Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/10473
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dc.contributor.authorAsfandyar, Maliken_US
dc.contributor.authorDr. NAWAZ Mehmooden_US
dc.contributor.authorHussain, Muddsseren_US
dc.date.accessioned2024-09-07T09:33:52Z-
dc.date.available2024-09-07T09:33:52Z-
dc.date.issued2016-
dc.identifier.citationAsfandyar, M., Nawaz, M., & Hussain, M. (2016). Accelerated CU decision based on enlarged CU sizes for HEVC UHD videos. In ICSIP (Ed.). 2016 IEEE international conference on signal and image processing (ICSIP). 2016 IEEE International Conference on Signal and Image Processing (ICSIP), Beijing, China (pp. 374-378). IEEE.en_US
dc.identifier.isbn9781509023776-
dc.identifier.isbn9781509023783-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/10473-
dc.description.abstractHigh Efficiency Video Coding (HEVC) is the latest video coding standard and has achieved 50% better compression performance compared to prior video standards competing with (2K, 4K, and 8K) video resolutions. HEVC adopts flexible quad-tree structure, resulting 60% of inter prediction complexity. A fast coding unit decision taking algorithm is proposed which can reduce the coding tree unit (CTU) inter mode complexity of HEVC, by enlarging the coding unit (CU), prediction unit (PU) and transform unit (TU) sizes. The algorithm first extends the coding unit size and then classifies it into different units with respect to the information collected from previous encoded frames. The probability model is used to take decision for the splitting of coding unit. Experimental results achieved an average reduction of encoding time by 62% with an average decrease of 0.01% dB in PSNR and negligible bit-rate increment.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.titleAccelerated CU decision based on enlarged CU sizes for HEVC UHD videosen_US
dc.typeConference Paperen_US
dc.relation.conference2016 IEEE International Conference on Signal and Image Processing (ICSIP)en_US
dc.identifier.doi10.1109/SIPROCESS.2016.7888287-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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