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Lecture
Huffman Coding
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Related lectures (27)
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Compression: Prefix-Free Codes
Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.
Data Compression and Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.
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.
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.
Entropy and Algorithms: Applications in Sorting and Weighing
Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.
Data Compression and Shannon's Theorem Summary
Summarizes Shannon's theorem, emphasizing the importance of entropy in data compression.
Data Compression and Entropy 2: Entropy as 'Question Game'
Explores entropy as a 'question game' to guess letters efficiently and its relation to data compression.
Entropy and Compression I
Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.
Data Compression and Shannon's Theorem: Performance Analysis
Explores Shannon's theorem on data compression and the performance of Shannon Fano codes.
