Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics

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21 October. 2019

Efficient Evaluation of Image Quality via
Deep-Learning Approximation of Perceptual
Metrics

Alessandro Artusi, Francesco Banterle, Fabio Carrara, and Alejandro Moreo

Congratulations to Dr. Alessandro Artusi, Team Leader of the DeepCamera Multidisciplinary Research Group, on his recently accepted publication with title "Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics".
 

Journal: IEEE Transaction on Image Processing Impact Factor 6.79

Digital Object Identifier: 10.1109/TIP.2019.2944079

 
Abstract:
Abstract—Image metrics based on Human Visual System (HVS) play a remarkable role in the evaluation of complex image processing algorithms. However, mimicking the HVS is known to be complex and computationally expensive (both in terms of time and memory), and its usage is thus limited to a few applications and to small input data. All of this makes such metrics not fully attractive in real-world scenarios. To address these issues, we propose Deep Image Quality Metric (DIQM), a deep-learning approach to learn the global image quality feature
(mean-opinion-score). DIQM can emulate existing visual metrics efficiently, reducing the computational costs by more than an order of magnitude with respect to existing implementations.

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