Perceptual Normalized Information Distance for Image Distortion Analysis Based on Kolmogorov Complexity

Image distortion analysis is a fundamental issue in many image processing problems, including compression, restoration, recognition, classification, and retrieval. In this work, we investigate the problem of image distortion measurement based on the theories of Kolmogorov complexity and normalized information distance (NID), which have rarely been studied in the context of image processing. Based on a wavelet domain Gaussian scale mixture model of images, we approximate NID using a Shannon entropy based method. This leads to a series of novel distortion measures that are competitive with state-of-the-art image quality assessment approaches.