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Color image quantization using the shuffled-frog leaping algorithm

机译:使用随机蛙跳算法对彩色图像进行量化

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摘要

Swann-based algorithms define a family of methods that consider a population of very simple individuals that cooperate to solve a difficult problem. The Shuffled frog-leaping algorithm is a method of this type that has been applied to solve different types of problems. This article describes the application of this algorithm to the color quantization problem. Although the selected method was developed to solve optimization problems, this work shows how it can be adapted to solve the problem proposed. The proposed method uses the mean squared error as the objective function of the optimization problem to be solved. To reduce the execution time of the algorithm, it is applied to a subset of pixels of the original image. As a result, a quantized palette is obtained that is used to define the quantized image. Computational results indicate that the proposed method can generate a quantized image with low computational cost. Moreover, the quality of the image generated is better than that of the images obtained by several well-known color quantization methods.
机译:基于Swann的算法定义了一系列方法,这些方法考虑了合作解决难题的非常简单的个体。随机蛙跳算法是这种类型的方法,已应用于解决不同类型的问题。本文介绍了该算法在颜色量化问题中的应用。尽管开发了选择的方法来解决优化问题,但这项工作表明了如何将其修改为解决所提出的问题的方法。所提出的方法使用均方误差作为要解决的优化问题的目标函数。为了减少算法的执行时间,将其应用于原始图像的像素子集。结果,获得了用于定义量化图像的量化调色板。计算结果表明,该方法能够以较低的计算量生成量化图像。而且,生成的图像的质量比通过几种众所周知的颜色量化方法获得的图像的质量更好。

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