Zerotrees and Pyramidal Lattice Vector Quantization for Wavelet Image Coding
基于零树、金字塔格型矢量量化的小波图像编码

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.