Multiple-model coding scheme for electrical signal compression

This paper proposes a low-latency Multiple-Model Coding (MMC) approach to compress sampled electrical signal waveforms under encoding rate constraints. The approach is window-based. Several parametric waveform models are put in competition to obtain a first coarse representation of the signal in each considered window. Then, different residual compression techniques are compared to minimize the residual reconstruction error. The model parameters are quantized, and the allocation of the rate budget among the two steps is optimized. Simulation results show that the proposed MMC approach achieves a higher signal-to-noise ratio than state-of-the-art solutions on periodic and transients signal waveforms.

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Publication Details

Journal
Signal Processing
Published
2025-04-18
DOI
https://doi.org/10.1016/j.sigpro.2025.110037
Primary Topic
Advanced Data Compression Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Multiple-model coding scheme for electrical signal compression

Zuxing Li, Patrick Panciatici, Thibault Prévost, Pablo Piantanida et al.
Signal Processing
Advanced Data Compression Techniques
article

Multiple-model coding scheme for electrical signal compression

Zuxing Li, Patrick Panciatici, Thibault Prévost, Pablo Piantanida, Michel Kieffer, Corentin Presvôts
article en

Abstract

This paper proposes a low-latency Multiple-Model Coding (MMC) approach to compress sampled electrical signal waveforms under encoding rate constraints. The approach is window-based. Several parametric waveform models are put in competition to obtain a first coarse representation of the signal in each considered window. Then, different residual compression techniques are compared to minimize the residual reconstruction error. The model parameters are quantized, and the allocation of the rate budget among the two steps is optimized. Simulation results show that the proposed MMC approach achieves a higher signal-to-noise ratio than state-of-the-art solutions on periodic and transients signal waveforms.

Signal ProcessingVol. 236
Centre National de la Recherche Scientifique (FR), Université Paris-Saclay (FR), CentraleSupélec (FR), Électricité de France (France) (FR)
Association Nationale de la Recherche et de la Technologie
Affordable and clean energy
Openalex Percentile: Top 94%
Advanced Data Compression Techniques
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Multiple-model coding scheme for electrical signal compression — Zuxing Li, Patrick Panciatici, et al. · Signal Processing (2025) | TGRS Research Map | TGRS