Image- and Gradient-based wavelet domain Syntheized Texture Quality Assessment (IGSTQA)


S. Alireza Golestaneh, and Lina J. Karam


Here we propose a training-free reduced-reference (RR) objective quality assessment method that quantifies the perceived quality of synthesized textures. The proposed reduced-reference synthesized texture quality assessment metric is based on measuring the spatial and statistical attributes of the texture image using both image- and gradient-based wavelet coefficients at multiple scales. Performance evaluations on two synthesized texture databases demonstrate that our proposed RR synthesized texture quality metric significantly outperforms both full-reference and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures.


Copyright (c) 2012-2015 Arizona Board of Regents. All Rights Reserved.
Contact: Alireza Golestaneh ([email protected]) and Lina Karam ([email protected])
Image, Video, and Usabilty (IVU) Lab, , Arizona State University
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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.
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– The Image, Video, and Usability Laboratory (IVU Lab, 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:
Alireza Golestaneh and L. J. Karam, “Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients Magnitudes”

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.

Please cite the following paper if you are using our code or this research helpful for your purpose:


author = {Alireza Golestaneh, S. and Karam, Lina J.},
title = {Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes},
booktitle = Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
month = {July},
year = {2018}

You can download the code from here (LINK). If the previous link does not work, download it from this GitHub LINK

You can download the “SynTEX Granularity Database” that is used in our paper from here: LINK