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Lecture
Data Compression and Shannon's Theorem: Recap
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Related lectures (26)
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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: Entropy Definition
Explores data compression through entropy definition, types, and practical examples, illustrating its role in efficient information storage and transmission.
Entropy and Compression I
Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.
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.
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.
Data Compression and Entropy: Conclusion
Covers the definition of entropy, Shannon–Fano algorithm, and upcoming topics.
Data Compression and Entropy: Basics and Introduction
Introduces data compression, entropy, and the importance of reducing redundancy in data.
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.
Data Compression and Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.
