Fundamental Distortion Limits of Analog-to-Digital Compression
Alon Kipnis, Yonina C. Eldar, Andrea J. Goldsmith

TL;DR
This paper characterizes the fundamental limits of analog-to-digital compression, revealing optimal sampling frequencies below Nyquist rate for minimal distortion under bitrate constraints, extending classical sampling theory.
Contribution
It provides a complete characterization of minimal distortion in combined sampling and lossy compression, including critical sampling rates below Nyquist for non-uniform spectral signals.
Findings
Existence of a critical sampling frequency below Nyquist rate for minimal distortion.
Minimal distortion depends on signal spectrum, sampling frequency, and bitrate.
Comparison shows fundamental limits outperform traditional PCM in certain regimes.
Abstract
Representing a continuous-time signal by a set of samples is a classical problem in signal processing. We study this problem under the additional constraint that the samples are quantized or compressed in a lossy manner under a limited bitrate budget. To this end, we consider a combined sampling and source coding problem in which an analog stationary Gaussian signal is reconstructed from its encoded samples. These samples are obtained by a set of bounded linear functionals of the continuous-time path, with a limitation on the average number of samples obtained per unit time available in this setting. We provide a full characterization of the minimal distortion in terms of the sampling frequency, the bitrate, and the signal's spectrum. Assuming that the signal's energy is not uniformly distributed over its spectral support, we show that for each compression bitrate there exists a…
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