Energy efficiency of image compression for virtual view image over wireless visual sensor network.
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EditorinChief
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Florida International University, USA
Associate EditorinChief
Dr. Kami Makki
Lamar University, USA
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Boise State University, USA
Editors
Dr. Marcelo Sampaio de Alencar ([email protected])
Federal University of Campina Grande, Brazil
Dr. Abderrahim Benslimane (abderrahim.benslimane@univavignon.fr)
University of Avignon, France
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World Class Telecommunications, Canada
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Queens University, Canada
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City University of Hong Kong, Hong Kong
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Dr. Chunming Liu, ([email protected])
TMobile, USA
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Dr. Alan Marshall ([email protected])
Queens University Belfast, Northern Ireland, UK
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Dr. Fabrice Theoleyre ([email protected])
CNRS, France
Dr. George S. Tombras ([email protected])
National and Kapodistrian University of Athens, Greece
Dr. Vijay Varadharajan ([email protected])
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Dr. Mohamad Watfa ([email protected])
American University of Beirut, Lebanon
Dr. Lei Zhang ([email protected])
Frostburg State University, USA
Dr. Qian Zhang ([email protected])
Hong Kong University of Science and Technology, Hong Kong
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Illinois Institute of Technology, USA
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University of Science and Technology of China, China
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2/2
Journal of Networks
ISSN 1796-2056
Volume 10, Number 6, June 2015
Contents
REGULAR PAPERS
Performances of OpenFlow-Based Software-Defined Networks: An overview
Fouad Benamrane, Mouad Ben mamoun, and Redouane Benaini
329
Downlink Channel Allocation Scheme deploying Cooperative Channel Monitoring for Cognitive
Cellular-FemtocellNetworks
Van-Toan Nguyen, Kien Duc Nguyen, Hoang Nam Nguyen, and Keattisak Sripimanwat
338
Study on Auto Detecting Defence Mechanisms against Application Layer Ddos Attacks in SIP Server
Muhammad Morshed Alam, Muhammad Yeasir Arafat, and Feroz Ahmed
344
Specification and Validation of Enhanced Mobile Agent-Enabled Anomaly Detection and
Verification in Resource Constrained Networks
Muhammad Usman, Vallipuram Muthukkumarasamy, and Xin-Wen Wu
353
A Lightweight Authentication Protocol Based on Partial Identifier for EPCglobal Class-1 Gen-2 Tags
Zhicai Shi, Fei Wu, Yongxiang Xia, Yihan Wang, Jian Dai, and Changzhi Wang
369
A Multi-Constrained Routing Algorithm for Software Defined Network Based on Nonlinear
Annealing
Lijie Sheng, Zhikun Song, and Jianhua Yang
376
Energy Efficiency of Image Compression for Virtual View Image over Wireless Visual Sensor
Network
Nyoman Putra Sastra, Wirawan, and Gamantyo Hendrantoro
385
JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
385
Energy Efficiency of Image Compression for
Virtual View Image over Wireless Visual Sensor
Network
Nyoman Putra Sastraa,b , Wirawana , and Gamantyo Hendrantoroa
a
Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia
Email: {wirawan, gamantyo}@ee,its.ac.id
b
Department of Electrical Engineering, Universitas Udayana, Bali, 80236 Indonesia
Email: [email protected]
Abstract— Many applications use virtual view to see objects
or sceneries in certain directions where there is no angle
of visual sensor viewing the directions. Generating indirect
virtual view images also save visual sensor node in a
network. In addition visual sensors have abilities to give
more information related to virtual view images. In fact,
generating virtual view images using Wireless Visual Sensor
Network is a challenging task, due to the limitation of
energy, computation, and bandwidth. Therefore this paper
proposes using combination of camera selection method
with parameters optimization of JPEG 2000 image compression and image transmission strategy applying mutual
information criteria. The camera selection method based
on the smallest value of disparity was adopted to reduce
transmission of number of images. In each node of visual
sensor, the optimization of JPEG 2000 image compression
using parameters selection such as DWT level and bit rate
was applied to reduce the size of the images. However image
quality in PSNR has to be preserved. By implementing the
sensor selection, the image compression, and the mutual
information strategic, the study results that the total energy
required to send one compressed image can be reduced
to 10.27% of energy consumption of uncompressed image.
Thus the image will be faster delivered.
