Lectures related to Data Compression: Source Coding

Compression: Prefix-Free Codes

Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.

Compression: Kraft Inequality

Explains compression and Kraft inequality in codes and sequences.

Huffman's Algorithm: Mechanizing the Proof

Explores the mechanization of Huffman's algorithm proof and functional implementation.

Source Coding and Prefix-Free Codes

Covers source coding, injective codes, prefix-free codes, and Kraft's inequality.

Information Theory: Source Coding & Channel Coding

Covers the fundamentals of information theory, focusing on source coding and channel coding.

Universal Compression: Lempel-Ziv Method

Covers the Universal Compression using the Lempel-Ziv method and demonstrates its superiority over other methods.

Conditional Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data compression techniques.

Source Coding Theorem

Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.

Data Compression and Shannon's Theorem: Entropy Calculation Example

Demonstrates the calculation of entropy for a specific example, resulting in an entropy value of 2.69.

Compression: Prediction

Covers the concepts of compression and prediction using prefix-free codes and distributions.

Information Theory and Coding

Covers source coding, Kraft's inequality, mutual information, Huffman procedure, and properties of tropical sequences.

Data Compression and Shannon-Fano Algorithm

Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.

Stochastic Processes: Sequences and Compression

Explores compression in stochastic processes through injective codes and prefix-free codes.

Entropy and Data Compression: Huffman Coding Techniques

Discusses entropy, data compression, and Huffman coding techniques, emphasizing their applications in optimizing codeword lengths and understanding conditional entropy.

Data Compression and Shannon's Theorem: Lossy Compression

Explores data compression, including lossless methods and the necessity of lossy compression for real numbers and signals.

Source Coding: Compression

Covers entropy, source coding, encoding maps, decodability, prefix-free codes, and Kraft-McMillan's inequality.

Shannon's Theorem

Introduces Shannon's Theorem on binary codes, entropy, and data compression limits.

Data Compression: Shannon-Fano Algorithm

Explores the Shannon-Fano algorithm for efficient data compression and its applications in lossless and lossy compression techniques.

Data Compression and Shannon's Theorem Summary

Summarizes Shannon's theorem, emphasizing the importance of entropy in data compression.

Compression: Prefix-free Codes

Explains how to design efficient prefix-free codes for compression.