Explores data compression through entropy definition, types, and practical examples, illustrating its role in efficient information storage and transmission.
Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.
Introduces data compression, exploring how redundancy in data can be reduced to achieve smaller file sizes without losing information.
Covers the Lempel-Ziv universal coding algorithm and invertible finite state machines in information theory.
Explores image compression principles, focusing on JPEG 2000, covering transform-based coding, quantization, entropy coding, region of interest, error resilience, and software implementations.
