Entropy and Compression I

This lecture covers the definition of entropy, minimum and maximum values, and coding. It explains the properties of entropy, the concept of concavity, and the Jensen's inequality. The lecture also discusses compression without loss, providing examples of algorithms like SMS language and Morse code. The Shannon-Fano algorithm is introduced, demonstrating how it can be used to compress data efficiently. The performance of the Shannon-Fano algorithm is analyzed, showing significant reduction in the number of bits required for representation. The lecture concludes by highlighting the importance of entropy in measuring information quantity and previews upcoming topics on coding algorithms and performance analysis.