Title :
Bayes risk-weighted vector quantization
Author_Institution :
Dept. of Electr. Eng., Stanford Univ., CA, USA
Abstract :
Lossy compression and classification algorithms both attempt to reduce a large collection of possible observations into a few representative categories so as to preserve essential information. A framework for combining classification and compression into one or two quantizers is described along with some examples and related to other quantizer-based classification schemes
Keywords :
Bayes methods; vector quantisation; Bayes risk-weighted vector quantization; classification algorithms; lossy compression algorithms; quantizer-based classification; Bit rate; Decoding; Distortion measurement; Entropy; Hardware; Image coding; Lagrangian functions; Nearest neighbor searches; Signal to noise ratio; Vector quantization;
Conference_Titel :
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location :
Alexandria, VA
Print_ISBN :
0-7803-2761-6
DOI :
10.1109/WITS.1994.513847
