Quantization of Bandlimited Graph Signals

Felix Krahmer, He Lyu, Rayan Saab, Anna Veselovska, Rongrong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2023 International Conference on Sampling Theory and Applications, SampTA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350328851
DOIs
StatePublished - 2023
Event2023 International Conference on Sampling Theory and Applications, SampTA 2023 - New Haven, United States
Duration: 10 Jul 202314 Jul 2023

Publication series

Name2023 International Conference on Sampling Theory and Applications, SampTA 2023

Conference

Conference2023 International Conference on Sampling Theory and Applications, SampTA 2023
Country/TerritoryUnited States
CityNew Haven
Period10/07/2314/07/23

Keywords

  • 1-bit quantization
  • bandlimited graph signals
  • graph signal processing
  • noise-shaping
  • quantization

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