Nonadaptive lossy encoding of sparse signals
Author(s)
Pai, Ruby J![Thumbnail](/bitstream/handle/1721.1/41623/216884029-MIT.pdf.jpg?sequence=5&isAllowed=y)
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Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Advisor
Vivek K. Goyal.
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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).
Date issued
2006Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.