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Improving multiscale recurrent pattern image coding with least-squares prediction mode

2009-11Conference paper Restricted access
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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.authorGraziosi, Danillo B.
dc.contributor.authorRodrigues, Nuno M. M.
dc.contributor.authorSilva, Eduardo A. B. da
dc.contributor.authorFaria, Sérgio M. M. de
dc.contributor.authorCarvalho, Murilo B. de
dc.contributor.authorFaria, Sergio
dc.contributor.authorM. M. Rodrigues, Nuno
dc.contributor.editor
dc.date.accessioned2025-04-21T10:18:42Z
dc.date.available2025-04-21T10:18:42Z
dc.date.issued2009-11
dc.description.abstractThe Multidimensional Multiscale Parser-based (MMP) image coding algorithm, when combined with flexible partitioning and predictive coding techniques (MMP-FP), provides state-of-the-art performance. In this paper we investigate the use of adaptive least-squares prediction in MMP. The linear prediction coefficients implicitly embed the local texture characteristics, and are computed based on a block’s causal neighborhood (composed of already reconstructed data). Thus, the intra prediction mode is adaptively adjusted according to the local context and no extra overhead is needed for signaling the coefficients. We add this new context-adaptive linear prediction mode to the other MMP prediction modes, that are based on the ones used in H.264/AVC; the best mode is chosen through rate-distortion optimization. Simulation results show that least-squares prediction is able to significantly increase MMP-FPs rate-distortion performance for smooth images, leading to better results than the ones of state-of-theart, transform-based methods. Yet with the addition of least-squares prediction MMP-FP presents no performance loss when used for encoding non-smooth images, such as text and graphics.eng
dc.identifier.citationD. B. Graziosi, N. M. M. Rodrigues, E. A. B. da Silva, S. M. M. de Faria and M. B. de Carvalho, "Improving multiscale recurrent pattern image coding with least-squares prediction mode," 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, 2009, pp. 2813-2816, doi: 10.1109/ICIP.2009.5414219.
dc.identifier.doi10.1109/icip.2009.5414219
dc.identifier.isbn978-142445654-3
dc.identifier.issn15224880
dc.identifier.urihttp://hdl.handle.net/10400.8/12816
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/5414219
dc.relation.ispartof2009 16th IEEE International Conference on Image Processing (ICIP)
dc.rights.uriN/A
dc.subjectImage Coding
dc.subjectRecurrent Pattern Matching
dc.subjectBlock Intra Prediction
dc.subjectLeast Square Minimization
dc.titleImproving multiscale recurrent pattern image coding with least-squares prediction modeeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2009-11
oaire.citation.conferencePlaceCairo, Egypt
oaire.citation.endPage2816
oaire.citation.startPage2813
oaire.citation.titleProceedings - International Conference on Image Processing, ICIP
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameFaria
person.familyNameM. M. Rodrigues
person.givenNameSergio
person.givenNameNuno
person.identifier.ciencia-id8815-4101-28DD
person.identifier.orcid0000-0002-0993-9124
person.identifier.orcid0000-0001-9536-1017
person.identifier.ridC-5245-2011
person.identifier.scopus-author-id14027853900
person.identifier.scopus-author-id7006052345
relation.isAuthorOfPublicationf69bd4d6-a6ef-4d20-8148-575478909661
relation.isAuthorOfPublicationb4ebe652-7f0e-4e67-adb0-d5ea29fc9e69
relation.isAuthorOfPublication.latestForDiscoveryf69bd4d6-a6ef-4d20-8148-575478909661

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The Multidimensional Multiscale Parser-based (MMP) image coding algorithm, when combined with flexible partitioning and predictive coding techniques (MMP-FP), provides state-of-the-art performance. In this paper we investigate the use of adaptive least-squares prediction in MMP. The linear prediction coefficients implicitly embed the local texture characteristics, and are computed based on a block’s causal neighborhood (composed of already reconstructed data). Thus, the intra prediction mode is adaptively adjusted according to the local context and no extra overhead is needed for signaling the coefficients. We add this new context-adaptive linear prediction mode to the other MMP prediction modes, that are based on the ones used in H.264/AVC; the best mode is chosen through rate-distortion optimization. Simulation results show that least-squares prediction is able to significantly increase MMP-FPs rate-distortion performance for smooth images, leading to better results than the ones of state-of-theart, transform-based methods. Yet with the addition of least-squares prediction MMP-FP presents no performance loss when used for encoding non-smooth images, such as text and graphics.
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