scholarly journals Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 213 ◽  
Author(s):  
Yizhong Wang ◽  
Li Xie ◽  
Siyao Zhou ◽  
Mengzhen Wang ◽  
Jun Chen
Keyword(s):  
Mean Squared Error ◽  
Source Coding ◽  
Rate Distortion ◽  
Asymptotic Rate ◽  
Squared Error ◽  
Simple Characterization ◽  
Distortion Functions ◽  
Distortion Analysis ◽  
Gaussian Source ◽  
Multivariate Gaussian

Consider a symmetric multivariate Gaussian source with ℓ components, which are corrupted by independent and identically distributed Gaussian noises; these noisy components are compressed at a certain rate, and the compressed version is leveraged to reconstruct the source subject to a mean squared error distortion constraint. The rate-distortion analysis is performed for two scenarios: centralized encoding (where the noisy source components are jointly compressed) and distributed encoding (where the noisy source components are separately compressed). It is shown, among other things, that the gap between the rate-distortion functions associated with these two scenarios admits a simple characterization in the large ℓ limit.

scholarly journals Distributed Recovery of a Gaussian Source in Interference with Successive Lattice Processing

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 845 ◽  
Author(s):  
Christian Chapman ◽  
Matthew Kinsinger ◽  
Ameya Agaskar ◽  
Daniel W. Bliss
Keyword(s):  
Upper Bound ◽  
Mean Squared Error ◽  
Source Coding ◽  
Power Reduction ◽  
Search Problem ◽  
Squared Error ◽  
Reduction Property ◽  
Gaussian Source ◽  
Parameter Search

A scheme for recovery of a signal by distributed listeners in the presence of Gaussian interference is constructed by exhausting an “iterative power reduction” property. An upper bound for the scheme’s achieved mean-squared-error distortion is derived. The strategy exposes a parameter search problem, which, when solved, causes the scheme to outperform others of its kind. Performance of a blocklength-one scheme is simulated and is seen to improve over plain source coding without compression in the presence of many interferers, and experiences less outages over ensembles of channels.

Perceptually Weighted Mean Squared Error Based Rate-Distortion Optimization for HEVC

2020 ◽  
Vol 66 (4) ◽  
pp. 824-834
Author(s):  
Xiuzhe Wu ◽  
Hanli Wang ◽  
Sudeng Hu ◽  
Sam Kwong ◽  
C.-C. Jay Kuo
Keyword(s):  
Mean Squared Error ◽  
Rate Distortion ◽  
Rate Distortion Optimization ◽  
Weighted Mean ◽  
Squared Error

scholarly journals Asymptotic Reverse Waterfilling Algorithm of NRDF for Certain Classes of Vector Gauss-Markov Processes

2020 ◽  
Author(s):  
Fotios Stavrou ◽  
Mikael Skoglund
Keyword(s):  
Markov Processes ◽  
Mean Squared Error ◽  
Iterative Scheme ◽  
Rate Distortion ◽  
Numerical Approach ◽  
Squared Error ◽  
Equivalent Characterization ◽  
Time Invariant ◽  
Waterfilling Algorithm

<div>In this paper, we revisit the asymptotic reverse-waterfilling characterization of the nonanticipative rate distortion</div><div>function (NRDF) derived for a time-invariant multidimensional Gauss-Markov processes with mean-squared error (MSE) distortion in [1]. We show that for certain classes of time-invariant multidimensional Gauss-Markov processes, the specific characterization behaves as a reverse-waterfilling algorithm obtained in matrix form ensuring that the numerical approach of [1, Algorithm 1] is optimal. In addition, we give an equivalent characterization that utilizes the eigenvalues of the involved matrices reminiscent of the well-known reverse-waterfilling algorithm in information theory. For the latter, we also propose a novel numerical approach to solve the algorithm optimally. The efficacy of our proposed iterative scheme compared to similar existing schemes is demonstrated via experiments. Finally, we use our new results to derive an analytical solution of the asymptotic NRDF for a correlated time-invariant two-dimensional Gauss-Markov process.</div>

scholarly journals Asymptotic Reverse Waterfilling Algorithm of NRDF for Certain Classes of Vector Gauss-Markov Processes

2020 ◽  
Author(s):  
Fotios Stavrou ◽  
Mikael Skoglund
Keyword(s):  
Markov Processes ◽  
Mean Squared Error ◽  
Iterative Scheme ◽  
Rate Distortion ◽  
Numerical Approach ◽  
Squared Error ◽  
Equivalent Characterization ◽  
Time Invariant ◽  
Waterfilling Algorithm

<div>In this paper, we revisit the asymptotic reverse-waterfilling characterization of the nonanticipative rate distortion</div><div>function (NRDF) derived for a time-invariant multidimensional Gauss-Markov processes with mean-squared error (MSE) distortion in [1]. We show that for certain classes of time-invariant multidimensional Gauss-Markov processes, the specific characterization behaves as a reverse-waterfilling algorithm obtained in matrix form ensuring that the numerical approach of [1, Algorithm 1] is optimal. In addition, we give an equivalent characterization that utilizes the eigenvalues of the involved matrices reminiscent of the well-known reverse-waterfilling algorithm in information theory. For the latter, we also propose a novel numerical approach to solve the algorithm optimally. The efficacy of our proposed iterative scheme compared to similar existing schemes is demonstrated via experiments. Finally, we use our new results to derive an analytical solution of the asymptotic NRDF for a correlated time-invariant two-dimensional Gauss-Markov process.</div>

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