Quality Modeling Under A Relaxed Natural Scene Statistics Model
Abhinau K. Venkataramanan Alan C. Bovik
Information-theoretic image quality assessment (IQA) models such as Visual
Information Fidelity (VIF) and Spatio-temporal Reduced Reference Entropic
Differences (ST-RRED) have enjoyed great success by seamlessly integrating
natural scene statistics (NSS) with information theory. The Gaussian Scale
Mixture (GSM) model that governs the wavelet subband coefficients of natural
images forms the foundation for these algorithms. However, the explosion of
user-generated content on social media, which is typically distorted by one or
more of many possible unknown impairments, has revealed the limitations of
NSS-based IQA models that rely on the simple GSM model. Here, we seek to
elaborate the VIF index by deriving useful properties of the Multivariate
Generalized Gaussian Distribution (MGGD), and using them to study the behavior
of VIF under a Generalized GSM (GGSM) model.