<title>Wavelet packet image coding with optimized zerotree quantization</title>

Abstract

This paper addresses the problems of how to exploit the space and frequency properties of the wavelet coefficients, and how to design a wavelet packet coder optimally in the rate and distortion sense. From the localization properties of the wavelets, the best quantizer for a wavelet coefficient is expected to match its local characteristics, i.e., to be adaptive both in space and frequency domain. Previous image coders tended to design quantizer in a band or a class level, which limited their performances as it is difficult for the localization properties of wavelets to be exploited. Contrasting with previous coders, we introduce a new image coding framework, where the compaction properties in frequency domain are exploited through the selection of wavelet packets, and the compaction properties in space domain are exploited with the tree-structured wavelet representations. For each wavelet coefficient, its model is estimated from the quantized causal neighborhoods, therefore, the optimal quantizer is spatial-varying and rate sensitive, and the optimization problem is no longer a joint optimization problem as in the SFQ-like coders. The simulation results demonstrate that the proposed coding performance is competitive, and often is superior than those of state of art zerotree-based coding schemes.