Finite automata and regularized edge-preserving wavelet transform scheme

Abstract

Summary form only given. We present an edge-preserving image compression technique based on the wavelet transform and iterative constrained least square regularization. This approach treats image reconstruction from lossy image compression as the process of image restoration. It utilizes the edge information detected from the source image as a priori knowledge for the subsequent reconstruction. Image restoration refers to the problem of estimating the source image from its degraded version. The reconstruction of DWT-coded images is formulated as a regularized image recovery problem and makes use of the edge information as the a priori knowledge about the source image to recover the details, as well as to reduce the ringing artifact of the DWT-coded image. To compromise the rate of edge information and DWT-coded image data, a scheme based on generalized finite automata (GFA) is used. GFA is used instead of vector quantization in order to achieve adaptive encoding of the edge image.