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AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

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Abstract

This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compressed image, and Track 2 targets the super-resolution of compressed video. In Track 1, we use the popular dataset DIV2K as the training, validation and test sets. In Track 2, we propose the LDV 3.0 dataset, which contains 365 videos, including the LDV 2.0 dataset (335 videos) and 30 additional videos. In this challenge, there are 12 teams and 2 teams that submitted the final results to Track 1 and Track 2, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution on compressed image and video. The proposed LDV 3.0 dataset is available at https://github.com/RenYang-home/LDV_dataset. The homepage of this challenge is at https://github.com/RenYang-home/AIM22_CompressSR.

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Notes

  1. 1.

    https://support.google.com/youtube/answer/2797468?hl=en.

  2. 2.

    https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware/tags/HM-16.20.

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Acknowledgments

We thank the sponsors of the AIM and Mobile AI 2022 workshops and challenges: AI Witchlabs, MediaTek, Huawei, Reality Labs, OPPO, Synaptics, Raspberry Pi, ETH Zürich (Computer Vision Lab) and University of Würzburg (Computer Vision Lab).

Author information

Authors and Affiliations

  1. Computer Vision Lab, ETH Zürich, Zürich, Switzerland

    Ren Yang & Radu Timofte

  2. Julius Maximilian University of Würzburg, Würzburg, Germany

    Radu Timofte

  3. Department of Computer Vision Technology (VIS), Baidu Inc., Beijing, China

    Xin Li, Qi Zhang, Fanglong Liu, Dongliang He, Fu Li, He Zheng & Weihang Yuan

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Lin Zhang, Yu Zhu, Xiaoran Qin, Chenghua Li, Cong Leng & Jian Cheng

  5. Noah’s Ark Lab, Huawei, Montreal, Canada

    Pavel Ostyakov, Dmitry Vyal, Magauiya Zhussip, Xueyi Zou & Youliang Yan

  6. Multimedia Lab, ByteDance Inc., Beijing, China

    Lei Li, Jingzhu Tang, Ming Chen & Shijie Zhao

  7. MAICRO, Nanjing, China

    Cong Leng & Jian Cheng

  8. AiRiA, Nanjing, China

    Cong Leng & Jian Cheng

  9. University of Milano - Bicocca, Milano, Italy

    Claudio Rota, Marco Buzzelli, Simone Bianco & Raimondo Schettini

  10. Samsung Research China - Beijing (SRC-B), Beijing, China

    Dafeng Zhang, Feiyu Huang, Shizhuo Liu, Xiaobing Wang & Zhezhu Jin

  11. University of Science and Technology of China, Hefei, China

    Bingchen Li & Xin Li

  12. ByteDance Inc., Beijing, China

    Mingxi Li & Ding Liu

  13. South China University of Technology, Guangzhou, China

    Wenbin Zou

  14. National University of Defense Technology, Changsha, China

    Peijie Dong & Xin Niu

  15. Jimei University, Xiamen, China

    Tian Ye

  16. Fujian Normal University, Fuzhou, China

    Wenbin Zou & Ming Tan

  17. China Design Group Inc., Nanjing, China

    Yunchen Zhang

  18. Aselsan, Ankara, Turkey

    Mustafa Ayazoglu

  19. Computer Vision Lab, Julius Maximilian University of Würzburg, Würzburg, Germany

    Radu Timofte & Marcos Conde

  20. MegaStudyEdu, Seoul, South Korea

    Ui-Jin Choi

  21. Xiaomi Inc., Beijing, China

    Zhuang Jia, Tianyu Xu & Yijian Zhang

  22. University of Electronic Science and Technology of China, Chengdu, China

    Mao Ye, Dengyan Luo & Xiaofeng Pan

  23. Xinjiang University, Xinjiang, China

    Liuhan Peng

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  1. Ren Yang
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  2. Radu Timofte
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  3. Xin Li
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Corresponding author

Correspondence to Ren Yang .

Editor information

Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

Appendix: Teams and Affiliations

Appendix: Teams and Affiliations

1.1 AIM 2022 Team

Challenge:

AIM 2022 Challenge on Super-Resolution of Compressed Image and Video

Organizer(s):

Ren Yang\(^{1}\) (ren.yang@vision.ee.ethz.ch),

Radu Timofte\(^{1,2}\) (radu.timofte@uni-wuerzburg.ch)

Affiliation(s):

\(^1\) Computer Vision Lab, ETH Zürich, Switzerland

\(^2\) Julius Maximilian University of Würzburg, Germany

1.2 VUE Team

Member(s):

Xin Li\(^{1}\) (lixin41@baidu.com), Qi Zhang\(^{1}\), Lin Zhang\(^{2}\), Fanglong Liu\(^{1}\), Dongliang He\(^{1}\), Fu li\(^{1}\), He Zheng\(^{1}\), Weihang Yuan\(^{1}\)

Affiliation(s):

\(^\text {1 } \) Department of Computer Vision Technology (VIS), Baidu Inc.

