An Effective Sharpness Assessment Method For Shallow Depth-Of-Field Images - Duan, Zhixiang; Li, Guangxin; Fan, Guoliang (Proceedings - International Conference on Image Processing, 2021)
No-reference (NR) image sharpness assessment is an important issue for image quality assessment and algorithm performance evaluation. Many objective NR sharpness assessment metrics have been proposed which are often intended to be strongly associated with the human visual system (HVS). However, recent studies show that common sharpness assessment indicators may misjudge the degree of blurring for images with shallow depth of field that are often used to highlight the main subject in the view. This paper proposes an efficient no-reference objective image sharpness assessment metric based on the product of bidirectional pixel intensity differences that is computed block-by-block (PBDB). This paper contributes the following: (1) the sharpness of shallow depth-of-field images can be accurately evaluated with the proposed algorithm when traditional methods do not work well. (2) Experimental results on three public datasets demonstrate competitiveness and effectiveness of the proposed algorithm when compared with several state-of-the-art methods.