一种基于显著不相关检验的近距分形图象编码方法

提出一种基于显著不相关检验的近距分形图象编码方法。对于标准测试图象（Ｌｅｎａ２５６×２５６×８ｐｐｂ），这种方法与子块分类方法相比，以解码图象质量（ＰＳＮＲ）下降２～３（ｄＢ）为代价，编码速度提高了７０～２００倍，且压缩比还有一定的提高；与普通近距分形方法相比，在解码图象质量（ＰＳＮＲ）不下降的情况下，编码速度提高约３２％；若以解码图象质量（ＰＳＮＲ）下降０．２～１（ｄＢ）为代价，编码速度提高３～９倍。

A Neighboring Search Fratcal lmage Coding Based on Testing Significant Uncorrelation of Blocks

In this paper, we proposed a very fast fractal image coding method, which is based on neighboring search for the matching of image blocks and testing of significant uncorrelation of blocks. For the standard test image(Lena 256×256×8ppb), comparing with the classification method, we recorded acceleration factors from 70 up to 200 with 2(dB) to 3(dB) deg radation of PSNR and some improvement of the compression ratio; comparing with the ordinary neighboring search method, we recorded a 32% acceleration factor without any degradation of PSNR, and if 0.2(dB) to 1(dB) degradation of PSNR are permitted, acceleration factors from 3 to 9 are recorded.