Pytorch Implementation of the paper "Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling (CVPR 2021)".
Project Page: Link
Paper (arXiv): Link
pip install numpy opencv-python lmdb pyyaml
Training and testing dataset can be found here.
We adopt the LMDB format and also provide the script in codes/data_scripts
.
For more detail, please refer to BasicSR.
Pretrained weight can be downloaded from Google Drive.
All the implementation is in /codes
. To run the code,
select the corresponding configuration file in /codes/options/
and run as following command (MIMO-VRN for example):
python train.py -opt options/train/train_MIMO-VRN.yml
python test.py -opt options/test/test_MIMO-VRN.yml
@InProceedings{Huang_2021_CVPR,
author = {Huang, Yan-Cheng and Chen, Yi-Hsin and Lu, Cheng-You and Wang, Hui-Po and Peng, Wen-Hsiao and Huang, Ching-Chun},
title = {Video Rescaling Networks With Joint Optimization Strategies for Downscaling and Upscaling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {3527-3536}
}
Our project is heavily based on Invertible-Image-Rescaling and they adopt BasicSR as basic framework.