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.