1 code implementation • 20 Apr 2025 • Zheng Chen, Jingkai Wang, Kai Liu, Jue Gong, Lei Sun, Zongwei Wu, Radu Timofte, Yulun Zhang, Jianxing Zhang, Jinlong Wu, Jun Wang, Zheng Xie, Hakjae Jeon, Suejin Han, Hyung-Ju Chun, Hyunhee Park, Zhicun Yin, Junjie Chen, Ming Liu, Xiaoming Li, Chao Zhou, WangMeng Zuo, Weixia Zhang, Dingquan Li, Kede Ma, Yun Zhang, Zhuofan Zheng, Yuyue Liu, Shizhen Tang, Zihao Zhang, Yi Ning, Hao Jiang, Wenjie An, Kangmeng Yu, Chenyang Wang, Kui Jiang, Xianming Liu, Junjun Jiang, Yingfu Zhang, Gang He, Siqi Wang, Kepeng Xu, Zhenyang Liu, Changxin Zhou, Shanlan Shen, Yubo Duan, Yiang Chen, Jin Guo, Mengru Yang, Jen-Wei Lee, Chia-Ming Lee, Chih-Chung Hsu, Hu Peng, Chunming He
This paper provides a review of the NTIRE 2025 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes.
no code implementations • 5 Oct 2024 • Ivan Molodetskikh, Artem Borisov, Dmitriy Vatolin, Radu Timofte, Jianzhao Liu, Tianwu Zhi, Yabin Zhang, Yang Li, Jingwen Xu, Yiting Liao, Qing Luo, Ao-Xiang Zhang, Peng Zhang, Haibo Lei, Linyan Jiang, Yaqing Li, Yuqin Cao, Wei Sun, Weixia Zhang, Yinan Sun, Ziheng Jia, Yuxin Zhu, Xiongkuo Min, Guangtao Zhai, Weihua Luo, Yupeng Z., Hong Y
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.
1 code implementation • 1 Sep 2024 • Wei Sun, Weixia Zhang, Yuqin Cao, Linhan Cao, Jun Jia, Zijian Chen, ZiCheng Zhang, Xiongkuo Min, Guangtao Zhai
To address this problem, we design a multi-branch deep neural network (DNN) to assess the quality of UHD images from three perspectives: global aesthetic characteristics, local technical distortions, and salient content perception.
1 code implementation • 14 May 2024 • Wei Sun, Weixia Zhang, Yanwei Jiang, HaoNing Wu, ZiCheng Zhang, Jun Jia, Yingjie Zhou, Zhongpeng Ji, Xiongkuo Min, Weisi Lin, Guangtao Zhai
We employ the fidelity loss to train the model via a learning-to-rank manner to mitigate inconsistencies in quality scores in the portrait image quality assessment dataset PIQ.
Ranked #1 on
Face Image Quality Assessment
on PIQ23
1 code implementation • 24 Apr 2024 • Marcos V. Conde, Saman Zadtootaghaj, Nabajeet Barman, Radu Timofte, Chenlong He, Qi Zheng, Ruoxi Zhu, Zhengzhong Tu, Haiqiang Wang, Xiangguang Chen, Wenhui Meng, Xiang Pan, Huiying Shi, Han Zhu, Xiaozhong Xu, Lei Sun, Zhenzhong Chen, Shan Liu, ZiCheng Zhang, HaoNing Wu, Yingjie Zhou, Chunyi Li, Xiaohong Liu, Weisi Lin, Guangtao Zhai, Wei Sun, Yuqin Cao, Yanwei Jiang, Jun Jia, Zhichao Zhang, Zijian Chen, Weixia Zhang, Xiongkuo Min, Steve Göring, Zihao Qi, Chen Feng
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.
no code implementations • 4 Apr 2024 • Chunyi Li, Tengchuan Kou, Yixuan Gao, Yuqin Cao, Wei Sun, ZiCheng Zhang, Yingjie Zhou, Zhichao Zhang, Weixia Zhang, HaoNing Wu, Xiaohong Liu, Xiongkuo Min, Guangtao Zhai
With the rapid advancements in AI-Generated Content (AIGC), AI-Generated Images (AIGIs) have been widely applied in entertainment, education, and social media.
no code implementations • 11 Mar 2024 • Weixia Zhang, Dingquan Li, Guangtao Zhai, Xiaokang Yang, Kede Ma
In this work, we show -- for the first time -- that NR-IQA models can be plugged into the maximum a posteriori (MAP) estimation framework for image enhancement.
1 code implementation • 11 Mar 2024 • Weixia Zhang, Chengguang Zhu, Jingnan Gao, Yichao Yan, Guangtao Zhai, Xiaokang Yang
However, performance evaluation research lags behind the development of talking head generation techniques.
1 code implementation • 28 Dec 2023 • HaoNing Wu, ZiCheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, Qiong Yan, Xiongkuo Min, Guangtao Zhai, Weisi Lin
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.
Ranked #1 on
Video Quality Assessment
on LIVE-FB LSVQ
1 code implementation • CVPR 2023 • Weixia Zhang, Guangtao Zhai, Ying WEI, Xiaokang Yang, Kede Ma
We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information.
1 code implementation • 3 Oct 2022 • Weixia Zhang, Dingquan Li, Xiongkuo Min, Guangtao Zhai, Guodong Guo, Xiaokang Yang, Kede Ma
No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references.
2 code implementations • 19 Aug 2021 • Bowen Li, Weixia Zhang, Meng Tian, Guangtao Zhai, Xianpei Wang
The inaccessibility of reference videos with pristine quality and the complexity of authentic distortions pose great challenges for this kind of blind video quality assessment (BVQA) task.
Ranked #4 on
Video Quality Assessment
on MSU NR VQA Database
2 code implementations • 28 Jul 2021 • Weixia Zhang, Kede Ma, Guangtao Zhai, Xiaokang Yang
In this paper, we present a simple yet effective continual learning method for blind image quality assessment (BIQA) with improved quality prediction accuracy, plasticity-stability trade-off, and task-order/-length robustness.
1 code implementation • 19 Feb 2021 • Weixia Zhang, Dingquan Li, Chao Ma, Guangtao Zhai, Xiaokang Yang, Kede Ma
In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data.
no code implementations • 22 Nov 2020 • Weixia Zhang, Chao Ma, Qi Wu, Xiaokang Yang
We then propose to recursively alternate the learning schemes of imitation and exploration to narrow the discrepancy between training and inference.
1 code implementation • 28 May 2020 • Weixia Zhang, Kede Ma, Guangtao Zhai, Xiaokang Yang
Nevertheless, due to the distributional shift between images simulated in the laboratory and captured in the wild, models trained on databases with synthetic distortions remain particularly weak at handling realistic distortions (and vice versa).
1 code implementation • 5 Jul 2019 • Weixia Zhang, Kede Ma, Jia Yan, Dexiang Deng, Zhou Wang
We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions.
Ranked #2 on
Video Quality Assessment
on MSU NR VQA Database
1 code implementation • 1 Jul 2019 • Weixia Zhang, Kede Ma, Guangtao Zhai, Xiaokang Yang
Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically distorted images.