dc.contributor.advisor | Tian, Chao | |
dc.creator | Song, Jianfeng | |
dc.date.accessioned | 2020-08-26T19:58:15Z | |
dc.date.available | 2020-08-26T19:58:15Z | |
dc.date.created | 2019-12 | |
dc.date.issued | 2019-10-30 | |
dc.date.submitted | December 2019 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/188802 | |
dc.description.abstract | High 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.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | HEVC | en |
dc.subject | CNN | en |
dc.title | HEVC Fast CU Decision for Intra-Prediction by CNN | en |
dc.type | Thesis | en |
thesis.degree.department | Electrical and Computer Engineering | en |
thesis.degree.discipline | Electrical Engineering | en |
thesis.degree.grantor | Texas A&M University | en |
thesis.degree.name | Master of Science | en |
thesis.degree.level | Masters | en |
dc.contributor.committeeMember | Braga-Neto, Ulisses | |
dc.contributor.committeeMember | Liu, Tie | |
dc.contributor.committeeMember | Wang, Zhangyang | |
dc.type.material | text | en |
dc.date.updated | 2020-08-26T19:58:16Z | |
local.etdauthor.orcid | 0000-0001-7286-3444 | |