# Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information

arXiv: https://arxiv.org/abs/2105.14962 · HTML: https://mlsanity.com/paper/2105.14962/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 22 of 22)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Residual Non-local Attention Networks for Image Restoration](https://mlsanity.com/paper/1903.10082/) (2019)
- [Non-Local Recurrent Network for Image Restoration](https://mlsanity.com/paper/1806.02919/) (2018)
- [Compression Artifacts Reduction by a Deep Convolutional Network](https://mlsanity.com/paper/1504.06993/) (2015)
- [Wide Activation for Efficient and Accurate Image Super-Resolution](https://mlsanity.com/paper/1808.08718/) (2018)
- [MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video](https://mlsanity.com/paper/1902.09707/) (2019)
- [Multi-Frame Quality Enhancement for Compressed Video](https://mlsanity.com/paper/1803.04680/) (2018)
- [Deep Generative Adversarial Compression Artifact Removal](https://mlsanity.com/paper/1704.02518/) (2017)
- [Enhancing Quality for HEVC Compressed Videos](https://mlsanity.com/paper/1709.06734/) (2017)
- [A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding](https://mlsanity.com/paper/1608.06690/) (2016)
- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results](https://mlsanity.com/paper/2104.10781/) (2021)
- [Non-Local ConvLSTM for Video Compression Artifact Reduction](https://mlsanity.com/paper/1910.12286/) (2019)
- [One-to-Many Network for Visually Pleasing Compression Artifacts Reduction](https://mlsanity.com/paper/1611.04994/) (2016)
- [Image Restoration by Estimating Frequency Distribution of Local Patches](https://mlsanity.com/paper/1805.09097/) (2018)
- [Quality-Gated Convolutional LSTM for Enhancing Compressed Video](https://mlsanity.com/paper/1903.04596/) (2019)
- [DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images](https://mlsanity.com/paper/1805.10558/) (2018)
- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study](https://mlsanity.com/paper/2104.10782/) (2021)
- [NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results](https://mlsanity.com/paper/2005.02291/) (2020)

## Built on by (most cited first, 4 of 4)

- [C3-STISR: Scene Text Image Super-resolution with Triple Clues](https://mlsanity.com/paper/2204.14044/) (2022)
- [Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reduction](https://mlsanity.com/paper/2108.02110/) (2021)
- [Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement](https://mlsanity.com/paper/2202.00011/) (2022)
- [Lightweight Hybrid Video Compression Framework Using Reference-Guided Restoration Network](https://mlsanity.com/paper/2303.11592/) (2023)

## Abstract (arXiv)

Many deep learning based video compression artifact removal algorithms have been proposed to recover high-quality videos from low-quality compressed videos. Recently, methods were proposed to mine spatiotemporal information via utilizing multiple neighboring frames as reference frames. However, these post-processing methods take advantage of adjacent frames directly, but neglect the information of the video itself, which can be exploited. In this paper, we propose an effective reference frame proposal strategy to boost the performance of the existing multi-frame approaches. Besides, we introduce a loss based on fast Fourier transformation~(FFT) to further improve the effectiveness of restoration. Experimental results show that our method achieves better fidelity and perceptual performance on MFQE 2.0 dataset than the state-of-the-art methods. And our method won Track 1 and Track 2, and was ranked the 2nd in Track 3 of NTIRE 2021 Quality enhancement of heavily compressed videos Challenge.