Nonadaptive lossy encoding of sparse signals
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216884029-MIT.pdf
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Full printable version
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12.27 MB
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Author(s)
Pai, Ruby J
Advisor(s)
Vivek K. Goyal.
Date Issued
2006
Publisher
Massachusetts Institute of Technology
Abstract
At high rate, a sparse signal is optimally encoded through an adaptive strategy that finds and encodes the signal's representation in the sparsity-inducing basis. This thesis examines how much the distortion rate (D(R)) performance of a nonadaptive encoder, one that is not allowed to explicitly specify the sparsity pattern, can approach that of an adaptive encoder. Two methods are studied: first, optimizing the number of nonadaptive measurements that must be encoded and second, using a binned quantization strategy. Both methods are applicable to a setting in which the decoder knows the sparsity basis and the sparsity level. Through small problem size simulations, it is shown that a considerable performance gain can be achieved and that the number of measurements controls a tradeoff between decoding complexity and achievable D(R).
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 67-68).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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