2 code implementations • 1 Aug 2019 • Dingquan Li, Tingting Jiang, Ming Jiang
We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network.
Ranked #6 on Video Quality Assessment on MSU NR VQA Database
1 code implementation • 10 Aug 2020 • Dingquan Li, Tingting Jiang, Ming Jiang
Experiments on two relevant datasets (KonIQ-10k and CLIVE) show that, compared to MAE or MSE loss, the new loss enables the IQA model to converge about 10 times faster and the final model achieves better performance.
Ranked #2 on Image Quality Assessment on MSU NR VQA Database
Blind Image Quality Assessment No-Reference Image Quality Assessment +1
1 code implementation • 9 Nov 2020 • Dingquan Li, Tingting Jiang, Ming Jiang
We focus on automatically assessing the quality of in-the-wild videos, which is a challenging problem due to the absence of reference videos, the complexity of distortions, and the diversity of video contents.
Ranked #1 on Video Quality Assessment on MSU NR VQA Database
1 code implementation • 18 Oct 2018 • Dingquan Li, Tingting Jiang, Ming Jiang
To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably.
1 code implementation • IEEE Transactions on Multimedia 2018 • Dingquan Li, Tingting Jiang, Weisi Lin, Ming Jiang
The proposed method, SFA, is compared with nine representative blur-specific NR-IQA methods, two general-purpose NR-IQA methods, and two extra full-reference IQA methods on Gaussian blur images (with and without Gaussian noise/JPEG compression) and realistic blur images from multiple databases, including LIVE, TID2008, TID2013, MLIVE1, MLIVE2, BID, and CLIVE.
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.
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.
1 code implementation • 29 Apr 2022 • Xiaoqing Fan, Ge Li, Dingquan Li, Yurui Ren, Wei Gao, Thomas H. Li
Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission.
1 code implementation • 2 Nov 2023 • Wei zhang, Dingquan Li, Ge Li, Wen Gao
This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network.
1 code implementation • 18 Mar 2024 • Yujia Liu, Chenxi Yang, Dingquan Li, Jianhao Ding, Tingting Jiang
To be specific, we present theoretical evidence showing that the magnitude of score changes is related to the $\ell_1$ norm of the model's gradient with respect to the input image.
Adversarial Robustness No-Reference Image Quality Assessment +1
1 code implementation • 19 Oct 2018 • Qin He, Dingquan Li, Tingting Jiang, Ming Jiang
So we propose a new no-reference method of tone-mapped image quality assessment based on multi-scale and multi-layer features that are extracted from a pre-trained deep convolutional neural network model.
Blind Image Quality Assessment No-Reference Image Quality Assessment Multimedia
1 code implementation • ICCV 2023 • Shuyi Jiang, Daochang Liu, Dingquan Li, Chang Xu
Approximately, 350 million people, a proportion of 8%, suffer from color vision deficiency (CVD).
1 code implementation • 17 Feb 2024 • Dingquan Li, Kede Ma, Jing Wang, Ge Li
The content-dependent hierarchical prior is constructed at the encoder side, which enables coarse-to-fine super resolution of the point cloud geometry at the decoder side.
1 code implementation • 26 Jun 2021 • Zhihua Wang, Dingquan Li, Kede Ma
Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse."
no code implementations • 29 Jul 2022 • Peibei Cao, Dingquan Li, Kede Ma
Learning-based image quality assessment (IQA) has made remarkable progress in the past decade, but nearly all consider the two key components -- model and data -- in isolation.
no code implementations • 10 Jan 2024 • Chenxi Yang, Yujia Liu, Dingquan Li, Tingting Jiang
Ensuring the robustness of NR-IQA methods is vital for reliable comparisons of different image processing techniques and consistent user experiences in recommendations.
no code implementations • 11 Mar 2024 • Weixia Zhang, Dingquan Li, Guangtao Zhai, Xiaokang Yang, Kede Ma
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.
no code implementations • 20 Apr 2024 • Chenxi Yang, Yujia Liu, Dingquan Li, Yan Zhong, Tingting Jiang
Meanwhile, it is important to note that the correlation, like ranking correlation, plays a significant role in NR-IQA tasks.