Compressed sensing (CS) studies the recovery of high dimensional signals from
their low dimensional linear measurements under a sparsity prior. This paper is
focused on the CS problem with quantized measurements. There have been research
results dealing with different scenarios including a single/multiple bits per
measurement, noiseless/noisy environment, and an unsaturated/saturated
quantizer. While the existing methods are only for one or more specific cases,
this paper presents a framework to unify all the above mentioned scenarios of
the quantized CS problem.