Generalized Gaussian scale mixtures: A model for wavelet coefficients of natural images

We develop a Generalized Gaussian scale mixture (GGSM) model of the wavelet coefficients of natural and distorted images. The GGSM model, which is more general than and which subsumes the Gaussian scale mixture (GSM) model, is shown to be a better representation of the statistics of the wavelet coefficients of both natural as well as distorted images. We demonstrate the utility of the model by applying it to various image processing applications, including blind distortion identification and no reference image quality assessment (NR-IQA). Similar to the GSM model, the GGSM model is useful for motivating the use of local divisive energy normalization, especially when the wavelet coefficients are computed on distorted pictures. We show that the GGSM model can lead to improved performance in distortion-related applications, while providing a more principled approach to the statistical processing of distorted image signals. The software release of a GGSM-based NR-IQA approach called DIIVINE-GGSM is available online at http://live.ece.utexas.edu/research/quality/diivine-ggsm.zip for further experimentation.