Perceptual characterization of images degraded by blur and noise: model.

Abstract

Reliable and economic methods for assessing image quality are essential for designing better imaging systems. Although reliable psychophysical methods are available for assessing perceptual image quality with the help of human subjects, the cost of performing such experiments prevents their use for evaluating large amounts of image material. This has led to an increasing demand for objective methods for estimating image quality. The perceived quality of an image is usually determined by several underlying perceptual attributes such as sharpness and noisiness. In the accompanying paper [J. Opt. Soc. Am. A 13, 1166-1177 (1996)] it is demonstrated that the relationships between images on the one hand and judgments on attributes and overall quality by subjects on the other hand can be characterized in a multidimensional perceptual space. In this perceptual space the images are represented by points, and the strengths of their perceptual attributes are modeled by the projections of these image positions onto the attribute axes. In analogy with the perceptual space we will introduce a psychometric space in which the positions of the images are determined by objective measures on the images. In the case of images degraded by blur and noise the stimulus coordinates are functions of the estimated spread of the blurring kernel and the estimated standard deviation of the noise, respectively. According to the model presented in this paper, the perceptual attributes of images can be estimated in three steps. In the first step the physical parameters (blur spread and noise standard deviation) are estimated from the images. In the second step these estimates are used to position the images in psychometric space. In the third step the attribute strengths are derived by projecting the alter image positions onto the attribute axes. We show that the attributes and the quality thus estimated correlate well with the perceived attributes and quality.