A Novel Technique of Error Concealment Method Selection in Texture Images Using ALBP Classifier

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dc.contributor.authorTothova, Zelmira
dc.contributor.authorPolec, Jaroslav
dc.coverage.issue2cs
dc.coverage.volume19cs
dc.date.accessioned2016-03-11T09:18:24Z
dc.date.available2016-03-11T09:18:24Z
dc.date.issued2010-06cs
dc.description.abstractThere are many error concealment techniques for image processing. In the paper, the focus is on restoration of image with missing blocks or macroblocks. Different methods can be optimal for different kinds of images. In recent years, great attention was dedicated to textures, and specific methods were developed for their processing. Many of them use classification of textures as an integral part. It is also of an advantage to know the texture classification to select the best restoration technique. In the paper, selection based on texture classification with advanced local binary patterns and spatial distribution of dominant patterns is proposed. It is shown, that for classified textures, optimal error concealment method can be selected from predefined ones, resulting then in better restoration. For testing, three methods of extrapolation and texture synthesis were used.en
dc.formattextcs
dc.format.extent331-337cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2010, vol. 19, č. 2, s. 331-337. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/57001
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2010/10_02_331_337.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectError concealmenten
dc.subjectre-synthesisen
dc.subjectinpaintingen
dc.subjecttextureen
dc.subjectextrapolationen
dc.subjectclassificationen
dc.titleA Novel Technique of Error Concealment Method Selection in Texture Images Using ALBP Classifieren
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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