  Inter prediction is an important module in video coding for temporal
redundancy removal, where similar reference blocks are searched from previously
coded frames and employed to predict the block to be coded. Although
traditional video codecs can estimate and compensate for block-level motions,
their inter prediction performance is still heavily affected by the remaining
inconsistent pixel-wise displacement caused by irregular rotation and
deformation. In this paper, we address the problem by proposing a deep frame
interpolation network to generate additional reference frames in coding
scenarios. First, we summarize the previous adaptive convolutions used for
frame interpolation and propose a factorized kernel convolutional network to
improve the modeling capacity and simultaneously keep its compact form. Second,
to better train this network, multi-domain hierarchical constraints are
introduced to regularize the training of our factorized kernel convolutional
network. For spatial domain, we use a gradually down-sampled and up-sampled
auto-encoder to generate the factorized kernels for frame interpolation at
different scales. For quality domain, considering the inconsistent quality of
the input frames, the factorized kernel convolution is modulated with
quality-related features to learn to exploit more information from high quality
frames. For frequency domain, a sum of absolute transformed difference loss
that performs frequency transformation is utilized to facilitate network
optimization from the view of coding performance. With the well-designed frame
interpolation network regularized by multi-domain hierarchical constraints, our
method surpasses HEVC on average 6.1% BD-rate saving and up to 11.0% BD-rate
saving for the luma component under the random access configuration.
