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dc.contributor.advisorTian, Chao
dc.creatorSong, Jianfeng
dc.date.accessioned2020-08-26T19:58:15Z
dc.date.available2020-08-26T19:58:15Z
dc.date.created2019-12
dc.date.issued2019-10-30
dc.date.submittedDecember 2019
dc.identifier.urihttps://hdl.handle.net/1969.1/188802
dc.description.abstractHigh Efficiency Video Coding (HEVC) is also know as H.265 was first official introduced in 2013, it is one of the video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. In the original algorithm, the coding unit depth decision was made by applying a recursive search from top depth 0 to bottom depth 3 on all possible quad-tree structures. Therefore this algorithm is considered to be very time consuming and computational expensive. In my research, I have compared two different methods of reducing the computational complexity for HEVC by using Neural Networks, and propose a new structure of neural network and provide the corresponding result.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectHEVCen
dc.subjectCNNen
dc.titleHEVC Fast CU Decision for Intra-Prediction by CNNen
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberBraga-Neto, Ulisses
dc.contributor.committeeMemberLiu, Tie
dc.contributor.committeeMemberWang, Zhangyang
dc.type.materialtexten
dc.date.updated2020-08-26T19:58:16Z
local.etdauthor.orcid0000-0001-7286-3444


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