The dataset and code of the paper "CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement".
CUDA==11.6 Python==3.7 Pytorch==1.13
conda create -n cpga python=3.7 -y && conda activate cpga
git clone --depth=1 https://github.com/VQE-CPGA/CPGA && cd VQE-CPGA/CPGA/
# given CUDA 11.6
python -m pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
python -m pip install tqdm lmdb pyyaml opencv-python scikit-image
cd ops/dcn/
bash build.sh
Check if DCNv2 work (optional)
python simple_check.py
Download raw and compressed videos
Please check [BaiduPan][qix5].
Edit YML
You need to edit option_CPGA_vcp_#_QP#.yml file.
Generate LMDB
The LMDB generation for speeding up IO during training.
python create_vcp.py --opt_path option_CPGA_vcp_#_QP#.yml
Finally, the VCP dataset root will be sym-linked to the folder ./data/ automatically.
We use the JCT-VC testing dataset in JCT-VC. Download raw and compressed videos [BaiduPan][qix5].
python train_CPGA.py --opt_path ./config/option_CPGA_vcp_LDB_22.yml
python test_CPGA.py --opt_path ./config/option_CPGA_vcp_LDB_22.yml
If this repository is helpful to your research, please cite our paper:
@inproceedings{2024qiang_cpga,
title={Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement},
author={Qiang Zhu, Jinhua Hao, Yukang Ding, Yu Liu, Qiao Mo, Ming Sun, Chao Zhou, Shuyuan Zhu},
booktitle={The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024},
volume={},
number={},
pages={},
year={2024}
}
@article{zhu2024deep,
title={Deep Compressed Video Super-Resolution With Guidance of Coding Priors},
author={Qiang Zhu, Feiyu Chen, Yu Liu, Shuyuan Zhu, Bing Zeng},
journal={ IEEE Transactions on Broadcasting },
volume={70},
issue={2},
pages={505-515},
year={2024}
publisher={IEEE},
doi={10.1109/TBC.2024.3394291}
}
@article{zhu2024compressed,
title={Compressed Video Quality Enhancement with Temporal Group Alignment and Fusion},
author={Qiang, Zhu and Yajun, Qiu and Yu, Liu and Shuyuan, Zhu and Bing, Zeng},
journal={IEEE Signal Processing Letters},
year={2024},
publisher={IEEE},
doi={10.1109/LSP.2024.3407536}
}
We adopt Apache License v2.0. For other licenses, please refer to DCNv2.