基于空间-色度多维向量模糊量化的彩色图象压缩

Color Image Compression by Fuzzy Quantization of Multidimensional Vectors

在Ｆｕｚｚｙｋ－ｍｅａｎｓ算法的基础上，考虑到真实图象中的色度信息与空间相关性，提出了空间－色度复合的多维向量模糊聚类量化方法。它能有效地压缩彩色图象信息，量化失真小，并在不使图象边缘钝化的条件下去除图象的高频噪声。

A fuzzy quantizer of multidimensional vectors for color image compression is proposed. It exploits the spatial redundancy as well as the chromatic information by defining the membership function with spatial chromatic combined vectors. As its hypersphere determined by the membership function shrinks, each vector, which is assigned to multiple clusters in the early stages of iteration, falls in one cluster. The initial codebook is generated by a constrained random initialization to help avoid the local minimum. Experiment results demonstrated that this approach of spatial chromatic vector quantization can achieve high compression efficiency. The results also revealed that the method is capable of diminishing noise without blurring edges in the image.