Image quality assessment-a multiscale geometric analysis-based framework and examples

Abstract

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, we introduce the fundamentals of IQA and give a broad treatment of the state-of-the-art. We focus on a novel framework for IQA to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA), contrast sensitivity function (CSF), andWeber's law of just noticeable difference (JND). Thorough empirical studies were carried out using the laboratory for image and video engineering (LIVE) database against subjective mean opinion score (MOS), and these demonstrate that the proposed framework has good consistency with subjective perception values and the objective assessment results well reflect the visual quality of the images.