  Deep learning has demonstrated tremendous break through in the area of
image/video processing. In this paper, a spatial-temporal residue network
(STResNet) based in-loop filter is proposed to suppress visual artifacts such
as blocking, ringing in video coding. Specifically, the spatial and temporal
information is jointly exploited by taking both current block and co-located
block in reference frame into consideration during the processing of in-loop
filter. The architecture of STResNet only consists of four convolution layers
which shows hospitality to memory and coding complexity. Moreover, to fully
adapt the input content and improve the performance of the proposed in-loop
filter, coding tree unit (CTU) level control flag is applied in the sense of
rate-distortion optimization. Extensive experimental results show that our
scheme provides up to 5.1% bit-rate reduction compared to the state-of-the-art
video coding standard.
