6,750 research outputs found
Source Coding in Networks with Covariance Distortion Constraints
We consider a source coding problem with a network scenario in mind, and
formulate it as a remote vector Gaussian Wyner-Ziv problem under covariance
matrix distortions. We define a notion of minimum for two positive-definite
matrices based on which we derive an explicit formula for the rate-distortion
function (RDF). We then study the special cases and applications of this
result. We show that two well-studied source coding problems, i.e. remote
vector Gaussian Wyner-Ziv problems with mean-squared error and mutual
information constraints are in fact special cases of our results. Finally, we
apply our results to a joint source coding and denoising problem. We consider a
network with a centralized topology and a given weighted sum-rate constraint,
where the received signals at the center are to be fused to maximize the output
SNR while enforcing no linear distortion. We show that one can design the
distortion matrices at the nodes in order to maximize the output SNR at the
fusion center. We thereby bridge between denoising and source coding within
this setup
Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
This paper addresses the problem of distributed coding of images whose
correlation is driven by the motion of objects or positioning of the vision
sensors. It concentrates on the problem where images are encoded with
compressed linear measurements. We propose a geometry-based correlation model
in order to describe the common information in pairs of images. We assume that
the constitutive components of natural images can be captured by visual
features that undergo local transformations (e.g., translation) in different
images. We first identify prominent visual features by computing a sparse
approximation of a reference image with a dictionary of geometric basis
functions. We then pose a regularized optimization problem to estimate the
corresponding features in correlated images given by quantized linear
measurements. The estimated features have to comply with the compressed
information and to represent consistent transformation between images. The
correlation model is given by the relative geometric transformations between
corresponding features. We then propose an efficient joint decoding algorithm
that estimates the compressed images such that they stay consistent with both
the quantized measurements and the correlation model. Experimental results show
that the proposed algorithm effectively estimates the correlation between
images in multi-view datasets. In addition, the proposed algorithm provides
effective decoding performance that compares advantageously to independent
coding solutions as well as state-of-the-art distributed coding schemes based
on disparity learning
Analysis-by-Synthesis-based Quantization of Compressed Sensing Measurements
We consider a resource-constrained scenario where a compressed sensing- (CS)
based sensor has a low number of measurements which are quantized at a low rate
followed by transmission or storage. Applying this scenario, we develop a new
quantizer design which aims to attain a high-quality reconstruction performance
of a sparse source signal based on analysis-by-synthesis framework. Through
simulations, we compare the performance of the proposed quantization algorithm
vis-a-vis existing quantization methods.Comment: 5 pages, Published in ICASSP 201
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