200 research outputs found
On practical design for joint distributed source and network coding
This paper considers the problem of communicating correlated information from multiple source nodes over a network of noiseless channels to multiple destination nodes, where each destination node wants to recover all sources. The problem involves a joint consideration of distributed compression and network information relaying. Although the optimal rate region has been theoretically characterized, it was not clear how to design practical communication schemes with low complexity. This work provides a partial solution to this problem by proposing a low-complexity scheme for the special case with two sources whose correlation is characterized by a binary symmetric channel. Our scheme is based on a careful combination of linear syndrome-based Slepian-Wolf coding and random linear mixing (network coding). It is in general suboptimal; however, its low complexity and robustness to network dynamics make it suitable for practical implementation
Iterative Slepian-Wolf Decoding and FEC Decoding for Compress-and-Forward Systems
While many studies have concentrated on providing theoretical analysis for the relay assisted compress-and-forward systems little effort has yet been made to the construction and evaluation of a practical system. In this paper a practical CF system incorporating an error-resilient multilevel Slepian-Wolf decoder is introduced and a novel iterative processing structure which allows information exchanging between the Slepian-Wolf decoder and the forward error correction decoder of the main source message is proposed. In addition, a new quantization scheme is incorporated as well to avoid the complexity of the reconstruction of the relay signal at the final decoder of the destination. The results demonstrate that the iterative structure not only reduces the decoding loss of the Slepian-Wolf decoder, it also improves the decoding performance of the main message from the source
REGION-BASED ADAPTIVE DISTRIBUTED VIDEO CODING CODEC
The recently developed Distributed Video Coding (DVC) is typically suitable for the
applications where the conventional video coding is not feasible because of its
inherent high-complexity encoding. Examples include video surveillance usmg
wireless/wired video sensor network and applications using mobile cameras etc. With
DVC, the complexity is shifted from the encoder to the decoder.
The practical application of DVC is referred to as Wyner-Ziv video coding (WZ)
where an estimate of the original frame called "side information" is generated using
motion compensation at the decoder. The compression is achieved by sending only
that extra information that is needed to correct this estimation. An error-correcting
code is used with the assumption that the estimate is a noisy version of the original
frame and the rate needed is certain amount of the parity bits. The side information is
assumed to have become available at the decoder through a virtual channel. Due to
the limitation of compensation method, the predicted frame, or the side information, is
expected to have varying degrees of success. These limitations stem from locationspecific
non-stationary estimation noise. In order to avoid these, the conventional
video coders, like MPEG, make use of frame partitioning to allocate optimum coder
for each partition and hence achieve better rate-distortion performance. The same,
however, has not been used in DVC as it increases the encoder complexity.
This work proposes partitioning the considered frame into many coding units
(region) where each unit is encoded differently. This partitioning is, however, done at
the decoder while generating the side-information and the region map is sent over to
encoder at very little rate penalty. The partitioning allows allocation of appropriate
DVC coding parameters (virtual channel, rate, and quantizer) to each region. The
resulting regions map is compressed by employing quadtree algorithm and
communicated to the encoder via the feedback channel. The rate control in DVC is
performed by channel coding techniques (turbo codes, LDPC, etc.). The performance
of the channel code depends heavily on the accuracy of virtual channel model that models estimation error for each region. In this work, a turbo code has been used and
an adaptive WZ DVC is designed both in transform domain and in pixel domain. The
transform domain WZ video coding (TDWZ) has distinct superior performance as
compared to the normal Pixel Domain Wyner-Ziv (PDWZ), since it exploits the
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spatial redundancy during the encoding. The performance evaluations show that the
proposed system is superior to the existing distributed video coding solutions.
Although the, proposed system requires extra bits representing the "regions map" to be
transmitted, fuut still the rate gain is noticeable and it outperforms the state-of-the-art
frame based DVC by 0.6-1.9 dB.
The feedback channel (FC) has the role to adapt the bit rate to the changing
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statistics between the side infonmation and the frame to be encoded. In the
unidirectional scenario, the encoder must perform the rate control. To correctly
estimate the rate, the encoder must calculate typical side information. However, the
rate cannot be exactly calculated at the encoder, instead it can only be estimated. This
work also prbposes a feedback-free region-based adaptive DVC solution in pixel
domain based on machine learning approach to estimate the side information.
Although the performance evaluations show rate-penalty but it is acceptable
considering the simplicity of the proposed algorithm.
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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
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, SlepianâWolf and WynerâZiv. 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
Distributed Joint Source-Channel Coding in Wireless Sensor Networks
Considering the fact that sensors are energy-limited and the wireless channel conditions in wireless sensor networks, there is an urgent need for a low-complexity coding method with high compression ratio and noise-resisted features. This paper reviews the progress made in distributed joint source-channel coding which can address this issue. The main existing deployments, from the theory to practice, of distributed joint source-channel coding over the independent channels, the multiple access channels and the broadcast channels are introduced, respectively. To this end, we also present a practical scheme for compressing multiple correlated sources over the independent channels. The simulation results demonstrate the desired efficiency
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