  Intra prediction is an important component of modern video codecs, which is
able to efficiently squeeze out the spatial redundancy in video frames. With
preceding pixels as the context, traditional intra prediction schemes generate
linear predictions based on several predefined directions (i.e. modes) for
blocks to be encoded. However, these modes are relatively simple and their
predictions may fail when facing blocks with complex textures, which leads to
additional bits encoding the residue. In this paper, we design a Progressive
Spatial Recurrent Neural Network (PS-RNN) that learns to conduct intra
prediction. Specifically, our PS-RNN consists of three spatial recurrent units
and progressively generates predictions by passing information along from
preceding contents to blocks to be encoded. To make our network generate
predictions considering both distortion and bit-rate, we propose to use Sum of
Absolute Transformed Difference (SATD) as the loss function to train PS-RNN
since SATD is able to measure rate-distortion cost of encoding a residue block.
Moreover, our method supports variable-block-size for intra prediction, which
is more practical in real coding conditions. The proposed intra prediction
scheme achieves on average 2.5% bit-rate reduction on variable-block-size
settings under the same reconstruction quality compared with HEVC.
