Stationarity in the Realizations of the Causal Rate-Distortion Function for One-Sided Stationary Sources
Milan S. Derpich (1), Marco A. Guerrero (1), Jan {\O}stergaard (2), ((1) Department of Electronic Engineering, Universidad T\'ecnica Federico, Santa Mar\'ia, Chile, (2) Department of Electronic Systems, Aalborg, University, Denmark)

TL;DR
This paper investigates the causal rate-distortion function for one-sided stationary sources, revealing limitations of stationarity in realizations and providing new bounds and characterizations for practical encoding schemes.
Contribution
It extends the understanding of the causal IRDF for one-sided stationary sources, showing when stationarity can be assumed and improving the definition for two-sided sources.
Findings
Causal IRDF cannot generally be realized by a stationary distribution.
For large classes of distortion criteria, the search can be restricted to jointly stationary output sequences.
Practical zero-delay encoders approach the IRDF within approximately 1.254 bits per sample.
Abstract
This paper derives novel results on the characterization of the the causal information rate-distortion function (IRDF) for arbitrarily-distributed one-sided stationary -th order Markov source x(1),x(2),.... It is first shown that Gorbunov and Pinsker's results on the stationarity of the realizations to the causal IRDF (stated for two-sided stationary sources) do not apply to the commonly used family of asymptotic average single-letter (AASL) distortion criteria. Moreover, we show that, in general, a reconstruction sequence cannot be both jointly stationary with a one-sided stationary source sequence and causally related to it. This implies that, in general, the causal IRDF for one-sided stationary sources cannot be realized by a stationary distribution. However, we prove that for an arbitrarily distributed one-sided stationary source and a large class of…
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Taxonomy
TopicsWireless Communication Security Techniques · Distributed Sensor Networks and Detection Algorithms · Sparse and Compressive Sensing Techniques
