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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★ 30 cited
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…