Reduced Reference Image Quality Assessment Based on Contourlet Domain and Natural Image Statistics

Reduced-reference (RR) image quality assessment metrics (IQA) evaluate the quality of images by extracting a parameter set from the original reference image and using this set in place of the actual reference image. In this paper, we propose a novel RR-IQA metric based on Contourlet transform. By combining Contourlet transform with a version of the hidden Markov model--Gaussian scale mixtures (GSM), the marginal distributions of neighbor coefficients in the Contourlet domain are modeled. With Contourlet transform as a pre-processing, the marginal histogram of coefficients in each subband can be well fitted by Guassian distribution after divisive normalization transforming. The standard derivation of the fitted Guassian transform and fitted error will be extracted as feature parameters. Experiments show that the proposed metric has good consistency with human subjective perception.