Fractal Image Compression Based on High Entropy Values Technique

Douaa Younis Abbaas
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Abstract

There are many attempts tried to improve the encoding stage of FIC because it consumed time. These attempts worked by reducing size of the search pool for pair range-domain matching but most of them led to get a bad quality, or a lower compression ratio of reconstructed image. This paper aims to present a method to improve performance of the full search algorithm by combining FIC (lossy compression) and another lossless technique (in this case entropy coding is used). The entropy technique will reduce size of the domain pool (i. e., number of domain blocks) based on the entropy value of each range block and domain block and then comparing the results of full search algorithm and proposed algorithm based on entropy technique to see each of which give best results (such as reduced the encoding time with acceptable values in both compression quali-ty parameters which are C. R (Compression Ratio) and PSNR (Image Quality). The experimental results of the proposed algorithm proven that using the proposed entropy technique reduces the encoding time while keeping compression rates and reconstruction image quality good as soon as possible.
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基于高熵值技术的分形图像压缩
由于FIC编码阶段耗时长,人们对其进行了许多改进。这些尝试都是通过减小搜索池的大小来实现对范围域匹配的,但大多数都导致重构图像的质量较差或压缩比较低。本文旨在提出一种结合FIC(有损压缩)和另一种无损技术(在这种情况下使用熵编码)来提高全搜索算法性能的方法。熵技术将根据每个范围块和域块的熵值减小域池的大小(即域块的数量),然后将全搜索算法的结果与基于熵技术的算法的结果进行比较,以查看每种算法的最佳结果(例如在压缩质量参数C. R(压缩比)和PSNR(图像质量)都可以接受的情况下减少编码时间)。实验结果表明,采用该算法在保持较好的压缩率和重构图像质量的同时,减少了编码时间。
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