Abstract
Image quality assessment (IQA) is critically important for the image-processing field. IQA aims to build a computational model to predict human perceived image quality, accurately and automatically. Until now, great efforts have been employed to design IQA metrics. In this paper, we systematically and comprehensively review the fundamental, brief history, and state-of-the-art developments of IQA, with emphasis on natural image quality assessment (NIQA). First, the definition of image quality is discussed, which contains three aspects and lead to different philosophies of designing IQA metrics. Afterwards, classic NIQA metrics are presented with some further discussions. Widely used databases and the performances of classic NIQA metrics on them are also listed. We highlight the most significant works and some open issues about the developments of IQA, and provide the benchmarks for the researchers and scholars who work on IQA.
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