FRIQUEE Software release.


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-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------
Copyright (c) 2016 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)  D. Ghadiyaram and A. C. Bovik, "FRIQUEE Software Release" 
URL: http://live.ece.utexas.edu/research/quality/FRIQUEE_release.zip, 2016

2) D. Ghadiyaram and A. C. Bovik, "Perceptual Quality Prediction on Authentically Distorted Images Using a
Bag of Features Approach," http://arxiv.org/abs/1609.04757 (under review)


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.

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Author  : Deepti Ghadiyaram
Version : 1.0

The authors are with the Laboratory for Image and Video Engineering
(LIVE), Department of Electrical and Computer Engineering, The
University of Texas at Austin, Austin, TX.

Kindly report any suggestions or corrections to [email protected]

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This is a demonstration of a new Feature Maps Driven No-Reference Image Quality Evaluator Engine (FRIQUEE)
The algorithm is described in detail in :

D. Ghadiyaram and A. C. Bovik, "Perceptual Quality Prediction on Authentically Distorted Images Using a
Bag of Features Approach," http://arxiv.org/abs/1609.04757 (under review)


You can change this program as you like and use it anywhere, but please
refer to its original source cite our paper as given below:

D. Ghadiyaram and A. C. Bovik, "Perceptual Quality Prediction on Authentically Distorted Images Using a
Bag of Features Approach," http://arxiv.org/abs/1609.04757 (under review)

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Running on Matlab 


Input : A test image loaded in a 3 dimensional array

Output: 

a) A set of 560 image quality features (FRIQUEE-ALL features, as described in the paper)

b) A quality score of the image. The score typically has a value 
between 0 and 100 (0 represents the worst quality, 100 the best).
  
Usage: Please see example.m for details on how to use this script

Dependencies: 

We are also making a trained model publicly available. This model is trained on all the 1162 test images of the LIVE-Challenge Database using a Support Vector Regressor (SVR) with a radial basis function. The parameters used for training arE:
C (cost) = 2^8;  gamma = 2^-6;

Binaries: - Two mex files svmpredict and svmtrain are also provided with the release. These are compiled on a UNIX environment. If you are on a different OS, please download LIBSVM and compile the files appropriately.

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Note on training: 
This release version of FRIQUEE was trained on the entire LIVE database.


This program uses LibSVM binaries.
Below is the requried copyright notice for the binaries distributed with this release.

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LibSVM tools: svmpredict, svmtrain (binaries)
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Copyright (c) 2000-2009 Chih-Chung Chang and Chih-Jen Lin
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