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Lossy and lossless image encoding using multi‐scale recurrent pattern matching

2013-08Artigo científico Acesso restrito
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorGraziosi, Danillo Bracco
dc.contributor.authorRodrigues, Nuno Miguel
dc.contributor.authorSilva, Eduardo A.B. da
dc.contributor.authorCarvalho, Murilo B. de
dc.contributor.authorFaria, Sérgio Manuel Maciel de
dc.date.accessioned2026-03-05T11:22:47Z
dc.date.available2026-03-05T11:22:47Z
dc.date.issued2013-08
dc.description.abstractIn this study, the authors investigate the use of multi‐scale recurrent pattern matching paradigm for lossless image compression. The multi‐scale multidimensional parser (MMP) algorithm is a successful implementation of this paradigm for lossy image compression, and can naturally perform lossless compression since it was first derived from a Lempel–Ziv lossless scheme. However, neither its recently adopted coding tools had been adapted for lossless coding nor a thorough analysis of its performance had been carried out. In this work, the authors evaluate MMP's lossless compression capability, proposing modifications for some of its predictions modes, as well as the inclusion of an adaptive prediction mode based on least squares. The residual information is also coded with well‐known techniques used in lossless compression. Experimental results for MMP show that the algorithm achieves a good performance for images such as computed generated graphics and scanned documents, whereas keeping a competitive performance for natural images. Since the algorithm's structure is exactly the same for lossless and lossy compression, the obtained results suggest that MMP is able to achieve a high compression performance for a wide range of images and rates, from lossy to lossless, without any prior analysis of the image to be coded.eng
dc.description.sponsorshipThis work has been supported by the Fundação Para a Ciência e Tecnologia (FCT), under project PTDC/EEA-TEL/66462/2006/, and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), under the program PDEE (Programa de Doutorado no País com Estágio no Exterior), process 5443-09-1.
dc.identifier.citationGraziosi, D. B., Rodrigues, N. M., Da Silva, E. A., De Carvalho, M. B., & De Faria, S. M. M. (2013). Lossy and lossless image encoding using multi‐scale recurrent pattern matching. IET Image Processing, 7(6), 556-566.
dc.identifier.doi10.1049/iet-ipr.2012.0538
dc.identifier.issn1751-9659
dc.identifier.issn1751-9667
dc.identifier.urihttp://hdl.handle.net/10400.8/15784
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitution of Engineering and Technology (IET)
dc.relationSCODE - Scanned COmpound Document Encoder
dc.relation.hasversionhttps://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-ipr.2012.0538
dc.relation.ispartofIET Image Processing
dc.relation.ispartofseries566
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleLossy and lossless image encoding using multi‐scale recurrent pattern matchingeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberPTDC/EEA-TEL/66462/2006
oaire.awardTitleSCODE - Scanned COmpound Document Encoder
oaire.awardURIhttp://hdl.handle.net/10400.8/15029
oaire.citation.endPage566
oaire.citation.issue6
oaire.citation.startPage556
oaire.citation.titleIET Image Processing
oaire.citation.volume7
oaire.fundingStreamConcurso para Projectos de I&D em todos os Domínios Científicos - 2006
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameM. M. Rodrigues
person.givenNameNuno
person.identifier.ciencia-id6917-B121-4E34
person.identifier.orcid0000-0001-9536-1017
person.identifier.scopus-author-id7006052345
relation.isAuthorOfPublicationb4ebe652-7f0e-4e67-adb0-d5ea29fc9e69
relation.isAuthorOfPublication.latestForDiscoveryb4ebe652-7f0e-4e67-adb0-d5ea29fc9e69
relation.isProjectOfPublication20b2d92b-a0d6-4b2e-880c-d5c4b01210e2
relation.isProjectOfPublication.latestForDiscovery20b2d92b-a0d6-4b2e-880c-d5c4b01210e2

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