【摘要】 基于小波变换的图像压缩算法在较低码率时出现的Ｇｉｂｂｓ效应多年来一直未能得到很好的解决，其主要原因是，纯粹基于像素值的ＭＳＥ（ｍｅａｎｓｑｕａｒｅｅｒｒｏｒ）准则对于图像边缘对应的小波系数分配了较少的比特数．文章在详细分析了Ｓｈａｐｉｒｏ和Ｓａｉｄ＆Ｐｅａｒｌｍａｎ等人提出的零树小波压缩算法的基础上，从抑制高频噪声和图像边缘对应系数的自适应量化等方面对原来的算法进行了改进，给出了相应的对比实验结果．文章的主要意义在于，提出了识别与压缩相结合的思想，由于小波变换的空间局部化特性，因此可以进行非常灵活的比特分配，从而在一定程度上减少了Ｇｉｂｂｓ效应的影响更多还原

【Abstract】 Gibbs phenomenon, which occurs in the waveletbased image compression algorithms under low bit rates, remains an open question for many years. The main cause is that purelypixelvaluebased MSE(mean square error) criteria can not allocate enough bits to the wavelet coefficients corresponding to edges in image. With detail analysis of zerotree wavelet image compression algorithm originally proposed by Shapiro and then wellmodified by Said and Pearlman, the algorithm is improved by suppressing high frequency noises as well as adaptively quantizing coefficients around edges. Experimental results are comparatively given. The main contribution of this paper is the idea of combination of recognition and compression. With the aid of the spatial localization property of wavelet transform, a very flexible bit allocation scheme can be realized, and therefore Gibbs phenomenon is reduced to some extent.更多还原