An Abstraction and Structural Information Based Depth Perception Evaluation Metric

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dc.contributor.authorNur Yilmaz, Gokce
dc.contributor.authorBayrak, Huseyin
dc.date.accessioned2020-06-25T18:23:27Z
dc.date.available2020-06-25T18:23:27Z
dc.date.issued2017
dc.departmentKırıkkale Üniversitesi
dc.description25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY
dc.description.abstractDeveloping reliable and efficient 3 Dimensional (3D) video depth perception evaluation metrics is currently a trending research topic for supporting the advancement of the 3D video services. This support can be proliferated by utilizing effective 3D video features while modeling these metrics. In this study, a Reduced Reference (RR) depth perception evaluation metric using significant depth level and structural information as effective 3D video features is developed. The significant depth level and structural information in the Depth Maps (DM) are determined using abstraction filter and Canny edge detection algorithm, respectively. The performance assessment results of the proposed RR metric present that it is quite effective for ensuring advanced 3D video services.en_US
dc.description.sponsorshipTurk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Univen_US
dc.identifier.citationclosedAccessen_US
dc.identifier.isbn978-1-5090-6494-6
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85026311856
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12587/7106
dc.identifier.wosWOS:000413813100585
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIeeeen_US
dc.relation.ispartof2017 25Th Signal Processing And Communications Applications Conference (Siu)
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject3D videoen_US
dc.subjectabstraction filteren_US
dc.subjectdepth perceptionen_US
dc.subjectReduced Reference (RR) metricen_US
dc.subjectstructural complexityen_US
dc.titleAn Abstraction and Structural Information Based Depth Perception Evaluation Metricen_US
dc.typeConference Object

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