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- W1497001491 abstract "In this note we study the under-addressed quantization stage implicit in any compressed sensing signal acquisition paradigm. We also study the problem of compressing the bit-stream resulting from the quantization. We propose using Sigma-Delta (a#x03A3;a#x0394;) quantization followed by a compression stage comprised of a discrete Johnson-Linden Strauss embedding, and a subsequent reconstruction scheme based on convex optimization. We show that this encoding/decoding method yields near-optimal rate-distortion guarantees for sparse and compressible signals and is robust to noise. Our results hold for sub-Gaussian (including Gaussian and Bernoulli) random compressed sensing measurements, and they hold for high bit-depth quantizers as well as for coarse quantizers including 1-bit quantization." @default.
- W1497001491 created "2016-06-24" @default.
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- W1497001491 date "2015-04-01" @default.
- W1497001491 modified "2023-10-17" @default.
- W1497001491 title "Near-Optimal Compression for Compressed Sensing" @default.
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- W1497001491 doi "https://doi.org/10.1109/dcc.2015.31" @default.
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