Information Theory and Coding

Overview

Introduction to Information Theory and Coding - Definition of Information Measure and Entropy - Extension of An Information Source and Markov Source - Adjoint of An Information Source, Joint and Conditional Information Measures - Properties of Joint and Conditional Information Measures and a Markov Source - Asymptotic Properties of Entropy and Problem Solving in Entropy - Block Code and Its Properties - Instantaneous Code and Its Properties - Kraft-Mcmillan Equality and Compact Codes - Shannon's First Theorem - Coding Strategies and Introduction to Huffman Coding - Huffman Coding and Proof of Its Optimality - Competitive Optimality of The Shannon Code - Non-Binary Huffman Code and Other Codes - Adaptive Huffman Coding - Shannon-Fano-Elias Coding and Introduction to Arithmetic Coding


Introduction to Information Channel - Equivocation and Mutual Information - Properties of Different Information Channels - Reduction of Information Channels - Properties of Mutual Information and Introduction to Channel Capacity - Calculation of Channel Capacity for Different Information Channel - Shannon's Second Theorem - Discussion on Error Free Communication Over Noisy Channel - Error Free Communication Over a Binary Symmetric Channel - Differential Entropy and Evaluation of Mutual Information - Channel Capacity of a Bandlimited Continuous Channel - Introduction to Rate-Distortion Theory - Definition and Properties of Rate-Distortion Functions - Calculation of Rate-Distortion Functions - Computational Approach For Calculation of Rate-Distortion Functions - Introduction to Quantization - Lloyd-Max Quantizer - Companded Quantization - Variable Length Coding and Problem Solving In Quantizer Design - Vector Quantization - Transform - Transform Coding