Vector quantizers trained on small training sets

David Cohn, Eve A. Riskin, Richard Ladner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We examine how the performance of a memoryless vector quantizer (VQ) changes as a function of its training set size. By relating the training distortion of such a codebook to its test (true) distortion, we demonstrate that one may obtain 'good' codebooks at a fraction of the computational cost by training on a small random subset of the blocks in the target image.

Original languageEnglish
Title of host publicationProceedings of the 1993 IEEE International Symposium on Information Theory
Pages176
Number of pages1
StatePublished - 1993
EventProceedings of the 1993 IEEE International Symposium on Information Theory - San Antonio, TX, USA
Duration: 17 Jan 199322 Jan 1993

Publication series

NameProceedings of the 1993 IEEE International Symposium on Information Theory

Conference

ConferenceProceedings of the 1993 IEEE International Symposium on Information Theory
CitySan Antonio, TX, USA
Period17/01/9322/01/93

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