Analog-to-Digital Compression: A New Paradigm for Converting Signals to Bits
For engineers designing signal acquisition systems, this work provides a foundational framework to improve efficiency by integrating sampling and quantization, potentially reducing bit requirements without sacrificing information.
This paper proposes a new paradigm for analog-to-digital conversion that jointly considers sampling and quantization, rather than treating them separately, to optimize the conversion under bit constraints.
Processing, storing and communicating information that originates as an analog signal involves conversion of this information to bits. This conversion can be described by the combined effect of sampling and quantization, as illustrated in Fig. 1. The digital representation is achieved by first sampling the analog signal so as to represent it by a set of discrete-time samples and then quantizing these samples to a finite number of bits. Traditionally, these two operations are considered separately. The sampler is designed to minimize information loss due to sampling based on characteristics of the continuous-time input. The quantizer is designed to represent the samples as accurately as possible, subject to a constraint on the number of bits that can be used in the representation. The goal of this article is to revisit this paradigm by illuminating the dependency between these two operations. In particular, we explore the requirements on the sampling system subject to constraints on the available number of bits for storing, communicating or processing the analog information.