A Comparison Study of Vector Quantization Codebook Design Algorithms Based On The Equidistortion Principle

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

1 Citation (Scopus)

Abstract

This paper discusses vector quantization codebook design algorithms based on the equidistortion principle, which is effective to minimize quantization error evaluated by the mean squared error. In this paper, we review some representative vector quantization codebook design algorithms including our law-of-the-jungle algorithm. Those algorithms are then compared from the viewpoint of vector quantization performance. In addition, computational efficiency of each algorithm is also examined. Experimental results show that the law-of-the-jungle algorithm is the most promising for practical vector quantization applications, in terms of both vector quantization performance and computational efficiency.

Original languageEnglish
Title of host publication21st IASTED International Multi-Conference on Applied Informatics
Pages255-261
Number of pages7
Publication statusPublished - 2003 Dec 1
Event21st IASTED International Multi-Conference on Applied Informatics - Innsbruck, Austria
Duration: 2003 Feb 102003 Feb 13

Publication series

NameIASTED International Multi-Conference on Applied Informatics
Volume21

Other

Other21st IASTED International Multi-Conference on Applied Informatics
CountryAustria
CityInnsbruck
Period03/2/1003/2/13

Keywords

  • Competitive learning
  • Neural networks
  • The equidistortion principle
  • Vector quantization

ASJC Scopus subject areas

  • Computer Science(all)

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