Image compression based on vector quantization using cuckoo search optimization technique

Most common vector quantization (VQ) is Linde Buzo Gray (LBG), that designs a local optimal codebook for image compression. Recently firefly algorithm (FA), particle swarm optimization (PSO) and Honey bee mating optimization (HBMO) were designed which generate near global codebook, but search proces...

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Detalles Bibliográficos
Autores: Chiranjeevi, Karri, Jena, Umaranjan R.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/72685
Acceso en línea:https://hdl.handle.net/10230/72685
http://dx.doi.org/10.1016/j.asej.2016.09.009
Access Level:acceso abierto
Palabra clave:Cuckoo search (CS)
Firefly algorithm (FA)
Particle swarm optimization (PSO)
Linde-Buzo-Gray (LBG)
Vector quantizationImage compression
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spelling Image compression based on vector quantization using cuckoo search optimization techniqueChiranjeevi, KarriJena, Umaranjan R.Cuckoo search (CS)Firefly algorithm (FA)Particle swarm optimization (PSO)Linde-Buzo-Gray (LBG)Vector quantizationImage compressionMost common vector quantization (VQ) is Linde Buzo Gray (LBG), that designs a local optimal codebook for image compression. Recently firefly algorithm (FA), particle swarm optimization (PSO) and Honey bee mating optimization (HBMO) were designed which generate near global codebook, but search process follows Gaussian distribution function. FA experiences a problem when brighter fireflies are insignificant and PSO undergoes instability in convergence when particle velocity is very high. So, we proposed Cuckoo search (CS) metaheuristic optimization algorithm, that optimizes the LBG codebook by levy flight distribution function which follows the Mantegna's algorithm instead of Gaussian distribution. Cuckoo search consumes 25% of convergence time for local and 75% of convergence time for global codebook, so it guarantees the global codebook with appropriate mutation probability and this behavior is the major merit of CS. Practically we observed that cuckoo search algorithm has high peak signal to noise ratio (PSNR) and better fitness value compared to LBG, PSO-LBG, Quantum PSO-LBG, HBMO-LBG and FA-LBG at the cost of high convergence time.Elsevier2026202620182026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/10230/72685http://dx.doi.org/10.1016/j.asej.2016.09.009reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésAin Shams Engineering Journal. 2018;9(4):1417-31© 2016 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/726852026-06-12T07:21:37Z
dc.title.none.fl_str_mv Image compression based on vector quantization using cuckoo search optimization technique
title Image compression based on vector quantization using cuckoo search optimization technique
spellingShingle Image compression based on vector quantization using cuckoo search optimization technique
Chiranjeevi, Karri
Cuckoo search (CS)
Firefly algorithm (FA)
Particle swarm optimization (PSO)
Linde-Buzo-Gray (LBG)
Vector quantizationImage compression
title_short Image compression based on vector quantization using cuckoo search optimization technique
title_full Image compression based on vector quantization using cuckoo search optimization technique
title_fullStr Image compression based on vector quantization using cuckoo search optimization technique
title_full_unstemmed Image compression based on vector quantization using cuckoo search optimization technique
title_sort Image compression based on vector quantization using cuckoo search optimization technique
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 Cuckoo search (CS)
Firefly algorithm (FA)
Particle swarm optimization (PSO)
Linde-Buzo-Gray (LBG)
Vector quantizationImage compression
topic Cuckoo search (CS)
Firefly algorithm (FA)
Particle swarm optimization (PSO)
Linde-Buzo-Gray (LBG)
Vector quantizationImage compression
description Most common vector quantization (VQ) is Linde Buzo Gray (LBG), that designs a local optimal codebook for image compression. Recently firefly algorithm (FA), particle swarm optimization (PSO) and Honey bee mating optimization (HBMO) were designed which generate near global codebook, but search process follows Gaussian distribution function. FA experiences a problem when brighter fireflies are insignificant and PSO undergoes instability in convergence when particle velocity is very high. So, we proposed Cuckoo search (CS) metaheuristic optimization algorithm, that optimizes the LBG codebook by levy flight distribution function which follows the Mantegna's algorithm instead of Gaussian distribution. Cuckoo search consumes 25% of convergence time for local and 75% of convergence time for global codebook, so it guarantees the global codebook with appropriate mutation probability and this behavior is the major merit of CS. Practically we observed that cuckoo search algorithm has high peak signal to noise ratio (PSNR) and better fitness value compared to LBG, PSO-LBG, Quantum PSO-LBG, HBMO-LBG and FA-LBG at the cost of high convergence time.
publishDate 2018
dc.date.none.fl_str_mv 2018
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/10230/72685
http://dx.doi.org/10.1016/j.asej.2016.09.009
url https://hdl.handle.net/10230/72685
http://dx.doi.org/10.1016/j.asej.2016.09.009
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Ain Shams Engineering Journal. 2018;9(4):1417-31
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://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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