dc.contributor.author | Frnda, Jaroslav | |
dc.contributor.author | Pavličko, Michal | |
dc.contributor.author | Ďurica, Marek | |
dc.contributor.author | Ševčík, Lukáš | |
dc.contributor.author | Vozňák, Miroslav | |
dc.contributor.author | Fournier-Viger, Philippe | |
dc.contributor.author | Lin, Jerry Chun-Wei | |
dc.date.accessioned | 2022-04-27T09:09:04Z | |
dc.date.available | 2022-04-27T09:09:04Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Intelligent Data Analysis. 2021, vol. 25, issue 3, p. 571-587. | cs |
dc.identifier.issn | 1088-467X | |
dc.identifier.issn | 1571-4128 | |
dc.identifier.uri | http://hdl.handle.net/10084/146082 | |
dc.description.abstract | This 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.iso | en | cs |
dc.publisher | IOS Press | cs |
dc.relation.ispartofseries | Intelligent Data Analysis | cs |
dc.relation.uri | https://doi.org/10.3233/IDA-205085 | cs |
dc.rights | Copyright ©2022 IOS Press All rights reserved. | cs |
dc.subject | ACR | cs |
dc.subject | neural network | cs |
dc.subject | SSIM | cs |
dc.subject | QoE | cs |
dc.subject | QoS | cs |
dc.subject | video assessment methods | cs |
dc.title | A new perceptual evaluation method of video quality based on neural network | cs |
dc.type | article | cs |
dc.identifier.doi | 10.3233/IDA-205085 | |
dc.type.status | Peer-reviewed | cs |
dc.description.source | Web of Science | cs |
dc.description.volume | 25 | cs |
dc.description.issue | 3 | cs |
dc.description.lastpage | 587 | cs |
dc.description.firstpage | 571 | cs |
dc.identifier.wos | 000644439500006 | |