Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients
This work addresses the challenge of evaluating synthesized texture quality for applications in computer graphics and vision, representing an incremental improvement over existing methods.
The paper tackles the problem of perceptual quality assessment for synthesized textures by proposing a training-free reduced-reference metric based on multi-scale spatial and statistical attributes from image and gradient wavelet coefficients, achieving significant outperformance over state-of-the-art full-reference and reduced-reference metrics in evaluations on two databases.
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.