Spatial-Temporal Residue Network Based In-Loop Filter for Video Coding

Abstract

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.

4 pages, 2 figures, accepted by VCIP2017

Cited by in corpus (5)

• Image and Video Compression with Neural Networks: A Review
• A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC
• Recent Advances on HEVC Inter-frame Coding: From Optimization to Implementation and Beyond
• Combining Progressive Rethinking and Collaborative Learning: A Deep Framework for In-Loop Filtering
• Enhanced Intra Prediction for Video Coding by Using Multiple Neural Networks