  We study the average distortion introduced by scalar, vector, and entropy
coded quantization of compressive sensing (CS) measurements. The asymptotic
behavior of the underlying quantization schemes is either quantified exactly or
characterized via bounds. We adapt two benchmark CS reconstruction algorithms
to accommodate quantization errors, and empirically demonstrate that these
methods significantly reduce the reconstruction distortion when compared to
standard CS techniques.
