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 This copyright statement may not be removed from any file containing it or from modifications to these files. This copyright notice must also be included in any file or product that is derived from the source files. Redistribution and use of this code in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - 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 in the documentation and/or other materials provided with the distribution. - 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. DISCLAIMER: This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the Arizona Board of Regents, Arizona State University, IVU Lab members, authors or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.
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