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Conference Paper: Improving Deep Video Compression by Resolution-Adaptive Flow Coding
Title | Improving Deep Video Compression by Resolution-Adaptive Flow Coding |
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Authors | |
Issue Date | 2020 |
Publisher | Springer |
Citation | 16th European Conference on Computer Vision (ECCV 2020), Glasgow, UK, 23-28 August 2020. In Vedaldi, A, Bischof, H, Brox, T, et al. (Eds.), Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II, p. 193-209. Cham: Springer, 2020 How to Cite? |
Abstract | In the learning based video compression approaches, it is an essential issue to compress pixel-level optical flow maps by developing new motion vector (MV) encoders. In this work, we propose a new framework called Resolution-adaptive Flow Coding (RaFC) to effectively compress the flow maps globally and locally, in which we use multi-resolution representations instead of single-resolution representations for both the input flow maps and the output motion features of the MV encoder. To handle complex or simple motion patterns globally, our frame-level scheme RaFC-frame automatically decides the optimal flow map resolution for each video frame. To cope different types of motion patterns locally, our block-level scheme called RaFC-block can also select the optimal resolution for each local block of motion features. In addition, the rate-distortion criterion is applied to both RaFC-frame and RaFC-block and select the optimal motion coding mode for effective flow coding. Comprehensive experiments on four benchmark datasets HEVC, VTL, UVG and MCL-JCV clearly demonstrate the effectiveness of our overall RaFC framework after combing RaFC-frame and RaFC-block for video compression. |
Persistent Identifier | http://hdl.handle.net/10722/321912 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
Series/Report no. | Lecture Notes in Computer Science ; 12347 LNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics |
DC Field | Value | Language |
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dc.contributor.author | Hu, Zhihao | - |
dc.contributor.author | Chen, Zhenghao | - |
dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Lu, Guo | - |
dc.contributor.author | Ouyang, Wanli | - |
dc.contributor.author | Gu, Shuhang | - |
dc.date.accessioned | 2022-11-03T02:22:18Z | - |
dc.date.available | 2022-11-03T02:22:18Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | 16th European Conference on Computer Vision (ECCV 2020), Glasgow, UK, 23-28 August 2020. In Vedaldi, A, Bischof, H, Brox, T, et al. (Eds.), Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II, p. 193-209. Cham: Springer, 2020 | - |
dc.identifier.isbn | 9783030585358 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321912 | - |
dc.description.abstract | In the learning based video compression approaches, it is an essential issue to compress pixel-level optical flow maps by developing new motion vector (MV) encoders. In this work, we propose a new framework called Resolution-adaptive Flow Coding (RaFC) to effectively compress the flow maps globally and locally, in which we use multi-resolution representations instead of single-resolution representations for both the input flow maps and the output motion features of the MV encoder. To handle complex or simple motion patterns globally, our frame-level scheme RaFC-frame automatically decides the optimal flow map resolution for each video frame. To cope different types of motion patterns locally, our block-level scheme called RaFC-block can also select the optimal resolution for each local block of motion features. In addition, the rate-distortion criterion is applied to both RaFC-frame and RaFC-block and select the optimal motion coding mode for effective flow coding. Comprehensive experiments on four benchmark datasets HEVC, VTL, UVG and MCL-JCV clearly demonstrate the effectiveness of our overall RaFC framework after combing RaFC-frame and RaFC-block for video compression. | - |
dc.language | eng | - |
dc.publisher | Springer | - |
dc.relation.ispartof | Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; 12347 | - |
dc.relation.ispartofseries | LNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics | - |
dc.title | Improving Deep Video Compression by Resolution-Adaptive Flow Coding | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-030-58536-5_12 | - |
dc.identifier.scopus | eid_2-s2.0-85097228080 | - |
dc.identifier.spage | 193 | - |
dc.identifier.epage | 209 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.publisher.place | Cham | - |