Quantization of Bandlimited Graph Signals

MCML Authors

Felix Krahmer

Prof. Dr.

Principal Investigator

* Former Principal Investigator

Anna Veselovska

Dr.

→ Group Massimo Fornasier
 Applied Numerical Analysis

Abstract

Graph models and graph-based signals are becoming increasingly important in machine learning, natural sciences, and modern signal processing. In this paper, we address the problem of quantizing bandlimited graph signals. We introduce two classes of noise-shaping algorithms for graph signals that differ in their sampling methodologies. We demonstrate that these algorithms can be efficiently used to construct quantized representatives of bandlimited graph-based signals with bounded amplitude. Moreover, for one of the algorithms, we provide theoretical guarantees on the relative error between the quantized representative and the true signal.

inproceedings KLS+23

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SampTA 2023

14th International Conference on Sampling Theory and Applications. Yale, CT, USA, Jul 10-14, 2023.

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Authors

F. Krahmer • H. Lyu • R. Saab • A. Veselovska • R. Wang

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Links

DOI

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Research Area

 A2 | Mathematical Foundations