Wavelet image coding with zero-tree of wavelet coefficients
Weon Lee · 2001
We introduce a new wavelet image coding framework using context-based zerotree quantization where an unique and efficient method for optimization of zerotree quantization is proposed. Because of the localization properties of wavelets, when a wavelet coeficient is to be quantized, the best quantizer is expected to be designed to match the statistics of the wavelet coefficients in its neighborhood, that is, the quantizer should be adaptive both in space and frequency domain. In the paper, we describe the proposed coding algorithm. where the spatial-varying models are estimated from the quantized causal neighborhoods and the zerotree pruning is based on the Lagrangian cost that can be evaluated fiom the statistics nearby the three. In this way, optimization of zerotree quantization is no longer a joint optimization problem as in SFQ. Simulation results demonstrate that the coding performance is competitive, and sometimes is superior to the best results of zerotree-based coding reported in SFQ.