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Article: Hierarchical tensor approximation of multidimensional visual data

TitleHierarchical tensor approximation of multidimensional visual data
Authors
KeywordsHierarchical Transformation
Multidimensional Image Compression
Multilinear Models
Progressive Transmission
Tensor Ensemble Approximation
Texture Synthesis
Issue Date2008
PublisherI E E E. The Journal's web site is located at http://www.computer.org/tvcg
Citation
Ieee Transactions On Visualization And Computer Graphics, 2008, v. 14 n. 1, p. 186-199 How to Cite?
AbstractVisual data comprise of multiscale and inhomogeneous signals. In this paper, we exploit these characteristics and develop a compact data representation technique based on a hierarchical tensor-based transformation. In this technique, an original multidimensional data set Is transformed Into a hierarchy of signals to expose its multiscale structures. The signal at each level of the hierarchy Is further divided Into a number of smaller tensors to expose its spatially inhomogeneous structures. These smaller tensors are further transformed and pruned using a tensor approximation technique. Our hierarchical tensor approximation supports progressive transmission and partial decompression. Experimental results indicate that our technique can achieve higher compression ratios and quality than previous methods, including wavelet transforms, wavelet packet transforms, and single-level tensor approximation. We have successfully applied our technique to multiple tasks involving multidimensional visual data, including medical and scientific data visualization, data-driven rendering, and texture synthesis. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/152376
ISSN
2015 Impact Factor: 1.4
2015 SCImago Journal Rankings: 0.917
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWu, Qen_US
dc.contributor.authorXia, Ten_US
dc.contributor.authorChen, Cen_US
dc.contributor.authorLin, HYSen_US
dc.contributor.authorWang, Hen_US
dc.contributor.authorYu, Yen_US
dc.date.accessioned2012-06-26T06:37:46Z-
dc.date.available2012-06-26T06:37:46Z-
dc.date.issued2008en_US
dc.identifier.citationIeee Transactions On Visualization And Computer Graphics, 2008, v. 14 n. 1, p. 186-199en_US
dc.identifier.issn1077-2626en_US
dc.identifier.urihttp://hdl.handle.net/10722/152376-
dc.description.abstractVisual data comprise of multiscale and inhomogeneous signals. In this paper, we exploit these characteristics and develop a compact data representation technique based on a hierarchical tensor-based transformation. In this technique, an original multidimensional data set Is transformed Into a hierarchy of signals to expose its multiscale structures. The signal at each level of the hierarchy Is further divided Into a number of smaller tensors to expose its spatially inhomogeneous structures. These smaller tensors are further transformed and pruned using a tensor approximation technique. Our hierarchical tensor approximation supports progressive transmission and partial decompression. Experimental results indicate that our technique can achieve higher compression ratios and quality than previous methods, including wavelet transforms, wavelet packet transforms, and single-level tensor approximation. We have successfully applied our technique to multiple tasks involving multidimensional visual data, including medical and scientific data visualization, data-driven rendering, and texture synthesis. © 2008 IEEE.en_US
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://www.computer.org/tvcgen_US
dc.relation.ispartofIEEE Transactions on Visualization and Computer Graphicsen_US
dc.subjectHierarchical Transformationen_US
dc.subjectMultidimensional Image Compressionen_US
dc.subjectMultilinear Modelsen_US
dc.subjectProgressive Transmissionen_US
dc.subjectTensor Ensemble Approximationen_US
dc.subjectTexture Synthesisen_US
dc.titleHierarchical tensor approximation of multidimensional visual dataen_US
dc.typeArticleen_US
dc.identifier.emailYu, Y:yzyu@cs.hku.hken_US
dc.identifier.authorityYu, Y=rp01415en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/TVCG.2007.70406en_US
dc.identifier.scopuseid_2-s2.0-36349032384en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-36349032384&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume14en_US
dc.identifier.issue1en_US
dc.identifier.spage186en_US
dc.identifier.epage199en_US
dc.identifier.isiWOS:000250787500016-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridWu, Q=51964899100en_US
dc.identifier.scopusauthoridXia, T=35876042700en_US
dc.identifier.scopusauthoridChen, C=9333688600en_US
dc.identifier.scopusauthoridLin, HYS=15050533500en_US
dc.identifier.scopusauthoridWang, H=8732047300en_US
dc.identifier.scopusauthoridYu, Y=8554163500en_US

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