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Versatile Learned Video Compression. (arXiv:2111.03386v2 [eess.IV] UPDATED)
Jan. 6, 2022, 2:10 a.m. | Runsen Feng, Zongyu Guo, Zhizheng Zhang, Zhibo Chen
cs.CV updates on arXiv.org arxiv.org
Learned video compression methods have demonstrated great promise in catching
up with traditional video codecs in their rate-distortion (R-D) performance.
However, existing learned video compression schemes are limited by the binding
of the prediction mode and the fixed network framework. They are unable to
support various inter prediction modes and thus inapplicable for various
scenarios. In this paper, to break this limitation, we propose a versatile
learned video compression (VLVC) framework that uses one model to support all
possible prediction …
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