# MAXIMUM LIKELIHOOD ESTIMATION OF COMPOSITE

Published on Jan 1, 1983

Abstract

Published on Jan 1, 1983

Abstract

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The concept of composite source coding for efficient image transmission or storage is addressed in the paper, and specific properties characterising the component sources are determined. An approach is developed which decomposes typical image signals into two independent additive components. Each component is subsequently encoded separately for storage or transmission. These individual components are combined at the receiver (or recovery stage) to produce a replica of the original signal. Specia...

In this work the concept of composite source coding is addressed with particular reference to image signals and specific properties characterising the component sources are determined. The physical problem of source decomposition is formulated under a general formula in such a way that dynamic programming is used as a key algorithm to achieve efficiently the signal partitioning. The resulting solution is optimum in the sense that the rate required to transmit the component signals is minimum for...

Several state-of-the-art mathematical models useful in image processing are considered. These models include the traditional fast unitary transforms, autoregessive and state variable models as well as two-dimensional linear prediction models. These models introduced earlier [51], [52] as low-order finite difference approximations of partial differential equations are generalized and extended to higher order in the framework of linear prediction theory. Applications in several image Processing pr...

With the continuing growth of modern communications technology, demand for image transmission and storage is increasing rapidly. Advances in computer technology for mass storage and digital processing have paved the way for implementing advanced data compression techniques to improve the efficiency of transmission and storage of images. In this paper a large variety of algorithms for image data compression are considered. Starting with simple techniques of sampling and pulse code modulation (PCM...

Abstract : Much current interest in the areas of communication and control is devoted to a study of estimation theory. This text provides a comprehensive treatment of estimation theory which should be suitable for graduate level engineers. There are nine chapters in the text: Introduction to Estimation Theory, Review of Probability Theory and Random Variables, Stochast Processes, Gauss-Markov Processes and Stochastic Differential Equations, Decision Theory, Basic Estimation Theory, The Optimum L...

Autoregressive-moving average dynamic models are construtted for nonstationary processes which exhibit cyclic behavior. Recursive linear mean-square prediction and filtering policies are then formulated which utilize these models. Application to a problem in two-dimensional image restoration is considered in detail.

Rate-distortion functions for 2-dimensional homogeneous isotropic images are compared with the performance of five source encoders designed for such images. Both unweighted and frequency weighted mean-square error distortion measures are considered. The coders considered are a) differential pulse code modulation (DPCM) using six previous samples or picture elements (pels) in the prediction--herein called 6-pel DPCM, b) simple DPCM using single-sample prediction, c) 6-pel DPCM followed by entropy...

From the Publisher: An introduction to the mathematical theory of multistage decision processes, this text takes a functional equation approach to the discovery of optimum policies. Written by a leading developer of such policies, it presents a series of methods, uniqueness and existence theorems, and examples for solving the relevant equations. The text examines existence and uniqueness theorems, the optimal inventory equation, bottleneck problems in multistage production processes, a new forma...

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