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NREUP 2009

Virginia State University

Title: Wavelet and Image Compression

Directors: Dawit Haile


Dates of Program: May 26 - July 3, 2009

Summary: Wavelets are collections of functions that can be used to decompose signals into various frequency components at an appropriate resolution for a range of spatial scales. The idea of decomposing a signal into frequency components has been heavily exploited with the use of Fourier decompositions which use sines and cosines as their basis functions. The clear advantage of wavelets over traditional Fourier methods is that they are localized in both space or time and frequency. Wavelet Transforms provide powerful techniques of converting continuous analog data sets to a digital framework. One particular important application is the ability to compress data to allow for more compact and efficient storage. We will use one such transformation - the Haar Wavelet Transform (HWT) and study its applications in image compression and recovery by giving particular emphasis to the storage of recovery of various images. We also will explore how to achieve high compression ratio in images using 2D-HWT by applying different compression thresholds for the wavelet coefficients.

Student Researchers Supported by MAA:

  • Maurice Brown
  • Brian Dadson
  • Reneisha Hill
  • Lynette Obiero
  • Clarence Sims

Program Contacts:

Bill Hawkins

Michael Pearson
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