# Non-Local ConvLSTM for Video Compression Artifact Reduction

arXiv: https://arxiv.org/abs/1910.12286 · HTML: https://mlsanity.com/paper/1910.12286/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2019.

## Built on by (most cited first, 19 of 19)

- [FVC: A New Framework towards Deep Video Compression in Feature Space](https://mlsanity.com/paper/2105.09600/) (2021)
- [RegNet: Self-Regulated Network for Image Classification](https://mlsanity.com/paper/2101.00590/) (2021)
- [Learning Dual Memory Dictionaries for Blind Face Restoration](https://mlsanity.com/paper/2210.08160/) (2022)
- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results](https://mlsanity.com/paper/2104.10781/) (2021)
- [COMISR: Compression-Informed Video Super-Resolution](https://mlsanity.com/paper/2105.01237/) (2021)
- [Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution](https://mlsanity.com/paper/2208.03012/) (2022)
- [NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results](https://mlsanity.com/paper/2204.09314/) (2022)
- [AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results](https://mlsanity.com/paper/2208.11184/) (2022)
- [Multi-modality Deep Restoration of Extremely Compressed Face Videos](https://mlsanity.com/paper/2107.05548/) (2021)
- [Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reduction](https://mlsanity.com/paper/2108.02110/) (2021)
- [NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results](https://mlsanity.com/paper/2505.03007/) (2025)
- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study](https://mlsanity.com/paper/2104.10782/) (2021)
- [End-to-end Transformer for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2210.13827/) (2022)
- [Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information](https://mlsanity.com/paper/2105.14962/) (2021)
- [Online Video Quality Enhancement with Spatial-Temporal Look-up Tables](https://mlsanity.com/paper/2311.13616/) (2023)
- [Multi-frame Joint Enhancement for Early Interlaced Videos](https://mlsanity.com/paper/2109.14151/) (2021)
- [Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution](https://mlsanity.com/paper/2212.14046/) (2022)
- [Lightweight Hybrid Video Compression Framework Using Reference-Guided Restoration Network](https://mlsanity.com/paper/2303.11592/) (2023)
- [Blind Quality Enhancement of Compressed Video via Fine-Grained Degradation-Guided Sequential Inference](https://mlsanity.com/paper/2511.16137/) (2025)

## Abstract (arXiv)

Video compression artifact reduction aims to recover high-quality videos from low-quality compressed videos. Most existing approaches use a single neighboring frame or a pair of neighboring frames (preceding and/or following the target frame) for this task. Furthermore, as frames of high quality overall may contain low-quality patches, and high-quality patches may exist in frames of low quality overall, current methods focusing on nearby peak-quality frames (PQFs) may miss high-quality details in low-quality frames. To remedy these shortcomings, in this paper we propose a novel end-to-end deep neural network called non-local ConvLSTM (NL-ConvLSTM in short) that exploits multiple consecutive frames. An approximate non-local strategy is introduced in NL-ConvLSTM to capture global motion patterns and trace the spatiotemporal dependency in a video sequence. This approximate strategy makes the non-local module work in a fast and low space-cost way. Our method uses the preceding and following frames of the target frame to generate a residual, from which a higher quality frame is reconstructed. Experiments on two datasets show that NL-ConvLSTM outperforms the existing methods.