Modified Bat Algorithm with Local Search for Fractal Image Compression of Bitmap Images

A. Gálvez, A. Iglesias
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Abstract

This paper introduces a new method for IFS fractal image compression of bitmap images. The method is based on the bat algorithm, a powerful swarm intelligence method for optimization. This algorithm is modified with two additional features: a new population model based on strong elitism and new random individuals, and the inclusion of mutation operators. The modified method is also coupled with a local search heuristics for further enhancement. An illustrative example is used to analyze the performance of this approach. Our experiments on a fractal image show that the method performs very well, being able to capture the underlying structure of the original image with good visual quality. We also compared our method with other alternative approaches in the literature and found that it outperforms all of them for the given example. However, the numerical results show that there is also room for further improvement. We conclude that the method is promising and can potentially become a very useful and effective technique for fractal image compression of bitmap images.
基于局部搜索的改进Bat算法对位图图像进行分形压缩
介绍了一种对位图图像进行IFS分形图像压缩的新方法。该方法基于蝙蝠算法,这是一种强大的群体智能优化方法。该算法增加了两个特征:基于强精英主义和新随机个体的新种群模型,以及包含突变算子。改进后的方法还结合了局部搜索启发式算法以进一步增强。通过实例分析了该方法的性能。我们在一个分形图像上的实验表明,该方法性能很好,能够以良好的视觉质量捕获原始图像的底层结构。我们还将我们的方法与文献中的其他替代方法进行了比较,发现对于给定的示例,它的性能优于所有方法。然而,数值结果表明,还有进一步改进的空间。结果表明,该方法具有较好的应用前景,有望成为位图图像分形压缩的一种有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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