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

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dc.authorid0000-0002-7008-4778en_US
dc.contributor.authorGezer, Murat
dc.contributor.authorGargari, Sepideh Nahavandi
dc.contributor.authorGuz, Umit
dc.contributor.authorGürkan, Hakan
dc.date.accessioned2021-03-20T20:12:31Z
dc.date.available2021-03-20T20:12:31Z
dc.date.issued2019
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_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 JPEG2000.en_US
dc.description.sponsorshipCoordination Office for Scientific Research Projects, FMV ISIK UniversityIsik University [10B301]en_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.identifier.doi10.1007/s11760-019-01454-zen_US
dc.identifier.endpage1130en_US
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1123en_US
dc.identifier.urihttp://doi.org/10.1007/s11760-019-01454-z
dc.identifier.urihttps://hdl.handle.net/20.500.12885/597
dc.identifier.volume13en_US
dc.identifier.wosWOS:000481886600010en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorGürkan, Hakan
dc.language.isoenen_US
dc.publisherSpringer London Ltden_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.subjectCT compressionen_US
dc.subjectComputed tomographyen_US
dc.subjectClassified energy and pattern blocksen_US
dc.subjectQuadtreeen_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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