在Fuzzyk-means算法的基础上，考虑到真空蝇的色度信息与空间相关性，提出了空间一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。该方法在彩色图象在实验中取得了良好的效果。

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.