The data saving capability of "compressed sensing (sampling)" in signal discretization is disputed and found to be far below the theoretical upper bound defined by the signal sparsity. It is demonstrated on a simple and intuitive example, that, in a realistic scenario for signals that are believed to be sparse, one can achieve a substantially larger saving than compressing sensing can. It is also shown that frequent assertions in the literature that "compressed sensing" can beat the Nyquist sampling approach are misleading substitutions of terms and are rooted in misinterpretation of the sampling theory.
- compressed sensing
- sampling theorem