90 research outputs found
Rate-distortion function upper bounds for Gaussian vectors and their applications in coding AR sources
source coding; rate-distortion function (RDF); Gaussian vector; autoregressive (AR)
source; discrete Fourier transform (DFT
On the relation of nonanticipative rate distortion function and filtering theory
In this paper the relation between nonanticipative rate distortion function
(RDF) and Bayesian filtering theory is investigated using the topology of weak
convergence of probability measures on Polish spaces. The relation is
established via an optimization on the space of conditional distributions of
the so-called directed information subject to fidelity constraints. Existence
of the optimal reproduction distribution of the nonanticipative RDF is shown,
while the optimal nonanticipative reproduction conditional distribution for
stationary processes is derived in closed form. The realization procedure of
nonanticipative RDF which is equivalent to joint-source channel matching for
symbol-by-symbol transmission is described, while an example is introduced to
illustrate the concepts.Comment: 6 pages, 4 figures, final version submitted for publication at 12th
Biannual European Control Conference (ECC), 201
Distributed Remote Vector Gaussian Source Coding with Covariance Distortion Constraints
In this paper, we consider a distributed remote source coding problem, where
a sequence of observations of source vectors is available at the encoder. The
problem is to specify the optimal rate for encoding the observations subject to
a covariance matrix distortion constraint and in the presence of side
information at the decoder. For this problem, we derive lower and upper bounds
on the rate-distortion function (RDF) for the Gaussian case, which in general
do not coincide. We then provide some cases, where the RDF can be derived
exactly. We also show that previous results on specific instances of this
problem can be generalized using our results. We finally show that if the
distortion measure is the mean squared error, or if it is replaced by a certain
mutual information constraint, the optimal rate can be derived from our main
result.Comment: This is the final version accepted at ISIT'1
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