Measuring images: Differences, quality, and appearance

One goal of image quality modeling is to predict human judgments of quality between image pairs, without needing knowledge of the image origins. This concept can be thought of as device-independent image quality modeling. The first step towards this goal is the creation of a model capable of predicting perceived magnitude differences between image pairs. A modular color image difference framework has recently been introduced with this goal in mind. This framework extends traditional CIE color difference formulae to include modules of spatial vision and adaptation, sharpness detection, contrast detection, and spatial localization. The output of the image difference framework is an error map, which corresponds to spatially localized color differences. This paper reviews the modular framework, and introduces several new techniques for reducing the multi-dimensional error map into a single metric. In addition to predicting overall image differences, the strength of the modular framework is its ability to predict the distinct mechanisms that cause the differences. These mechanisms can be thought of as attributes of image appearance. We examine the individual mechanisms of image appearance, such as local contrast, and compare them with overall perceived differences. Through this process, it is possible to determine the perceptual weights of multi-dimensional image differences. This represents the first stage in the development of an image appearance model designed for image difference and image quality modeling.