6.342 Wavelets, Approximation, and Compression

Spring 2011


Lectures: MW 11:00-12:30

Room 4-153
Credit: 12 (3-0-9) (Grad H)


Instructor: Vivek Goyal, vgoyal@mit.edu


Text: Fourier and Wavelet Signal Processing by Martin Vetterli, Jelena Kovacevic and Vivek K Goyal
Prerequisites: 18.06; and 6.341 or 6.450 (high proficiency in 18.06 and 6.011 can be plenty; interested students should inquire with the instructor)
Overview: Hilbert space formulation of continuous-time and discrete-time signals. Sampling.  Orthogonal and biorthogonal signal expansions.  Uncertainty principles and the time-frequency plane.  Two-channel filter banks, iterated filter banks, and discrete wavelet transforms.  Multiresolution analysis.  Wavelet bases, regularity, approximation properties, and nonlinear approximation.  Basics of quantization and source coding. Compression, denoising, and other image processing using wavelets. Advanced topics from the current research literature.

Several term projects from the past two offerings of the course have been very successful.  With additional work, they have led to papers and theses. These include (list still under construction):


More information about the course:

Class Poster
Spring 2009 Class Syllabus (will be updated soon)
Spring 2007 Stellar Website

Spring 2009 Stellar Website

Spring 2011 Stellar Website