Texture synthesis quality assessment using perceptual texture similarity

Xinghui Dong*, Huiyu Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number105591
JournalKnowledge-Based Systems
Volume194
Early online date03 Feb 2020
DOIs
Publication statusPublished - 22 Apr 2020
Externally publishedYes

Bibliographical note

Funding Information:
H. Zhou was supported by UK EPSRC under Grant EP/N011074/1 , Royal Society-Newton Advanced Fellowship under Grant NA160342 , and European Union’s Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie grant agreement No 720325 .

Funding Information:
H. Zhou was supported by UK EPSRC under Grant EP/N011074/1, Royal Society-Newton Advanced Fellowship under Grant NA160342, and European Union's Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie grant agreement No 720325.

Publisher Copyright:
© 2020 Elsevier B.V.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Perceptual texture similarity
  • Random forests
  • Texture
  • Texture synthesis quality assessment

ASJC Scopus subject areas

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence

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