(untitled) (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2024)
Image Quality Assessment (IQA) plays a critical role in ensuring the effectiveness of various image-based applications, including medical imaging, autonomous driving, entertainment media, and more. This work offers an in-depth assessment of IQA techniques, emphasizing the improvements in no-reference (NR) techniques generated by deep learning while also highlighting conventional full-reference (FR) and reduced-reference (RR) models. The survey includes a comparison of metrics, datasets, and methods used for both synthetic and real-world images. We discuss the difficulties when evaluating IQA models and offer ideas for future study, with a focus on addressing a variety of distortions, including aspects of human perception and resolving data scarcity problems through semi-supervised learning.