Signal Denoising and Compression - igheyas/WaveletTransformation GitHub Wiki
📋 Week 8: Signal Denoising & Compression 📝 Outline Goals
Wavelet Shrinkage (Thresholding)
Hard vs. Soft Threshold
Universal (Donoho–Johnstone) Threshold
Advanced Denoising Algorithms
VisuShrink, SureShrink, BayesShrink
Wavelet‐Based Compression
Coefficient Quantization
Embedded Coding (SPIHT)
Zerotree Concept
Rate–Distortion Trade-offs
Exercises
- Goals By the end of this week you will:
Learn how to remove noise by zeroing or shrinking small wavelet coefficients.
Understand the choice of threshold and its impact on mean‐square error.
See how wavelet coefficients can be quantized and coded for compression.
Analyze the trade‐off between bit-rate and reconstruction quality.