Reliable one-bit quantization of bandlimited graph data via single-shot noise shaping

TL;DR

This paper introduces a single-shot noise shaping technique for quantizing bandlimited graph data to few bits, including one-bit, with high accuracy and rigorous error bounds.

Contribution

It presents a novel noise shaping method that enables reliable, low-bit quantization of graph data, outperforming existing approaches.

Findings

Achieves state-of-the-art quantization performance.

Supports arbitrary bit levels, including one-bit quantization.

Provides rigorous error bounds for the quantization process.

Abstract

Graph data are ubiquitous in natural sciences and machine learning. In this paper, we consider the problem of quantizing graph structured, bandlimited data to few bits per entry while preserving its information under low-pass filtering. We propose an efficient single-shot noise shaping method that achieves state-of-the-art performance and comes with rigorous error bounds. In contrast to existing methods it allows reliable quantization to arbitrary bit-levels including the extreme case of using a single bit per data coefficient.