Genetic programming for structural similarity design at multiple spatial scales

Bibliographic Details
Main Author: Bakurov, Illya
Publication Date: 2022
Other Authors: Buzzelli, Marco, Castelli, Mauro, Schettini, Raimondo, Vanneschi, Leonardo
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/141872
Summary: Bakurov, I., Buzzelli, M., Castelli, M., Schettini, R., & Vanneschi, L. (2022). Genetic programming for structural similarity design at multiple spatial scales. In GECCO ’22. Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 911-919). (GECCO 2022 - The Genetic and Evolutionary Computation Conference, July 9-13, Boston, US). Association for Computing Machinery (ACM). ISBN 978-1-4503-9237-2/22/07 ---- Funding Information: FCT Portugal partially supported this work, under the grand SFRH/BD/137277/2018, and through projects BINDER (PTDC/CCI-INF/29168/2017) and AICE (DSAIPA/DS/ 0113/2019).
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spelling Genetic programming for structural similarity design at multiple spatial scalesGenetic ProgrammingImage Quality AssessmentStructural SimilarityMulti-Scale Structural Similarity IndexDilated ConvolutionsSpatially-Varying KernelsMulti-Scale ContextMulti-Scale ProcessingEvolutionary ComputationImage ProcessingArtificial IntelligenceSoftwareTheoretical Computer ScienceBakurov, I., Buzzelli, M., Castelli, M., Schettini, R., & Vanneschi, L. (2022). Genetic programming for structural similarity design at multiple spatial scales. In GECCO ’22. Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 911-919). (GECCO 2022 - The Genetic and Evolutionary Computation Conference, July 9-13, Boston, US). Association for Computing Machinery (ACM). ISBN 978-1-4503-9237-2/22/07 ---- Funding Information: FCT Portugal partially supported this work, under the grand SFRH/BD/137277/2018, and through projects BINDER (PTDC/CCI-INF/29168/2017) and AICE (DSAIPA/DS/ 0113/2019).The growing production of digital content and its dissemination across the worldwide web require eficient and precise management. In this context, image quality assessment measures (IQAMs) play a pivotal role in guiding the development of numerous image processing systems for compression, enhancement, and restoration. The structural similarity index (SSIM) is one of the most common IQAMs for estimating the similarity between a pristine reference image and its corrupted variant. The multi-scale SSIM is one of its most popular variants that allows assessing image quality at multiple spatial scales. This paper proposes a two-stage genetic programming (GP) approach to evolve novel multi-scale IQAMs, that are simultaneously more effective and efficient. We use GP to perform feature selection in the first stage, while the second stage generates the final solutions. The experimental results show that the proposed approach outperforms the existing MS-SSIM. A comprehensive analysis of the feature selection indicates that, for extracting multi-scale similarities, spatially-varying convolutions are more effective than dilated convolutions. Moreover, we provide evidence that the IQAMs learned for one database can be successfully transferred to previously unseen databases. We conclude the paper by presenting a set of evolved multi-scale IQAMs and providing their interpretation.ACM - Association for Computing MachineryInformation Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNBakurov, IllyaBuzzelli, MarcoCastelli, MauroSchettini, RaimondoVanneschi, Leonardo2022-07-14T22:14:54Z2022-07-012022-07-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion9application/pdfhttp://hdl.handle.net/10362/141872eng978-1-4503-9327-2PURE: 45372673https://doi.org/10.1145/3512290.3528783info: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:RCAAP2024-07-22T01:36:30Zoai:run.unl.pt:10362/141872Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:34:16.412303Repositórios 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 Genetic programming for structural similarity design at multiple spatial scales
title Genetic programming for structural similarity design at multiple spatial scales
spellingShingle Genetic programming for structural similarity design at multiple spatial scales
Bakurov, Illya
Genetic Programming
Image Quality Assessment
Structural Similarity
Multi-Scale Structural Similarity Index
Dilated Convolutions
Spatially-Varying Kernels
Multi-Scale Context
Multi-Scale Processing
Evolutionary Computation
Image Processing
Artificial Intelligence
Software
Theoretical Computer Science
title_short Genetic programming for structural similarity design at multiple spatial scales
title_full Genetic programming for structural similarity design at multiple spatial scales
title_fullStr Genetic programming for structural similarity design at multiple spatial scales
title_full_unstemmed Genetic programming for structural similarity design at multiple spatial scales
title_sort Genetic programming for structural similarity design at multiple spatial scales
author Bakurov, Illya
author_facet Bakurov, Illya
Buzzelli, Marco
Castelli, Mauro
Schettini, Raimondo
Vanneschi, Leonardo
author_role author
author2 Buzzelli, Marco
Castelli, Mauro
Schettini, Raimondo
Vanneschi, Leonardo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Bakurov, Illya
Buzzelli, Marco
Castelli, Mauro
Schettini, Raimondo
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Genetic Programming
Image Quality Assessment
Structural Similarity
Multi-Scale Structural Similarity Index
Dilated Convolutions
Spatially-Varying Kernels
Multi-Scale Context
Multi-Scale Processing
Evolutionary Computation
Image Processing
Artificial Intelligence
Software
Theoretical Computer Science
topic Genetic Programming
Image Quality Assessment
Structural Similarity
Multi-Scale Structural Similarity Index
Dilated Convolutions
Spatially-Varying Kernels
Multi-Scale Context
Multi-Scale Processing
Evolutionary Computation
Image Processing
Artificial Intelligence
Software
Theoretical Computer Science
description Bakurov, I., Buzzelli, M., Castelli, M., Schettini, R., & Vanneschi, L. (2022). Genetic programming for structural similarity design at multiple spatial scales. In GECCO ’22. Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 911-919). (GECCO 2022 - The Genetic and Evolutionary Computation Conference, July 9-13, Boston, US). Association for Computing Machinery (ACM). ISBN 978-1-4503-9237-2/22/07 ---- Funding Information: FCT Portugal partially supported this work, under the grand SFRH/BD/137277/2018, and through projects BINDER (PTDC/CCI-INF/29168/2017) and AICE (DSAIPA/DS/ 0113/2019).
publishDate 2022
dc.date.none.fl_str_mv 2022-07-14T22:14:54Z
2022-07-01
2022-07-01T00:00:00Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/141872
url http://hdl.handle.net/10362/141872
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-1-4503-9327-2
PURE: 45372673
https://doi.org/10.1145/3512290.3528783
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 9
application/pdf
dc.publisher.none.fl_str_mv ACM - Association for Computing Machinery
publisher.none.fl_str_mv ACM - Association for Computing Machinery
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
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
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
_version_ 1833596800827654144

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