A dual-model approach to blind quality assessment of noisy images
Physiological and psychological evidences exist that the human visual system (HVS) has different behavioral patterns under low and high noise/artifact levels. We propose in this paper a dual-model approach to blind or no-reference (NR) image quality assessment (IQA) of noisy images through differentiating near-threshold and suprathreshold noise conditions. The underlying assumption for the proposed dual-model method is that for images with low level near-threshold noise, HVS tries to gauge the strength of the noise, so the image quality can be well approximated via measuring strength of the noise. And for images with their contents overwhelmed by high level suprathreshold noise, the HVS tries to recover meaningful structure from the noisy pixels using past experiences and prior knowledge encoded into an internal generative model of the brain. So image quality is closely related to the agreement between the noisy observation and the internal generative model explainable part of the image. More specifically, under the near-threshold noise condition, a noise level estimation algorithm based on natural image statistics is used, while under suprathreshold condition, an active inference model based on the free energy principle is adopted. The near-and suprathreshold models can be seamlessly integrated through a transformation between both estimates. The proposed dual-model algorithm has been tested on additive Gaussian noise contaminated images. Experimental results and comparative studies suggest that although being a no-reference approach, the proposed algorithm has prediction accuracy comparable to some of the best full-reference (FR) IQA methods.