基于零树、金字塔格型矢量量化的小波图像编码

近年来，金字塔格型矢量量化（PLVQ）方法由于其本身的特点和性质引起了人们的广泛兴趣，而文献1]介绍的基于零树（ZR）的编码也获得了极大的成功，根据信号的二进小波分解特点和小图象系数的分布特点，得用D4格将PLVQ和零树结构起来，提出了一种基于零树和金字塔型矢量量化的小波图象编码方法，该方法首先采用金字塔 型矢量方法来量化小波图象系数，以得到非零格点和零格点；然后采用复合熵编码来处理非零格点；最后为了有效确定非零格点的位置，也就是为了有效地处理，也就是为了有效地处理零格点，又引进了重要图的概念，在此基础上，从下往上、从上往下二次扫描重要图，再采用改进的零树编码方法来处理零格点，本文乘法优于传统的基于游长的熵编码。

Zerotrees and Pyramidal Lattice Vector Quantization for Wavelet Image Coding

Pyramidal lattice vector quantization(PLVQ) has drawn extensive attention by its promising property recently. The coding method based on zerotree (ZR) coding 1] has made a great coup during the last few years. Based on the traits of dyadic wavelet decomposition of signal and that of the distribution of wavelet image coefficients, PLVQ and ZR are conjoined by making use of D-4 lattice. Firstly, Pyramidal lattice vector quantization is adopted to quantize wavelet image coefficients. Nonzero lattice vectors and zero lattice vectors are formed. Secondly, nonzero lattice vectors are dealt with by adopting complex entropy coding. Finally, in order to fix on the position of nonzero lattice vector effectively, that is, to deal with zero lattice vectors effectively, the concept of significant map is introduced into. The significant map is scanned two times from down to up and from up to down. Based on this and the probability distribution of zerotree roots, zero lattice vectors are disposed by adopting improved zerotree coding. Experimental results demonstrate that the proposed algorithm performs better than traditional entropy coding based on runlength.