Data Compression and Shannon's Theorem: Shannon-Fano Coding

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Data Compression and Entropy: Basics and Introduction

Introduces data compression, entropy, and the importance of reducing redundancy in data.

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: Entropy Calculation Example

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

Conditional Entropy: Huffman Coding

Explores conditional entropy and Huffman coding for efficient data compression techniques.

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.

Data Compression and Shannon's Theorem: Recap

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

Data Compression and Entropy: Conclusion

Covers the definition of entropy, Shannon–Fano algorithm, and upcoming topics.

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: Performance Analysis

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

Data Compression and Shannon's Theorem: Huffman Codes

Explores the performance of Shannon-Fano algorithm and introduces Huffman codes for efficient data compression.