  Motion estimation and motion compensation are indispensable parts of inter
prediction in video coding. Since the motion vector of objects is mostly in
fractional pixel units, original reference pictures may not accurately provide
a suitable reference for motion compensation. In this paper, we propose a deep
reference picture generator which can create a picture that is more relevant to
the current encoding frame, thereby further reducing temporal redundancy and
improving video compression efficiency. Inspired by the recent progress of
Convolutional Neural Network(CNN), this paper proposes to use a dilated CNN to
build the generator. Moreover, we insert the generated deep picture into
Versatile Video Coding(VVC) as a reference picture and perform a comprehensive
set of experiments to evaluate the effectiveness of our network on the latest
VVC Test Model VTM. The experimental results demonstrate that our proposed
method achieves on average 9.7% bit saving compared with VVC under low-delay P
configuration.
