Maximum likelihood estimation of composite source models for image coding

Random processes of considerable importance in signal processing often exhibit short term stationary statistical attributes whilst in the long term appear to behave in a non-stationary manner. Image signals belong to this category. In this work we introduce a class of composite source models as a means of representing consistently signals of this nature, with a particular application in mind concerned with coding. A composite likelihood function is derived whose subsequent maximization yields estimates of the parameters which are associated with the source models. It is a fact that maximization of the ML function is almost intractable by analytical means. However by introducing optimization techniques based on dynamic programming, ML estimation of composite source models is simplified drastically. Further it is shown that source models so estimated yield coding systems which require the least possible transmission rate for pre-specified levels of average distortion in the reconstruction of image signals.