Data Compression and Shannon's Theorem

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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: Recap

Explores entropy, compression algorithms, and optimal coding methods for data compression.

Data Compression and Shannon's Theorem: Entropy Calculation Example

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

Conditional Entropy and Information Theory Concepts

Discusses conditional entropy and its role in information theory and data compression.

Data Compression and Entropy: Compression

Explores lossless data compression techniques, emphasizing efficient message representation and encoding strategies.

Information Theory: Review and Mutual Information

Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.

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.

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Data Compression and Shannon's Theorem: Performance Analysis

Explores Shannon's theorem on data compression and the performance of Shannon Fano codes.

Source Coding Theorem

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