Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering

@article{Xu2018QuantizedCS,
  title={Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering},
  author={Chunlei Xu and Laurent Jacques},
  journal={CoRR},
  year={2018},
  volume={abs/1801.05870}
}
Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals with quantized, finite precision representations, i.e., a mandatory process involved in any practical sensing model. While the resolution of this quantization clearly impacts the quality of signal reconstruction, there even exist incompatible combinations of quantization functions and sensing matrices that proscribe arbitrarily low reconstruction error when the number of… CONTINUE READING
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