A study on the effect of neighbourhood functions for noise robust vector quantisers

Author

Andrew, Lachlan L H ; Palaniswami, M.

Author_Institution

Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia

Volume

6

fYear

1994

fDate

27 Jun- 2 Jul 1994

Firstpage

4159

Abstract

By the use of noise robust compression, separate error correction can be reduced. This paper studies a number of neighbourhood functions for the SOFM for designing image vector quantiser codebooks for noisy channels. They include a neighbourhood recently proposed for the scalar coding of speech and a novel neighbourhood which makes the SOFM functionally equivalent to the popular LBG algorithm. The simulation results of these neighbourhood functions on two images provide insight into the problem of selecting an appropriate topology for the design of vector quantiser codebooks for noisy channels

Keywords

error correction codes; image coding; noise; self-organising feature maps; vector quantisation; LBG algorithm; SOFM; error correction; image vector quantiser codebooks; neighbourhood functions; noise robust compression; noise robust vector quantisers; noisy channels; scalar coding; speech; Error correction; Gold; Hypercubes; Image coding; Network topology; Neurons; Noise generators; Noise robustness; Quantization; Speech coding;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374881

Filename

374881