No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, H.-G. | - |
dc.contributor.author | Shin, S.-S. | - |
dc.contributor.author | Kim, S.-W. | - |
dc.contributor.author | Lee, G.Y. | - |
dc.date.accessioned | 2022-01-25T06:40:53Z | - |
dc.date.available | 2022-01-25T06:40:53Z | - |
dc.date.issued | 2021-06 | - |
dc.identifier.issn | 1225-6463 | - |
dc.identifier.issn | 2233-7326 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/54383 | - |
dc.description.abstract | This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods. © 2021 ETRI | - |
dc.format.extent | 11 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | John Wiley and Sons Inc | - |
dc.title | No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model | - |
dc.type | Article | - |
dc.identifier.doi | 10.4218/etrij.2020-0160 | - |
dc.identifier.bibliographicCitation | ETRI Journal, v.43, no.3, pp 538 - 548 | - |
dc.identifier.kciid | ART002732888 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000634966500001 | - |
dc.identifier.scopusid | 2-s2.0-85103422439 | - |
dc.citation.endPage | 548 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 538 | - |
dc.citation.title | ETRI Journal | - |
dc.citation.volume | 43 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | 3D shearlet transform | - |
dc.subject.keywordAuthor | conditional constraints | - |
dc.subject.keywordAuthor | deep residual bidirectional gated recurrent neural network | - |
dc.subject.keywordAuthor | natural scene statistics | - |
dc.subject.keywordAuthor | no-reference video quality assessment | - |
dc.subject.keywordPlus | Logistic regression | - |
dc.subject.keywordPlus | Sports | - |
dc.subject.keywordPlus | Motion information | - |
dc.subject.keywordPlus | No-reference video quality assessments | - |
dc.subject.keywordPlus | Objective functions | - |
dc.subject.keywordPlus | Perceptual quality | - |
dc.subject.keywordPlus | Shearlet transforms | - |
dc.subject.keywordPlus | Spatio temporal features | - |
dc.subject.keywordPlus | Spatio-temporal models | - |
dc.subject.keywordPlus | Spatiotemporal correlation | - |
dc.subject.keywordPlus | Recurrent neural networks | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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