Image compression using orthogonalized independent components bases

Abstract

In this paper we address the orthogonalization of independent component analysis (ICA) to obtain transform-based image coders. We consider several classes of training images, from which we extract the independent components, followed by orthogonalization, obtaining bases for image coding. Experimental tests show the generalization ability of ICA of natural images, and the adaptation ability to specific classes. The proposed fixed size block coders have lower transform complexity than JPEG. They outperform JPEG, on several classes of images, for a given range of compression ratios, according to both standard (SNR) and perceptual (picture quality scale - PQS) measures. For some image classes, the visual quality of the images obtained with our coders is similar to that obtained by JPEG2000, which is currently the state of the art still image coder. On fingerprint images, our fixed and variable size block coders perform competitively with the special-purpose wavelet-based coder developed by the FBI.