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dc.contributor.authorFrnda, Jaroslav
dc.contributor.authorPavličko, Michal
dc.contributor.authorĎurica, Marek
dc.contributor.authorŠevčík, Lukáš
dc.contributor.authorVozňák, Miroslav
dc.contributor.authorFournier-Viger, Philippe
dc.contributor.authorLin, Jerry Chun-Wei
dc.date.accessioned2022-04-27T09:09:04Z
dc.date.available2022-04-27T09:09:04Z
dc.date.issued2021
dc.identifier.citationIntelligent Data Analysis. 2021, vol. 25, issue 3, p. 571-587.cs
dc.identifier.issn1088-467X
dc.identifier.issn1571-4128
dc.identifier.urihttp://hdl.handle.net/10084/146082
dc.description.abstractThis paper proposes a novel method for video quality evaluation based on machine learning technique. The current research deals with the correct interpretation of objective video quality evaluation (Quality of Service - QoS) in relation to subjective end-user perception (Quality of Experience - QoE), typically expressed by mean opinion score (MOS). Our method allows us to interconnect results obtained from video objective and subjective assessment methods in the form of a neural network (computing model inspired by biological neural networks). So far, no unified interpretation scale has been standardized for both approaches, therefore it is difficult to determine the level of end-user satisfaction obtained from the objective assessment. Thus, contribution of the proposed method lies in description of the way to create a hybrid metric that delivers fast and reliable subjective score of perceived video quality for internet television (IPTV) broadcasting companies.cs
dc.language.isoencs
dc.publisherIOS Presscs
dc.relation.ispartofseriesIntelligent Data Analysiscs
dc.relation.urihttps://doi.org/10.3233/IDA-205085cs
dc.rightsCopyright ©2022 IOS Press All rights reserved.cs
dc.subjectACRcs
dc.subjectneural networkcs
dc.subjectSSIMcs
dc.subjectQoEcs
dc.subjectQoScs
dc.subjectvideo assessment methodscs
dc.titleA new perceptual evaluation method of video quality based on neural networkcs
dc.typearticlecs
dc.identifier.doi10.3233/IDA-205085
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume25cs
dc.description.issue3cs
dc.description.lastpage587cs
dc.description.firstpage571cs
dc.identifier.wos000644439500006


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