Quality assessment for real out-of-focus blurred images

Images are vulnerable to different kinds of distortions, such as blur, noise, blockiness etc, which all degrade the image quality. Among the distorted images, out-of-focus blurred images are frequently-encountered and occupy a large proportion. However, few efforts have been done to quality evaluation for these images. In this paper, we devise a dedicated quality evaluation scheme to automatically infer the quality of out-of-focus blurred images, which is named GPSQ (Gradient magnitude and Phase congruency-based and Saliency-guided Quality model). In GPSQ, a pair of low-level features, including gradient magnitude (GM) and phase congruency (PC), are extracted to characterize the image local blurriness. Then saliency detection is performed on the image to generate a corresponding saliency map. Finally, we weight the local structure map with the saliency map to estimate the visual quality of the out-of-focus blurred image. Experimental results demonstrate the proposed GPSQ delivers high consistency with subjective evaluation results.