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dc.contributor.authorSendjasni, Abderrezzaq
dc.contributor.authorLarabi, Mohamed-Chaker
dc.contributor.authorAlaya Cheikh, Faouzi
dc.date.accessioned2023-03-14T13:29:48Z
dc.date.available2023-03-14T13:29:48Z
dc.date.created2022-11-19T14:04:05Z
dc.date.issued2022
dc.identifier.citationIEEE transactions on circuits and systems for video technology 2022, 32 (11), 7301-7316.en_US
dc.identifier.issn1051-8215
dc.identifier.urihttps://hdl.handle.net/11250/3058201
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.titleConvolutional Neural Networks for Omnidirectional Image Quality Assessment: A Benchmarken_US
dc.title.alternativeConvolutional Neural Networks for Omnidirectional Image Quality Assessment: A Benchmarken_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber7301-7316en_US
dc.source.volume32en_US
dc.source.journalIEEE transactions on circuits and systems for video technology (Print)en_US
dc.source.issue11en_US
dc.identifier.doi10.1109/TCSVT.2022.3181235
dc.identifier.cristin2076724
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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