Two-Step Image Quality Assessment (2stepQA) Software release.
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Copyright (c) 2017 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:
- X. Yu, C. G. Bampis, P. Gupta and A. C. Bovik "Predicting Encoded Picture Quality in Two Steps is a Better Way," in arXiv:1801.02016 [eess.IV]
- X. Yu, C. G. Bampis, Praful Gupta and A. C. Bovik, "2stepQA Software Release" URL: http://live.ece.utexas.edu/research/quality/2stepQA_release.zip, 2017
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Authors : Xiangxu Yu, Christos Bampis and Praful Gupta
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.
The current release implements 2stepQA, an efficient image quality reduced-reference predictor. 2stepQA integrates both no-reference (NR) and full-reference perceptual quality measurements into the quality prediction process.The no-reference module accounts for the possibly imperfect quality of the source (reference) image, while the full-reference component measures the quality differences between the source image and its possibly further distorted version. A simple, yet very efficient, multiplication step fuses the two sources of information into a reliable objective prediction score. The current release contains example images for testing.