Blind video quality assessment via spatiotemporal statistical analysis of adaptive cube size 3D-DCT coefficients

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dc.contributor.authorCemiloglu, Enes
dc.contributor.authorYilmaz, Gokce Nur
dc.date.accessioned2021-01-14T18:10:42Z
dc.date.available2021-01-14T18:10:42Z
dc.date.issued2020
dc.departmentKKÜ
dc.descriptionCemiloglu, Enes/0000-0002-0934-9140
dc.description.abstractThere is an urgent need for a robust video quality assessment (VQA) model that can efficiently evaluate the quality of a video content varying in terms of the distortion and content type in the absence of the reference video. Considering this need, a novel no reference (NR) model relying on the spatiotemporal statistics of the distorted video in a three-dimensional (3D)-discrete cosine transform (DCT) domain is proposed in this study. While developing the model, as the first contribution, the video contents are adaptively segmented into the cubes of different sizes and spatiotemporal contents in line with the human visual system (HVS) properties. Then, the 3D-DCT is applied to these cubes. Following that, as the second contribution, different efficient features (i.e. spectral behaviour, energy variation, distances between spatiotemporal frequency bands, and DC variation) associated with the contents of these cubes are extracted. After that, these features are associated with the subjective experimental results obtained from the EPFL-PoliMi video database using the linear regression analysis for building the model. The evaluation results present that the proposed model, unlike many top-performing NR-VQA models (e.g. V-BLIINDS, VIIDEO, and SSEQ), achieves high and stable performance across the videos with different contents and distortions.en_US
dc.identifier.citationBu makale açık erişimli değildir.en_US
dc.identifier.doi10.1049/iet-ipr.2019.0275
dc.identifier.endpage852en_US
dc.identifier.issn1751-9659
dc.identifier.issn1751-9667
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85083270873
dc.identifier.scopusqualityQ2
dc.identifier.startpage845en_US
dc.identifier.urihttps://doi.org/10.1049/iet-ipr.2019.0275
dc.identifier.urihttps://hdl.handle.net/20.500.12587/12735
dc.identifier.volume14en_US
dc.identifier.wosWOS:000526816700005
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherINST ENGINEERING TECHNOLOGY-IETen_US
dc.relation.ispartofIET IMAGE PROCESSING
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdiscrete cosine transformsen_US
dc.subjectdistortionen_US
dc.subjectregression analysisen_US
dc.subjectvideo signal processingen_US
dc.subjectvideo databasesen_US
dc.subjectfeature extractionen_US
dc.subjectspatiotemporal phenomenaen_US
dc.subjectblind video quality assessmenten_US
dc.subjectspatiotemporal statistical analysisen_US
dc.subjectadaptive cube size 3D-DCT coefficientsen_US
dc.subjectrobust video quality assessment modelen_US
dc.subjectvideo contenten_US
dc.subjectreference videoen_US
dc.subjectdistorted videoen_US
dc.subjectspatiotemporal contentsen_US
dc.subjecthuman visual system propertiesen_US
dc.subjectspatiotemporal frequency bandsen_US
dc.subjectEPFL-PoliMi video databaseen_US
dc.subjectNR-VQA modelsen_US
dc.subjectno reference modelen_US
dc.subjectthree-dimensional-discrete cosine transform domainen_US
dc.subjectHVS propertiesen_US
dc.subjectfeature extractionen_US
dc.subjectlinear regression analysisen_US
dc.titleBlind video quality assessment via spatiotemporal statistical analysis of adaptive cube size 3D-DCT coefficientsen_US
dc.typeArticle

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