Abstract
The discrepancy between an image and its “reblurred” version indicates the extent of blur in the image. This paper presents a novel no-reference image sharpness evaluator leveraging the discrepancy measures of structural degradation in both the spatial and wavelet domains. Specifically, local structural degradation of an input image is characterized by the discrepancy measures of orientation selectivity-based visual patterns and log-Gabor filter responses between the image and its corresponding reblurred version respectively. Considering the influence of viewing distance on image quality, the global sharpness discrepancy is measured through inter-resolution self-similarities. Finally, the computed discrepancies are utilized as sharpness-aware features and then a support vector regressor is employed to map the feature vectors into quality scores. The performance of the proposed method is evaluated on six public image quality databases, including two real blurred image databases. Experimental results demonstrate that our proposed method achieves state-of-the-art performances across all these databases.
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