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Conference Paper: Hierarchical tensor approximation of multidimensional images

TitleHierarchical tensor approximation of multidimensional images
Authors
KeywordsAdaptive Bases
Image Compression
Multi-Scale Analysis
Multilinear Models
Tensor Ensemble Approximation
Issue Date2006
PublisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349
Citation
Proceedings - International Conference On Image Processing, Icip, 2006, v. 4, p. IV49-IV52 How to Cite?
AbstractVisual data comprises of multi-scale and inhomogeneous signals. In this paper, we exploit these characteristics and develop an adaptive data approximation technique based on a hierarchical tensor-based transformation. In this technique, an original multi-dimensional image is transformed into a hierarchy of signals to expose its multi-scale 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 collective tensor approximation technique. Experimental results indicate that our technique can achieve higher compression ratios than existing functional approximation methods, including wavelet transforms, wavelet packet transforms and single-level tensor approximation. © 2007 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/151919
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorWu, Qen_US
dc.contributor.authorXia, Ten_US
dc.contributor.authorYu, Yen_US
dc.date.accessioned2012-06-26T06:30:48Z-
dc.date.available2012-06-26T06:30:48Z-
dc.date.issued2006en_US
dc.identifier.citationProceedings - International Conference On Image Processing, Icip, 2006, v. 4, p. IV49-IV52en_US
dc.identifier.issn1522-4880en_US
dc.identifier.urihttp://hdl.handle.net/10722/151919-
dc.description.abstractVisual data comprises of multi-scale and inhomogeneous signals. In this paper, we exploit these characteristics and develop an adaptive data approximation technique based on a hierarchical tensor-based transformation. In this technique, an original multi-dimensional image is transformed into a hierarchy of signals to expose its multi-scale 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 collective tensor approximation technique. Experimental results indicate that our technique can achieve higher compression ratios than existing functional approximation methods, including wavelet transforms, wavelet packet transforms and single-level tensor approximation. © 2007 IEEE.en_US
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349en_US
dc.relation.ispartofProceedings - International Conference on Image Processing, ICIPen_US
dc.subjectAdaptive Basesen_US
dc.subjectImage Compressionen_US
dc.subjectMulti-Scale Analysisen_US
dc.subjectMultilinear Modelsen_US
dc.subjectTensor Ensemble Approximationen_US
dc.titleHierarchical tensor approximation of multidimensional imagesen_US
dc.typeConference_Paperen_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/ICIP.2007.4379951en_US
dc.identifier.scopuseid_2-s2.0-48149085589en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-48149085589&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume4en_US
dc.identifier.spageIV49en_US
dc.identifier.epageIV52en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridWu, Q=51964899100en_US
dc.identifier.scopusauthoridXia, T=35876042700en_US
dc.identifier.scopusauthoridYu, Y=8554163500en_US

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