[challenge website], [arXiv]
The Grand Challenge on Video Super-Resolution for Video Conferencing at ICME 2025 is intended to stimulate research in the area of Video Super Resolution in communication systems.
This ICME grand challenge focuses on video super-resolution for video conferencing, where the low-resolution video is encoded using the H.265 codec with fixed Quantization Parameters (QP). The goal is to upscale the input LR videos by a specific factor and provide HR videos with perceptually enhanced quality (including compression artifact removal). We follow the low-delay scenario in the entire challenge, where no future frames should be used to enhance the current frame.
There are three tracks specific to video content:
- Track 1: General purpose real-world video content, x4 upscaling
- Track 2: Talking Head videos, x4 upscaling
- Track 3: Screen sharing videos, x3 upscaling
This repository aims to provide the training set and validation set for each track. The test sets will be provided close to the end of the competition to the registered teams.
For more information about the challenge, timeline, and registration, visit the website.
The description of the datasets, baseline models, and final results of challenge are presented in ICME2025 paper:
If you use this dataset in a publication please cite the following paper:
@article{naderi2025icme,
title={ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing},
author={Naderi, Babak and Cutler, Ross and Cho, Juhee and Khongbantabam, Nabakumar and Ivkovic, Dejan},
journal={arXiv preprint arXiv:2506.12269},
year={2025}
}
- Clone the repository:
git clone https://github.com/microsoft/VSR-Challenge.git
cd VSR-Challenge- Install the required libraries:
cd download
pip install -r requirements.txt-
Download the txt file with the list of URLs from each track's description page (Track 1, Track 2, Track 3).
-
Change LOCAL_PATH to a path in your file system and run the following command. Replace file_list.txt with the list of files for the training/validation set of the track you are interested in:
python downloader.py --list-of-files file_list.txt --local-path LOCAL_PATHThis project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
This dataset is released under the Community Data License Agreement – Permissive, Version 2.0 - CDLA. See the LICENSE-DATA file for more details. For information on data sourced from third parties, refer to the NOTICE file.
MIT License
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