30,408 research outputs found
Distributed Quantization for Compressed Sensing
We study distributed coding of compressed sensing (CS) measurements using
vector quantizer (VQ). We develop a distributed framework for realizing
optimized quantizer that enables encoding CS measurements of correlated sparse
sources followed by joint decoding at a fusion center. The optimality of VQ
encoder-decoder pairs is addressed by minimizing the sum of mean-square errors
between the sparse sources and their reconstruction vectors at the fusion
center. We derive a lower-bound on the end-to-end performance of the studied
distributed system, and propose a practical encoder-decoder design through an
iterative algorithm.Comment: 5 Pages, Accepted for presentation in ICASSP 201
Nonlinear Information Bottleneck
Information bottleneck (IB) is a technique for extracting information in one
random variable that is relevant for predicting another random variable
. IB works by encoding in a compressed "bottleneck" random variable
from which can be accurately decoded. However, finding the optimal
bottleneck variable involves a difficult optimization problem, which until
recently has been considered for only two limited cases: discrete and
with small state spaces, and continuous and with a Gaussian joint
distribution (in which case optimal encoding and decoding maps are linear). We
propose a method for performing IB on arbitrarily-distributed discrete and/or
continuous and , while allowing for nonlinear encoding and decoding
maps. Our approach relies on a novel non-parametric upper bound for mutual
information. We describe how to implement our method using neural networks. We
then show that it achieves better performance than the recently-proposed
"variational IB" method on several real-world datasets
LDMIC: Learning-based Distributed Multi-view Image Coding
Multi-view image compression plays a critical role in 3D-related
applications. Existing methods adopt a predictive coding architecture, which
requires joint encoding to compress the corresponding disparity as well as
residual information. This demands collaboration among cameras and enforces the
epipolar geometric constraint between different views, which makes it
challenging to deploy these methods in distributed camera systems with randomly
overlapping fields of view. Meanwhile, distributed source coding theory
indicates that efficient data compression of correlated sources can be achieved
by independent encoding and joint decoding, which motivates us to design a
learning-based distributed multi-view image coding (LDMIC) framework. With
independent encoders, LDMIC introduces a simple yet effective joint context
transfer module based on the cross-attention mechanism at the decoder to
effectively capture the global inter-view correlations, which is insensitive to
the geometric relationships between images. Experimental results show that
LDMIC significantly outperforms both traditional and learning-based MIC methods
while enjoying fast encoding speed. Code will be released at
https://github.com/Xinjie-Q/LDMIC.Comment: Accepted by ICLR 202
Low-Complexity Approaches to Slepian–Wolf Near-Lossless Distributed Data Compression
This paper discusses the Slepian–Wolf problem of distributed near-lossless compression of correlated sources. We introduce practical new tools for communicating at all rates in the achievable region. The technique employs a simple “source-splitting” strategy that does not require common sources of randomness at the encoders and decoders. This approach allows for pipelined encoding and decoding so that the system operates with the complexity of a single user encoder and decoder. Moreover, when this splitting approach is used in conjunction with iterative decoding methods, it produces a significant simplification of the decoding process. We demonstrate this approach for synthetically generated data. Finally, we consider the Slepian–Wolf problem when linear codes are used as syndrome-formers and consider a linear programming relaxation to maximum-likelihood (ML) sequence decoding. We note that the fractional vertices of the relaxed polytope compete with the optimal solution in a manner analogous to that observed when the “min-sum” iterative decoding algorithm is applied. This relaxation exhibits the ML-certificate property: if an integral solution is found, it is the ML solution. For symmetric binary joint distributions, we show that selecting easily constructable “expander”-style low-density parity check codes (LDPCs) as syndrome-formers admits a positive error exponent and therefore provably good performance
Joint Reconstruction of Multi-view Compressed Images
The distributed representation of correlated multi-view images is an
important problem that arise in vision sensor networks. This paper concentrates
on the joint reconstruction problem where the distributively compressed
correlated images are jointly decoded in order to improve the reconstruction
quality of all the compressed images. We consider a scenario where the images
captured at different viewpoints are encoded independently using common coding
solutions (e.g., JPEG, H.264 intra) with a balanced rate distribution among
different cameras. A central decoder first estimates the underlying correlation
model from the independently compressed images which will be used for the joint
signal recovery. The joint reconstruction is then cast as a constrained convex
optimization problem that reconstructs total-variation (TV) smooth images that
comply with the estimated correlation model. At the same time, we add
constraints that force the reconstructed images to be consistent with their
compressed versions. We show by experiments that the proposed joint
reconstruction scheme outperforms independent reconstruction in terms of image
quality, for a given target bit rate. In addition, the decoding performance of
our proposed algorithm compares advantageously to state-of-the-art distributed
coding schemes based on disparity learning and on the DISCOVER
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
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