Triple-Frame-Based Bi-Directional Motion Estimation for Motion-Compensated Frame Interpolation

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dc.contributor.authorChoi, Giyongko
dc.contributor.authorHeo, Pyeong Gangko
dc.contributor.authorPark, HyunWookko
dc.date.accessioned2019-05-29T02:25:57Z-
dc.date.available2019-05-29T02:25:57Z-
dc.date.created2019-05-28-
dc.date.issued2019-05-
dc.identifier.citationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, v.29, no.5, pp.1251 - 1258-
dc.identifier.issn1051-8215-
dc.identifier.urihttp://hdl.handle.net/10203/262264-
dc.description.abstractIn this paper, new motion estimation (ME) and motion vector refinement (MVR) methods for motion-compensated frame interpolation (MCFI) are proposed. To estimate motion vectors (MVs) with great reliability, triple-frame-based bi-directional ME (TFBME), which employs three sequential frames, is developed. In TFBME, the middle frame works as a reference to estimate MVs between two end frames, which leads to performance improvement especially when objects move linearly through three sequential frames. In addition, a modified bi-directional ME method, which employs only two successive frames, is combined with TFBME to handle cases in which objects move non-linearly through three sequential frames. In our proposed MVR, artifacts on interpolated frames are first detected by a convolutional neural network and then corrected by weighted vector median filtering. Experimental results show that the proposed MCFI method outperforms conventional methods in both subjective and objective evaluations.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleTriple-Frame-Based Bi-Directional Motion Estimation for Motion-Compensated Frame Interpolation-
dc.typeArticle-
dc.identifier.wosid000467063100002-
dc.identifier.scopusid2-s2.0-85047603390-
dc.type.rimsART-
dc.citation.volume29-
dc.citation.issue5-
dc.citation.beginningpage1251-
dc.citation.endingpage1258-
dc.citation.publicationnameIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY-
dc.identifier.doi10.1109/TCSVT.2018.2840842-
dc.contributor.localauthorPark, HyunWook-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorConvolutional neural networks-
dc.subject.keywordAuthorframe rate up-conversion-
dc.subject.keywordAuthormotion-compensated frame interpolation-
dc.subject.keywordAuthormotion estimation-
dc.subject.keywordAuthormotion vector refinement-
dc.subject.keywordPlusVIDEO-
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