System-Aware Compression

TL;DR

This paper introduces a system-aware compression method that optimizes rate-distortion performance by incorporating system structure, using ADMM to adapt standard compression techniques for improved efficiency in noisy, system-influenced signals.

Contribution

It proposes a novel compression framework that considers system layout and noise, enabling globally optimized compression through a system-adjusted signal approach.

Findings

Significant gains in signal and video compression performance.

Theoretical validation for Gaussian signals.

Effective adaptation of standard compression methods to system-aware scenarios.

Abstract

Many information systems employ lossy compression as a crucial intermediate stage among other processing components. While the important distortion is defined by the system's input and output signals, the compression usually ignores the system structure, therefore, leading to an overall sub-optimal rate-distortion performance. In this paper we propose a compression methodology for an operational rate-distortion optimization considering a known system layout, modeled using linear operators and noise. Using the alternating direction method of multipliers (ADMM) technique, we show that the design of the new globally-optimized compression reduces to a standard compression of a "system adjusted" signal. Essentially, the proposed framework leverages standard compression techniques to address practical settings of the noisy source coding problem. We further explain the main ideas of our method by theoretically studying the case of a cyclo-stationary Gaussian signal. We present experimental results for coding of one-dimensional signals and for video compression using the HEVC standard, showing significant gains by the adjustment to an acquisition-rendering system.