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eess.IV2024
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives
Chenxi Yang, Yujia Liu, Dingquan Li +2
Deep neural networks have demonstrated impressive success in No-Reference Image Quality Assessment (NR-IQA). However, recent researches highlight the vulnerability of NR-IQA models…
eess.IV2020★ 85 cited
Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment
Dingquan Li, Tingting Jiang, Ming Jiang
Currently, most image quality assessment (IQA) models are supervised by the MAE or MSE loss with empirically slow convergence. It is well-known that normalization can facilitate fa…
eess.IV2018
Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images
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. The neglect of the high-level semantic inform…