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Pre-fetching based on video
analysis for interactive region-ofinterest streaming of soccer
sequences
Authors:
Aditya Mavlankar and Bernd
Girod
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Information Systems Laboratory,
Department of Electrical Engineering
Stanford University, Stanford, CA 94305,
USA
Email: {maditya, bgirod}@stanford.edu
Speaker :童耀民 MA1G0222
2013.03.22
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Outline
1. INTRODUCTION
2. ROI PREDICTION AND PRE-FETCHING
Trajectory Prediction
Prediction Using H.264/AVC Motion Vectors
Prediction Tracking Soccer Ball
Prediction Tracking Soccer Ball and Players
3. EXPERIMENTAL RESULTS
4. CONCLUSIONS
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INTRODUCTION
We consider a video streaming
system in which the user can
interactively watch an arbitrary
region of a high-spatial-resolution
scene.
Region-of-interest
helps pre-fetch
encoded video.
(RoI) prediction
select slices of
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INTRODUCTION
Despite the availability of highresolution video, challenges in
delivering
this
high-resolution
content to the client are posed by
the limited resolution of the display
and/or limited data rate for
communications.
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INTRODUCTION
The goal of the paper is to find out
whether domain-specific techniques
can predict the client’s RoI more
accurately.
The more accurate the RoI prediction
the lower is the percentage of missing
pixels.
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INTRODUCTION
In this paper, we focus on
interactive viewing of soccer and
investigate
whether
domainspecific RoI prediction based on
semantic video analysis is more
accurate than RoI prediction
based on general techniques that
apply to any type of content.
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INTRODUCTION
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ROI PREDICTION AND PREFETCHING
As part of earlier work, we have
developed a graphical user interface
[2,3] to allow the user to select an RoI
while watching the video.
The application supports continuous
zoom to provide smooth control of
the zoom factor.
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ROI PREDICTION AND PREFETCHING
The high-resolution layers are
encoded using independent slices.
We choose the high-resolution
layer that corresponds closest to
the user’s zoom factor.
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ROI PREDICTION AND PREFETCHING
If some required high-resolution slices
are unavailable, we conceal the
error by upsampling portions of the
thumbnail video.
We compare the performance of
four RoI predictors in this paper.
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ROI PREDICTION AND PREFETCHING
The goal of each predictor is to
predict the RoI in frame n + d
when frame n is rendered on
screen.
The zoom factor for frame n + d is
predicted to be the same as the
zoom factor observed for frame n.
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ROI PREDICTION AND PREFETCHING
2.1. Trajectory Prediction
We adapt the autoregressive
moving
average
(ARMA)
prediction algorithm of [13] to
extrapolate the coordinates of the
RoI center.
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ROI PREDICTION AND PREFETCHING
2.2. Prediction Using H.264/AVC
Motion Vectors
This algorithm, proposed in our earlier
work [12], exploits the motion vectors
(MVs) contained within the encoded
bitstream of the thumbnail video
frames that are buffered at the client.
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ROI PREDICTION AND PREFETCHING
2.2. Prediction Using H.264/AVC
Motion Vectors
The MVs are used to find a
plausible propagation of the RoI
center pixel in every subsequent
frame up to frame n+d.
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ROI PREDICTION AND PREFETCHING
2.3. Prediction Tracking Soccer
Ball
The RoI is simply predicted to be
centered around the ball.
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ROI PREDICTION AND PREFETCHING
2.4. Prediction Tracking Soccer
Ball and Players
We have developed our own
algorithm for player tracking using
background subtraction and blob
tracking based on MVs.
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EXPERIMENTAL RESULTS
We use the Soccer1 sequence
having 2560 × 704 pixels and 25
frames/sec.
The RoI display is 480 × 240 pixels.
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EXPERIMENTAL RESULTS
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EXPERIMENTAL RESULTS
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EXPERIMENTAL RESULTS
PSNR (Peak Signal to Noise Ratio)：
也是訊雜比，只是訊號部分的值通通改用該訊號度量的最大
值。以訊號度量範圍為0到255當作例子來計算PSNR時，訊
號部分均當成是其能夠度量的最大值，也就是255，而不是
原來的訊號
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CONCLUSIONS
For long look-ahead, RoI prediction is
very challenging for both kinds of
techniques
and
incurs
a
large
percentage of missing pixels.
Nevertheless, we found that the domainspecific technique performs better
though only by about 1 dB, while the
drop in PSNR with respect to perfect RoI
prediction is more than 3 dB.
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