  Conventional video compression approaches use the predictive coding
architecture and encode the corresponding motion information and residual
information. In this paper, taking advantage of both classical architecture in
the conventional video compression method and the powerful non-linear
representation ability of neural networks, we propose the first end-to-end
video compression deep model that jointly optimizes all the components for
video compression. Specifically, learning based optical flow estimation is
utilized to obtain the motion information and reconstruct the current frames.
Then we employ two auto-encoder style neural networks to compress the
corresponding motion and residual information. All the modules are jointly
learned through a single loss function, in which they collaborate with each
other by considering the trade-off between reducing the number of compression
bits and improving quality of the decoded video. Experimental results show that
the proposed approach can outperform the widely used video coding standard
H.264 in terms of PSNR and be even on par with the latest standard H.265 in
terms of MS-SSIM. Code is released at https://github.com/GuoLusjtu/DVC.
