Synthesized texture quality assessment via multi-scale spatial and statistical texture attributes of image and gradient magnitude coefficients

S. Alireza Golestaneh, Lina Karam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Perceptual quality assessment for synthesized textures is a challenging task. In this paper, we propose a trainingfree 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.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PublisherIEEE Computer Society
Pages851-857
Number of pages7
Volume2018-June
ISBN (Electronic)9781538661000
DOIs
StatePublished - Dec 13 2018
Event31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States
Duration: Jun 18 2018Jun 22 2018

Other

Other31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
CountryUnited States
CitySalt Lake City
Period6/18/186/22/18

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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    Golestaneh, S. A., & Karam, L. (2018). Synthesized texture quality assessment via multi-scale spatial and statistical texture attributes of image and gradient magnitude coefficients. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 (Vol. 2018-June, pp. 851-857). [8575268] IEEE Computer Society. https://doi.org/10.1109/CVPRW.2018.00117