Grey wolf optimization for color quantization

Color quantization is an image processing operation that attempts to reduce the number of distinct colors used to represent an image without significant loss of quality. This operation is useful as an initial step to perform further processing on the image. The interest in this operation has led to several solution methods being proposed over the years. Within these methods several swarm-based methods have been used in recent years to solve the problem. This article discusses the application of one of these methods, called grey wolf optimization, to perform color quantization. This algorithm has generated good results when applied to a variety of optimization problems. Among the features that differentiate this algorithm from other swarm algorithms are its ability to converge toward better solutions, its speed, and the existence of a single control parameter. The article describes in detail how the grey wolf optimization method should be adapted to perform color quantization, so that it generates a quantized palette that allows the quantized image to be represented. In this case, each individual in the group represents a quantized palette, which is improved with the information provided by the group. The detailed description of the algorithm is complemented by an extensive testing section that compares the results of the proposed method to those of 16 others techniques. The results, based on the comparison of MSE, MAE, PSNR, SSIM, and runtime, show that grey wolf optimization can generate good quality images, better than most of the compared methods. • The Grey wolf optimization method is improved to apply it to color quantization. • The method is tested on 24 images commonly used in color quantization literature. • Extensive experiments and statistical analysis show that the proposal is effective. • It obtains better results than most of the 16 methods with which it is compared.