Publication:
Evaluation of adaptation methods for multi-view video

dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.departmentN/A
dc.contributor.departmentN/A
dc.contributor.kuauthorTekalp, Ahmet Murat
dc.contributor.kuauthorGürler, Cihat Göktuğ
dc.contributor.kuauthorSavaş, Saadet Sedef
dc.contributor.kuprofileFaculty Member
dc.contributor.kuprofilePhD Student
dc.contributor.kuprofilePhD Student
dc.contributor.otherDepartment of Electrical and Electronics Engineering
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGraduate School of Sciences and Engineering
dc.contributor.schoolcollegeinstituteGraduate School of Sciences and Engineering
dc.contributor.yokid26207
dc.contributor.yokidN/A
dc.contributor.yokidN/A
dc.date.accessioned2024-11-09T23:51:20Z
dc.date.issued2012
dc.description.abstractMulti-view video (MVV) is the next step in the evaluation of 3DTV. Using IP networks as the transport medium seems to be the most promising solution because MVV has flexible bitrate requirements that can change based on the number of views requested from the receiver. With the advanced streaming technologies like scalable video coding, the capability of video services over IP has been greatly enhanced. However, a successful MVV delivery service cannot be achieved without properly addressing the perceived quality of experience (QoE) of MVV. QoE is an important issue especially in adaptive video streaming in which the quality of the content varies to match the available channel capacity. This study evaluates the effect of different scaling methods some of which are unique to MVV and propose a novel systematic adaptation strategy in order to deliver the best QoE under diverse network conditions. Extensive subjective tests are conducted to compare different scaling methods on MVV by using high definition contents.
dc.description.indexedbyScopus
dc.description.indexedbyWoS
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsorshipThe Institute of Electrical and Electronics Engineers (IEEE)
dc.description.sponsorshipIEEE Signal Processing Society
dc.description.sponsorshipArmy Research Office
dc.description.sponsorshipDigimarc
dc.description.sponsorshipDisney Research
dc.identifier.doi10.1109/ICIP.2012.6467349
dc.identifier.isbn9781-4673-2533-2
dc.identifier.issn1522-4880
dc.identifier.linkhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84875834564anddoi=10.1109%2fICIP.2012.6467349andpartnerID=40andmd5=2f7459c4c2c9e943f7c052c13ef596d1
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-84875834564
dc.identifier.urihttp://dx.doi.org/10.1109/ICIP.2012.6467349
dc.identifier.urihttps://hdl.handle.net/20.500.14288/14676
dc.identifier.wos319334902081
dc.keywordsAdaptation methods
dc.keywordsAdaptation strategies
dc.keywordsAdaptive video streaming
dc.keywordsAvailable channels
dc.keywordsBit rates
dc.keywordsDelivery service
dc.keywordsHigh-definition contents
dc.keywordsIP networks
dc.keywordsMultiview video
dc.keywordsNetwork condition
dc.keywordsNumber of views
dc.keywordsPerceived quality
dc.keywordsScaling method
dc.keywordsStreaming technology
dc.keywordsSubjective tests
dc.keywordsVideo services
dc.keywordsDigital television
dc.keywordsImage processing
dc.keywordsMultimedia systems
dc.keywordsQuality of service
dc.keywordsScalable video coding
dc.keywordsQuality control
dc.languageEnglish
dc.publisherIEEE
dc.sourceProceedings - International Conference on Image Processing, ICIP
dc.subjectElectrical electronics engineering
dc.titleEvaluation of adaptation methods for multi-view video
dc.typeConference proceeding
dspace.entity.typePublication
local.contributor.authorid0000-0003-1465-8121
local.contributor.authoridN/A
local.contributor.authoridN/A
local.contributor.kuauthorTekalp, Ahmet Murat
local.contributor.kuauthorGürler, Cihat Göktuğ
local.contributor.kuauthorSavaş, Saadet Sedef
relation.isOrgUnitOfPublication21598063-a7c5-420d-91ba-0cc9b2db0ea0
relation.isOrgUnitOfPublication.latestForDiscovery21598063-a7c5-420d-91ba-0cc9b2db0ea0

Files