Automated image quality assessment and its applications in computer vision - Zhou, Phoebe Huixin (Nanyang Technological University, 2022)
The practical adoption of Convolutional Neural Networks (CNNs) in computer vision is widespread. However, CNN performance is heavily dependent on the perceptual quality of images. It is therefore necessary to monitor the quality of data input in order to verify that CNN predictions are reliable. While various algorithmic methods have been proposed to perform image quality prediction, the generalizability of these Image Quality Assessment (IQA) methods across diverse real-world scenarios is still the subject of ongoing research. Furthermore, few studies have used IQA to assess the robustness of CNNs to image quality degradations.