Blind quality index for camera images with natural scene statistics and patch-based sharpness assessment

• A blind quality index for camera images is proposed.
• Our method needs merely 11 features, far less than the majority of general-purpose train-based NR-IQA metrics.
• Our method is the first to propose modular framework for camera images.

The current image quality metrics work on the assumption that an image contains single and simulated distortions which are not representative of real camera images. In this paper we address the problem of quality assessment of camera images from two respects, natural scene statistics (NSS) and local sharpness, and associated three types of features. The first type of four features measures the naturalness of an image, inspired by a recent finding that there exists high correlation between structural degradation information and free energy entropy on natural scene images and this regulation will be gradually devastated as more distortions are introduced. The second type of four features originates from an observation concerning the NSS that a broad spectrum of statistics of distorted images can be caught by the generalized Gaussian distribution (GGD). Both the two types of features above belong to the NSS-based models, but they come from the considerations of local auto-regression (AR) and global histogram, respectively. The third type of three features focuses on estimating the local sharpness, which works by computing log-energies in discrete wavelet transform domain. Finally our quality metric is achieved via a SVR-based machine learning tool, and its performance is proved to be statistically better than state-of-the-art competitors on the CID2013 database dedicated to the quality assessment of camera images.