  Image quality assessment(IQA) is of increasing importance for image-based
applications. Its purpose is to establish a model that can replace humans for
accurately evaluating image quality. According to whether the reference image
is complete and available, image quality evaluation can be divided into three
categories: full-reference(FR), reduced-reference(RR), and non-reference(NR)
image quality assessment. Due to the vigorous development of deep learning and
the widespread attention of researchers, several non-reference image quality
assessment methods based on deep learning have been proposed in recent years,
and some have exceeded the performance of reduced -reference or even
full-reference image quality assessment models. This article will review the
concepts and metrics of image quality assessment and also video quality
assessment, briefly introduce some methods of full-reference and semi-reference
image quality assessment, and focus on the non-reference image quality
assessment methods based on deep learning. Then introduce the commonly used
synthetic database and real-world database. Finally, summarize and present
challenges.
