NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

Xin LI; Kun YUAN; Yajing PEI; Yiting LU; Ming SUN; Chao ZHOU; Zhibo CHEN; Radu TIMOFTE; Wei SUN; Haoning WU; Zicheng ZHANG; Jun JIA; Zhichao ZHANG; Linhan CAO; Qiubo CHEN; Xiongkuo MIN; Weisi LIN; Guangtao ZHAI; Jian Hui SUN; Tianyi WANG; Lei LI; Han KONG; Wenxuan WANG; Bing LI; Cheng LUO; Haiqiang WANG; Xiangguang CHEN; Wenhui MENG; Xiang PAN; Huiying SHI; Han ZHU; Xiaozhong XU; Lei SUN; Zhenzhong CHEN; Shan LIU; Fangyuan KONG; Haotian FAN; Yifang XU; Haoran XU; Mengduo YANG; Jie ZHOU; Jiaze LI; Shijie WEN; Mai XU; Da LI; Shunyu YAO; Jiazhi DU; Wangmeng ZUO; Zhibo LI; Shuai HE; Anlong MING; Huiyuan FU; Huadong MA; Yong WU; Fie XUE; Guozhi ZHAO; Lina DU; Jie GUO; Yu ZHANG; Huimin ZHENG; Junhao CHEN; Yue LIU; Dulan ZHOU; Kele XU; Qisheng XU; Tao SUN; Zhixiang DING; Yuhang HU

doi:10.1109/CVPRW63382.2024.00643

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

Xin LI^*
, Kun YUAN
, Yajing PEI
, Yiting LU
, Ming SUN
, Chao ZHOU
, Zhibo CHEN
, Radu TIMOFTE
, Wei SUN
, Haoning WU
, Zicheng ZHANG
, Jun JIA
, Zhichao ZHANG
, Linhan CAO
, Qiubo CHEN
, Xiongkuo MIN
, Weisi LIN
, Guangtao ZHAI
, Jian Hui SUN
, Tianyi WANG

Lei LI, Han KONG, Wenxuan WANG, Bing LI, Cheng LUO, Haiqiang WANG, Xiangguang CHEN, Wenhui MENG, Xiang PAN, Huiying SHI, Han ZHU, Xiaozhong XU, Lei SUN, Zhenzhong CHEN, Shan LIU, Fangyuan KONG, Haotian FAN, Yifang XU, Haoran XU, Mengduo YANG, Jie ZHOU, Jiaze LI, Shijie WEN, Mai XU, Da LI, Shunyu YAO, Jiazhi DU, Wangmeng ZUO, Zhibo LI, Shuai HE, Anlong MING, Huiyuan FU, Huadong MA, Yong WU, Fie XUE, Guozhi ZHAO, Lina DU, Jie GUO, Yu ZHANG, Huimin ZHENG, Junhao CHEN, Yue LIU, Dulan ZHOU, Kele XU, Qisheng XU, Tao SUN, Zhixiang DING, Yuhang HU

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

27 Citations (Scopus)

Abstract

This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://github.com/lixinustc/KVQ-Challenge-CVPR-NTIRE2024.

Original language	English
Title of host publication	Proceedings: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
Publisher	IEEE
Pages	6415-6431
Number of pages	17
ISBN (Electronic)	9798350365474
ISBN (Print)	9798350365481
DOIs	https://doi.org/10.1109/CVPRW63382.2024.00643
Publication status	Published - 17 Jun 2024
Externally published	Yes
Event	2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024 - Seattle, United States Duration: 16 Jun 2024 → 22 Jun 2024

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Conference

Conference	2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
Country/Territory	United States
City	Seattle
Period	16/06/24 → 22/06/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Funding

This work was partially supported by the Humboldt Foundation. We thank the NTIRE 2024 sponsors: Meta Reality Labs, OPPO, Kuaishou, Huawei and University of Würzburg (Computer Vision Lab). We also thank Kuaishou for sponsoring this challenge.

