  Most of the existing neural video compression methods adopt the predictive
coding framework, which first generates the predicted frame and then encodes
its residue with the current frame. However, as for compression ratio,
predictive coding is only a sub-optimal solution as it uses simple subtraction
operation to remove the redundancy across frames. In this paper, we propose a
deep contextual video compression framework to enable a paradigm shift from
predictive coding to conditional coding. In particular, we try to answer the
following questions: how to define, use, and learn condition under a deep video
compression framework. To tap the potential of conditional coding, we propose
using feature domain context as condition. This enables us to leverage the high
dimension context to carry rich information to both the encoder and the
decoder, which helps reconstruct the high-frequency contents for higher video
quality. Our framework is also extensible, in which the condition can be
flexibly designed. Experiments show that our method can significantly
outperform the previous state-of-the-art (SOTA) deep video compression methods.
When compared with x265 using veryslow preset, we can achieve 26.0% bitrate
saving for 1080P standard test videos.
