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

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35:42:29 Hours

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English

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Thu, 11-Aug-2022

Course description

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

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Free

Lectures

41

Skill level

Beginner

Expiry period

Lifetime

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