GRNN-NRIQ Software release.

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-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------
Copyright (c) 2012 Jiangnan University and The University of Texas at Austin
All rights reserved.

Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, 
modify, and distribute this code (the source files) and its documentation for
any purpose, provided that the copyright notice in its entirety appear in all copies of this code, and the 
original source of this code, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu)
and Center for Perceptual Systems (CPS, http://www.cps.utexas.edu) at the University of Texas at Austin (UT Austin, 
http://www.utexas.edu), is acknowledged in any publication that reports research using this code. The research
is to be cited in the bibliography as:

1) Chaofeng li, Alan Bovik and Xiaojun Wu. Blind Image Quality Assessment Using a General Regression Neural Network, IEEE Transactions on Neural Networks, 22(5), 2011. 793-799.

2) Chaofeng li, Alan Bovik and Xiaojun Wu, " GRNN-NRIQ Software Release", 
URL: http://live.ece.utexas.edu/research/quality/GRNN_NRIQ.zip, 2012.

IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, 
OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS
AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS,
AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.

-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------%

Author  : Chaofeng Li 
Version : 1.0

The authors are with the School of IoT Engineering,Jiangnan University,Wuxi,CHINA

Kindly report any suggestions or corrections to wxlichaofeng@126.com

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This is a demonstration of the GRNN based No Reference image Quality Index(GRNN-NRQI). The algorithm is described in:

Chaofeng li, Alan Bovik and Xiaojun Wu. Blind Image Quality Assessment Using a General Regression Neural Network, IEEE Transactions on Neural Networks, 22(5), 2011. 793-799.

You can change this program as you like and use it anywhere, but please
refer to its original source (cite our paper and our web page at
http://live.ece.utexas.edu/research/quality/niqe_release.zip).

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grnn_predict_15_all_new.m is the program code for blind image quality assessment by using GRNN.

train_data_15,_25,_35,_45,_55 are the image feature for five divided dataset, and train_dmos_new_15,_25,_35,_45,_55 are the corresponding DMOS.

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