一种改进的零树小波图像压缩算法

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(mean square error)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

An Improved Zero-tree Wavelet Image Compression Algorithm

Gibbs phenomenon, which occurs in the wavelet-based image compression algorithms under low bit rates, remains an open question for many years. The main cause is that purely-pixel-value-based MSE(mean square error) criteria can not allocate enough bits to the wavelet coefficients corresponding to edges in image. With detail analysis of zero-tree wavelet image compression algorithm originally proposed by Shapiro and then well-modified 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.