Data Compression and Shannon's Theorem: Lossy Compression

This lecture covers the concept of data compression, including lossless compression with Shannon-Fano algorithm, Shannon's theorem, optimal compression with Huffman code, and the necessity of lossy compression for representing real numbers or sampling signals. It explains the limitations imposed by Shannon's entropy bound and provides examples of ambiguous codes leading to catastrophic outcomes. The lecture also discusses the compromise between memory space and distortion in lossy image compression, as well as advanced algorithms like JPEG and JPEG 2000. It further explores lossy compression in sound, highlighting the psychoacoustic effect of masking and the significant reduction in file size achieved by formats like MP3.