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Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks

Platform : Image Processing

IEEE Projects Years : 2012

Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks

Abstract:
This article1 presents the design of a networked system for joint
compression, rate control and error correction of video over resource-constrained
embedded devices based on the theory of compressed sensing. The objective of
this work is to design a cross-layer system that jointly controls the video encoding
rate, the transmission rate, and the channel coding rate to maximize the received
video quality. First, compressed sensing based video encoding for transmission
over wireless multimedia sensor networks (WMSNs) is studied. It is shown that
compressed sensing can overcome many of the current problems of video over
WMSNs, primarily encoder complexity and low resiliency to channel errors. A rate
controller is then developed with the objective of maintaining fairness among
video streams while maximizing the received video quality. It is shown that the
rate of compressed sensed video can be predictably controlled by varying only the
compressed sensing sampling rate. It is then shown that the developed rate
controller can be interpreted as the iterative solution to a convex optimization
problem representing the optimization of the rate allocation across the network.
The error resiliency properties of compressed sensed images and videos are then
studied, and an optimal error detection and correction scheme is presented for
video transmission over lossy channels.
Architecture:
Existing System:
In existing layered protocol stacks based on the IEEE
802.11 and 802.15.4 standards, frames are split into multiple packets. If even a
single bit is flipped due to channel errors, after a cyclic redundancy check, the
entire packet is dropped at a final or intermediate receiver. This can cause the
video decoder to be unable to decode an independently coded (I) frame, thus
leading to loss of the entire sequence of video frames.
Disadvantages:
Instead, ideally, when one bit is in error, the effect on the
reconstructed video should be unperceivable, with minimal overhead. In addition,
the perceived video quality should gracefully and proportionally degrade with
decreasing channel quality.
Proposed System:
With the proposed controller, nodes adapt the rate of change
of their transmitted video quality based on an estimate of the impact that a change
in the transmission rate will have on the received video quality. While the
proposed method is general, it works particularly well for security videos. In
addition, all of these techniques require that the encoder has access to the entire
video frame (or even multiple frames) before encoding the video.
Advantages:
The proposed CSV encoder is designed to: i) encode video at
low complexity for the encoder; ii) take advantage of the temporal correlation
between frames.
Modules:-
1. CS Video Encoder (CSV)
The CSV video encoder uses compressed sensing to encode video by
exploiting the spatial and temporal redundancy within the individual
frames and between adjacent frames, respectively.
Sensing the channel : those that have the cost of sensing channel have
higher energy consumption and so they are not suitable for WMSNs.
Using extra packets: Using retransmission time of dropped packets
includes not only retransmission request but also transmission of dropped
packet. These methods waste a great amount of energy for congestion
detection in sensor nodes.
Low cost: Some methods do not necessitate extra cost for congestion
detection. These methods are the most suitable for congestion detection in
WMSNs.
2. Rate Change Aggressiveness Based on Video
Quality:
With the proposed controller, nodes adapt the rate of change of
their transmitted video quality based on an estimate of the impact that a change in
the transmission rate will have on the received video quality. The rate controller
Uses the information about the estimated received video quality directly in the rate
control decision. If the sending node estimates that the received video quality is
high, and round trip time measurements indicate that current network congestion
condition would allow a rate increase, the node will increase the rate less
aggressively than a node estimating lower video quality and the same round trip
time. Conversely, if a node is sending low quality video, it will gracefully decrease
its data rate, even if the RT T indicates a congested network. This is obtained by
basing the rate control decision on the marginal distortion factor, i.e., a measure of
the effect of a rate change on video distortion.
3. Video Transmission Using Compressed Sensing:
We develop a video encoder
based on compressed sensing. We show that, by using the difference between the
CS Samples of two frames, we can capture and compress the frames based on the
temporal correlation at low complexity without using motion vectors.
4. Adaptive Parity-Based Transmission:
For a fixed number of bits per frame, the
perceptual quality of video streams can be further improved by dropping error
samples that would contribute to image reconstruction with incorrect information.
Which shows the reconstructed image quality both with and without including
samples containing errors? It assume that the receiver knows which samples have
errors, they demonstrate that there is a very large possible gain in received image
quality if those samples containing errors can be removed.
We studied adaptive parity with compressed sensing for image
transmission, where we showed that since the transmitted samples constitute an
unstructured, random, incoherent combination of the original image pixels, in CS,
unlike traditional wireless imaging systems, no individual sample is more
important for image reconstruction than any other sample. Instead, the number of
correctly received samples is the only main factor in determining the quality of the
received image.
System Configuration:
Hardware Required:
 System : Pentium IV 2.4 GHz
 Hard Disk : 40 GB
 Floppy Drive : 1.44 MB
 Monitor : 15 VGA color
 Mouse : Logitech
 Keyboard : 110 Keys enhanced
 RAM : 512MB
Software Required:
 O/S : Windows XP.
 Language : C#.Net


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