AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results

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Détails bibliographiques
Auteurs principaux:	Conde, Marcos V., Zadtootaghaj, Saman, Barman, Nabajeet, Timofte, Radu, He, Chenlong, Zheng, Qi, Zhu, Ruoxi, Tu, Zhengzhong, Wang, Haiqiang, Chen, Xiangguang, Meng, Wenhui, Pan, Xiang, Shi, Huiying, Zhu, Han, Xu, Xiaozhong, Sun, Lei, Chen, Zhenzhong, Liu, Shan, Zhang, Zicheng, Wu, Haoning, Zhou, Yingjie, Li, Chunyi, Liu, Xiaohong, Lin, Weisi, Zhai, Guangtao, Sun, Wei, Cao, Yuqin, Jiang, Yanwei, Jia, Jun, Zhang, Zhichao, Chen, Zijian, Zhang, Weixia, Min, Xiongkuo, Göring, Steve, Qi, Zihao, Feng, Chen
Format:	Preprint
Publié:	2024
Sujets:	Computer Vision and Pattern Recognition Multimedia
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

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author	Conde, Marcos V. Zadtootaghaj, Saman Barman, Nabajeet Timofte, Radu He, Chenlong Zheng, Qi Zhu, Ruoxi Tu, Zhengzhong Wang, Haiqiang Chen, Xiangguang Meng, Wenhui Pan, Xiang Shi, Huiying Zhu, Han Xu, Xiaozhong Sun, Lei Chen, Zhenzhong Liu, Shan Zhang, Zicheng Wu, Haoning Zhou, Yingjie Li, Chunyi Liu, Xiaohong Lin, Weisi Zhai, Guangtao Sun, Wei Cao, Yuqin Jiang, Yanwei Jia, Jun Zhang, Zhichao Chen, Zijian Zhang, Weixia Min, Xiongkuo Göring, Steve Qi, Zihao Feng, Chen
author_facet	Conde, Marcos V. Zadtootaghaj, Saman Barman, Nabajeet Timofte, Radu He, Chenlong Zheng, Qi Zhu, Ruoxi Tu, Zhengzhong Wang, Haiqiang Chen, Xiangguang Meng, Wenhui Pan, Xiang Shi, Huiying Zhu, Han Xu, Xiaozhong Sun, Lei Chen, Zhenzhong Liu, Shan Zhang, Zicheng Wu, Haoning Zhou, Yingjie Li, Chunyi Liu, Xiaohong Lin, Weisi Zhai, Guangtao Sun, Wei Cao, Yuqin Jiang, Yanwei Jia, Jun Zhang, Zhichao Chen, Zijian Zhang, Weixia Min, Xiongkuo Göring, Steve Qi, Zihao Feng, Chen
contents	This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted code and models. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content.
format	Preprint
id	arxiv_https___arxiv_org_abs_2404_16205
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results Conde, Marcos V. Zadtootaghaj, Saman Barman, Nabajeet Timofte, Radu He, Chenlong Zheng, Qi Zhu, Ruoxi Tu, Zhengzhong Wang, Haiqiang Chen, Xiangguang Meng, Wenhui Pan, Xiang Shi, Huiying Zhu, Han Xu, Xiaozhong Sun, Lei Chen, Zhenzhong Liu, Shan Zhang, Zicheng Wu, Haoning Zhou, Yingjie Li, Chunyi Liu, Xiaohong Lin, Weisi Zhai, Guangtao Sun, Wei Cao, Yuqin Jiang, Yanwei Jia, Jun Zhang, Zhichao Chen, Zijian Zhang, Weixia Min, Xiongkuo Göring, Steve Qi, Zihao Feng, Chen Computer Vision and Pattern Recognition Multimedia This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted code and models. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content.
title	AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
topic	Computer Vision and Pattern Recognition Multimedia
url	https://arxiv.org/abs/2404.16205

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