Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/70307
DC FieldValue
dc.titleFast rate-distortion optimization in H.264/AVC video coding
dc.contributor.authorPan, F.
dc.contributor.authorChoo, K.
dc.contributor.authorLe, T.M.
dc.date.accessioned2014-06-19T03:10:37Z
dc.date.available2014-06-19T03:10:37Z
dc.date.issued2005
dc.identifier.citationPan, F.,Choo, K.,Le, T.M. (2005). Fast rate-distortion optimization in H.264/AVC video coding. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3683 LNAI : 433-441. ScholarBank@NUS Repository.
dc.identifier.isbn3540288961
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/70307
dc.description.abstractOne of the new features in the H.264/AVC encoder is the use of Lagrangian Rate-Distortion Optimised (RDO) mode decision at the macroblock (MB) level. This brute-force algorithm searches through the 10 different MB coding modes to obtain the best one for encoding that MB, and is hence computationally expensive. This paper proposes a novel algorithm where the RDO can be reorganized in a better order such that the most likely MB modes will be tried first, and an early termination of the RDO process will be used once the calculated rate-distortion cost (RDcost) is below a preset threshold. The preset threshold is dependent on the RDcost of previous and neighbouring MBs that have been coded. This is based on the observation that the RDcost of an MB in the current frame is highly correlated to a co-located MB in the previous frame. Experimental results have shown that the new algorithm has dramatically reduced the encoding times of up to 61.8%, with negligible increases in bitrates or PSNR. © Springer-Verlag Berlin Heidelberg 2005.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume3683 LNAI
dc.description.page433-441
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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