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Lecture
Data Compression: Source Coding
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Related lectures (26)
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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.
