A family of efficient and channel error resilient wavelet/subband image coders

We present a new wavelet/subband framework that allows the efficient and effective quantization/coding of subband coefficients in both noiseless and noisy channel environments, Two different models, one based on a zero-tree structure and another based on a quadtree and context-based modeling structure, are introduced for coding the locations of significant subband coefficients. Then, several multistage residual lattice vector quantizers are proposed for the quantization of such coefficients. The proposed framework features relatively simple modeling and quantization/coding structures that produce a bit stream containing two distinct bit sequences, which can then be protected differently according to their importance and channel noise sensitivity levels. The resulting wavelet/subband image coding algorithms provide good tradeoffs between compression performance and resilience to channel errors. In fact, experimental results indicate that for both noiseless and noisy channels, the resulting coders outperform most of the source-channel coders reported in the literature. More importantly, our coders are substantially more robust than all previously reported source-channel coders with respect to varying channel error conditions, This is a desired feature in low-bandwidth wireless applications.