Lectures related to Entropy and Compression I

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.

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 Entropy Definition

Explores the concept of entropy as the average number of questions needed to guess a randomly chosen letter in a sequence, emphasizing its enduring relevance in information theory.

Lecture: Shannon

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Data Compression and Shannon's Theorem: Performance Analysis

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