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.
Original language | English |
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Title of host publication | 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 505-508 |
Number of pages | 4 |
ISBN (Electronic) | 9789881476852 |
DOIs | |
State | Published - 2 Jul 2018 |
Event | 10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States Duration: 12 Nov 2018 → 15 Nov 2018 |
Publication series
Name | 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings |
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Conference
Conference | 10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 |
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Country/Territory | United States |
City | Honolulu |
Period | 12/11/18 → 15/11/18 |
Bibliographical note
Publisher Copyright:© 2018 APSIPA organization.