  In this paper, a dual learning-based method in intra coding is introduced for
PCS Grand Challenge. This method is mainly composed of two parts: intra
prediction and reconstruction filtering. They use different network structures,
the neural network-based intra prediction uses the full-connected network to
predict the block while the neural network-based reconstruction filtering
utilizes the convolutional networks. Different with the previous filtering
works, we use a network with more powerful feature extraction capabilities in
our reconstruction filtering network. And the filtering unit is the block-level
so as to achieve a more accurate filtering compensation. To our best knowledge,
among all the learning-based methods, this is the first attempt to combine two
different networks in one application, and we achieve the state-of-the-art
performance for AI configuration on the HEVC Test sequences. The experimental
result shows that our method leads to significant BD-rate saving for provided 8
sequences compared to HM-16.20 baseline (average 10.24% and 3.57% bitrate
reductions for all-intra and random-access coding, respectively). For HEVC test
sequences, our model also achieved a 9.70% BD-rate saving compared to HM-16.20
baseline for all-intra configuration.
