TY - JOUR
T1 - On robustness of adaptive quantization for subband coding of images
AU - Man, Hong
AU - Smith, Mark J.T.
AU - Kossentini, Faouzi
PY - 1999
Y1 - 1999
N2 - 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.
AB - 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.
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M3 - Conference article
AN - SCOPUS:0032651347
SN - 0277-786X
VL - 3653
SP - 188
EP - 199
JO - Proceedings of SPIE - The International Society for Optical Engineering
JF - Proceedings of SPIE - The International Society for Optical Engineering
IS - I
T2 - Proceedings of the 1999 Visual Communications and Image Processing
Y2 - 25 January 1999 through 27 January 1999
ER -