Structural similarity index (SSIM) revisited

Detalhes bibliográficos
Autor(a) principal: Bakurov, Illya
Data de Publicação: 2022
Outros Autores: Buzzelli, Marco, Schettini, Raimondo, Castelli, Mauro, Vanneschi, Leonardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/127077
Resumo: Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2022). Structural similarity index (SSIM) revisited: A data-driven approach. Expert Systems with Applications, 189, 1-19. [116087]. [Advanced online publication on 27 October 2021]. https://doi.org/10.1016/j.eswa.2021.116087--------------Funding Information: This work was supported by national funds through the FCT (Funda??o para a Ci?ncia e a Tecnologia), Portugal by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCIINF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency, Slovenia (research core funding no. P5-0410).
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spelling Structural similarity index (SSIM) revisitedA data-driven approachImage quality assessment measuresStructural similarityEvolutionary computationScale selectionImage processingEngineering(all)Computer Science ApplicationsArtificial IntelligenceBakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2022). Structural similarity index (SSIM) revisited: A data-driven approach. Expert Systems with Applications, 189, 1-19. [116087]. [Advanced online publication on 27 October 2021]. https://doi.org/10.1016/j.eswa.2021.116087--------------Funding Information: This work was supported by national funds through the FCT (Funda??o para a Ci?ncia e a Tecnologia), Portugal by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCIINF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency, Slovenia (research core funding no. P5-0410).Several contemporaneous image processing and computer vision systems rely upon the full-reference image quality assessment (IQA) measures. The single-scale structural similarity index (SS-SSIM) is one of the most popular measures, and it owes its success to the mathematical simplicity, low computational complexity, and implicit incorporation of Human Visual System’s (HVS) characteristics. In this paper, we revise the original parameters of SSIM and its multi-scale counterpart (MS-SSIM) to increase their correlation with subjective evaluation. More specifically, we exploit the evolutionary computation and the swarm intelligence methods on five popular IQA databases, two of which are dedicated distance-changed databases, to determine the best combination of parameters efficiently. Simultaneously, we explore the effect of different scale selection approaches in the context of SS-SSIM. The experimental results show that with a proper fine-tuning (1) the performance of SS-SSIM and MS-SSIM can be improved, in average terms, by 8% and by 3%, respectively, (2) the SS-SSIM after the so-called standard scale selection achieves similar performance as if applying computationally more expensive state-of-the-art scale selection methods or MS-SSIM; moreover, (3) there is evidence that the parameters learned on a given database can be successfully transferred to other (previously unseen) databases; finally, (4) we propose a new set of reference parameters for SSIM’s variants and provide their interpretation.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBakurov, IllyaBuzzelli, MarcoSchettini, RaimondoCastelli, MauroVanneschi, Leonardo2024-01-24T01:31:43Z2022-03-012022-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19application/pdfhttp://hdl.handle.net/10362/127077eng0957-4174PURE: 34615726https://doi.org/10.1016/j.eswa.2021.116087info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:07:11Zoai:run.unl.pt:10362/127077Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:01.915783Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Structural similarity index (SSIM) revisited
A data-driven approach
title Structural similarity index (SSIM) revisited
spellingShingle Structural similarity index (SSIM) revisited
Bakurov, Illya
Image quality assessment measures
Structural similarity
Evolutionary computation
Scale selection
Image processing
Engineering(all)
Computer Science Applications
Artificial Intelligence
title_short Structural similarity index (SSIM) revisited
title_full Structural similarity index (SSIM) revisited
title_fullStr Structural similarity index (SSIM) revisited
title_full_unstemmed Structural similarity index (SSIM) revisited
title_sort Structural similarity index (SSIM) revisited
author Bakurov, Illya
author_facet Bakurov, Illya
Buzzelli, Marco
Schettini, Raimondo
Castelli, Mauro
Vanneschi, Leonardo
author_role author
author2 Buzzelli, Marco
Schettini, Raimondo
Castelli, Mauro
Vanneschi, Leonardo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Bakurov, Illya
Buzzelli, Marco
Schettini, Raimondo
Castelli, Mauro
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Image quality assessment measures
Structural similarity
Evolutionary computation
Scale selection
Image processing
Engineering(all)
Computer Science Applications
Artificial Intelligence
topic Image quality assessment measures
Structural similarity
Evolutionary computation
Scale selection
Image processing
Engineering(all)
Computer Science Applications
Artificial Intelligence
description Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2022). Structural similarity index (SSIM) revisited: A data-driven approach. Expert Systems with Applications, 189, 1-19. [116087]. [Advanced online publication on 27 October 2021]. https://doi.org/10.1016/j.eswa.2021.116087--------------Funding Information: This work was supported by national funds through the FCT (Funda??o para a Ci?ncia e a Tecnologia), Portugal by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCIINF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency, Slovenia (research core funding no. P5-0410).
publishDate 2022
dc.date.none.fl_str_mv 2022-03-01
2022-03-01T00:00:00Z
2024-01-24T01:31:43Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/127077
url http://hdl.handle.net/10362/127077
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0957-4174
PURE: 34615726
https://doi.org/10.1016/j.eswa.2021.116087
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 19
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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