Two-Bit Quantization and Its Priority Under Low Sampling Rate

In recent years, extreme quantization methods-particularly one-bit quantization-have garnered significant attention in signal processing and data acquisition systems. While one-bit quantization offers substantial benefits in terms of resource efficiency, including reduced data storage, lower transmission bandwidth, and simplified hardware implementation, it also introduces severe challenges such as high quantization noise, harmonic distortion, and degraded signal fidelity. To address these limitations, this paper investigates a more balanced low-bit quantization approach, employing two bits per sample instead of one. From a harmonic suppression perspective, the considered two-bit quantization strategy demonstrates superior performance in mitigating quantization noise compared to its one-bit counterpart. This method effectively suppresses dominant harmonic distortions, particularly lower-order harmonics that significantly degrade signal quality. Furthermore, a key advantage of this approach is its adaptability to varying sampling rates-it maintains noise suppression efficacy not only in high-speed sampling scenarios but also under low sampling rate conditions. This flexibility makes it particularly suitable for applications where computational resources or power consumption are constrained.