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Texture Synthesis Quality Assessment Using Perceptual Texture Similarity

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journal contribution
posted on 26.02.2020, 14:12 by Xinhui Dong, Huiyu Zhou
Texture synthesis plays an important role in computer game and movie industries. Although it has been widely studied, the assessment of the quality of the synthesised textures has received little attention. Inspired by the research progress in perceptual texture similarity estimation, we propose a Texture Synthesis Quality Assessment (TSQA) approach. To our knowledge, this is the first attempt to exploit perceptual texture similarity for the TSQA task. In particular, we introduce two perceptual similarity principles for synthesis quality assessment. Correspondingly, we train two Random Forest (RF) regressors. Given a pair of sample and synthesised textures, the two regressors can be used to predict the global and local quality scores of the synthesised texture respectively. An overall score is generated from the two scores. Our results show that the deep Bag-of-Words (BoW) descriptors, extracted by a pre-trained Convolutional Neural Network (CNN), perform better than, or comparably to, the other nine types of hand-crafted or CNN descriptors and an image quality assessment measure, together with the proposed TSQA approach.

History

Citation

Knowledge-Based Systems, 2020, https://doi.org/10.1016/j.knosys.2020.105591

Author affiliation

School of Informatics

Version

AM (Accepted Manuscript)

Published in

Knowledge-Based Systems

Publisher

Elsevier

issn

0950-7051

Acceptance date

30/01/2020

Copyright date

2020

Available date

03/02/2020

Publisher version

https://www.sciencedirect.com/science/article/pii/S095070512030068X

Language

en