Index Terms— Wireless Sensor Network, virtual view images, image transmission, image compression, Linux embedded
I. I NTRODUCTION
W
IRELESS Sensor Network (WSN) is a wireless
communication system with limited resources,
especially on energy, computation, and bandwidth [1], [2].
The use of WSN platform in visual data transmission,
such as image and video, is known as WVSN [3]. Visual
sensor or camera generates information in the form of
images, or video, to be further processed for various visual
applications at a distributed processing unit or a central
processing. WVSN is widely used in many applications
such as environmental observation and surveillance system, which is designed with limited resources to provide
optimal information to users. This limitation of resources,
mainly on energy source of WVSN, creates a challenge in
system design since the size of data is larger than scalar
data.
© 2015 ACADEMY PUBLISHER
doi:10.4304/jnw.10.6.385-393
Visual applications require various angle of view in
order to observe an object. Even though visual sensors
are distributed in many places, certain FoV may not
be available. Moreover, observation of one particular
direction is changing in time because of the movement or
displacement of objects and weather condition. To address
user demand of viewing an object from certain angle,
more than one camera is required.
More visual sensor means better view or scenery reconstruction, but limitation of energy, computation, and
bandwidth become issues to be addressed. Thus, an image
compression technique is required to save those resources.
A visual sensor selection method is also needed to generate virtual image with the lowest size of data transmission.
In wireless network, energy required to transmit information is larger than energy to process data [3]. To save
energy, there are some issues to be considered in this
paper: 1) selecting images to be transmitted which has
differences of the information from predefine image of
each visual sensor, 2) Applying distributed compression
to the captured images, 3) Reducing number of images or
visual sensors as basic image to generate image on certain
FoV
The first issue will be addressed in Section 2.A.4 that
discuss implementation of mutual information strategy.
Section 2.A.1.up to Section 2.A.3 will meet the second
issue by optimizing JPEG 2000 parameters, which is
adapted in Imote2 with its OS Linux Embedded. Last
issue will be responded in Section 2.C and Section 2.D,
which will discuss selection technique of visual sensor, or
camera who provide maximal information of images, in
other words, from all available visual sensors, only few
are selected to provide maximal information.
The method of visual sensor selection is required to
reduce number of visual sensors. There are many visual
selection methods that had been developed by some
researchers. In [4]–[6], cameras are communicating to find
number of active cameras to create space that is desired in
scene. They developed distributed processing and centered
algorithms. These three researches require three processes
on sensor nodes and these processes require extended time
that lead to significant energy consumption. None of them
is aimed to directly reconstruct virtual image in WVSN
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JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
platform.
In [7] and [8], researches of multiview image from
overlapping images captured by a group of visual sensors
in WVSN environment have been conducted to broaden
the view. In [7], visual sensor selection is based on
spatial correlation and entropy between images. The
implementation of image joining in WVSN or WMSN
in [8] is mainly about the effort of resources utilization reduction, especially on memory usage, by reducing
less important information and transmitting only image’s
portion of a visual sensor that overlapped with images
from other sensor visual. There is no explanation of
energy consumption in that research. In [8] also nothing
specifically addresses the selection of visual sensors to
evoke the image of a particular FoV. Ref. [9] is research
of generating duplicates of foot ball play scene. In this
method, image is constructed from two and three visual
sensors located near the FoV without explicitly describing
the criteria of visual sensor selection.
In our previous research [10], we have developed a
method of image construction in WVSN. This method
has successfully generated virtual image with low PSNR
compared to capture image by visual sensor in the FoV.
The research had not implemented image compression
after capturing using visual sensor. This causes the image
file size becomes larger, such that sending one image to
central processing requires up to 10 minutes of time, with
the cost of high energy consumption.Additionally, the research did not make effort of reducing data transmission.
Moreover, In this study did not implement the mutual
information criterion for the selection of images to be
sent.
Based on the discussion of the above studies, the
main contribution of this research is to give solution for
energy saving in distributed processing. The distributed
processing by means of image compression and image
selection which have different information to the information already stored in each visual sensor node. The
use of energy on each process is measured. The quality of
virtual view image is improved and compared to the result
in [10]. WVSN platform XScale PXA 271 processor,
visual sensor SOC OV 7670, and sensor board with
Linux Embedded OS [11]–[13] are used in this research.
This paper is organized as follows. Section II describes
proposed scheme and system design. Section III presents
testing, measurement and analysis of the system. Finally,
section IV concludes the research.
