S3: A spectral and spatial measure of local perceived sharpness in natural images

Abstract—This paper presents an algorithmdesigned tomeasure the local perceived sharpness in an image. Ourmethod utilizes both spectral and spatial properties of the image: For each block, we measure the slope of the magnitude spectrum and the total spatial variation. These measures are then adjusted to account for visual perception, and then, the adjusted measures are combined via a weighted geometric mean. The resulting measure, i.e., (spectral and spatial sharpness), yields a perceived sharpness map in which greater values denote perceptually sharper regions. This map can be collapsed into a single index, which quantifies the overall per-ceived sharpness of the whole image. We demonstrate the utility of the measure for within-image and across-image sharpness pre-diction, no-reference image quality assessment of blurred images, and monotonic estimation of the standard deviation of the impulse response used in Gaussian blurring. We further evaluate the accu-racy of in local sharpness estimation by comparing maps to sharpness maps generated by human subjects. We show that can generate sharpness maps, which are highly correlated with the human-subject maps. Index Terms—Blur, human visual system (HVS), natural scenes, sharpness, spectral slope, total variation (TV). I