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Complexity Estimation for Load Balancing of 360-Degree Intra Versatile Video Coding

2020-10Conference paper Open access
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datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg10:Reduzir as Desigualdades
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorFilipe, Jose N.
dc.contributor.authorMonteiro Carreira, João Filipe
dc.contributor.authorTavora, Luis M. N.
dc.contributor.authorFaria, Sergio
dc.contributor.authorNavarro, Antonio
dc.contributor.authorAssuncao, Pedro A. A.
dc.date.accessioned2025-07-09T17:38:44Z
dc.date.available2025-07-09T17:38:44Z
dc.date.issued2020-10
dc.descriptionArticle number - 9195224; Conference name - 34th IEEE Workshop on Signal Processing Systems, SiPS 2020; Conference city - Coimbra; Conference date - 20 October 2020 - 22 October 2020; Conference code - 164857
dc.description.abstractThe ever increasing demand for image and video content poses new requirements to support higher resolutions and richer representation formats, creating new challenges in coding algorithms. The forthcoming Versatile Video Coding (VVC) standard aims to increase the coding efficiency of existing algorithms and it is particularly suitable for Ultra-High Definition (UHD) resolutions and 360° video. However, since coding efficiency gains are obtained at the cost of increased complexity, fast computational approaches are needed to cope with realtime requirements, such as parallel processing. Thus, this work presents a contribution towards efficient parallel encoding of 360° video, based on coding complexity estimation and nonuniform data-level splitting (slice-based) for load balancing across multiple processors. A machine learning approach is proposed to estimate the complexity of intra coding VVC, using uncorrelated features, obtained through Principal Component Analysis (PCA) and Extremely Randomised Trees (ERT). Then, a complexity-balanced slice partition is devised, taking advantage of the clustered complexity inherent to Equirectangular Projection (ERP). It is shown that coding complexity is estimated with an accuracy of 92.25%, and the encoding time is reduced by 8.50%, when compared to the case where the 360° frames are evenly split.eng
dc.description.sponsorshipThis work was supported by Programa Operacional Regional do Centro, project ARoundVision CENTRO-01-0145-FEDER-030652 and by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020, Portugal.
dc.identifier.citationJ. N. Filipe, J. Carreira, L. M. N. Tavora, S. M. M. Faria, A. Navarro and P. A. A. Assuncao, "Complexity Estimation for Load Balancing of 360-Degree Intra Versatile Video Coding," 2020 IEEE Workshop on Signal Processing Systems (SiPS), Coimbra, Portugal, 2020, pp. 1-5, doi: https://doi.org/10.1109/SiPS50750.2020.9195224.
dc.identifier.doi10.1109/sips50750.2020.9195224
dc.identifier.isbn978-1-7281-8099-1
dc.identifier.issn2374-7390
dc.identifier.urihttp://hdl.handle.net/10400.8/13594
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE Canada
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/9195224
dc.relation.ispartof2020 IEEE Workshop on Signal Processing Systems (SiPS)
dc.rights.uriN/A
dc.subjectVideo Coding Complexity
dc.subjectLoad-balancing
dc.subjectVVC
dc.subjectComplexity Estimation
dc.titleComplexity Estimation for Load Balancing of 360-Degree Intra Versatile Video Codingeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2020-10
oaire.citation.conferencePlaceCoimbra, Portugal
oaire.citation.titleIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMonteiro Carreira
person.familyNamede Oliveira Pegado de Noronha E Távora
person.familyNameFaria
person.familyNameAssunção
person.givenNameJoão Filipe
person.givenNameLuís Miguel
person.givenNameSergio
person.givenNamePedro
person.identifier.ciencia-id911C-4634-594A
person.identifier.ciencia-id121C-FADA-D750
person.identifier.ciencia-id8815-4101-28DD
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person.identifier.orcid0000-0002-5711-5875
person.identifier.orcid0000-0002-8580-1979
person.identifier.orcid0000-0002-0993-9124
person.identifier.orcid0000-0001-9539-8311
person.identifier.ridC-5245-2011
person.identifier.ridA-4827-2017
person.identifier.scopus-author-id14027853900
person.identifier.scopus-author-id6701838347
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The ever increasing demand for image and video content poses new requirements to support higher resolutions and richer representation formats, creating new challenges in coding algorithms. The forthcoming Versatile Video Coding (VVC) standard aims to increase the coding efficiency of existing algorithms and it is particularly suitable for Ultra-High Definition (UHD) resolutions and 360° video. However, since coding efficiency gains are obtained at the cost of increased complexity, fast computational approaches are needed to cope with realtime requirements, such as parallel processing. Thus, this work presents a contribution towards efficient parallel encoding of 360° video, based on coding complexity estimation and nonuniform data-level splitting (slice-based) for load balancing across multiple processors. A machine learning approach is proposed to estimate the complexity of intra coding VVC, using uncorrelated features, obtained through Principal Component Analysis (PCA) and Extremely Randomised Trees (ERT). Then, a complexity-balanced slice partition is devised, taking advantage of the clustered complexity inherent to Equirectangular Projection (ERP). It is shown that coding complexity is estimated with an accuracy of 92.25%, and the encoding time is reduced by 8.50%, when compared to the case where the 360° frames are evenly split.
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