ABSTRACT
This chapter describes state-of-the-art objective quality metrics to assess the quality of digital images. It also describes approaches that have been shown to be competitive with bottom-up Human Visual System (HVS)-based approaches in predicting image quality. These methods additionally demonstrate advantages over bottom-up HVS-based measures in several aspects. Digital images and video are prolific in the world owing to the ease of acquisition, processing, storage and transmission. Many common image processing operations such as compression, dithering and printing affect the quality of the image. The HVS is very good at evaluating the quality of an image blindly, that is, without a reference “perfect” image to compare it against. It is, however, rather difficult to perform this task automatically using a computer. Traditional approaches to image quality assessment use a bottom-up approach, where models of the HVS are used to derive quality metrics. Bottom-up HVS-based approaches are those that combine models for different properties of the HVS in defining a quality metric.
