Zobrazeno 1 - 10
of 447
pro vyhledávání: '"ZHANG Weixia"'
Autor:
Molodetskikh, Ivan, Borisov, Artem, Vatolin, Dmitriy, Timofte, Radu, Liu, Jianzhao, Zhi, Tianwu, Zhang, Yabin, Li, Yang, Xu, Jingwen, Liao, Yiting, Luo, Qing, Zhang, Ao-Xiang, Zhang, Peng, Lei, Haibo, Jiang, Linyan, Li, Yaqing, Cao, Yuqin, Sun, Wei, Zhang, Weixia, Sun, Yinan, Jia, Ziheng, Zhu, Yuxin, Min, Xiongkuo, Zhai, Guangtao, Luo, Weihua, Z., Yupeng, Y, Hong
This paper presents the Video Super-Resolution (SR) Quality Assessment (QA) Challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. The task of this challenge was to develop an objective QA me
Externí odkaz:
http://arxiv.org/abs/2410.04225
Autor:
Sun, Wei, Zhang, Weixia, Cao, Yuqin, Cao, Linhan, Jia, Jun, Chen, Zijian, Zhang, Zicheng, Min, Xiongkuo, Zhai, Guangtao
UHD images, typically with resolutions equal to or higher than 4K, pose a significant challenge for efficient image quality assessment (IQA) algorithms, as adopting full-resolution images as inputs leads to overwhelming computational complexity and c
Externí odkaz:
http://arxiv.org/abs/2409.00749
Autor:
Sun, Wei, Zhang, Weixia, Jiang, Yanwei, Wu, Haoning, Zhang, Zicheng, Jia, Jun, Zhou, Yingjie, Ji, Zhongpeng, Min, Xiongkuo, Lin, Weisi, Zhai, Guangtao
Portrait images typically consist of a salient person against diverse backgrounds. With the development of mobile devices and image processing techniques, users can conveniently capture portrait images anytime and anywhere. However, the quality of th
Externí odkaz:
http://arxiv.org/abs/2405.08555
Autor:
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
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
Externí odkaz:
http://arxiv.org/abs/2404.16205
Autor:
Li, Chunyi, Kou, Tengchuan, Gao, Yixuan, Cao, Yuqin, Sun, Wei, Zhang, Zicheng, Zhou, Yingjie, Zhang, Zhichao, Zhang, Weixia, Wu, Haoning, Liu, Xiaohong, Min, Xiongkuo, Zhai, Guangtao
With the rapid advancements in AI-Generated Content (AIGC), AI-Generated Images (AIGIs) have been widely applied in entertainment, education, and social media. However, due to the significant variance in quality among different AIGIs, there is an urg
Externí odkaz:
http://arxiv.org/abs/2404.03407
The rapid advancement of Artificial Intelligence Generated Content (AIGC) technology has propelled audio-driven talking head generation, gaining considerable research attention for practical applications. However, performance evaluation research lags
Externí odkaz:
http://arxiv.org/abs/2403.06421
Contemporary no-reference image quality assessment (NR-IQA) models can effectively quantify the perceived image quality, with high correlations between model predictions and human perceptual scores on fixed test sets. However, little progress has bee
Externí odkaz:
http://arxiv.org/abs/2403.06406
Autor:
Wu, Haoning, Zhang, Zicheng, Zhang, Weixia, Chen, Chaofeng, Liao, Liang, Li, Chunyi, Gao, Yixuan, Wang, Annan, Zhang, Erli, Sun, Wenxiu, Yan, Qiong, Min, Xiongkuo, Zhai, Guangtao, Lin, Weisi
The explosion of visual content available online underscores the requirement for an accurate machine assessor to robustly evaluate scores across diverse types of visual contents. While recent studies have demonstrated the exceptional potentials of la
Externí odkaz:
http://arxiv.org/abs/2312.17090
We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge fr
Externí odkaz:
http://arxiv.org/abs/2303.14968
Autor:
Zhang, Weixia, Li, Dingquan, Min, Xiongkuo, Zhai, Guangtao, Guo, Guodong, Yang, Xiaokang, Ma, Kede
No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA models are extensively studied in computational vision, and are widely use
Externí odkaz:
http://arxiv.org/abs/2210.00933