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}
}