II. T HE PROPOSED S CHEME
Multivisual sensors are used in WVSN to provide
multiview and multi resolution services, and also for environment surveillance. With the availability of resources,
image can be constructed by sending all image captured
by visual sensor. In WVSN application, visual sensor
node processes the image or video before transmission.
Virtual view image is image on peculiar FoV where
no camera is directly available to serve users. Many
applications have taken advantages of the virtual view
© 2015 ACADEMY PUBLISHER
Figure 1. System Schema.
image, for examples, (1) object recognition as a result
of captured object from different point of view, (2) view
widen of specific scenery, and (3) virtual reality such as
to see player and ball position in foot ball game.
WVSN inherently has limited resources; as a result a
method is required to save resources by sending the image
that was selected based on some defined criteria. Fig. 1
shows the platform of generating virtual view image in
WVSN. Initially, system is setup whereas sensor node
position is calibrated. Each sensor node will send its
captured image to central processor as a referenced image.
When an image is requested based on FoV, the system will
calculate and calibrate the requested FoV according to all
images in the central processor. A sensor node, then, is
selected to send the image which is employed as a virtual
view image foundation. Next step, the sensor node will
capture and compressed new images. The new images are
compared to the referenced image. Mutual information is
used as decision basis in sending the image. Finally the
image will be foundation in building virtual view image.
A. Image Processing in Visual Sensor Node
1) Wireless Sensor Network Platform: In this research
we use Imote2 as WSN platform. Imote2 has modules of
radio board and visual sensor board. The radio board uses
Intel Xscale PXA271 processor with 32 MB SDRAM,
32 MB flash, IO port as GPIO, 2x SPI, 3x UART, PC,
SDIO, USM host, USB client, and integrated wireless
communication IEEE 802.15.4 (CC2420). The Visual
sensor board provides miniature speaker and line output,
color image and video camera chip, omni Vision OV7670,
audio capture and playback CODEC WM890, onboard
microphone and line input, and PIR (Passive Infrared)
motion sensor. Table I displays the Imote2 consumption
energy parameters.
2) Embedded Linux Operating System: Linux embedded has been successfully developed in Imote2 board [12],
[13]. With this Linux embedded system, development of
JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
TABLE I.
PARAMETERS OF I MOTE 2.
Initial Energy
Active Camera withdraws
PXA Processor Active
Image Capture (including Sensing)
Radio Active
Read from SD Flash
Write in SD Flash
Image Dimension
Image Size
200 Joules
40 mW
192.3 mW
0.04 J
46.39 µJ/bit
5.32 nJ/bit
7.65 nJ/bit
640x480 pixel
900 KB
Algorithm 1 Installation step of Linux embedded implementation
1: Linux Embedded Implementation
a. Compiling Linux Kernel for JSVN Implementation
b. Flashing Bootloader, Linux Kernel, and File system
to JSN Platform
2: Visual Sensor Implementation
c. Modified Visual Sensor for Linux Implementation
d. Cross compiling and upload Linux OV7670 driver
e. Cross compiling and upload binary coding to
capture image
3: JPEG 2000 Compression Implementation in JSN
f. OpenJPEG Library modification
g. OpenJPEG Cross compiling for XScale Processor
f. Upload binary code from Linux Host to JSN with
Embedded Linux
applications for scalar and multimedia data becomes easier and more dynamic. The advantage of Linux embedded
OS is the ability of replacing the TinyOS bootloader,
which support images size up to 28 MB and independent
configuration and data storage. The communication model
is technology independent (programming via wireless
may be possible with same communication protocol), and
the flexibility of Linux to be modified on working or
running system.
The main steps of the implementation of Linux embedded are installing or uploading bootloader, kernel, and
file system. Bootloader or bootloader object (Blop) is
the basic system start-up and used to initialize system
components, i.e. system memory initialization, system
clock management, and GPIO initialization. When Blob
finishes system initialization, system will automatically
run Linux kernel. Kernel is generally read in system file,
known as JFFS2. The JFFS2 itself is a system file that had
been activated by bootloader. Linux Embedded system
that is installed in flash memory puts the system file in
the end of memory. The implementation steps are shown
below.
3) Image Compression on Visual Sensor: The DWT
processes of JPEG 2000 compression method are performed after preprocessing, including tiling, level offset, and Irreversible Colour Transform (ICT) [14]. The
tiling process is the process of dividing source image
into uniform size of non-overlappingsquared tiles, except
for tiles on image boundaries. Each tile is compressed
© 2015 ACADEMY PUBLISHER
387
TABLE II.
