S3: A Spectral and Spatial Sharpness Measure

Abstract

This paper presents a block-based 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, S 3 (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 S 3 measure for within-image and across-image sharpness prediction, for global blur estimation, and for no-reference image quality assessment of blurred images.

The impact of visual attributes on online image diffusion

“…On the other hand, unintentional blur is perceived as poor technique, degrading aesthetic value. We implemented Vu et al [46] S3 algorithm for mapping sharpness levels. Although the concept of sharpness can be subjective, the S3 algorithm achieves good results by combining both spectral analysis and local contrasts to create a sharpness map, quantifying the sharpness of each pixel.…”

No-Reference Image Quality Assessment and Blind Deblurring with Sharpness Metrics Exploiting Fourier Phase Information

“…As we saw in Section 4.3, these indices tend to grow rapidly with the size of an image, which does not really correspond to our visual perception. One possibility to deal with this problem could be to use a "visual summation" principle [37], and define the overall sharpness of an image as the maximal sharpness of all its fixed-size (say, 32 × 32) sub-parts. A less extreme variant could be to weight the sharpness of each sub-part by some sort of saliency measure.…”

“…The concept of local phase coherence, originally introduced and developed in [29,19,20] for edge detection purposes, was later linked to the perception of blur by Wang and Simoncelli [39], which ultimately led to the definition of a no-reference image quality index [17]. Closer to our work lies the index [37] which combines some spectral and spatial characteristics.…”

Main subject detection via adaptive feature refinement

“…In Ref. 40 we describe an algorithm for generating a local sharpness map, which indicates the degree of sharpness for each region in an image. Given an image X, let S(X) denote the sharpness map of X.…”