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This paper presents an algorithm designed to measure the local perceived sharpness in an image. Our method 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., S3 (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 perceived sharpness of the whole image. We demonstrate the utility of the S3 measure for within-image and across-image sharpness prediction, 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 accuracy of S3 in local sharpness estimation by comparing S3 maps to sharpness maps generated by human subjects. We show that S3 can generate sharpness maps, which are highly correlated with the human-subject maps.