Repository logo
 
Publication

Low complexity high efficiency coding of light fields using ensemble classifiers

2020-01Journal article Restricted access
Simple item page
datacite.subject.fosEngenharia e Tecnologia::Outras Engenharias e Tecnologias
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
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.
dc.contributor.authorAsif, Muhammad
dc.date.accessioned2025-10-21T16:25:43Z
dc.date.available2025-10-21T16:25:43Z
dc.date.issued2020-01
dc.description.abstractLight field images can be efficiently compressed using standard video codecs, such as the High Efficiency Video Coding (HEVC). However, the huge amount of data combined with the high computational complexity of HEVC, poses limitations on high-speed light field capturing and storage. This paper presents a contribution for low complexity encoding of light fields, in different formats using HEVC, based on a Random Forests ensemble classifier. Optimal features for training the classifier are found through a score fusion based approach. Using the HEVC still image profile, the proposed method gives speed-up of 56.23% for sub-aperture images. For pseudo video format, the proposed method outperforms others available in the literature, yielding an average speed-up of 62.18%, 56.54% and 44.73% for Random Access, Low-delay Main and All-Intra profiles respectively, with negligible decrease in RD performance. These are novel results in fast coding of light fields, which are useful for further research and benchmarking.eng
dc.identifier.citationMuhammad Tahir, Imtiaz A. Taj, Pedro A. Assuncao, Muhammad Asif, Low complexity high efficiency coding of light fields using ensemble classifiers, Journal of Visual Communication and Image Representation, Volume 66, 2020, 102742, ISSN 1047-3203, https://doi.org/10.1016/j.jvcir.2019.102742.
dc.identifier.doi10.1016/j.jvcir.2019.102742
dc.identifier.eissn1095-9076
dc.identifier.issn1047-3203
dc.identifier.urihttp://hdl.handle.net/10400.8/14346
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S1047320319303633?via%3Dihub
dc.relation.ispartofJournal of Visual Communication and Image Representation
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectLight fields
dc.subjectHEVC
dc.subjectFast encoding
dc.subjectMachine learning
dc.subjectRandom forests
dc.titleLow complexity high efficiency coding of light fields using ensemble classifierseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage12
oaire.citation.startPage1
oaire.citation.titleJournal of Visual Communication and Image Representation
oaire.citation.volume66
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

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Low complexity high efficiency coding of light fields using ensemble classifiers.pdf
Size:
2.89 MB
Format:
Adobe Portable Document Format
Description:
Light field images can be efficiently compressed using standard video codecs, such as the High Efficiency Video Coding (HEVC). However, the huge amount of data combined with the high computational complexity of HEVC, poses limitations on high-speed light field capturing and storage. This paper presents a contribution for low complexity encoding of light fields, in different formats using HEVC, based on a Random Forests ensemble classifier. Optimal features for training the classifier are found through a score fusion based approach. Using the HEVC still image profile, the proposed method gives speed-up of 56.23% for sub-aperture images. For pseudo video format, the proposed method outperforms others available in the literature, yielding an average speed-up of 62.18%, 56.54% and 44.73% for Random Access, Low-delay Main and All-Intra profiles respectively, with negligible decrease in RD performance. These are novel results in fast coding of light fields, which are useful for further research and benchmarking.
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.32 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ESTG - Artigos em revistas internacionais