Publication Date
1996
Document Type
Dissertation/Thesis
First Advisor
Tahernezhadi, Mansour
Degree Name
M.S. (Master of Science)
Legacy Department
Department of Electrical Engineering
LCSH
Algorithms; Signal processing--Digital techniques
Abstract
Digital signal processing algorithms are being used in many applications such as noise, interference cancellation, speech processing, equalization, and image processing. Equalization algorithms in digital communications and image compression algorithms in image processing gained a lot of importance due to their wide spread use in real life. In this thesis, we present algorithms for digital communications and image processing. Blind equalization is a process in which the equalization of the received signal can be done without the aid of any training sequence. Considerable work has been done in case of symbol space blind equalizers but the results are assumed in case of fractionally spaced blind equalizers. In the first part of the thesis we presented the result for fractionally spaced blind equalizers by extending the results of symbol spaced blind equalizers. Subband and wavelet decompositions are powerful tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their frequency splitting, which allows for efficient coding matched to statistics of each frequency band and to the characteristics of the human visual system. We investigate subband coding techniques for lossless image compression. Compression with Reversible Embedded Wavelets (CREW) is one of the techniques used to compress the continuous-tone still images using wavelets and pyramidal decomposition. Here, we present a new and different implementation for coding wavelet coefficients which provides even better performance than the CREW coding. We provide implementation results on the JPEG test set of images and compare them with state-of-the-art predictive techniques and other techniques based on subband decomposition.
Recommended Citation
Yellapantula, Ramakrishna V., "Development of digital signal processing algorithms for image and communication applications" (1996). Graduate Research Theses & Dissertations. 2188.
https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/2188
Extent
viii, 54, [55-58] pages
Language
eng
Publisher
Northern Illinois University
Rights Statement
In Copyright
Rights Statement 2
NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.
Media Type
Text
Comments
Includes bibliographical references (pages [55-58])