158 research outputs found
Distributed Successive Approximation Coding using Broadcast Advantage: The Two-Encoder Case
Traditional distributed source coding rarely considers the possible link
between separate encoders. However, the broadcast nature of wireless
communication in sensor networks provides a free gossip mechanism which can be
used to simplify encoding/decoding and reduce transmission power. Using this
broadcast advantage, we present a new two-encoder scheme which imitates the
ping-pong game and has a successive approximation structure. For the quadratic
Gaussian case, we prove that this scheme is successively refinable on the
{sum-rate, distortion pair} surface, which is characterized by the
rate-distortion region of the distributed two-encoder source coding. A
potential energy saving over conventional distributed coding is also
illustrated. This ping-pong distributed coding idea can be extended to the
multiple encoder case and provides the theoretical foundation for a new class
of distributed image coding method in wireless scenarios.Comment: In Proceedings of the 48th Annual Allerton Conference on
Communication, Control and Computing, University of Illinois, Monticello, IL,
September 29 - October 1, 201
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
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
Wyner-Ziv coding based on TCQ and LDPC codes and extensions to multiterminal source coding
Driven by a host of emerging applications (e.g., sensor networks and wireless
video), distributed source coding (i.e., Slepian-Wolf coding, Wyner-Ziv coding and
various other forms of multiterminal source coding), has recently become a very active
research area.
In this thesis, we first design a practical coding scheme for the quadratic Gaussian
Wyner-Ziv problem, because in this special case, no rate loss is suffered due to
the unavailability of the side information at the encoder. In order to approach the
Wyner-Ziv distortion limit D??W Z(R), the trellis coded quantization (TCQ) technique
is employed to quantize the source X, and irregular LDPC code is used to implement
Slepian-Wolf coding of the quantized source input Q(X) given the side information
Y at the decoder. An optimal non-linear estimator is devised at the joint decoder
to compute the conditional mean of the source X given the dequantized version of
Q(X) and the side information Y . Assuming ideal Slepian-Wolf coding, our scheme
performs only 0.2 dB away from the Wyner-Ziv limit D??W Z(R) at high rate, which
mirrors the performance of entropy-coded TCQ in classic source coding. Practical
designs perform 0.83 dB away from D??W Z(R) at medium rates. With 2-D trellis-coded
vector quantization, the performance gap to D??W Z(R) is only 0.66 dB at 1.0 b/s and
0.47 dB at 3.3 b/s.
We then extend the proposed Wyner-Ziv coding scheme to the quadratic Gaussian
multiterminal source coding problem with two encoders. Both direct and indirect
settings of multiterminal source coding are considered. An asymmetric code design
containing one classical source coding component and one Wyner-Ziv coding component
is first introduced and shown to be able to approach the corner points on the
theoretically achievable limits in both settings. To approach any point on the theoretically
achievable limits, a second approach based on source splitting is then described.
One classical source coding component, two Wyner-Ziv coding components, and a
linear estimator are employed in this design. Proofs are provided to show the achievability
of any point on the theoretical limits in both settings by assuming that both
the source coding and the Wyner-Ziv coding components are optimal. The performance
of practical schemes is only 0.15 b/s away from the theoretical limits for the
asymmetric approach, and up to 0.30 b/s away from the limits for the source splitting
approach
Improved Modeling of the Correlation Between Continuous-Valued Sources in LDPC-Based DSC
Accurate modeling of the correlation between the sources plays a crucial role
in the efficiency of distributed source coding (DSC) systems. This correlation
is commonly modeled in the binary domain by using a single binary symmetric
channel (BSC), both for binary and continuous-valued sources. We show that
"one" BSC cannot accurately capture the correlation between continuous-valued
sources; a more accurate model requires "multiple" BSCs, as many as the number
of bits used to represent each sample. We incorporate this new model into the
DSC system that uses low-density parity-check (LDPC) codes for compression. The
standard Slepian-Wolf LDPC decoder requires a slight modification so that the
parameters of all BSCs are integrated in the log-likelihood ratios (LLRs).
Further, using an interleaver the data belonging to different bit-planes are
shuffled to introduce randomness in the binary domain. The new system has the
same complexity and delay as the standard one. Simulation results prove the
effectiveness of the proposed model and system.Comment: 5 Pages, 4 figures; presented at the Asilomar Conference on Signals,
Systems, and Computers, Pacific Grove, CA, November 201
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