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.

翻译：信息论图像质量评估（IQA）模型（如视觉信息保真度（VIF）和时空缩减参考熵差（ST-RRED））通过将自然场景统计（NSS）与信息论无缝融合取得了巨大成功。控制自然图像小波子带系数的高斯尺度混合（GSM）模型构成了这些算法的基础。然而，社交媒体上用户生成内容的爆炸式增长（通常受到一种或多种未知损害的扭曲）揭示了基于NSS的IQA模型（依赖简单GSM模型）的局限性。本文旨在通过推导多元广义高斯分布（MGGD）的有用性质，并利用这些性质研究VIF在广义GSM（GGSM）模型下的行为，来详细阐述VIF指标。