SynTEX

Summary

In this texture database (SynTEX), we evaluate the effect of texture regularity and granularity on the perceived quality of synthesized textures. The textures were synthesized using various paramteric and non-parametric texture synthesis algorithms. Please refer to the papers listed below and to the Readme files that are included with the SynTEX database folders for more information.

By downloading the SynTEX database, you agree to the following COPYRIGHT NOTICE, CONDITIONS, AND DISCLAIMER.

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SynTEX Database 
ivulab.asu.edy/databases/textures/syntex 
Copyright (c) 2013-2015 Arizona Board of Regents.  All Rights Reserved.

Contact: Lina Karam ([email protected]), S. Alireza  Golestaneh ([email protected]), Mahesh Subedar ([email protected]), and Srenivas Varadarajan ([email protected])

Image, Video, and Usabilty (IVU) Lab, http://ivulab.asu.edu , Arizona State University

This copyright statement may not be removed from any file containing it or from modifications to these files.
This copyright notice must also be included in any file or product that is derived from the database.

Redistribution and use of this database with or without modification are permitted provided that the following conditions are met:
– Redistribution of the database must retain the above copyright notice, this list of conditions and the following disclaimer.
– The Image, Video, and Usability Laboratory (IVU Lab, http://ivulab.asu.edu) is acknowledged in any publication that reports research results using this database, or modifications of this database.
– The following papers are to be cited in any publication that reports research results using this database, or modifications of this database as follows:

1. S. Alireza Golestaneh, Mahesh M. Subedar and Lina J. Karam, “The Effect of
Texture Granularity on Texture Synthesis Quality,” SPIE Optical Engineering + Applications
Symposium, Applications of Digital Image Processing XXXVIII Conference, August 2015.

2. S. Varadarajan and L. J. Karam. “A no-reference perceptual texture regularity metric,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1894-1898, May 2013.

DISCLAIMER:

This database 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 database, even if advised of the possibility of such damage. 
 
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