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Lecture
Compression: Prefix-Free Codes
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Related lectures (28)
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Compression: Kraft Inequality
Explains compression and Kraft inequality in codes and sequences.
Source Coding: Compression
Covers entropy, source coding, encoding maps, decodability, prefix-free codes, and Kraft-McMillan's inequality.
Entropy and Algorithms: Applications in Sorting and Weighing
Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.
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.
Data Compression and Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.
Source Coding Theorem
Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.
Stochastic Processes: Sequences and Compression
Explores compression in stochastic processes through injective codes and prefix-free codes.
Source Coding Theorems: Entropy and Source Models
Covers source coding theorems, entropy, and various source models in information theory.
Data Compression and Shannon's Theorem Summary
Summarizes Shannon's theorem, emphasizing the importance of entropy in data compression.
