Bi-Directional Deep Contextual Video Compression
- URL: http://arxiv.org/abs/2408.08604v1
- Date: Fri, 16 Aug 2024 08:45:25 GMT
- Title: Bi-Directional Deep Contextual Video Compression
- Authors: Xihua Sheng, Li Li, Dong Liu, Shiqi Wang,
- Abstract summary: We introduce a bi-directional deep contextual video compression scheme tailored for B-frames, termed DCVC-B.
First, we develop a bi-directional motion difference context propagation method for effective motion difference coding.
Second, we propose a bi-directional contextual compression model and a corresponding bi-directional temporal entropy model.
Third, we propose a hierarchical quality structure-based training strategy, leading to an effective bit allocation across large groups of pictures.
- Score: 17.195099321371526
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep video compression has made remarkable process in recent years, with the majority of advancements concentrated on P-frame coding. Although efforts to enhance B-frame coding are ongoing, their compression performance is still far behind that of traditional bi-directional video codecs. In this paper, we introduce a bi-directional deep contextual video compression scheme tailored for B-frames, termed DCVC-B, to improve the compression performance of deep B-frame coding. Our scheme mainly has three key innovations. First, we develop a bi-directional motion difference context propagation method for effective motion difference coding, which significantly reduces the bit cost of bi-directional motions. Second, we propose a bi-directional contextual compression model and a corresponding bi-directional temporal entropy model, to make better use of the multi-scale temporal contexts. Third, we propose a hierarchical quality structure-based training strategy, leading to an effective bit allocation across large groups of pictures (GOP). Experimental results show that our DCVC-B achieves an average reduction of 26.6% in BD-Rate compared to the reference software for H.265/HEVC under random access conditions. Remarkably, it surpasses the performance of the H.266/VVC reference software on certain test datasets under the same configuration.
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