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Hadamard transform based fast partial distortion elimination algorithm for lossless and lossy motion estimation

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dc.contributor.authorJin, Soonjong-
dc.contributor.authorLee, Hyuk-
dc.contributor.authorJeong, Jechang-
dc.date.accessioned2022-12-21T03:06:00Z-
dc.date.available2022-12-21T03:06:00Z-
dc.date.created2022-09-16-
dc.date.issued2008-05-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/178630-
dc.description.abstractIn this paper, based on ordered Hadamard transform and its probability model, a fast partial distortion elimination algorithm is proposed. For lossless motion estimation, we propose DC and AC constraints to model local block complexities using ordered Hadamard transform. Using sum of these constraints, we obtain the optimized search order in the matching error calculation by descending order of local constraints. And for the fast search in lossy motion estimation, using probability model which is approximated by the cumulative distribution function of constraints, we also propose the accurate total matching error prediction algorithm without all matching error calculations. The proposed algorithm reduces the motion estimation complexity up to 92.4% for lossy motion estimation and 89.4% for lossless motion estimation on average, compared with the conventional full search algorithm.-
dc.language영어-
dc.language.isoen-
dc.publisherIEEE-
dc.titleHadamard transform based fast partial distortion elimination algorithm for lossless and lossy motion estimation-
dc.typeArticle-
dc.contributor.affiliatedAuthorJeong, Jechang-
dc.identifier.doi10.1109/CISP.2008.14-
dc.identifier.scopusid2-s2.0-52149110573-
dc.identifier.bibliographicCitationProceedings - 1st International Congress on Image and Signal Processing, CISP 2008, v.4, pp.201 - 205-
dc.relation.isPartOfProceedings - 1st International Congress on Image and Signal Processing, CISP 2008-
dc.citation.titleProceedings - 1st International Congress on Image and Signal Processing, CISP 2008-
dc.citation.volume4-
dc.citation.startPage201-
dc.citation.endPage205-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusAlgorithms-
dc.subject.keywordPlusChlorine compounds-
dc.subject.keywordPlusDistribution functions-
dc.subject.keywordPlusEstimation-
dc.subject.keywordPlusImage coding-
dc.subject.keywordPlusLearning algorithms-
dc.subject.keywordPlusMathematical transformations-
dc.subject.keywordPlusModal analysis-
dc.subject.keywordPlusProbability-
dc.subject.keywordPlusProbability distributions-
dc.subject.keywordPlusSignal processing-
dc.subject.keywordPlusVector quantization-
dc.subject.keywordPlusCumulative distribution function-
dc.subject.keywordPlusEstimation complexity-
dc.subject.keywordPlusFast search-
dc.subject.keywordPlusFull search algorithm-
dc.subject.keywordPlusHadamard transform-
dc.subject.keywordPlusInternational congresses-
dc.subject.keywordPlusLocal constraints-
dc.subject.keywordPlusLossless-
dc.subject.keywordPlusLossless motion estimation-
dc.subject.keywordPlusMatching errors-
dc.subject.keywordPlusPartial distortion elimination-
dc.subject.keywordPlusProbability modeling-
dc.subject.keywordPlusMotion estimation-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/4566644-
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