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.

通过推导多元广义高斯分布的有用特性，并利用它们研究 Visual Information Fidelity 在广义 GSM 模型下的行为，我们寻求详细说明 VIF 指数。

在放松的自然场景统计模型下的质量建模

Quality Modeling Under A Relaxed Natural Scene Statistics Model

In coded aperture snapshot spectral imaging (CASSI) system, the real-world
hyperspectral image (HSI) can be reconstructed from the captured compressive
image in a snapshot. Model-based HSI reconstruction methods employed
hand-crafted priors to solve the reconstruction problem, but most of which
achieved limited success due to the poor representation capability of these
hand-crafted priors. Deep learning based methods learning the mappings between
the compressive images and the HSIs directly achieved much better results. Yet,
it is nontrivial to design a powerful deep network heuristically for achieving
satisfied results. In this paper, we propose a novel HSI reconstruction method
based on the Maximum a Posterior (MAP) estimation framework using learned
Gaussian Scale Mixture (GSM) prior. Different from existing GSM models using
hand-crafted scale priors (e.g., the Jeffrey's prior), we propose to learn the
scale prior through a deep convolutional neural network (DCNN). Furthermore, we
also propose to estimate the local means of the GSM models by the DCNN. All the
parameters of the MAP estimation algorithm and the DCNN parameters are jointly
optimized through end-to-end training. Extensive experimental results on both
synthetic and real datasets demonstrate that the proposed method outperforms
existing state-of-the-art methods. The code is available at
this https URL

本文提出了一种新颖的基于深度学习和高斯混合先验的 Maximum a Posteriori (MAP) 估计框架的高光谱成像重建方法，该方法通过深度卷积神经网络 (DCNN) 学习尺度先验和局部均值参数，在合成和真实数据集上均取得了超过现有前沿方法的最佳结果。