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 relative isolation.
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 • 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."
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 • 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.
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.
Blind Image Quality Assessment
No-Reference Image Quality Assessment
+1
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.
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 • 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.