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Fast video encoding based on random forests

2019-02-05Journal article Open access
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg10:Reduzir as Desigualdades
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorTahir, Muhammad
dc.contributor.authorTaj, Imtiaz A.
dc.contributor.authorAssuncao, Pedro A. A.
dc.contributor.authorAsif, Muhammad
dc.date.accessioned2025-07-25T15:18:04Z
dc.date.available2025-07-25T15:18:04Z
dc.date.issued2019-02-05
dc.description.abstractMachine learning approaches have been increasingly used to reduce the high computational complexity of high-efficiency video coding (HEVC), as this is a major limiting factor for real-time implementations, due to the decision process required to find optimal coding modes and partition sizes for the quad-tree data structures defined by the standard. This paper proposes a systematic approach to reduce the computational complexity of HEVC based on an ensemble of online and offline Random Forests classifiers. A reduced set of features for training the Random Forests classifier is proposed, based on the rankings obtained from information gain and a wrapper-based approach. The best model parameters are also obtained through a consistent and generalizable method. The proposed Random Forests classifier is used to model the coding unit and transform unit-splitting decision and the SKIP-mode prediction, as binary classification problems, taking advantage from the combination of online and offline approaches, which adapts better to the dynamic characteristics of video content. Experimental results show that, on average, the proposed approach reduces the computational complexity of HEVC by 62.64% for the random access (RA) profile and 54.57% for the low-delay (LD) main profile, with an increase in BD-Rate of 2.58% for RA and 2.97% for LD, respectively. These results outperform the previous works also using ensemble classifiers for the same purpose.eng
dc.description.sponsorshipThe authors would like to thank the anonymous reviewers for their comments and suggestions to improve this work. Pedro A. Assuncao would like to acknowledge the support of Fundação para a Ciência e Tecnologia (FCT) by Instituto de Telecomunicações (IT), grant UID/EEA/50008/2013, and Project ARound-Vision SAICT-45-2017-POCI-01-0145-FEDER-030652, PTDC/EEICOM/30652/2017, Portugal.
dc.identifier.citationTahir, M., Taj, I.A., Assuncao, P.A. et al. Fast video encoding based on random forests. J Real-Time Image Proc 17, 1029–1049 (2020). https://doi.org/10.1007/s11554-019-00854-1.
dc.identifier.doi10.1007/s11554-019-00854-1
dc.identifier.eissn1861-8219
dc.identifier.issn1861-8200
dc.identifier.urihttp://hdl.handle.net/10400.8/13780
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/article/10.1007/s11554-019-00854-1
dc.relation.ispartofJournal of Real-Time Image Processing
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectFast video coding
dc.subjectHEVC
dc.subjectRandom forests in HEVC
dc.subjectMachine learning in HEVC
dc.titleFast video encoding based on random forestseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F50008%2F2013/PT
oaire.citation.endPage1049
oaire.citation.issue4
oaire.citation.startPage1029
oaire.citation.titleJournal of Real-Time Image Processing
oaire.citation.volume17
oaire.fundingStream5876
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
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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Machine learning approaches have been increasingly used to reduce the high computational complexity of high-efficiency video coding (HEVC), as this is a major limiting factor for real-time implementations, due to the decision process required to find optimal coding modes and partition sizes for the quad-tree data structures defined by the standard. This paper proposes a systematic approach to reduce the computational complexity of HEVC based on an ensemble of online and offline Random Forests classifiers. A reduced set of features for training the Random Forests classifier is proposed, based on the rankings obtained from information gain and a wrapper-based approach. The best model parameters are also obtained through a consistent and generalizable method. The proposed Random Forests classifier is used to model the coding unit and transform unit-splitting decision and the SKIP-mode prediction, as binary classification problems, taking advantage from the combination of online and offline approaches, which adapts better to the dynamic characteristics of video content. Experimental results show that, on average, the proposed approach reduces the computational complexity of HEVC by 62.64% for the random access (RA) profile and 54.57% for the low-delay (LD) main profile, with an increase in BD-Rate of 2.58% for RA and 2.97% for LD, respectively. These results outperform the previous works also using ensemble classifiers for the same purpose.
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