--- license: apache-2.0 --- # MFQEv2 Dataset For some video enhancement/restoration tasks, lossless reference videos are necessary. We open-source the dataset used in our [MFQEv2 paper](https://arxiv.org/abs/1902.09707), which includes 108 lossless YUV videos for training and 18 test videos recommended by [ITU-T](https://ieeexplore.ieee.org/document/6317156). ## 1. Content - 108 lossless YUV videos for training. - 18 lossless YUV videos for test, recommended by ITU-T. - An HEVC compression tool box. 43.1 GB in total. ## 2. Download Raw Videos [[Dropbox]](https://www.dropbox.com/sh/tphdy1lmlpz7zq3/AABR4Qim-P-3xGtouWk6ohi5a?dl=0) or [[百度网盘 (key: mfqe)]](https://pan.baidu.com/s/1oBZf75bFGRanLmQQLAg4Ew) ## 3. Compress Videos We compress both training and test videos by [HM](https://hevc.hhi.fraunhofer.de/) 16.5 at low delay P (LDP) mode with QP=37. The video compression toolbox is provided at the dataset folder. We will get: ```tex MFQEv2_dataset/ ├── train_108/ │ ├── raw/ │ └── HM16.5_LDP/ │ └── QP37/ ├── test_18/ │ ├── raw/ │ └── HM16.5_LDP/ │ └── QP37/ ├── video_compression/ │ └── ... └── README.md ``` ### Ubuntu 1. `cd video_compression/` 2. Edit `option.yml`. 3. `chmod +x TAppEncoderStatic` 4. `python unzip_n_compress.py` ### Windows 1. Unzip `train_108.zip` and `test_18.zip` manually! 2. `cd video_compression\` 3. Edit `option.yml` (e.g., `system: windows`). 4. `python unzip_n_compress.py` ## 4. Citation If you find this helpful, please star and cite: ```tex @article{2019xing, doi = {10.1109/tpami.2019.2944806}, url = {https://doi.org/10.1109%2Ftpami.2019.2944806}, year = 2021, month = {mar}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, volume = {43}, number = {3}, pages = {949--963}, author = {Zhenyu Guan and Qunliang Xing and Mai Xu and Ren Yang and Tie Liu and Zulin Wang}, title = {{MFQE} 2.0: A New Approach for Multi-Frame Quality Enhancement on Compressed Video}, journal = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence} } ```