Crowding Optimization Method to Improve Fractal Image Compressions Based Iterated Function
Crowding Optimization Method to Improve Fractal Image Compressions Based Iterated Function
复制标题
基于迭代函数改进分形图像压缩的拥挤优化方法
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Neseif J. Al
中科院分区：
文献类型：
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作者：
S. S. Al;N. Al;Neseif J. Al
分形图是由迭代函数系统理论产生的几何图案。一种被称为分形图像压缩的流行技术基于这一理论，该理论假设图像中的冗余可以通过分块自相似来利用，并且原始图像可以通过分形码的有限迭代来近似。在其他图像压缩技术中，该技术提供了高压缩比。然而，它也存在一些缺点，如图像质量与计算成本成反比。已经提出了许多方法来在质量和成本之间找到折衷方案。遗传算法作为一种有效的优化方法被用来解决这一问题。本文采用一种改进的遗传算法--排挤算法来优化目标图像中的搜索空间，使其在单次运行中能够很好地逼近全局最优解。实验结果表明，与经典的分形图像压缩方法相比，该方法在保持高质量图像的同时，减少了编码时间，具有较好的效率。
Fractals are geometric patterns generated by Iterated Function System theory. A popular technique known as fractal image compression is based on this theory, which assumes that redundancy in an image can be exploited by block-wise self-similarity and that the original image can be approximated by a finite iteration of fractal codes. This technique offers high compression ratio among other image compression techniques. However, it presents several drawbacks, such as the inverse proportionality between image quality and computational cost. Numerous approaches have been proposed to find a compromise between quality and cost. As an efficient optimization approach, genetic algorithm is used for this purpose. In this paper, a crowding method, an improved genetic algorithm, is used to optimize the search space in the target image by good approximation to the global optimum in a single run. The experimental results for the proposed method show good efficiency by decreasing the encoding time while retaining a high quality image compared with the classical method of fractal image compression.