| Paper ID | SPTM-20.3 |
| Paper Title |
Graph Signal Compression via Task-Based Quantization |
| Authors |
Pei Li, Nanjing University of Posts and Telecommunivations, China; Nir Shlezinger, Ben-Gurion University of the Negev, Israel; Haiyang Zhang, Weizmann Institute of Science, Israel; Baoyun Wang, Nanjing University of Posts and Telecommunications, China; Yonina C. Eldar, Weizmann Institute of Science, Israel |
| Session | SPTM-20: Signal Processing over Graphs and Sparsity-Aware Signal Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SIPG] Signal and Information Processing over Graphs |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 continuous-amplitude 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. |