Access to Document

10.1109/CVPRW63382.2024.00643

Cite this

LI, X., YUAN, K., PEI, Y., LU, Y., SUN, M., ZHOU, C., CHEN, Z., TIMOFTE, R., SUN, W., WU, H., ZHANG, Z., JIA, J., ZHANG, Z., CAO, L., CHEN, Q., MIN, X., LIN, W., ZHAI, G., SUN, J. H., ... HU, Y. (2024). NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results. In Proceedings: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024 (pp. 6415-6431). (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). IEEE. https://doi.org/10.1109/CVPRW63382.2024.00643

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abstract = "This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://github.com/lixinustc/KVQ-Challenge-CVPR-NTIRE2024.",

author = "Xin LI and Kun YUAN and Yajing PEI and Yiting LU and Ming SUN and Chao ZHOU and Zhibo CHEN and Radu TIMOFTE and Wei SUN and Haoning WU and Zicheng ZHANG and Jun JIA and Zhichao ZHANG and Linhan CAO and Qiubo CHEN and Xiongkuo MIN and Weisi LIN and Guangtao ZHAI and SUN, \{Jian Hui\} and Tianyi WANG and Lei LI and Han KONG and Wenxuan WANG and Bing LI and Cheng LUO and Haiqiang WANG and Xiangguang CHEN and Wenhui MENG and Xiang PAN and Huiying SHI and Han ZHU and Xiaozhong XU and Lei SUN and Zhenzhong CHEN and Shan LIU and Fangyuan KONG and Haotian FAN and Yifang XU and Haoran XU and Mengduo YANG and Jie ZHOU and Jiaze LI and Shijie WEN and Mai XU and Da LI and Shunyu YAO and Jiazhi DU and Wangmeng ZUO and Zhibo LI and Shuai HE and Anlong MING and Huiyuan FU and Huadong MA and Yong WU and Fie XUE and Guozhi ZHAO and Lina DU and Jie GUO and Yu ZHANG and Huimin ZHENG and Junhao CHEN and Yue LIU and Dulan ZHOU and Kele XU and Qisheng XU and Tao SUN and Zhixiang DING and Yuhang HU",

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LI, X, YUAN, K, PEI, Y, LU, Y, SUN, M, ZHOU, C, CHEN, Z, TIMOFTE, R, SUN, W, WU, H, ZHANG, Z, JIA, J, ZHANG, Z, CAO, L, CHEN, Q, MIN, X, LIN, W, ZHAI, G, SUN, JH, WANG, T, LI, L, KONG, H, WANG, W, LI, B, LUO, C, WANG, H, CHEN, X, MENG, W, PAN, X, SHI, H, ZHU, H, XU, X, SUN, L, CHEN, Z, LIU, S, KONG, F, FAN, H, XU, Y, XU, H, YANG, M, ZHOU, J, LI, J, WEN, S, XU, M, LI, D, YAO, S, DU, J, ZUO, W, LI, Z, HE, S, MING, A, FU, H, MA, H, WU, Y, XUE, F, ZHAO, G, DU, L, GUO, J, ZHANG, Y, ZHENG, H, CHEN, J, LIU, Y, ZHOU, D, XU, K, XU, Q, SUN, T, DING, Z & HU, Y 2024, NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results. in Proceedings: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, pp. 6415-6431, 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024, Seattle, United States, 16/06/24. https://doi.org/10.1109/CVPRW63382.2024.00643

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results. / LI, Xin; YUAN, Kun; PEI, Yajing et al.
Proceedings: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024. IEEE, 2024. p. 6415-6431 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - PAN, Xiang

AU - SHI, Huiying

AU - ZHU, Han

AU - XU, Xiaozhong

AU - SUN, Lei

AU - CHEN, Zhenzhong

AU - LIU, Shan

AU - KONG, Fangyuan

AU - FAN, Haotian

AU - XU, Yifang

AU - XU, Haoran

AU - YANG, Mengduo

AU - ZHOU, Jie

AU - LI, Jiaze

AU - WEN, Shijie

AU - XU, Mai

AU - LI, Da

AU - YAO, Shunyu

AU - DU, Jiazhi

AU - ZUO, Wangmeng

AU - LI, Zhibo

AU - HE, Shuai

AU - MING, Anlong

AU - FU, Huiyuan

AU - MA, Huadong

AU - WU, Yong

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AU - GUO, Jie

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AU - LIU, Yue

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AB - This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://github.com/lixinustc/KVQ-Challenge-CVPR-NTIRE2024.

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LI X, YUAN K, PEI Y, LU Y, SUN M, ZHOU C et al. NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results. In Proceedings: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024. IEEE. 2024. p. 6415-6431. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). doi: 10.1109/CVPRW63382.2024.00643

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

Abstract

Publication series

Conference

Bibliographical note

Funding

Access to Document

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