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

TL;DR

This paper introduces a training-free reduced-reference method for assessing the perceptual quality of synthesized textures by analyzing multi-scale spatial and statistical attributes of image and gradient coefficients, outperforming existing metrics.

Contribution

It presents a novel RR quality assessment metric based on wavelet coefficients that effectively predicts perceived texture quality without training.

Findings

Outperforms state-of-the-art quality metrics

Effective in predicting perceived visual quality

Validated on synthesized texture databases

Abstract

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