Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling (CVPR 2021)
Pytorch Implementation of the paper "Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling (CVPR 2021)".
Project Page: Link
Paper: Link
Prerequisite
- Python 3 via Anaconda (recommended)
- PyTorch >= 1.4.0
- NVIDIA GPU + CUDA
- Python Package:
pip install numpy opencv-python lmdb pyyaml
Dataset Preparation
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.
Usage
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):
Training
python train.py -opt options/train/train_MIMO-VRN.yml
Testing
python test.py -opt options/test/test_MIMO-VRN.yml
Quantitative Results
HR Reconstruction on Vid4
Qualitative Results
HR Reconstruction on Vid4
LR Reconstruction on Vid4
Citation
Acknowledgement
Our project is heavily based on Invertible-Image-Rescaling and they adopt BasicSR as basic framework.