Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients

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...

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Bibliographic Details
Main Authors: Golestaneh, S. Alireza, Karam, Lina J.
Format: Conference Proceeding
Language:English
Subjects:
Entropy
Feature extraction
Indexes
Measurement
Quality assessment
Visualization
Online Access:Request full text
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Description
Summary: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.
ISSN:2160-7516
DOI:10.1109/CVPRW.2018.00117