  In VP9 video codec, the sizes of blocks are decided during encoding by
recursively partitioning 64$\times$64 superblocks using rate-distortion
optimization (RDO). This process is computationally intensive because of the
combinatorial search space of possible partitions of a superblock. Here, we
propose a deep learning based alternative framework to predict the intra-mode
superblock partitions in the form of a four-level partition tree, using a
hierarchical fully convolutional network (H-FCN). We created a large database
of VP9 superblocks and the corresponding partitions to train an H-FCN model,
which was subsequently integrated with the VP9 encoder to reduce the intra-mode
encoding time. The experimental results establish that our approach speeds up
intra-mode encoding by 69.7% on average, at the expense of a 1.71% increase in
the Bjontegaard-Delta bitrate (BD-rate). While VP9 provides several built-in
speed levels which are designed to provide faster encoding at the expense of
decreased rate-distortion performance, we find that our model is able to
outperform the fastest recommended speed level of the reference VP9 encoder for
the good quality intra encoding configuration, in terms of both speedup and
BD-rate.
