Joint Lossy Compression for a Vector Gaussian Source under Individual Distortion Criteria

TL;DR

This paper advances the understanding of joint lossy compression for vector Gaussian sources by analyzing cases where the semidefinite condition is not satisfied, providing new theoretical limits and explicit rate-distortion functions that incorporate correlations.

Contribution

It refines existing results under the semidefinite condition and derives new bounds and explicit rate-distortion functions for cases without it, highlighting the role of correlations in compression efficiency.

Findings

Lower-dimensional reconstructions are necessary when SDC is not satisfied.

Probability of SDC satisfaction decays exponentially with source length.

Explicit RDF incorporating correlations quantifies compression gains.

Abstract

This paper investigates the joint compression problem of a vector Gaussian source, where an individual distortion constraint is imposed on each source component. It is known that the rate-distortion function (RDF) is lower-bounded by the rate derived from the Hadamard inequality, which becomes exact when the semidefinite condition (SDC) holds. However, existing works often overlook the case where the SDC is not satisfied. Moreover, even when the SDC holds, a quantitative characterization of how correlations enable more efficient compression is lacking. In this work, we refine the results when the SDC is satisfied and derive new theoretical results when the SDC is not satisfied, thereby establishing theoretical limits for practical source compression with correlations. Specifically, we examine the properties of optimal source reconstruction and provide upper bounds on its dimension, showing that lower-dimensional reconstructions are essential for efficient compression when the SDC does not hold. Within a scalable two-type correlation (2TC) covariance framework, we prove that the probability of satisfying the SDC decays exponentially with source length, emphasizing the importance of exploring theoretical limits when the SDC is not met. Additional, we determine the component-wise correlations that a vector source should possess to achieve the Hadamard compression rate, revealing the trade-off between distortion constraints and correlations. More importantly, by deriving an explicit RDF with correlations incorporated, we quantitatively characterize the gain in compression efficiency achieved by fully leveraging source correlations.