Low-Bit Quantization of Bandlimited Graph Signals via Iterative Methods

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Authors: Felix Krahmer, Lyu, He, Rayan Saab, Jinna Qian, Anna Veselovska, Rongrong Wang
Cited by: 0

Abstract

We study the quantization of real-valued bandlimited signals on graphs, focusing on low-bit representations. We propose iterative noise-shaping algorithms for quantization, including sampling approaches with and without vertex replacement. The methods leverage the spectral properties of the graph Laplacian and exploit graph incoherence to achieve high-fidelity approximations. Theoretical guarantees are provided for the random sampling method, and extensive numerical experiments on synthetic and real-world graphs illustrate the efficiency and robustness of the proposed schemes.

Fields of Study & Subjects

Sparse and Compressive Sensing Techniques Mathematical Analysis and Transform Methods Advanced Graph Neural Networks

Publication details

Journal / source
arXiv (Cornell University)
Publication date
26 January 2026
Document type
Preprint