Universal code (data compression)

Applied sciences
Information engineering
Information theory
Coding theory

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Related lectures (25)

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Conditional Entropy: Huffman Coding

Explores conditional entropy and Huffman coding for efficient data compression techniques.

Huffman Coding: Optimal Prefix-Free Codes

Explores Huffman coding, demonstrating its optimality in average codeword length and prefix-free property.

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.

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 Summary

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

Universal Source Coding

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

Shannon's Theorem

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

Huffman Coding

Explores Huffman coding by comparing it to organizing a kitchen for efficiency.