Abstract
This article presents a color quantization technique that combines two previously proposed approaches: the Binary splitting method and the Iterative ant-tree for color quantization method. The resulting algorithm can obtain good quality images with low time consumption. In addition, the iterative nature of the proposed method allows the quality of the quantized image to improve as the iterations progress, although it also allows a good initial image to be quickly obtained. The proposed method was compared to 13 other color quantization techniques and the results showed that it could generate better quantized images than most of the techniques assessed. The statistical significance of the improvement obtained using the new method is confirmed by applying a statistical test to the results of all the methods compared.
Highlights
Color quantization is a process that attempts to reduce the colors of an image
In addition to the Neuquant method [33], other color quantization methods using Neural networks have been presented in [34,35,36]. Another group of algorithms based on this approach are those that use a swarm of individuals to solve a complex problem. Some methods of this type applied to color quantization are the Particle swarm optimization algorithm [37,38], the Ant-tree for color quantization (ATCQ) method [39], the Iterative ant-tree for color quantization (ITATCQ) method [40], the Artificial bee colony algorithm combined with K-means [41], the Artificial bee colony algorithm combined with ATCQ [42], the Firefly algorithm combined with ATCQ [43] and the Shuffled-frog leaping algorithm [44]
mean squared error (MSE) is computed by Equation (12), where pi represents a pixel of the original image and pi0 represents the pixel in the same position as pi but in the quantized image
Summary
Color quantization is a process that attempts to reduce the colors of an image. Let us consider a color image including n pixels. The palette used to represent this image can include 2563 different colors. The color quantization problem consists of defining a new palette which includes fewer colors than the palette used to represent the original image. Once this palette containing q colors has been defined, it is used to represent the new image, called quantized image. The quantized palette should be defined in such a way that it allows a new image, as similar to the original as possible, to be obtained
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