基于相关信息特征最近邻搜索的快速分形图像编码

Nearest Neighbor Search for Fast Fractal Image Encoding Based on Correlation Information Feature

针对分形图像压缩算法编码时间过长的问题,提出采用相关信息特征作为最近邻搜索特征的快速分形编码算法.通过深入分析图像子块的结构特性,提出相关信息特征的定义,证明并分析了采用该特征进行最近邻搜索操作的合理性.与传统特征相比,相关信息特征能够更好地反映子块的结构特性,所以基于相关信息特征的最近邻搜索能够更准确地确定后续局部匹配的范围.实验表明,在编码时间相同的情况下,本文算法较其他三种同类算法能够得到更好的解码图像质量.

To solve the problem of long time consuming in the fractal encoding process,a fast fractal encoding algorithm based on correlation information feature is proposed.By analyzing the blocks＇ structural characteristics in detail,the definition of correlation information feature is proposed and the rationality of adopting correlation information feature in nearest neighbor search is proved and analyzed.Correlation information feature can reflect the structural characteristics better than the conventional features,so the local matching space can be more accurately determined by the feature＇s nearest neighbor search.Experiments show that compared with three other similar fast fractal encoding algorithms,the proposed algorithm can provide better decoded image quality in the case of the same encoding time.