Data Compression and Shannon's Theorem: Definitions

Related lectures (26)

Page 1 of 3

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

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

Explains compression and Kraft inequality in codes and sequences.

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

Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.

Discusses conditional entropy and its role in data compression techniques.

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

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

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

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