CAVSR: Compression-Aware Video Super-Resolution [CVPR 2023]

Introduction

In this paper, we propose a novel and practical compression-aware video super-resolution model, which could adapt its video enhancement process to the estimated compression level.

A compression encoder is designed to model compression levels of input frames, and a base VSR model is then conditioned on the implicitly computed representation by inserting compression-aware modules.
In addition, we propose to further strengthen the VSR model by taking full advantage of meta data that is embedded naturally in compressed video streams in the procedure of information fusion.

Getting Started

Installation

pip install -r requirements.txt
python setup.py develop

Evaluation

Copy the dataset and checkpoints to the workplace.

Run scripts:

python basicsr/test.py   -opt script/test_sota.yml

License

All assets and code are under the Apache 2.0 license unless specified otherwise.

Bibtex

If this work is helpful for your research, please consider citing the following BibTeX entry.

@InProceedings{Wang_2023_CVPR,
    title     = {Compression-Aware Video Super-Resolution},
    author    = {Wang, Yingwei and Isobe, Takashi and Jia, Xu and Tao, Xin and Lu, Huchuan and Tai, Yu-Wing},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2023},
}

aprBlue / CAVSR