IEEE International Conference on Image Processing,
An Adaptive Linear System
Framework for Image Distortion Analysis
Zhou
Wang and Eero P. Simoncelli
Laboratory for Computational Vision, New York
University, New York, NY 10003
Abstract: We
describe a framework for decomposing the distortion between two images into a
linear combination of components. Unlike conventional linear bases such as those
in Fourier or wavelet decompositions, a subset of the components in our
representation are not fixed, but are adaptively computed from the input
images. We show that this framework is a
generalization of a number of existing image comparison approaches. As an
example of a specific implementation, we select the components based on the
structural similarity principle, separating the overall image distortions into
non-structural distortions (those that do not change the structures of the objects
in the scene) and the remaining structural distortions. We demonstrate that the
resulting measure is effective in predicting image distortions as perceived by
human observers.