Title :
Multiresolution vector quantization
Author :
Effros, Michelle ; Dugatkin, Diego
Author_Institution :
Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA
Abstract :
Multiresolution source codes are data compression algorithms yielding embedded source descriptions. The decoder of a multiresolution code can build a source reproduction by decoding the embedded bit stream in part or in whole. All decoding procedures start at the beginning of the binary source description and decode some fraction of that string. Decoding a small portion of the binary string gives a low-resolution reproduction; decoding more yields a higher resolution reproduction; and so on. Multiresolution vector quantizers are block multiresolution source codes. This paper introduces algorithms for designing fixed- and variable-rate multiresolution vector quantizers. Experiments on synthetic data demonstrate performance close to the theoretical performance limit. Experiments on natural images demonstrate performance improvements of up to 8 dB over tree-structured vector quantizers. Some of the lessons learned through multiresolution vector quantizer design lend insight into the design of more sophisticated multiresolution codes.
Keywords :
block codes; image coding; image resolution; source coding; vector quantisation; binary source descriptions; block multiresolution source codes; data compression algorithms; decoding procedures; embedded bit stream; multiresolution vector quantization; natural images; progressive transmission; source reproduction; Algorithm design and analysis; Data compression; Decoding; Distortion measurement; Engineering profession; Image resolution; Lagrangian functions; Signal resolution; Source coding; Vector quantization; 65; Embedded source code design; fixed rate; multiuser; network; progressive transmission; successive refinement; variable rate;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.2004.838381