MERL – A MITSUBISHI ELECTRIC RESEARCH LABORATORY http://www.merl.com Rate-Distortion Modeling for Multiscale Binary Shape Coding Based on Markov Random Fields Anthony Vetro Yao Wang Huifang Sun TR-2003-31 March 2003 Abstract The purpose of this paper it to explore the relationship between the rate-distortion characteristics of multiscale binary shape and Markov Random Field (MRF) parameters. For coding, it is im- portant that the input parameters that will be used to define this relationship be able to distinguish between the same shape at different scales, as well as different shapes at the same scale. In this work, we consider an MRF model, referred to as the Chien model, which accounts for high-order spatial interactions among pixels. We propose to use the statistical moments of the Chien model as input to a neural network to accurately predict the rate and distortion of the binary shape when coded at various scales. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c Mitsubishi Electric Information Technology Center America, 2003 201 Broadway, Cambridge, Massachusetts 02139