分形图像编码是一种很有前途的限失真压缩方法，然而，它存在计算量大的缺点，导致其编码时间过长。分形编码的时间主要花费在一个通常较大的码本中搜索每个输入子块的最佳匹配块。[JP]针对此问题，提出了一个加快编码的方案，它基于图像块的规范化行列式，能够在相对小的搜索邻域内找到输入子块的最佳匹配块。对3幅512×512测试图像的实验结果显示，与全搜索基本分形算法比较，依赖于搜索邻域的大小，该算法既能在峰值信噪比相同的情况下实现编码速度加快30倍左右，也能在主观质量略有下降的情况下实现编码速度加快1 [KG-*7]

Fractal image coding is a promising lossy compression technique in terms of achievable compression ratios and decoded image quality; However, it has the primary disadvantage of high computational demands resulting in unacceptably long encoding times. Most of the encoding times are spent on searching for the best matched block to each of range blocks in a usually large domain pool. This paper thus proposed an accelerating scheme by the determinants of normalized range and domain blocks, which can find out the best matched block to an input range block in a relatively small search neighborhood. Experimental results on three popular 512×512 test images showed that, depending on the search neighborhood size, the proposed algorithm not only can achieve the speed up of about 30 times with the same PSNR(peak signal to noise ratio) as the baseline fractal algorithm with the full search, but also can obtain the speed up of 1 [KG-*7]000 times or more at the cost of tolerable degradation of the decoded image quality.