Spatial-Temporal Residue Network Based In-Loop Filter for Video Coding
This work addresses video compression efficiency for applications like streaming and storage, representing an incremental improvement with a specific gain.
The paper tackles visual artifacts like blocking and ringing in video coding by proposing a spatial-temporal residue network (STResNet) as an in-loop filter, achieving up to 5.1% bit-rate reduction compared to the state-of-the-art standard.
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.