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Convolution Neural Network based Video Coding Technique using Reference Video Synthesis
- Title
- Convolution Neural Network based Video Coding Technique using Reference Video Synthesis
- Authors
- Lee J.K.; Kim N.; Cho S.; Kang J.-W.
- Ewha Authors
- 강제원
- SCOPUS Author ID
- 강제원
- Issue Date
- 2019
- Journal Title
- 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
- Citation
- 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings, pp. 505 - 508
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Indexed
- SCOPUS
- Document Type
- Conference Paper
- Abstract
- In this paper, we propose a novel video coding technique that uses a virtual reference (VR) video frame, synthesized by a convolution neural network (CNN) for an inter-coding. Specifically, an encoder generates a VR frame from a video interpolation CNN (VI-CNN) using two reconstructed pictures, i.e., one from the forward reference frames and the other from the backward reference frames. The VR frame is included into the reference picture lists to exploit further temporal correlation in motion estimation and compensation. It is demonstrated by the experimental results that the proposed technique shows about 1.4% BD-rate reductions over the HEVC reference test model (HM 16.9) as an anchor in a Random Access (RA) coding scenario. © 2018 APSIPA organization.
- DOI
- 10.23919/APSIPA.2018.8659611
- ISBN
- 9789881476852
- Appears in Collections:
- 공과대학 > 전자전기공학전공 > Journal papers
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