Compression of the biomedical images using quadtree-based partitioned universally classified energy and pattern blocks

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dc.authorid0000-0002-4597-0954
dc.authorid0000-0002-4597-0954en_US
dc.contributor.authorGezer, Muraten_US
dc.contributor.authorGargari, Sepideh Nahavandien_US
dc.contributor.authorGüz, Ümiten_US
dc.contributor.authorGürkan, Hakanen_US
dc.date.accessioned2019-05-08T00:54:13Z
dc.date.available2019-05-08T00:54:13Z
dc.date.issued2019-03-15
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Electrical-Electronics Engineeringen_US
dc.description.abstractIn this work, an efficient low bit rate image coding/compression method based on the quadtree-based partitioned universally classified energy and pattern building blocks (QB-UCEPB) is introduced. The proposed method combines low bit rate robustness and variable-sized quantization benefits of the well-known classified energy and pattern blocks (CEPB) method and quadtree-based (QB) partitioning technique, respectively. In the new method, first, the QB-UCEPB is constructed in the form of variable length block size thanks to the quadtree-based partitioning rather than fixed block size partitioning which was employed in the conventional CEPB method. The QB-UCEPB is then placed to the transmitter side as well as receiver side of the communication channel as a universal codebook manner. Every quadtree-based partitioned block of the input image is encoded using three quantities: image block scaling coefficient, the index number of the QB-UCEB and the index number of the QB-UCPB. These quantities are sent from the transmitter part to the receiver part through the communication channel. Then, the quadtree-based partitioned input image blocks are reconstructed in the receiver part using a decoding algorithm, which exploits the mathematical model that is proposed. Experimental results show that using the new method, the computational complexity of the classical CEPB is substantially reduced. Furthermore, higher compression ratios, PSNR and SSIM levels are achieved even at low bit rates compared to the classical CEPB and conventional methods such as SPIHT, EZW and JPEG2000en_US
dc.description.sponsorshipThis research work was supported by the Coordination Office for Scientific Research Projects, FMV ISIK University (Project Number: 10B301)en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationGezer, M., Gargari, S. N., Güz, Ü. & Gürkan, H. (2019). Compression of the biomedical images using quadtree-based partitioned universally classified energy and pattern blocks. Signal, Image and Video Processing, 13(6), 1123-1130. doi:10.1007/s11760-019-01454-zen_US
dc.identifier.doi10.1007/s11760-019-01454-z
dc.identifier.endpage1130
dc.identifier.issn1863-1703en_US
dc.identifier.issn1863-1711en_US
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85063065072en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1123
dc.identifier.urihttps://hdl.handle.net/11729/1588
dc.identifier.urihttp://dx.doi.org/10.1007/s11760-019-01454-z
dc.identifier.volume13
dc.identifier.wosWOS:000481886600010en_US
dc.identifier.wosqualityQ3
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorGüz, Ümiten_US
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringer Londonen_US
dc.relation.ispartofSignal, Image and Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomedical image compressionen_US
dc.subjectClassified energy and pattern blocksen_US
dc.subjectComputed tomographyen_US
dc.subjectCT compressionen_US
dc.subjectQuadtreeen_US
dc.subjectCommunication channels (information theory)en_US
dc.subjectComputerized tomographyen_US
dc.subjectDigital image storageen_US
dc.subjectImage classificationen_US
dc.subjectImage codingen_US
dc.subjectTransmittersen_US
dc.subjectBiomedical imagesen_US
dc.subjectConventional methodsen_US
dc.subjectDecoding algorithmen_US
dc.subjectHigher compression ratiosen_US
dc.subjectPartitioning techniquesen_US
dc.subjectQuad treesen_US
dc.subjectScaling coefficientsen_US
dc.subjectImage compressionen_US
dc.titleCompression of the biomedical images using quadtree-based partitioned universally classified energy and pattern blocksen_US
dc.typeArticleen_US

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