This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of Just Noticeable Blur (JNB) into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content as well in images with same content.

This work received the Best Paper Award by the IEEE Signal Processing Society.


Copyright (c) 2007-2009 Arizona Board of Regents.  All Rights Reserved.
 Contact: Lina Karam ([email protected]) and Rony Ferzli ([email protected]) 
 Image, Video, and Usabilty (IVU) Lab, http://ivulab.asu.edu , Arizona State University
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 - Redistribution's of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
 - Redistribution's in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer
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 - The Image, Video, and Usability Laboratory (IVU Lab, http://ivulab.asu.edu) is acknowledged in any publication that
 reports research results using this code, copies of this code, or modifications of this code. 
 The code and our papers are to be cited in the bibliography as:

 R. Ferzli and L. J. Karam, "JNB Sharpness Metric Software", http://ivulab.asu.edu/Quality/JNBM

 R. Ferzli and L. J. Karam, "A No-Reference Objective Image Sharpness Metric Based on the Notion
of Just Noticeable Blur (JNB)," IEEE Transactions on Image Processing, vol. 18, no. 4, pp. 717-728, April 2009.

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