No-Reference Image Sharpness Assessment Based on Maximum Gradient and Variability of Gradients

Gradients are commonly used in image sharpness assessment methods. However, research has not fully addressed the direct relationship between gradients and the perceived sharpness. In this paper, we discover and validate through experiments that the maximum gradient is an effective indicator of the perceived image sharpness on a global or local scale. Based on these observations, we propose a novel and efficient no-reference image quality assessment (NR-IQA) method for blurry images. Our method uses two elements to predict the quality of blurry images: the maximum gradient and the variability of gradients. The maximum gradient represents the sharpest spot in an image, and the variability of gradients shows variations within the content of the image. According to the characteristics of human visual systems, these factors are significant for humans when judging the quality of blurry images. The method was tested using blurry image datasets from five public IQA databases. Compared with nine other state-of-the-art NR-IQA methods for blurry images, the experimental results demonstrate that our method is more consistent with humans' subjective evaluations. The MATLAB source code of our method is available at https://github.com/Atmegal/Sharpness-evaluation.