  Most video understanding methods are learned on high-quality videos. However,
in real-world scenarios, the videos are first compressed before the
transportation and then decompressed for understanding. The decompressed videos
may have lost the critical information to the downstream tasks. To address this
issue, we propose the first coding framework for compressed video
understanding, where another learnable analytic bitstream is simultaneously
transported with the original video bitstream. With the dedicatedly designed
self-supervised optimization target and dynamic network architectures, this new
stream largely boosts the downstream tasks yet with a small bit cost. By only
one-time training, our framework can be deployed for multiple downstream tasks.
Our framework also enjoys the best of both two worlds, (1) high efficiency of
industrial video codec and (2) flexible coding capability of neural networks
(NNs). Finally, we build a rigorous benchmark for compressed video
understanding on three popular tasks over seven large-scale datasets and four
different compression levels. The proposed Understanding oriented Video Coding
framework UVC consistently demonstrates significantly stronger performances
than the baseline industrial codec.
