7,616 research outputs found
Multi-View Video Packet Scheduling
In multiview applications, multiple cameras acquire the same scene from
different viewpoints and generally produce correlated video streams. This
results in large amounts of highly redundant data. In order to save resources,
it is critical to handle properly this correlation during encoding and
transmission of the multiview data. In this work, we propose a
correlation-aware packet scheduling algorithm for multi-camera networks, where
information from all cameras are transmitted over a bottleneck channel to
clients that reconstruct the multiview images. The scheduling algorithm relies
on a new rate-distortion model that captures the importance of each view in the
scene reconstruction. We propose a problem formulation for the optimization of
the packet scheduling policies, which adapt to variations in the scene content.
Then, we design a low complexity scheduling algorithm based on a trellis search
that selects the subset of candidate packets to be transmitted towards
effective multiview reconstruction at clients. Extensive simulation results
confirm the gain of our scheduling algorithm when inter-source correlation
information is used in the scheduler, compared to scheduling policies with no
information about the correlation or non-adaptive scheduling policies. We
finally show that increasing the optimization horizon in the packet scheduling
algorithm improves the transmission performance, especially in scenarios where
the level of correlation rapidly varies with time
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
Lossy compression of discrete sources via Viterbi algorithm
We present a new lossy compressor for discrete-valued sources. For coding a
sequence , the encoder starts by assigning a certain cost to each possible
reconstruction sequence. It then finds the one that minimizes this cost and
describes it losslessly to the decoder via a universal lossless compressor. The
cost of each sequence is a linear combination of its distance from the sequence
and a linear function of its order empirical distribution.
The structure of the cost function allows the encoder to employ the Viterbi
algorithm to recover the minimizer of the cost. We identify a choice of the
coefficients comprising the linear function of the empirical distribution used
in the cost function which ensures that the algorithm universally achieves the
optimum rate-distortion performance of any stationary ergodic source in the
limit of large , provided that diverges as . Iterative
techniques for approximating the coefficients, which alleviate the
computational burden of finding the optimal coefficients, are proposed and
studied.Comment: 26 pages, 6 figures, Submitted to IEEE Transactions on Information
Theor
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