Encoding compressive sensing measurements with Golomb-Rice codes

Under the compressive sensing theoretical framework a sparse signal can be acquired using few random measurements. This result implies that an analog signal can be compressed while it is being acquired. However, compressive sensing does not yet achieve the high compression rates obtained with standard data compression techniques. To improve the compression performance of compressive sensing, the measurements can be further encoded exploiting their statistical structure. This work explores the concept of encoding compressive sensing measurements using a low-complexity entropy encoder such as the Golomb-Rice encoder. It is found, through system-level numerical simulations, that a Golomb-Rice encoder can reduce the bitrate of compressive sensing by more than 1 bps. Balanced and unbalanced sensing matrices were used in the simulations. Balanced sensing matrices resulted in a slightly better average SNR and CR performance.