PyTorch implementation of "MGANet & DCC2020 paper", and we have merged the code of these two methods.

• ArXiv
• Data Compression Conference (DCC2020)

Related work

• TCSVT- Enhanced version of MGANet

The proposed MGANet framework

The guided encoder-decoder subnet

The code was developed using Python 3.6 & PyTorch 0.4 & CUDA 8.0. There may be a problem related to software versions. To fix the problem, you may look at the implementation in MGANet files and replace the syntax to match the new PyTorch environment.

Currently, we release our research code for testing. It should produce the same results as in the paper under LD and AI configurations and frame number 3.

• Pretrained models can be downloaded from this link!

https://drive.google.com/drive/folders/1xROBCUHgIX-3zqKMSHwKFEK8udIEicl1?usp=sharing

• It would be very easy to understand the test function and test on your own data.
• An example of test usage is shown as follows:

python MGANet_test_AI37.py --gpu_id 1 --is_training False

• We will update the training code and database for TUs' partition of compressed video by HEVC for training and better reading after recent paper deadline.

• Here we provide quality enhancement results of compressed video for 18 widely used sequences for visual and quantitative comparisons.

If you use any part of our code, or our method is useful for your research, please consider citing:

@article{MGANET,
  author={Xiandong, Meng and Xuan, Deng and Shuyuan, Zhu and Shuaicheng, Liu and Chuan, Wang and Chen, Chen and Bing, Zeng},
  title={MGANet: A Robust Model for Quality Enhancement of Compressed Video},
  journal={arXiv:1811.09150}
}

@inproceedings{Meng_DCC2020,
  author={Xiandong, Meng and Xuan, Deng and Shuyuan, Zhu and Shuaicheng, Liu and Bing, Zeng},
  title={Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement},
  booktitle={Data Compression Conference(DCC)},
  year={2020}
}

@ARTICLE{MENG_tcsvt,
author={Xiandong Meng and Xuan Deng and Shuyuan Zhu and Xinfeng Zhang and Bing Zeng},
title={A Robust Quality Enhancement Method Based on Joint Spatial-Temporal Priors for Video Coding},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
note={{DOI}:10.1109/TCSVT.2020.3019919},
year={2020},
}

We are glad to hear if you have any suggestions and questions. Please send email to [email protected]