Analog-to-Digital Compression: A New Paradigm for Converting Signals to Bits

TL;DR

This paper introduces a new perspective on analog-to-digital conversion by examining the interdependence of sampling and quantization, aiming to optimize information retention within bit constraints.

Contribution

It revisits the traditional separate design of sampling and quantization, highlighting their dependency and proposing a unified approach for better analog information representation.

Findings

Sampling and quantization are interdependent processes.

Constraints on bit usage influence sampling requirements.

Unified design improves information preservation.

Abstract

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.