Review of Quality Assessment Algorithms on the Realistic Blurred Image Database (BID2011)

Accurate realistic blurred image quality assessment (RB-IQA) is challenging due to potential occurrence of blurring in image acquistion, compression and transmission. Derived from the Blurred Image Database (BID2011), a technical review is executed by screening literatures that cited the database for fully understanding the current achievement on realistic image sharpness estimation. By removing review and non-English-written papers and other irrelevant publications, 43 technical papers remain. Generally, the technical algorithms can grouped into shallow- and deep-learning categories. Notably, deep-learning-based RB-IQA algorithms via advanced learning strategies (transfer learning, rank learning, self-supvised learning, continual learning, meta-learning and domain adaptation) are predominantly developed, and remarkable progress has been made. RB-IQA is crucial for evaluating digital imaging devices and benchmarking restoration algorithms, and in future work, efforts should be made to improve the RB-IQA performance closer to human visual perception.