The application of wavelet neural networks to adaptive transform coding of one dimensional signals

Author

Jarrin, K.M.

Author_Institution

Mitre Corp., McLean, VA, USA

Volume

5

fYear

1995

fDate

9-12 May 1995

Firstpage

3347

Abstract

The inverse receptive field partition (IRFP) algorithm developed from the receptive field partition (RFP) algorithm, operates on a wavelet basis function network. This technique is a fast method of basis function selection. It begins with the highest resolution wavelet basis functions as its seed functions. Like RFP, IRFP then uses receptive field activation principle during training. This principle insures only wavelets within a specified interval are chosen as candidates. By way of the receptive activation principle, wavelets are selected with the best fit from a precalculated development pool and moved to the main pool. The selection of wavelet coefficients for the main pool is based on best fit across all resolutions. Overall functional fit can be controlled by global MSE and pruning thresholds

Keywords

adaptive codes; adaptive signal processing; learning (artificial intelligence); neural nets; signal resolution; transform coding; wavelet transforms; adaptive transform coding; basis function selection; global MSE thresholds; global pruning thresholds; inverse receptive field partition; multiresolutional analysis; one dimensional signals; receptive field activation; receptive field partition algorithm; training; wavelet basis function network; wavelet basis functions; wavelet coefficients; wavelet neural networks; Adaptive systems; Encoding; Filter bank; Image coding; Mirrors; Neural networks; Partitioning algorithms; Signal resolution; Transform coding; Wavelet coefficients;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on

Conference_Location

Detroit, MI

ISSN

1520-6149

Print_ISBN

0-7803-2431-5

Type

conf

DOI

10.1109/ICASSP.1995.479702

Filename

479702