Full-Reference Image Quality Expression via Genetic Programming

Bibliographic Details
Main Author: Bakurov, Illya
Publication Date: 2023
Other Authors: Buzzelli, Marco, Schettini, Raimondo, Castelli, Mauro, Vanneschi, Leonardo
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/149941
Summary: Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2023). Full-Reference Image Quality Expression via Genetic Programming. IEEE Transactions on Image Processing, 32, 1458-1473. https://doi.org/10.1109/TIP.2023.3244662--- This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) under the projects Algoritmos de Inteligência artificial no Consumo de crédito e conciliação de Endividamento (AICE) (DSAIPA/DS/0113/2019) and UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
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spelling Full-Reference Image Quality Expression via Genetic Programmingimage qualityfull-reference image quality assessmentimage similarityssimgenetic programmingSoftwareComputer Graphics and Computer-Aided DesignBakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2023). Full-Reference Image Quality Expression via Genetic Programming. IEEE Transactions on Image Processing, 32, 1458-1473. https://doi.org/10.1109/TIP.2023.3244662--- This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) under the projects Algoritmos de Inteligência artificial no Consumo de crédito e conciliação de Endividamento (AICE) (DSAIPA/DS/0113/2019) and UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBakurov, IllyaBuzzelli, MarcoSchettini, RaimondoCastelli, MauroVanneschi, Leonardo2023-03-02T22:27:05Z2023-03-012023-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16application/pdfapplication/pdfhttp://hdl.handle.net/10362/149941eng1941-0042PURE: 52561008https://doi.org/10.1109/TIP.2023.3244662info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:09:36Zoai:run.unl.pt:10362/149941Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:39:59.926636Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Full-Reference Image Quality Expression via Genetic Programming
title Full-Reference Image Quality Expression via Genetic Programming
spellingShingle Full-Reference Image Quality Expression via Genetic Programming
Bakurov, Illya
image quality
full-reference image quality assessment
image similarity
ssim
genetic programming
Software
Computer Graphics and Computer-Aided Design
title_short Full-Reference Image Quality Expression via Genetic Programming
title_full Full-Reference Image Quality Expression via Genetic Programming
title_fullStr Full-Reference Image Quality Expression via Genetic Programming
title_full_unstemmed Full-Reference Image Quality Expression via Genetic Programming
title_sort Full-Reference Image Quality Expression via Genetic Programming
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
full-reference image quality assessment
image similarity
ssim
genetic programming
Software
Computer Graphics and Computer-Aided Design
topic image quality
full-reference image quality assessment
image similarity
ssim
genetic programming
Software
Computer Graphics and Computer-Aided Design
description Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2023). Full-Reference Image Quality Expression via Genetic Programming. IEEE Transactions on Image Processing, 32, 1458-1473. https://doi.org/10.1109/TIP.2023.3244662--- This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) under the projects Algoritmos de Inteligência artificial no Consumo de crédito e conciliação de Endividamento (AICE) (DSAIPA/DS/0113/2019) and UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
publishDate 2023
dc.date.none.fl_str_mv 2023-03-02T22:27:05Z
2023-03-01
2023-03-01T00:00:00Z
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/10362/149941
url http://hdl.handle.net/10362/149941
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1941-0042
PURE: 52561008
https://doi.org/10.1109/TIP.2023.3244662
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16
application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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