure called incremental-encoding, consisting in starting the image quantization with a small number of colors, and including additional colors in a gradual form, until reaching the final number of quantization colors. In the experiments carried out we show that the incremental-encoding evolutionary algorithm improves the performance of the standard evolutionary algorithm in this problem. Also we show that it obtains better results than several existing color reduction techniques for color quantization problems.
Keywords: Color reduction, evolutionary algorithms, incremental-encoding, dithering
