Coder selection for lossy compression of still images
A criterion for selecting a lossy coder for still images is proposed. The "optimum" coder using the proposed criterion is selected to minimize the maximum Chernoff information between the distribution for the original and any distribution from the optimal achievable region. The resulting coder has the property that the best achievable probability of error over the problems of hypothesis testing between these distributions, is greater than the best Bayesian error probability for any other choice of coder. The coder selection procedure may be applied without a knowledge of what distortion measure is more suitable for images or a knowledge of the properties of the human visual perception. Several examples of coder selection are included to illustrate the procedure. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
coder selection lossy compression bayesian error probability achievable and non-achievable regions minimax chemoff criterion