Search Results for author: Dingquan Li

Found 10 papers, 9 papers with code

Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches

no code implementations29 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.

Image Quality Assessment

Deep Geometry Post-Processing for Decompressed Point Clouds

1 code implementation29 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.

Quantization

Semi-Supervised Deep Ensembles for Blind Image Quality Assessment

1 code implementation26 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."

Blind Image Quality Assessment Ensemble Learning

Continual Learning for Blind Image Quality Assessment

1 code implementation19 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.

Blind Image Quality Assessment Continual Learning

Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training

1 code implementation9 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.

Video Quality Assessment VQA

Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment

1 code implementation10 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

Quality Assessment of In-the-Wild Videos

2 code implementations1 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.

Image Classification Video Quality Assessment

Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information

1 code implementation19 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

Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images

1 code implementation18 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.

Blind Image Quality Assessment Image Quality Estimation +1

Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?

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.

Blind Image Quality Assessment Image Classification +2

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