The sparsity and compressibility of finitedimensional signals are of great interest in fields, such as compressed sensing. The notion of compressibility is also extended to infinite sequences of independent identically distributed or ergodic random variables based on the observed error in their nonlinear k-term approximation. In this paper, we use the entropy measure to study the compressibility of continuous-domain innovation processes (alternatively known as white noise). Specifically, we define such a measure as the entropy limit of the doubly quantized (time and amplitude) process. This provides a tool to compare the compressibility of various innovation processes. It also allows us to identify an analogue of the concept of "entropy dimension" which was originally defined by Rényi for random variables. Particular attention is given to stable and impulsive Poisson innovation processes. Here, our results recognize Poisson innovations as the more compressible ones with an entropy measure far below that of stable innovations. While this result departs from the previous knowledge regarding the compressibility of impulsive Poisson laws compared with continuous fat-tailed distributions, our entropy measure ranks α-stable innovations according to their tail.
For continuous-domain innovation processes (i.e., continuous-domain white noise), the notions of RID and RDE are defined in REF by vanishingly fine quantization of the time axis and the amplitude range.
How Compressible Are Innovation Processes?
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Computer Science Mathematics