Quantitative Evaluation of Computer Regenerated Images and Their Use in Storage-Restricted Environments

For a given set of images (or pattern samples), a probabilistic grammar may be developed to describe and recognize these images. This grammar, once obtained, may serve as an abstract representation for the images in question. Conversely, images may be regenerated from their grammatical representations. Images regenerated in this manner, however, may contain samples that are not found in the set of original images from which the grammatical representation was developed. A method for evaluating the quality of regenerated images with respect to the set of original images is discussed. Let I be the set of original images and O be the set of images regenerated from the abstract representation of I. Set O consists of two mutually exclusive subsets, A and B. A is the set of images found in I, and B is the set of images not found in I. It is shown that the clustering behavior of set B determines the quality of the regenerated images in the mean-square sense. By defining a scatter matrix for B, several factors that influence the image quality with respect to I are derived. Experimental results on the abstract representation and regeneration of a subset of the Munson data are demonstrated. An interesting application of this regeneration model to pattern recognition in storage-restricted environments is also presented.