  The previous deep video compression approaches only use the single scale
motion compensation strategy and rarely adopt the mode prediction technique
from the traditional standards like H.264/H.265 for both motion and residual
compression. In this work, we first propose a coarse-to-fine (C2F) deep video
compression framework for better motion compensation, in which we perform
motion estimation, compression and compensation twice in a coarse to fine
manner. Our C2F framework can achieve better motion compensation results
without significantly increasing bit costs. Observing hyperprior information
(i.e., the mean and variance values) from the hyperprior networks contains
discriminant statistical information of different patches, we also propose two
efficient hyperprior-guided mode prediction methods. Specifically, using
hyperprior information as the input, we propose two mode prediction networks to
respectively predict the optimal block resolutions for better motion coding and
decide whether to skip residual information from each block for better residual
coding without introducing additional bit cost while bringing negligible extra
computation cost. Comprehensive experimental results demonstrate our proposed
C2F video compression framework equipped with the new hyperprior-guided mode
prediction methods achieves the state-of-the-art performance on HEVC, UVG and
MCL-JCV datasets.
