Image Processing I: Quantization and Histogram Analysis

This lecture covers the fundamentals of image quantization, including quantizer specification, histogram analysis, uniform quantization, minimum error (Lloyd-Max) quantization, grayscale versus resolution, dithering, and the impact of reducing the number of gray levels. It also explores the iterative optimization algorithm (K means) and the properties of the Lloyd-Max quantizer. Additionally, it discusses the need for binary images in devices like printers and fax machines, and how the human visual system integrates black and white information. The lecture emphasizes the trade-off between spatial and grayscale resolution in image processing.