Multi-stage Spatio-Temporal Fusion Network for Fast and Accurate Video Bit-depth Enhancement
This is the official implementation of Multi-stage Spatio-Temporal Fusion Network for Fast and Accurate Video Bit-depth Enhancement (IEEE Transactions on Multimedia 2023) in Pytorch.
Environment
You can run conda env create -f mstfn.yml
to create the environment
- python=3.6
- ubuntu 20.04
- pytorch=1.6.0
- torchvision=0.7
- opencv-python==4.6
How to Test
We provide folders "./dataset/test_frames_16bit" and "./dataset/test_frames_12bit" to realize 4-bit to 16-bit and 8-bit to 12-bit BDE tasks respectively. When testing, prepare the testing dataset, and modify the dataset path and other related content in the code. We provide a test sequence(16bit and 12bit) for testing. You can directly test on the testing frames by running
$ python test.py
Note
- We provide recovery results in the folder "result". When testing, the predicted results are saved in the folder "test" .
- The files "./metrics/SSIM_PSNR.m" is used to calculate PSNR and SSIM.
Citation
If our work is helpful to you, please cite our paper:
@article{liu2023multi,
title={Multi-stage Spatio-Temporal Fusion Network for Fast and Accurate Video Bit-depth Enhancement},
author={Jing Liu and Zhiwei Fan and Ziwen Yang and Yuting Su and Xiaokang Yang},
journal={IEEE Transactions on Multimedia},
volume={},
pages={},
year={2023},
publisher={IEEE}
}