Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 강제원 | * |
dc.date.accessioned | 2020-07-16T16:30:09Z | - |
dc.date.available | 2020-07-16T16:30:09Z | - |
dc.date.issued | 2020 | * |
dc.identifier.issn | 2169-3536 | * |
dc.identifier.other | OAK-27211 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/254184 | - |
dc.description.abstract | In this paper, we propose a novel Convolutional Neural Network (CNN) based video coding technique using a video prediction network (VPN) to support enhanced motion prediction in High Efficiency Video Coding (HEVC). Specifically, we design a CNN VPN to generate a virtual reference frame (VRF), which is synthesized using previously coded frames, to improve coding efficiency. The proposed VPN uses two sub-VPN architectures in cascade to predict the current frame in the same time instance. The VRF is expected to have higher temporal correlation than a conventional reference frame, and, thus it is substituted for a conventional reference frame. The proposed technique is incorporated into the HEVC inter-coding framework. Particularly, the VRF is managed in a HEVC reference picture list, so that each prediction unit (PU) can choose a better prediction signal through Rate-Distortion optimization without any additional side information. Furthermore, we modify the HEVC inter-prediction mechanisms of Advanced Motion Vector Prediction and Merge modes adaptively when the current PU uses the VRF as a reference frame. In this manner, the proposed technique can exploit the PU-wise multi-hypothesis prediction techniques in HEVC. Since the proposed VPN can perform both the video interpolation and extrapolation, it can be used for Random Access (RA) and Low Delay B (LD) coding configurations. It is shown in experimental results that the proposed technique provides & x2212;2.9 & x0025; and & x2212;5.7 & x0025; coding gains, respectively, in RA and LD coding configurations as compared to the HEVC reference software, HM 16.6 version. | * |
dc.language | English | * |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | * |
dc.subject | Virtual private networks | * |
dc.subject | Encoding | * |
dc.subject | Video coding | * |
dc.subject | Kernel | * |
dc.subject | Interpolation | * |
dc.subject | Extrapolation | * |
dc.subject | Convolution | * |
dc.subject | deep learning | * |
dc.subject | convolutional neural network | * |
dc.subject | video prediction network | * |
dc.subject | inter-prediction | * |
dc.subject | virtual reference frame | * |
dc.subject | HEVC | * |
dc.subject | VVC | * |
dc.title | Deep Video Prediction Network-ased Inter-Frame Coding in HEVC | * |
dc.type | Article | * |
dc.relation.volume | 8 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 95906 | * |
dc.relation.lastpage | 95917 | * |
dc.relation.journaltitle | IEEE ACCESS | * |
dc.identifier.doi | 10.1109/ACCESS.2020.2993566 | * |
dc.identifier.wosid | WOS:000541139500061 | * |
dc.author.google | Lee, Jung-Kyung | * |
dc.author.google | Kim, Nayoung | * |
dc.author.google | Cho, Seunghyun | * |
dc.author.google | Kang, Je-Won | * |
dc.contributor.scopusid | 강제원(56367466400) | * |
dc.date.modifydate | 20240322125621 | * |