Information Theory and Coding: Source Coding

Related lectures (30)

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Explores Shannon's theorem on data compression and the performance of Shannon Fano codes.

Covers the Lempel-Ziv universal coding algorithm and invertible finite state machines in information theory.

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

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

Discusses conditional entropy and its role in information theory and data compression.

Explores the origins and interpretation of entropy, emphasizing its role in measuring disorder and information content in a system.

Explores entropy as a 'question game' to guess letters efficiently and its relation to data compression.

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

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Explores Claude Shannon's contributions to information theory and efficient communication and storage methods.