  Recently, deep image compression has shown a big progress in terms of coding
efficiency and image quality improvement. However, relatively less attention
has been put on video compression using deep learning networks. In the paper,
we first propose a deep learning based bi-predictive coding network, called
BP-DVC Net, for video compression. Learned from the lesson of the conventional
video coding, a B-frame coding structure is incorporated in our BP-DVC Net.
While the bi-predictive coding in the conventional video codecs requires to
transmit to decoder sides the motion vectors for block motion and the residues
from prediction, our BP-DVC Net incorporates optical flow estimation networks
in both encoder and decoder sides so as not to transmit the motion information
to the decoder sides for coding efficiency improvement. Also, a bi-prediction
network in the BP-DVC Net is proposed and used to precisely predict the current
frame and to yield the resulting residues as small as possible. Furthermore,
our BP-DVC Net allows for the compressive feature maps to be entropy-coded
using the temporal context among the feature maps of adjacent frames. The
BP-DVC Net has an end-to-end video compression architecture with newly designed
flow and prediction losses. Experimental results show that the compression
performance of our proposed method is comparable to those of H.264, HEVC in
terms of PSNR and MS-SSIM.
