On robustness of adaptive quantization for subband coding of images

In this paper, we present a generalized framework for the design of adaptive quantization that is able to achieve a good balance between high compression performance and channel error resilience. The unique feature of our proposed adaptive quantization technique is that it improves the channel error resilience of the compression system. It also provides a simple way to perform bit stream error sensitivity analysis, which previously was only available for fixed rate quantization schemes. The coder automatically classifies the compressed data sequence into separated subsequences with different error sensitivity levels, which enables a good adaptation to different channel models according to their noise statistics and error protection schemes. Two sets of adaptive quantization examples are provided for subband coding of images. The first set is based on a layered quantization/coding approach where our techniques directly quantizes the subband coefficients. The other set is designed for a conventional subband coding system with optimal bit allocation and fixed rate quantization at each subband. Under this second structure, the technique performs lossless compression on quantized subband coefficients. Experimental results have shown that our coders can obtain high quality compression performance with significantly improved resilience to channel errors.