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DC poleHodnotaJazyk
dc.contributor.authorKopp, Manfred
dc.contributor.editorSkala, Václav
dc.date.accessioned2015-09-25T12:55:50Z
dc.date.available2015-09-25T12:55:50Z
dc.date.issued1996
dc.identifier.citationJournal of WSCG. 1996, vol. 4, no. 1-2.en
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.issn1213-6964 (online)
dc.identifier.urihttp://wscg.zcu.cz/wscg1996/wscg96.htm#abs
dc.identifier.urihttp://hdl.handle.net/11025/15990
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesJournal of WSCGen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectkomprese obrazucs
dc.subjectvlnkycs
dc.subjectadaptivní 2D vlnková dekompozicecs
dc.titleLossless wavelet based image compression with adaptive 2D decompositionen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translated2D wavelets are usually generated from 1D wavelets through the rectangular or through the square decomposition scheme. In this paper a new adaptive 2D decomposition scheme for compression related applications is presented. The adaptive 2D decomposition selects 2D wavelet functions based on the compression of the coefficients, but needs only the same number of 1D filter operations as the rectangular decomposition for the compression and even less for the decompression. Results for lossless image compression have shown improvements in the compression rate between 1% and 10% compared to the square decomposition. Only in the case of very small images (below 50x50) the adaptive decomposition was outperformed by the square decomposition because of the overhead to store the selection, which 2D wavelets should be used.en
dc.subject.translatedimage compressionen
dc.subject.translatedwaveletsen
dc.subject.translatedadaptive 2D wavelet decompositionen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 4, number 1-2 (1996)

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