Multivariate Statistics for Blind Image Quality Applications

Many existing no-reference image quality approaches exploit only univariate statistical models of bandpass image coefficients, thereby neglecting the higher-order correlations that occur between adjacent coefficients which may be modified by distortion. We modeled multivariate natural image statistics in the spatial domain as following a Multivariate Generalized Gaussian distribution that provides useful information regarding the type and severity of distortions in image signals. We have also found that gaussianity assumptions when estimating local variances are violated in the presence of distortions, hence we estimate local energies using a generalized Gaussian distribution. To this end, we propose Multivariate-Generalized Contrast Normalization (MV-GCN): a multivariate approach which integrates a generalized contrast normalization step. We demonstrate the potential of our model in various image quality applications.