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- W2800105997 abstract "We investigate lossy compressed sensing (CS) of a hidden, or remote, source, where a sensor observes a sparse information source indirectly. The compressed noisy measurements are communicated to the decoder for signal reconstruction with the aim to minimize the mean square error distortion. An analytically tractable lower bound to the remote rate-distortion function (RDF), i.e., the conditional remote RDF, is derived by providing support side information to the encoder and decoder. For this setup, the best encoder separates into an estimation step and a transmission step. A variant of the Blahut–Arimoto algorithm is developed to numerically approximate the remote RDF. Furthermore, a novel entropy coding based quantized CS method is proposed. Numerical results illustrate the main rate-distortion characteristics of the lossy CS, and compare the performance of practical quantized CS methods against the proposed limits." @default.
- W2800105997 created "2018-05-17" @default.
- W2800105997 creator A5028268404 @default.
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- W2800105997 date "2018-01-01" @default.
- W2800105997 modified "2023-10-05" @default.
- W2800105997 title "Rate-Distortion Performance of Lossy Compressed Sensing of Sparse Sources" @default.
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- W2800105997 doi "https://doi.org/10.1109/tcomm.2018.2834349" @default.
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