Data Compression and Shannon's Theorem: Huffman Codes

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Explores data compression through entropy definition, types, and practical examples, illustrating its role in efficient information storage and transmission.

Data Compression and Shannon's Theorem: Recap

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

Data Compression and Entropy: Basics and Introduction

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

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

Explores Shannon-Fano coding for efficient data compression and its comparison to Huffman coding.

Entropy and Algorithms: Applications in Sorting and Weighing

Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.

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.

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.

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.

Compression: Kraft Inequality

Explains compression and Kraft inequality in codes and sequences.

Huffman Coding

Explores Huffman coding by comparing it to organizing a kitchen for efficiency.