\(^\text {2 } \) Institute of Automation, Chinese Academy of Sciences

1.3 NoahTerminalCV Team

Member(s):

Pavel Ostyakov (ostyakov.pavel@huawei.com), Dmitry Vyal, Magauiya Zhussip, Xueyi Zou, Youliang Yan

Affiliation(s):

Noah’s Ark Lab, Huawei

1.4 BSR Team

Member(s):

Lei Li (lilei.leili@bytedance.com), Jingzhu Tang, Ming Chen, Shijie Zhao

Affiliation(s):

Multimedia Lab, ByteDance Inc.

1.5 CASIA LCVG Team

Member(s):

Yu Zhu\(^{1}\) (zhuyu.cv@gmail.com), Xiaoran Qin\(^{1}\), Chenghua Li\(^{1}\), Cong Leng\(^{1,2,3}\), Jian Cheng\(^{1,2,3}\)

Affiliation(s):

\(^1\) Institute of Automation, Chinese Academy of Sciences, Beijing, China

\(^2\) MAICRO, Nanjing, China

\(^3\) AiRiA, Nanjing, China

1.6 IVL Team

Member(s):

Claudio Rota (c.rota30@campus.unimib.it), Marco Buzzelli, Simone Bianco, Raimondo Schettini

Affiliation(s):

University of Milano - Bicocca, Italy

1.7 Samsung Research China - Beijing (SRC-B)

Member(s):

Dafeng Zhang (dfeng.zhang@samsung.com), Feiyu Huang, Shizhuo Liu, Xiaobing Wang, Zhezhu Jin

Affiliation(s):

Samsung Research China - Beijing (SRC-B), China

1.8 USTC-IR

Member(s):

Bingchen Li (lbc31415926@mail.ustc.edu.cn), Xin Li

Affiliation(s):

University of Science and Technology of China, Hefei, China

1.9 MSDRSR

Member(s):

Mingxi Li (li_mx_0318@163.com), Ding Liu\(^{1}\)

Affiliation(s):

\(^{1}\) ByteDance Inc.

1.10 Giantpandacv Team

Member(s):

Wenbin Zou\(^{1,4}\) (alexzou14@foxmail.com), Peijie Dong\(^{2}\), Tian Ye\(^{3}\), Yunchen Zhang\(^{5}\), Ming Tan\(^{4}\), Xin Niu\(^{2}\)

Affiliation(s):

\(^1\) South China University of Technology, Guangzhou, China

\(^2\) National University of Defense Technology, Changsha, China

\(^3\) Jimei University, Xiamen, China

\(^4\) Fujian Normal University, Fuzhou, China

\(^5\) China Design Group Inc., Nanjing, China

1.11 Aselsan Research Team

Member(s):

Mustafa Ayazoğlu (mayazoglu@aselsan.com.tr)

Affiliation(s):

Aselsan (www.aselsan.com.tr), Ankara, Turkey

1.12 SRMUI Team

Member(s):

Marcos V. Conde\(^1\) (marcos.conde-osorio@uni-wuerzburg.de), Ui-Jin Choi\(^2\), Radu Timofte\(^1\)

Affiliation(s):

\(^1\) Computer Vision Lab, Julius Maximilian University of Würzburg, Germany

\(^2\) MegaStudyEdu, South Korea

1.13 MVideo Team

Member(s):

Zhuang Jia (jiazhuang@xiaomi.com), Tianyu Xu, Yijian Zhang

Affiliation(s):

Xiaomi Inc.

1.14 UESTC+XJU CV Team

Member(s):

Mao Ye (cvlab.uestc@gmail.com), Dengyan Luo, Xiaofeng Pan

Affiliation(s):

University of Electronic Science and Technology of China, Chengdu, China

1.15 cvlab Team

Member(s):

Liuhan Peng\(^1\) (pengliuhan@gmail.com), Mao Ye\(^2\)

Affiliation(s):

\(^1\) Xinjiang University, Xinjiang, China

\(^2\) University of Electronic Science and Technology of China, Chengdu, China

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Yang, R. et al. (2023). AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13803. Springer, Cham. https://doi.org/10.1007/978-3-031-25066-8_8

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