DocumentCode :
290185
Title :
An efficient neural prediction for vector quantization
Author :
Fioravanti, Roberto ; Fioravanti, Stefano ; Giusto, Daniele D.
Author_Institution :
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
Volume :
v
fYear :
1994
fDate :
19-22 Apr 1994
Abstract :
A novel predictive coding scheme for VQ is presented, called dynamic codebook reordering VQ (DCRVQ). Residual correlations between neighboring codevectors are exploited by a nonlinear prediction, that is a neural one. As a matter of fact, on the basis of the previously decoded codevectors, a multilayer neural network makes a prediction, and this result is used to reorganize the codebook in a dynamic way. This allows for efficient Huffman compression of codevector addresses after reordering
Keywords :
Huffman codes; correlation methods; image coding; multilayer perceptrons; prediction theory; vector quantisation; DCRVQ; codevector addresses; dynamic codebook reordering VQ; efficient Huffman compression; efficient neural prediction; multilayer neural network; neighboring codevectors; nonlinear prediction; predictive coding scheme; reorganization; residual correlations; vector quantization; Bit rate; Data compression; Decoding; Encoding; Image coding; Multi-layer neural network; Neural networks; Predictive coding; Rate-distortion; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
ISSN :
1520-6149
Print_ISBN :
0-7803-1775-0
Type :
conf
DOI :
10.1109/ICASSP.1994.389440
Filename :
389440
Link To Document :
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