2026-09-19T06:12:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/416232022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Vivek K. Goyal.
Pai, Ruby J
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-05-19T16:02:11Z
2008-05-19T16:02:11Z
2006
2006
http://hdl.handle.net/1721.1/41623
216884029
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 67-68).
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).
by Ruby J. Pai.
M.Eng.
68 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Nonadaptive lossy encoding of sparse signals
Thesis
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