Image Quality Assessment - A Multiscale Geometric Analysis-Based Framework and Examples

This chapter is about objective image quality assessment (IQA), which has been recognized as an effective and efficient way to predict the visual quality of distorted images. Basically, IQA has three different dependent degrees on original images, namely, full-reference (FR), noreference (NR), and reduced-reference (RR). To begin with, the fundamentals of IQA are introduced and a broad treatment of the current state-of-the-art IQA are also given. A novel framework for IQA is focused upon to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA), contrast sensitivity function (CSF), and Weber’s law of just noticeable difference (JND). Thorough empirical studies were carried out upon the laboratory for image and video engineering (LIVE) database against subjectivemean opinion score (MOS) and demonstrate that the proposed framework has good consistency with subjective perception values and the objective assessment results well reflect the visual quality of images.