A new compression scheme is described that aims at improving the fidelity of reconstructed images through the parallel application of both lossless and lossy compression techniques. The purpose of the scheme is to obtain compression ratios higher than those obtained by lossless compression schemes and at the same time produce reconstructed images with better fidelity than those normally obtained with lossy techniques. Tests so far have shown that the integrated lossless/lossy (IL/L) compression scheme consistently improves the fidelity of reconstructed images compared to well-known image compression algorithms. Specifically, the new scheme gives better fidelity (20% to 55% reduction in mean square error) than the DCT algorithm under equal compression ratios.>

The authors present an iterative algorithm for implementing the relative entropy method for PMF (probability mass function) estimation. Numerical examples are included using synthetic PMF's and actual speech data. Benchmark measurements of the algorithm are compared to results for the maximum-entropy method.>

The inverse isoperimetric problem of the entropy functional is considered in this Correspondence. This problem can be stated as follows: Given a known probability density function (pdf), what prior constraints are needed in order for this pdf to be the one that maximizes the entropy functional? The solution is given in terms of a Theorem, and it is compared to the Rozenberg-Rubichev solution. The Theorem is also used in simple applications, one of which illuminates the relationship of the maximum entropy principle (MEP) to the concept of "relative frequencies."