基于小波变换的图象压缩是图象压缩的一项成功技术，并且具有越来越重要的作用，但基于小波变换的图象压缩算法在比特率较低时出现的边缘模糊现象仍然是一个公认的难题.为了在一定程度上减少比特率较低时，出现的边缘模糊现象，提出了一种基于势函数模糊聚类量化的新方法，用于对经过小波变换分解后所形成的数字图象高频子带小波系数进行量化.在量化过程中还考虑了高频子带的小波系数的分布特性和高频子带的小波系数对保存边缘、纹理等信息的重要性程度，也利用了模糊集合的特性.实验证明，在低比特率下，这种方法能较好地保存图象边缘和纹理等信息，从而在一定程度上提高了重构图象的主观质量.该方法在小波图象压缩的模糊聚类量化领域进行了一定的尝试.

The wavelet-based image compression is a successful technology, and plays more and more important role in the image compression fields. But the edge fuzzy phenomenon, which occurs in the wavelet-based image compression algorithms under low bit rates, remains an open question. In order to reduce the edge fuzzy phenomenon under low bit rates to some extent, a new method of wavelet image compression based on potential fuzzy clustering quantization has been presented in this paper. The potential fuzzy clustering method is applied to quantize the detail sub band images' wavelet coefficients after the image has been decomposed by the wavelet transform. This method has two advantages. One is it considers the statistical characteristics of each detail sub band images' wavelet coefficients and the importance of detail sub band images' wavelet coefficients for saving the edge and texture information of the original image. The second advantage is it makes use of the characteristics of fuzzy set. The experimental results show that this method can get satisfying results, the edge and texture information can be saved well under low bit rates, the edge fuzzy phenomenon is reduced to some extent. And the subjective quality of the reconstructed image is improved. This paper has made some tries on fuzzy clustering quantization method in the wavelet-based image compression fields.