Data Compression and Entropy: Basics and Introduction

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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 Shannon's Theorem Summary

Summarizes Shannon's theorem, emphasizing the importance of entropy in data compression.

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.

Conditional Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data 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: Shannon-Fano Coding

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

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: introduction

Introduces data compression, exploring how redundancy in data can be reduced to achieve smaller file sizes without losing information.

Data Compression and Entropy: Compression

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

Data Compression and Shannon's Theorem: Huffman Codes

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