Reduced-Complexity Model Selection and Rate Allocation for Multiple-Model Electrical Signal Compression

TL;DR

This paper introduces a reduced-complexity multiple-model coding approach for electrical signal compression that optimizes model selection and rate allocation to meet quality constraints more efficiently than existing methods.

Contribution

It proposes a joint optimization framework for model and rate selection in MMC, including three approaches with reduced complexity, improving compression efficiency under quality constraints.

Findings

Reduces rate for a given distortion compared to state-of-the-art methods.

Employs Golden Section search for efficient rate determination.

Uses rate-distortion models to select promising models and rates.

Abstract

This paper adapts a Multiple-Model Coding (MMC) approach for sampled electrical signal waveforms to satisfy reconstructed signal quality constraints. The baseline MMC approach consists of two stages processing vectors of Voltage and Current Signal (VCS) of constant size and producing bitstreams of constant rate but varying quality. In the proposed approach, the parametric model and the rate allocated to the first stage, as well as the residual compression method of the second stage and its associated rate, are jointly optimized to achieve a target distortion of the reconstructed signal. Three approaches are proposed. An exhaustive search serves as a baseline for comparison. Then, an approach involving a Golden Section search is exploited to determine the rate of the first stage with reduced complexity. Finally, rate-distortion models of the compression efficiency for each model in the first stage are employed to obtain a subset of promising models in the first stage and reduced-size search intervals for the rate selection in both stages. Simulation results demonstrate that the proposed reduced-complexity MMC approach reduces the rate for a given distortion constraint compared to state-of-the-art solutions for VCS with equivalent complexity.