TY - JOUR
T1 - Graph Signal Compression via Task-Based Quantization
AU - Li, Pei
AU - Shlezinger, Nir
AU - Zhang, Haiyang
AU - Wang, Baoyun
AU - Eldar, Yonina C.
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under grant No 61971238, by the Benoziyo Endowment Fund for the Advancement of Science, the Estate of Olga Klein – Astrachan, the European Union’s Horizon 2020 research and innovation program under grant No. 646804-ERC-COG-BNYQ, and by the Israel Science Foundation under grant No. 0100101. P. Li and B. Wang are with the School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China (e-mail: {2017010211; bywang}@njupt.edu.cn) N. Shlezinger is with the School of ECE, Ben-Gurion University of the Negev, Beer-Sheva, Israel (e-mail: nirshlezinger1@gmail.com) H. Zhang and Y. C. Eldar are with the Faculty of Math and CS, Weizmann Institute of Science, Rehovot, Israel (e-mail: {haiyang.zhang; yonina.eldar}@weizmann.ac.il).
Publisher Copyright:
©2021 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
KW - Graph signal compression
KW - Task-based quantization
UR - http://www.scopus.com/inward/record.url?scp=85115141780&partnerID=8YFLogxK
U2 - 10.1109/ICASSP39728.2021.9414657
DO - 10.1109/ICASSP39728.2021.9414657
M3 - Conference article
AN - SCOPUS:85115141780
SN - 1520-6149
VL - 2021-June
SP - 5514
EP - 5518
JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
T2 - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021
Y2 - 6 June 2021 through 11 June 2021
ER -