Non Linear Autoregressive Networks with Exogenous Variables for QoE Prediction (NARX-QoE) Software release.
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Copyright (c) 2017 The University of Texas at Austin
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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:
C. G. Bampis, Z. Li, I. Katsavounidis and A. C. Bovik, "Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience," in IEEE Transactions on Image Processing, vol. 27, no. 7, pp. 3316-3331, July 2018.
C. G. Bampis, Z. Li and A. C. Bovik, "Continuous Prediction of Streaming Video QoE Using Dynamic Networks," in IEEE Signal Processing Letters, vol. 24, no. 7, pp. 1083-1087, July 2017.
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 : Christos Bampis
Version : 2.0
Comments: This code allows experimentation on three different datasets. In the new version, a Hammertein-Wiener implementation is also added. It should be noted that the related parameters (e.g. number of pole and zeros in the transfer function) need to be carefully selected for better performance in each of the three databases.
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.
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