Sampling_Quantization.pptx that provide a comprehensive overview of quantization in python

sampling and quantization

1. Sampling and Quantization in Digital Signal Processing From Analog to Digital Conversion Prof. Dr. Mariam Rehman

2. Introduction What is Signal Conversion? - Converting analog signals into digital form - Essential for communication, audio, video, and data processing

3. Analog to Digital Conversion Process Steps: 1. Sampling 2. Quantization 3. Encoding

4. Sampling Definition: - Process of measuring the amplitude of an analog signal at uniform intervals Nyquist Theorem: - Sampling rate ≥ 2 × maximum frequency of the signal Example: Audio (20 kHz → needs at least 40 kHz sampling)

5. Types of Sampling - Ideal Sampling - Natural Sampling - Flat-top Sampling

6. Quantization Definition: - Approximating sampled values to nearest discrete level Why needed? - Digital systems store only finite precision Key Point: - Introduces Quantization Error / Noise

7. Quantization Methods - Uniform Quantization (step size is same) - Non-Uniform Quantization (µ-law, A-law)

8. Quantization Error Definition: - Difference between actual sample and quantized value Causes signal distortion Reduced by: - Increasing number of quantization levels (bit-depth) Example: CD audio uses 16-bit → 65,536 levels

9. Applications - Audio (CDs, MP3) - Image (JPEG, PNG) - Video (MP4, Streaming) - Telecommunication (VoIP, Mobile)

10. Summary - Sampling = Taking snapshots of signal in time - Quantization = Rounding snapshots to nearest digital level - Both are essential for digital signal processing

11. Q&A Any Questions?