No-reference image quality assessment based on deep learning
Yifan Wang, Fan Zhang, Sheng Chang, Xinhong Zhang · 2023
Images form the basis of human vision and are an important source of information for both human perception and machine pattern recognition. Since the development of the image quality evaluation field, a large number of image quality evaluation algorithms have emerged. In recent years, deep learning has become a hot area and has also been applied to the image domain. This paper focuses on reference-free image quality evaluation and reviews the no-reference image quality assessment based on deep learning. Firstly, the classification of IQA, technical indexes for IQA algorithm evaluation, and several public IQA databases available online are introduced. Then, several deep learning models applied to NR-IQA are discussed, compared, and evaluated in detail. Finally, an outlook on future research is provided.