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No-reference lightweight estimation of 3D video objective quality

2014-10-27Comunicação em conferência Acesso restrito
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datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorSoares, João R. S.
dc.contributor.authorCruz, Luis A. da Silva
dc.contributor.authorAssunção, Pedro
dc.contributor.authorMarinheiro, Rui
dc.date.accessioned2026-05-19T13:34:10Z
dc.date.available2026-05-19T13:34:10Z
dc.date.issued2014-10-27
dc.description.abstractA no-reference (NR) method based on an artificial neural network (ANN) approach is proposed in this paper to estimate the objective quality of video-plus-depth streams subject to packet loss in depth data. A novel aspect of this method is the use of information only taken from packet headers, up to the network abstraction layer (NAL), requiring a very low complexity parsing of the compressed video streams. A maximum of seven packet-layer parameters were found to be enough to provide accurate objective quality estimates given by the structural similarity index (SSIM). The accuracy of the quality estimates, evaluated by comparison with the actual SSIM quality scores, is shown to be sufficiently high (e.g., Pearson Linear Correlation Coefficient over 0.92) for lightweight implementations of 3D video quality monitors at end-user receivers and also at network nodes.eng
dc.description.sponsorshipThis work was supported by Instituto de Telecomunicações, project 3DVQM, IT/LA/P01131/2011 and FCT, project PTDC/EEATEL/120666/2010
dc.identifier.citationJ. R. S. Soares, L. A. da Silva Cruz, P. Assuncão and R. Marinheiro, "No-reference lightweight estimation of 3D video objective quality," 2014 IEEE International Conference on Image Processing (ICIP), Paris, France, 2014, pp. 763-767, doi: 10.1109/ICIP.2014.7025153
dc.identifier.doi10.1109/icip.2014.7025153
dc.identifier.eissn2381-8549
dc.identifier.isbn978-1-4799-5751-4
dc.identifier.issn1522-4880
dc.identifier.urihttp://hdl.handle.net/10400.8/16299
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relation.hasversionhttps://ieeexplore.ieee.org/abstract/document/7025153
dc.relation.ispartof2014 IEEE International Conference on Image Processing (ICIP)
dc.rights.uriN/A
dc.subject3D video quality
dc.subjectVideo-plus-depth
dc.subjectNo-reference model
dc.subjectPacket-layer model
dc.subjectArtificial neural network
dc.titleNo-reference lightweight estimation of 3D video objective qualityeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2014-10-27
oaire.citation.conferencePlaceParis
oaire.citation.endPage767
oaire.citation.startPage763
oaire.citation.title2014 IEEE International Conference on Image Processing (ICIP)
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAssunção
person.givenNamePedro
person.identifier.ciencia-id6811-3984-C17B
person.identifier.orcid0000-0001-9539-8311
person.identifier.ridA-4827-2017
person.identifier.scopus-author-id6701838347
relation.isAuthorOfPublication25649bb9-f135-48e8-8d0f-3706b86701d3
relation.isAuthorOfPublication.latestForDiscovery25649bb9-f135-48e8-8d0f-3706b86701d3

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