33,425 research outputs found
Block coding for stationary Gaussian sources with memory under a square-error fidelity criterion
In this paper, we present a new version of the source coding theorem for the block coding of stationary Gaussian sources with memory under a square-error distortion criterion. For both time-discrete and time-continuous Gaussian sources, the average square-error distortion of the optimum block source code of rate R > R(D) is shown to decrease at least exponentially in block-length to D, where R(D) is the square-error criterion rate distortion function of the stationary Gaussian source with memory. In both cases, the exponent of convergence of average distortion is explicitly derived
Lossy Compression of Exponential and Laplacian Sources using Expansion Coding
A general method of source coding over expansion is proposed in this paper,
which enables one to reduce the problem of compressing an analog
(continuous-valued source) to a set of much simpler problems, compressing
discrete sources. Specifically, the focus is on lossy compression of
exponential and Laplacian sources, which is subsequently expanded using a
finite alphabet prior to being quantized. Due to decomposability property of
such sources, the resulting random variables post expansion are independent and
discrete. Thus, each of the expanded levels corresponds to an independent
discrete source coding problem, and the original problem is reduced to coding
over these parallel sources with a total distortion constraint. Any feasible
solution to the optimization problem is an achievable rate distortion pair of
the original continuous-valued source compression problem. Although finding the
solution to this optimization problem at every distortion is hard, we show that
our expansion coding scheme presents a good solution in the low distrotion
regime. Further, by adopting low-complexity codes designed for discrete source
coding, the total coding complexity can be tractable in practice.Comment: 8 pages, 3 figure
Characterization of Information Channels for Asymptotic Mean Stationarity and Stochastic Stability of Non-stationary/Unstable Linear Systems
Stabilization of non-stationary linear systems over noisy communication
channels is considered. Stochastically stable sources, and unstable but
noise-free or bounded-noise systems have been extensively studied in
information theory and control theory literature since 1970s, with a renewed
interest in the past decade. There have also been studies on non-causal and
causal coding of unstable/non-stationary linear Gaussian sources. In this
paper, tight necessary and sufficient conditions for stochastic stabilizability
of unstable (non-stationary) possibly multi-dimensional linear systems driven
by Gaussian noise over discrete channels (possibly with memory and feedback)
are presented. Stochastic stability notions include recurrence, asymptotic mean
stationarity and sample path ergodicity, and the existence of finite second
moments. Our constructive proof uses random-time state-dependent stochastic
drift criteria for stabilization of Markov chains. For asymptotic mean
stationarity (and thus sample path ergodicity), it is sufficient that the
capacity of a channel is (strictly) greater than the sum of the logarithms of
the unstable pole magnitudes for memoryless channels and a class of channels
with memory. This condition is also necessary under a mild technical condition.
Sufficient conditions for the existence of finite average second moments for
such systems driven by unbounded noise are provided.Comment: To appear in IEEE Transactions on Information Theor
A Universal Scheme for WynerâZiv Coding of Discrete Sources
We consider the WynerâZiv (WZ) problem of lossy compression where the decompressor observes a noisy version of the source, whose statistics are unknown. A new family of WZ coding algorithms is proposed and their universal optimality is proven. Compression consists of sliding-window processing followed by LempelâZiv (LZ) compression, while the decompressor is based on a modification of the discrete universal denoiser (DUDE) algorithm to take advantage of side information. The new algorithms not only universally attain the fundamental limits, but also suggest a paradigm for practical WZ coding. The effectiveness of our approach is illustrated with experiments on binary images, and English text using a low complexity algorithm motivated by our class of universally optimal WZ codes
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