Graph Signal Compression via Task-Based Quantization

Pei Li, Nir Shlezinger, Haiyang Zhang, Baoyun Wang, Yonina C. Eldar

Research output: Contribution to journalConference articlepeer-review

Abstract

Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be compressed in order to be stored and conveyed. The common framework for graph signal compression is based on sampling, resulting in a set of continuousamplitude samples, which in turn have to be quantized into a finite bit representation. In this work we study the joint design of graph signal sampling along with the quantization of these samples, for graph signal compression. We focus on bandlimited graph signals, and show that the compression problem can be represented as a task-based quantization setup, in which the task is to recover the spectrum of the signal. Based on this equivalence, we propose a joint design of the sampling and recovery mechanisms for a fixed quantization mapping, and present an iterative algorithm for dividing the available bit budget among the discretized samples. Our numerical evaluations demonstrate that the proposed scheme achieves reconstruction accuracy within a small gap of that achievable with infinite resolution quantizers, while compressing high-dimensional graph signals into finite bit streams.

Original languageEnglish
Pages (from-to)5514-5518
Number of pages5
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume2021-June
DOIs
StatePublished - 1 Jan 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Keywords

  • Graph signal compression
  • Task-based quantization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Graph Signal Compression via Task-Based Quantization'. Together they form a unique fingerprint.

Cite this