Edge preservation ratio for image sharpness assessment

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Abstract
Image sharpness is one of the most determining factors for image readability and scene understanding. How to accurately quantify it is a hot topic. This paper systematically validates a previously proposed index for full-reference image sharpness assessment (edge preservation ratio, EPR). Based on Gaussian blurring images in LIVE, CSIQ, TID2008 and TID2013 databases, we firstly evaluated EPR accuracy on five edge detectors on LIVE and selected an optimal one for further analysis. Then nine state-of-the-art image quality assessment metrics are compared, including full-reference, no-reference and dedicated image sharpness assessment categories. Experimental results demonstrate (1) Canny is an optimal edge detector for EPR implementation; (2) EPR is a top-ranking image sharpness assessment metric that outperforms PSNR and SSIM and rivals FSIM; and (3) EPR accords more closely with human subjective judgment than involved image sharpness assessment metrics. This study also indicates that image sharpness assessment is still full of challenges and utilizing deep learning architectures to learning the direct mapping from images to quality will be a trend in the near future.

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