Reduced-reference synthesized-texture quality assessment based on multi-scale spatial and statistical texture attributes
In this paper, we propose a reduced-reference (RR) objective quality assessment method that quantifies the perceived quality of synthesized textures. The proposed metric is based on measuring the granularity, regularity, and statistical attributes of the texture image. Furthermore, the proposed RR metric exhibits a significantly low overhead as compared to existing RR metrics by only requiring the transmission of 7 parameters as side information. Performance evaluations on two synthesized texture databases demonstrate that the proposed RR metric outperforms both full-reference (FR) and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures.