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Optimization of image coding algorithms and architectures using genetic algorithms

Research output: Contribution to journalArticle

Original languageEnglish
Pages549 - 558
JournalIEEE Transactions on Industrial Electronics
Journal publication dateOct 1996
Journal issue5
Volume43
DOIs
StatePublished

Abstract

This paper addresses the application of genetic algorithm (GA)-based optimization techniques to problems in image and video coding, demonstrating the success of GAs when used to solve real design problems with both performance and implementation constraints. Issues considered include problem representation, problem complexity, and fitness evaluation methods. For offline problems, such as the design of two-dimensional filters and filter banks, GAs are shown to be capable of producing results superior to conventional approaches. In the case of problems with real-time constraints, such as motion estimation, fractal search and vector quantization codebook design, GAs can provide solutions superior to those reported using conventional techniques with comparable implementation complexity. The use of GAs to jointly optimize algorithm performance in the context of a selected implementation strategy is emphasized throughout and several design examples are included

Additional information

Terms of use: Copyright © 1996 IEEE. Reprinted from IEEE Transactions on Industrial Electronics. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Publisher: Institute of Electrical and Electronics Engineers (IEEE)

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Research areas

  • genetic algorithms, vector quantization, video coding, motion estimation, image coding, fractal coding, digital filters

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