Zerotree edge adaptive coder for low bit rate image transmission

The problem of quantizing sub-images of a multiresolution image decomposition while preserving edges is considered. For this purpose, we propose a coding algorithm which exploits both spatial and frequency location of wavelet coefficients within and across scales. This algorithm is dedicated to lour bit rate image coding. In this paper, we develop a new constrained quantizer based on a lagrangian formulation called Edge Adaptive Quantizer (EAQ). Given a significance map, this algorithm preserves significant coefficients while smoothing elsewhere. This is done by introducing a spatial adaptation term based on Markov Random Fields (MRF). A new criterion based on spatial models and entropy constraint is then derived. With this new formulation, a practical solution to the multiresolution optimization problem is presented in the form of a bit allocation procedure. An optimal quantizer is constructed minimizing this new criterion for a target bit rate. Experiments using constraint quantization demonstrate PSNR gains over standard uniform scalar quantization and appreciable visual improvements. A simple extension of the algorithm allows for the use of other scalar quantizers.