Source Coding: Compression

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Related lectures (26)

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Kraft-McMillan Theorem

Explores the Kraft-McMillan theorem, proving the existence of uniquely decodable prefix-free codes.

Source Coding Theorems: Entropy and Source Models

Covers source coding theorems, entropy, and various source models in information theory.

Shannon's Theorem

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

Compression: Strong Connection and Prefix-Free Codes

Explores the relationship between code word length and probability distribution, focusing on designing prefix-free codes for efficient compression.

Compression: Prefix-free Codes

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

Information Theory: Source Coding, Cryptography, Channel Coding

Covers source coding, cryptography, and channel coding in communication systems, exploring entropy, codes, error channels, and future related courses.

Conditional Entropy: Huffman Coding

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

Compression: Prediction

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

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.

Compression

Covers the concept of compression and constructing prefix-free codes based on given information.