Lecture
Compression with Loss: Data and Signals
Related lectures (26)
Entropy and Compression I
Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.
Universal Compression: Lempel-Ziv Method
Covers the Universal Compression using the Lempel-Ziv method and demonstrates its superiority over other methods.
Lossless Compression: Shannon-Fano and HuffmanMOOC: Information, Calcul, Communication: Introduction à la pensée informatique
Explores lossless compression using Shannon-Fano and Huffman algorithms, showcasing Huffman's superior efficiency and speed over Shannon-Fano.
Data Compression: Entropy Definition
Explores data compression through entropy definition, types, and practical examples, illustrating its role in efficient information storage and transmission.
Space Trusses & Frames
Covers space trusses, frames, and machines, emphasizing equilibrium analysis and structural design principles.
Signals & Systems I: Cross-Correlation and Convolution
Explores cross-correlation, signal detection, Hilbert spaces, orthogonal approximation, and image compression using DCT.
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: Shannon-Fano Coding
Explores Shannon-Fano coding for efficient data compression and its comparison to Huffman coding.
Data Compression and Shannon's Theorem: Shannon's Theorem Demonstration
Covers the demonstration of Shannon's theorem, focusing on data compression.
Vision-Language-Action Models: Training and Applications
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.