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

Abstract

The notion of a perceptual space is useful for characterizing images in terms of their perceptual image quality. It is shown that images degraded by blur and noise can be assigned positions in a multidimensional perceptual space and that quality and its underlying attributes sharpness and noisiness can be associated with directions (or vectors) in this space. The perceptual space is constructed using multidimensional scaling (MDS) techniques. Two different MDS approaches are used, one making use of the perceived dissimilarity between the images and another making use of the scaled perceptual attributes sharpness and noisiness. The two alternative approaches lead to stimulus configurations that can be approximately related by a linear transformation, and the results from both MDS approaches can be combined to produce one perceptual space for each scene. A two-dimensional perceptual space adequately represents the processed images for all three scenes that are used, and the perceptual spaces obtained for all scenes are very similar. The directions of the attribute vectors in the perceptual space indicate that unsharpness and noisiness are approximately orthogonal attributes, which implies that there is little interaction between these attributes. The impairment vector, whose direction is opposite to that of the quality vectors, lies between the unsharpness and noisiness vectors at an angle of approximately 30 degrees between the unsharpness and impairment vectors.