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

arXiv: https://arxiv.org/abs/1709.08462 · HTML: https://mlsanity.com/paper/1709.08462/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [Interactive Face Video Coding: A Generative Compression Framework](https://mlsanity.com/paper/2302.09919/) (2023)
- [MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation](https://mlsanity.com/paper/2309.14216/) (2023)
- [Multi-Density Attention Network for Loop Filtering in Video Compression](https://mlsanity.com/paper/2104.12865/) (2021)
- [Distortion-Aware Loop Filtering of Intra 360^o Video Coding with Equirectangular Projection](https://mlsanity.com/paper/2202.09802/) (2022)
- [Deep Learning-based Filtering for Video Coding: A Survey on Architectures, Algorithms, and Complexity Analysis](https://mlsanity.com/paper/2607.16319/) (2026)

## Abstract (arXiv)

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.