Welcome to Lecture 36 of the course "Digital Signal Processing" by Prof.David Koilpllai
Full Course: https://study.iitm.ac.in/es/course_pages/EE3101.html

Video Overview
This lecture explores the essential concept of quantization in signal processing. We examine how signals transition from continuous time to discrete time and then to discrete amplitude, focusing on how quantization reduces infinite precision to a finite number of bits for digital representation. The lecture explains the role of anti-aliasing filters in preventing distortion during sampling and outlines the quantization process using uniform quantization with rounding and two's complement representation. We then analyze quantization error as a white noise process and derive the signal-to-quantization noise ratio (SQNR), explaining how the number of bits influences SQNR and illustrating the -6 dB per bit rule commonly used in practice. This session will strengthen your understanding of how signals are prepared for digital processing and storage.

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