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Graph-Cut-based Compression Algorithm for Compressed-Sensed Image Acquisition

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dc.contributor.author Alaydin, Julide Gulen
dc.contributor.author Gülen, Seden Hazal
dc.contributor.author Trocan, Maria
dc.contributor.author Töreyin, Behçet Uğur
dc.date.accessioned 2020-04-19T23:50:53Z
dc.date.available 2020-04-19T23:50:53Z
dc.date.issued 2014
dc.identifier.issn 2165-0608
dc.identifier.uri http://hdl.handle.net/20.500.12416/3370
dc.description.abstract The purpose of the paper is to find the best quantizer allocation for compressed-sensed acquired images, by using a graph-cut quantizer allocation method. The compressed sensed acquisition is realized in a block-based manner, using a random projection matrix, and on the obtained block measurements a graph-cut-based quantizer allocation method is applied, in order to further reduce the bitrate associated to the measurements. Finally, the quantized measurements are reconstructed using a Smooth Projected Landweber recovery method. The proposed compression method for compressed sensed acquisition shows better results when compared to JPEG2000. tr_TR
dc.language.iso eng tr_TR
dc.publisher IEEE tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.title Graph-Cut-based Compression Algorithm for Compressed-Sensed Image Acquisition tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal 22nd IEEE Signal Processing and Communications Applications Conference (SIU) tr_TR
dc.contributor.authorID 19325 tr_TR
dc.identifier.startpage 2310 tr_TR
dc.identifier.endpage 2313 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü tr_TR


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