Data Compression and Shannon's Theorem

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Data Compression and Entropy Interpretation

Explores the origins and interpretation of entropy, emphasizing its role in measuring disorder and information content in a system.

Entropy and Algorithms

Explores entropy's role in coding strategies and search algorithms, showcasing its impact on information compression and data efficiency.

Information in Networked Systems: Functional Representation and Data Compression

Explores traditional information theory, data compression, data transmission, and functional representation lemmas in networked systems.

JPEG 2000: Image Compression

Explores image compression principles, focusing on JPEG 2000, covering transform-based coding, quantization, entropy coding, region of interest, error resilience, and software implementations.

Conditional Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data compression techniques.

Source Coding Theorems: Entropy and Source Models

Covers source coding theorems, entropy, and various source models in information theory.

Shannon's Theorem

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

Lossless Compression: Shannon-Fano and Huffman

Explores lossless compression using Shannon-Fano and Huffman algorithms, showcasing Huffman's superior efficiency and speed over Shannon-Fano.