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