Neural network vector predictors with application to image coding

Author

Rizvi, Syed A. ; Wang, Lin-Cheng ; Nasrabadi, Nasser M.

Author_Institution

Dept. of Electr. & Comput. Eng., State Univ. of New York, Buffalo, NY, USA

Volume

3

fYear

1995

fDate

23-26 Oct 1995

Firstpage

296

Abstract

A vector predictor is an integral part of the predictive vector quantization (PVQ) scheme. The performance of a predictor deteriorates as the vector dimension (block size) is increased. This makes it necessary to investigate new design techniques in order to design a vector predictor which gives better performance when compared to a conventional vector predictor. This paper investigates several neural network configurations which can be employed in order to design a vector predictor. The following architectures are investigated: (a) multilayer perceptron, (b) functional link network, and (c) radial basis function network. The performance of the above mentioned neural network vector predictors is evaluated and compared with that of a linear vector predictor

Keywords

feedforward neural nets; image coding; multilayer perceptrons; neural net architecture; prediction theory; vector quantisation; PVQ; block size; design techniques; functional link network; image coding; linear vector predictor; multilayer perceptron; neural network architectures; neural network configurations; neural network vector predictors; performance evaluation; predictive vector quantization; radial basis function network; vector dimension; Application software; Computer architecture; Image coding; Multi-layer neural network; Neural networks; Performance evaluation; Pixel; Predictive models; Radial basis function networks; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1995. Proceedings., International Conference on

Conference_Location

Washington, DC

Print_ISBN

0-8186-7310-9

Type

conf

DOI

10.1109/ICIP.1995.537635

Filename

537635