16,304 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
Statistical mechanical analysis of a hierarchical random code ensemble in signal processing
We study a random code ensemble with a hierarchical structure, which is
closely related to the generalized random energy model with discrete energy
values. Based on this correspondence, we analyze the hierarchical random code
ensemble by using the replica method in two situations: lossy data compression
and channel coding. For both the situations, the exponents of large deviation
analysis characterizing the performance of the ensemble, the distortion rate of
lossy data compression and the error exponent of channel coding in Gallager's
formalism, are accessible by a generating function of the generalized random
energy model. We discuss that the transitions of those exponents observed in
the preceding work can be interpreted as phase transitions with respect to the
replica number. We also show that the replica symmetry breaking plays an
essential role in these transitions.Comment: 24 pages, 4 figure
Scalable wavelet-based coding of irregular meshes with interactive region-of-interest support
This paper proposes a novel functionality in wavelet-based irregular mesh coding, which is interactive region-of-interest (ROI) support. The proposed approach enables the user to define the arbitrary ROIs at the decoder side and to prioritize and decode these regions at arbitrarily high-granularity levels. In this context, a novel adaptive wavelet transform for irregular meshes is proposed, which enables: 1) varying the resolution across the surface at arbitrarily fine-granularity levels and 2) dynamic tiling, which adapts the tile sizes to the local sampling densities at each resolution level. The proposed tiling approach enables a rate-distortion-optimal distribution of rate across spatial regions. When limiting the highest resolution ROI to the visible regions, the fine granularity of the proposed adaptive wavelet transform reduces the required amount of graphics memory by up to 50%. Furthermore, the required graphics memory for an arbitrary small ROI becomes negligible compared to rendering without ROI support, independent of any tiling decisions. Random access is provided by a novel dynamic tiling approach, which proves to be particularly beneficial for large models of over 10(6) similar to 10(7) vertices. The experiments show that the dynamic tiling introduces a limited lossless rate penalty compared to an equivalent codec without ROI support. Additionally, rate savings up to 85% are observed while decoding ROIs of tens of thousands of vertices
Rate-Distortion Analysis of Multiview Coding in a DIBR Framework
Depth image based rendering techniques for multiview applications have been
recently introduced for efficient view generation at arbitrary camera
positions. Encoding rate control has thus to consider both texture and depth
data. Due to different structures of depth and texture images and their
different roles on the rendered views, distributing the available bit budget
between them however requires a careful analysis. Information loss due to
texture coding affects the value of pixels in synthesized views while errors in
depth information lead to shift in objects or unexpected patterns at their
boundaries. In this paper, we address the problem of efficient bit allocation
between textures and depth data of multiview video sequences. We adopt a
rate-distortion framework based on a simplified model of depth and texture
images. Our model preserves the main features of depth and texture images.
Unlike most recent solutions, our method permits to avoid rendering at encoding
time for distortion estimation so that the encoding complexity is not
augmented. In addition to this, our model is independent of the underlying
inpainting method that is used at decoder. Experiments confirm our theoretical
results and the efficiency of our rate allocation strategy
Energy Management Policies for Energy-Neutral Source-Channel Coding
In cyber-physical systems where sensors measure the temporal evolution of a
given phenomenon of interest and radio communication takes place over short
distances, the energy spent for source acquisition and compression may be
comparable with that used for transmission. Additionally, in order to avoid
limited lifetime issues, sensors may be powered via energy harvesting and thus
collect all the energy they need from the environment. This work addresses the
problem of energy allocation over source acquisition/compression and
transmission for energy-harvesting sensors. At first, focusing on a
single-sensor, energy management policies are identified that guarantee a
maximal average distortion while at the same time ensuring the stability of the
queue connecting source and channel encoders. It is shown that the identified
class of policies is optimal in the sense that it stabilizes the queue whenever
this is feasible by any other technique that satisfies the same average
distortion constraint. Moreover, this class of policies performs an independent
resource optimization for the source and channel encoders. Analog transmission
techniques as well as suboptimal strategies that do not use the energy buffer
(battery) or use it only for adapting either source or channel encoder energy
allocation are also studied for performance comparison. The problem of
optimizing the desired trade-off between average distortion and delay is then
formulated and solved via dynamic programming tools. Finally, a system with
multiple sensors is considered and time-division scheduling strategies are
derived that are able to maintain the stability of all data queues and to meet
the average distortion constraints at all sensors whenever it is feasible.Comment: Submitted to IEEE Transactions on Communications in March 2011; last
update in July 201
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