Neural network minimization techniques for multistage orthogonal QMF filters with vanishing moments

Stuart Schweid, T. K. Sarkar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper a feedforward neural network is used to address multi-stage decomposition signal compression. The neural network uses back-propagation to update the filter coefficients of all stages simultaneously in each iteration. Back propagation is the vehicle that allows the optimization to proceed in a forward and backward direction, thus achieving a better signal compression than forward only stage by stage compression. In addition, the paper will address how to use the algorithm when all stages are constrained to be identical; a restriction that results in a wavelet type decomposition.

Original languageEnglish (US)
Title of host publicationProceedings of the Data Compression Conference
EditorsJames A. Storer, Martin Cohn
PublisherIEEE Computer Society
Number of pages1
ISBN (Print)0818656379
StatePublished - Jan 1 1994
EventProceedings of the Data Compression Conference - Snowbird, UT, USA
Duration: Mar 29 1994Mar 31 1994

Publication series

NameProceedings of the Data Compression Conference

Other

OtherProceedings of the Data Compression Conference
CitySnowbird, UT, USA
Period3/29/943/31/94

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Schweid, S., & Sarkar, T. K. (1994). Neural network minimization techniques for multistage orthogonal QMF filters with vanishing moments. In J. A. Storer, & M. Cohn (Eds.), Proceedings of the Data Compression Conference (Proceedings of the Data Compression Conference). IEEE Computer Society.