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A fast algorithm for macroblock mode selection in H.264 video coding

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dc.contributor.authorKim, Donghyung-
dc.contributor.authorKim, Jongho-
dc.contributor.authorKim, Seungjong-
dc.contributor.authorJeong, Jechang-
dc.date.accessioned2022-12-21T10:38:37Z-
dc.date.available2022-12-21T10:38:37Z-
dc.date.created2022-08-26-
dc.date.issued2006-09-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/181081-
dc.description.abstractTo improve coding efficiency, the H.264 video coding standard uses new coding tools, such as variable block size, quarter-pixel-accuracy motion estimation, multiple reference frames, intra prediction and a loop filter. Using these coding tools, H.264 achieves significant improvement in coding efficiency compared with existing standards. However, encoder complexity also increases tremendously. Among the tools, macroblock mode selection and motion estimation contribute most to total encoder complexity. This paper focuses on complexity reduction in macroblock mode selection. Of the macroblock modes which can be selected, inter8x8 and intra4x4 have the highest complexity. We propose two methods for complexity reduction of inter8x8 and intra4x4 by using the costs of the other macroblock modes. Simulation results show that the proposed methods save about 53% of total encoding time compared with the H.264 reference implementation, whereas the average PSNR decreases less than 0.05 dB.-
dc.language영어-
dc.language.isoen-
dc.publisherSPRINGER-VERLAG BERLIN-
dc.titleA fast algorithm for macroblock mode selection in H.264 video coding-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Donghyung-
dc.contributor.affiliatedAuthorJeong, Jechang-
dc.identifier.doi10.1007/11867586_43-
dc.identifier.scopusid2-s2.0-33749666701-
dc.identifier.wosid000241552700043-
dc.identifier.bibliographicCitationIMAGE ANALYSIS AND RECOGNITION, PT 1, v.4141, pp.458 - 467-
dc.relation.isPartOfIMAGE ANALYSIS AND RECOGNITION, PT 1-
dc.citation.titleIMAGE ANALYSIS AND RECOGNITION, PT 1-
dc.citation.volume4141-
dc.citation.startPage458-
dc.citation.endPage467-
dc.type.rimsART-
dc.type.docTypeArticle; Proceedings Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.subject.keywordPlusAlgorithms-
dc.subject.keywordPlusComputational complexity-
dc.subject.keywordPlusComputer simulation-
dc.subject.keywordPlusMotion estimation-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/11867586_43-
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서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
서울 의과대학 > 서울 의학교육학교실 > 1. Journal Articles

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