Geometric Methods for Wavelet-Based Image Compression

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameInternational Symposium on Optical Science and Technologyen_US
dc.citation.locationSan Diego, CAen_US
dc.contributor.authorWakin, Michaelen_US
dc.contributor.authorRomberg, Justinen_US
dc.contributor.authorChoi, Hyeokhoen_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T01:08:51Z
dc.date.available2007-10-31T01:08:51Z
dc.date.issued2003-08-01en
dc.date.modified2006-06-05en_US
dc.date.note2003-07-13en_US
dc.date.submitted2003-08-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractNatural images can be viewed as combinations of smooth regions, textures, and geometry. Wavelet-based image coders, such as the space-frequency quantization (SFQ) algorithm, provide reasonably efficient representations for smooth regions (using zerotrees, for example) and textures (using scalar quantization) but do not properly exploit the geometric regularity imposed on wavelet coefficients by features such as edges. In this paper, we develop a representation for wavelet coefficients in geometric regions based on the wedgelet dictionary, a collection of geometric atoms that construct piecewise-linear approximations to contours. Our <i>wedgeprint representation</i> implicitly models the coherency among geometric wavelet coefficients. We demonstrate that a simple compression algorithm combining wedgeprints with zerotrees and scalar quantization can achieve near-optimal rate-distortion performance <i>D</i>(<i>R</i>) ~ (log <i>R</i>)²/<i>R</i>² for the class of piecewise-smooth images containing smooth <i>C</i>² regions separated by smooth <i>C</i>² discontinuities. Finally, we extend this simple algorithm and propose a complete compression framework for natural images using a rate-distortion criterion to balance the three representations. Our Wedgelet-SFQ (WSFQ) coder outperforms SFQ in terms of visual quality and mean-square error.en_US
dc.description.sponsorshipTexas Instrumentsen_US
dc.description.sponsorshipOffice of Naval Researchen_US
dc.description.sponsorshipNational Science Foundationen_US
dc.identifier.citationM. Wakin, J. Romberg, H. Choi and R. G. Baraniuk, "Geometric Methods for Wavelet-Based Image Compression," 2003.
dc.identifier.doihttp://dx.doi.org/10.1117/12.506155en_US
dc.identifier.urihttps://hdl.handle.net/1911/20428
dc.language.isoeng
dc.publisherSPIEen_US
dc.subjectImage compression*
dc.subjectwavelets*
dc.subjectwedgelets*
dc.subjectedges*
dc.subjectgeometry*
dc.subject.keywordImage compressionen_US
dc.subject.keywordwaveletsen_US
dc.subject.keywordwedgeletsen_US
dc.subject.keywordedgesen_US
dc.subject.keywordgeometryen_US
dc.subject.otherWavelet based Signal/Image Processingen_US
dc.titleGeometric Methods for Wavelet-Based Image Compressionen_US
dc.typeConference paper
dc.type.dcmiText
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