Fast vector quantization using a Bat algorithm for image compression

Linde-Buzo-Gray (LBG), a traditional method of vector quantization (VQ) generates a local optimal codebook which results in lower PSNR value. The performance of vector quantization (VQ) depends on the appropriate codebook, so researchers proposed optimization techniques for global codebook generatio...

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Detalles Bibliográficos
Autores: Chiranjeevi, Karri, Jena, Umaranjan R.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/72688
Acceso en línea:https://hdl.handle.net/10230/72688
http://dx.doi.org/10.1016/j.jestch.2015.11.003
Access Level:acceso abierto
Palabra clave:Vector quantization
Linde-Buzo-Gray (LBG)
Particle swarm optimization (PSO)
Quantum particle swarm algorithm (QPSO)
Honey bee mating optimization (HBMO)
Firefly algorithm (FA)
Bat algorithm (BA)
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spelling Fast vector quantization using a Bat algorithm for image compressionChiranjeevi, KarriJena, Umaranjan R.Vector quantizationLinde-Buzo-Gray (LBG)Particle swarm optimization (PSO)Quantum particle swarm algorithm (QPSO)Honey bee mating optimization (HBMO)Firefly algorithm (FA)Bat algorithm (BA)Linde-Buzo-Gray (LBG), a traditional method of vector quantization (VQ) generates a local optimal codebook which results in lower PSNR value. The performance of vector quantization (VQ) depends on the appropriate codebook, so researchers proposed optimization techniques for global codebook generation. Particle swarm optimization (PSO) and Firefly algorithm (FA) generate an efficient codebook, but undergoes instability in convergence when particle velocity is high and non-availability of brighter fireflies in the search space respectively. In this paper, we propose a new algorithm called BA-LBG which uses Bat Algorithm on initial solution of LBG. It produces an efficient codebook with less computational time and results very good PSNR due to its automatic zooming feature using adjustable pulse emission rate and loudness of bats. From the results, we observed that BA-LBG has high PSNR compared to LBG, PSO-LBG, Quantum PSO-LBG, HBMO-LBG and FA-LBG, and its average convergence speed is 1.841 times faster than HBMO-LBG and FA-LBG but no significance difference with PSO.Elsevier2026202620162026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/10230/72688http://dx.doi.org/10.1016/j.jestch.2015.11.003reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésEngineering Science and Technology, an International Journal. 2016;19(2):769-81© 2016, Karabuk University. Publishing services by Elsevier B.V. Under a Creative Commons license.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/726882026-06-12T07:21:37Z
dc.title.none.fl_str_mv Fast vector quantization using a Bat algorithm for image compression
title Fast vector quantization using a Bat algorithm for image compression
spellingShingle Fast vector quantization using a Bat algorithm for image compression
Chiranjeevi, Karri
Vector quantization
Linde-Buzo-Gray (LBG)
Particle swarm optimization (PSO)
Quantum particle swarm algorithm (QPSO)
Honey bee mating optimization (HBMO)
Firefly algorithm (FA)
Bat algorithm (BA)
title_short Fast vector quantization using a Bat algorithm for image compression
title_full Fast vector quantization using a Bat algorithm for image compression
title_fullStr Fast vector quantization using a Bat algorithm for image compression
title_full_unstemmed Fast vector quantization using a Bat algorithm for image compression
title_sort Fast vector quantization using a Bat algorithm for image compression
dc.creator.none.fl_str_mv Chiranjeevi, Karri
Jena, Umaranjan R.
author Chiranjeevi, Karri
author_facet Chiranjeevi, Karri
Jena, Umaranjan R.
author_role author
author2 Jena, Umaranjan R.
author2_role author
dc.subject.none.fl_str_mv Vector quantization
Linde-Buzo-Gray (LBG)
Particle swarm optimization (PSO)
Quantum particle swarm algorithm (QPSO)
Honey bee mating optimization (HBMO)
Firefly algorithm (FA)
Bat algorithm (BA)
topic Vector quantization
Linde-Buzo-Gray (LBG)
Particle swarm optimization (PSO)
Quantum particle swarm algorithm (QPSO)
Honey bee mating optimization (HBMO)
Firefly algorithm (FA)
Bat algorithm (BA)
description Linde-Buzo-Gray (LBG), a traditional method of vector quantization (VQ) generates a local optimal codebook which results in lower PSNR value. The performance of vector quantization (VQ) depends on the appropriate codebook, so researchers proposed optimization techniques for global codebook generation. Particle swarm optimization (PSO) and Firefly algorithm (FA) generate an efficient codebook, but undergoes instability in convergence when particle velocity is high and non-availability of brighter fireflies in the search space respectively. In this paper, we propose a new algorithm called BA-LBG which uses Bat Algorithm on initial solution of LBG. It produces an efficient codebook with less computational time and results very good PSNR due to its automatic zooming feature using adjustable pulse emission rate and loudness of bats. From the results, we observed that BA-LBG has high PSNR compared to LBG, PSO-LBG, Quantum PSO-LBG, HBMO-LBG and FA-LBG, and its average convergence speed is 1.841 times faster than HBMO-LBG and FA-LBG but no significance difference with PSO.
publishDate 2016
dc.date.none.fl_str_mv 2016
2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10230/72688
http://dx.doi.org/10.1016/j.jestch.2015.11.003
url https://hdl.handle.net/10230/72688
http://dx.doi.org/10.1016/j.jestch.2015.11.003
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Engineering Science and Technology, an International Journal. 2016;19(2):769-81
dc.rights.none.fl_str_mv © 2016, Karabuk University. Publishing services by Elsevier B.V. Under a Creative Commons license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2016, Karabuk University. Publishing services by Elsevier B.V. Under a Creative Commons license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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