Successive refinement of information

The successive refinement of information consists of first approximating data using a few bits of information, then iteratively improving the approximation as more and more information is supplied. The goal is to achieve an optimal description at each stage. In general, an ongoing description which is rate-distortion optimal whenever it is interrupted is sought. It is shown that in order to achieve optimal successive refinement the necessary and sufficient conditions are that the solutions of the rate distortion problem can be written as a Markov chain. In particular, all finite alphabet signals with Hamming distortion satisfy these requirements. It is also shown that the same is true for Gaussian signals with squared error distortion and for Laplacian signals with absolute error distortion. A simple counterexample with absolute error distortion and a symmetric source distribution which shows that successive refinement is not always achievable is presented. >