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结合K均值聚类和KD-Tree搜索的快速分形编码方法

Fast Fractal Coding by Combining K-Mean Clustering and KD-Tree Search

  • 摘要: 利用部分失真搜索求解传统K均值聚类算法中的最近邻搜索问题,显著地减少了传统算法的乘法次数,从而提高了聚类速度;然后用改进后的聚类算法来加速分形编码:首先将定义域块聚类并为每个类建立一棵KD-Tree,编码时对每个值域块先后用部分失真搜索与近似最近邻搜索得到与其距离最近的若干KD-Tree及其上的若干最近邻,而其最优匹配块即由后者产生.实验结果表明,相对于全局搜索,该方法能大幅度地提高编码速度和较大地提高压缩比,而解码质量只有很小的下降;相对于同类方法,在相同压缩比下有更好的加速效果和解码质量.

     

    Abstract: In this paper,we first present a fast K-mean clustering algorithm by using Partial Distortion Search (PDS) to complete the nearest neighbor searching in traditional K-mean clustering algorithm.Then we use the improved algorithm to speed up fractal image coding:cluster the domain blocks and build a KD-Tree for each cluster;and for each range block,its best-match domain block is obtained by PDS and approximate nearest neighbor searching (ANNS) from some nearest neighbors of each of some nearest KD-Trees. Experimental results show that,in comparison with exhaustive searching,only with slight degradation of decoded image quality,the proposed method improves the coding speed and compression ratio greatly,and in contrast to similar methods,it provides better speed-up and image quality under the same compression ratio.

     

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