  Sigma Delta quantization, a quantization method which first surfaced in the
1960s, has now been used widely in various digital products such as cameras,
cell phones, radars, etc. The method samples an input signal at a rate higher
than the Nyquist rate, thus achieves great robustness to quantization noise.
Compressed Sensing (CS) is a frugal acquisition method that utilizes the
possible sparsity of the signals to reduce the required number of samples for a
lossless acquisition. One can deem the reduced number as an effective
dimensionality of the set of sparse signals and accordingly, define an
effective oversampling rate as the ratio between the actual sampling rate and
the effective dimensionality. A natural conjecture is that the error of Sigma
Delta quantization, previously shown to decay with the vanilla oversampling
rate, should now decay with the effective oversampling rate when carried out in
the regime of compressed sensing. Confirming this intuition is one of the main
goals in this direction.
  The study of quantization in CS has so far been limited to proving error
convergence results for Gaussian and sub-Gaussian sensing matrices, as the
number of bits and/or the number of samples grow to infinity. In this paper, we
provide a first result for the more realistic Fourier sensing matrices. The
major idea is to randomly permute the Fourier samples before feeding them into
the quantizer. We show that the random permutation can effectively increase the
low frequency power of the measurements, thus enhance the quality of
$\Sigma\Delta$ quantization.