PARAMETERS OF JPEG 2000.
Image Dimesion
DWT Level
Compression
Bit Rate
Size of Block code (bc)
Tile size
640 x 480 x 3
3
Lossless
0.01
64x64
640 x 480 (1 tile)
independently using its own parameters. The aim of tiling
is to reduce memory utilization.
Preprocessed image source becomes input for blocks
of wavelet transform. DWT is a two dimensions filter
separable along its columnand row. The filter works by
applying low pass filter (L) and high pass filter (H) on
each sample, row by row, and columnby column. The
output is then applied again to the same filter. As the
result, image is divided into sub-bands: low-low (LL1)
low-high (LH1), high-low (HL1) and high-high (HH1)
[15]. High pass sub-band refers to the residues of original
image, which is required in the perfect reconstruction of
the image from its low resolution version. DWT level
can be raised to create transformation level two with sub
bands LL2, LH2, HL2, HH2, and so on.
The implementation of JPEG 2000 in this research
is using library OpenJPEG [16] on WSN platform. The
JPEG 2000 parameters are altered for system optimization
that is reducing energy consumption while keeping the
image quality. From our preliminary experiments, as
shown in installation step of Linux embedded implementation, the parameters of JPEG 2000 being used in this
implementation is as in II. These parameters have low
energy consumption with adequate PSNR.
4) Mutual Information to Determine Image Transmission: Joint histogram of two images can be used to
estimate joint probability distribution from grey values of
image by dividing each value of histogram with its total
value. Shannon entropy for joint distribution is defined
as:
X
p(i, j) log2 p(i, j)
(1)
H=−
i,J
The value of joint entropy of two images defines their
degree of correlation. In registry image, low joint entropy
means that both images are similar. Indifferent to registry
image, for camera selection, large value of joint entropy
is preferable to get more information.
Another technique to see information contain within
two or more images is by computing mutual information.
Mutual information is one of many quantities to state how
much one random variable provides information about
another random variable. The mutual information of two
images, I and J, is defined as:
M (i, j) = H(i) + H(j) − H(i, j);
(2)
Where H(i, j) is joint entropy of two images I and J. The
equation shows that low joint entropy means high mutual
information. In other words, whenever mutual information
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JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
y0 = f 0
of two images taken from visual sensors tends to be
similar, there is no information is required to be sent from
sensor node to central processing. The sensor only sends
image, after being compressed, when there is a significant
change in mutual information value.
B. Visual Sensor Selection
Disparity is difference of depth of a point P , on the
same scenery subjected to different field of view, which is
shown in 2. Disparity changes as rotation and translation
difference of one field of view to other field of view [17].
To simplify the implementation, virtual view movement
in this study only the horizontal rotation, which are in the
left and right point P .
Two visual sensors used to generate images with the
same scenery, as in 2, and according to [17], one point in
the first camera C has projection point below:
y
y
x=f
y=f
(3)
z
z
0
Projection that point on the second camera C , is stated
by below equation:
y0
y0
y‘ = f 0
0
z
z
Coordinate system C 0 can be expressed
system C rotated by R and followed by
[tx ty tz ]
2 0 3
2
3 2
X
X
tx
4 Y 0 5 = R̄ 4 Y 5 + 4 ty
Z0
Z
tz
From this system model, there is no
axes X and Z (rotation on horizontal
Rotation equation becomes [15]:
0
cos β 0 sin β
0
1
0
R̄ = @
− sin β 0 cos β
(4)
as coordinate
translation T ,
3
5
(5)
rotation around
direction only).
1
(6)
A
0
x =f
0X
0
Z0
=
0
cos β ff x + sin β ff
© 2015 ACADEMY PUBLISHER
− sin β
f
+ cos β
1
+ cos β
(8)
*
*
d sin β **
1 ** d sin β
+
4 * d + cosβ
d − cosβ *
*
*
*
−d cos β
1 ** d cos β
−1+
+ 1**
+ *
4 d + sinβ
d − sinβ
δ=
(7)
(9)
First visual sensor is selected by searching smallest
rotation angle β that has minimum disparity between
visual sensors C and desired FoV. Second visual sensors
selected by smallest disparity with opposite direction to
β, seen from virtual view. As example, let position of
first visual sensor be on the right side of virtual view, so
the position of second visual sensor is on the left side
of virtual view. From this two points, the FoV disparity
when seen from both visual sensors position is defined
by equation below:
δi0 =
d1
d1 + |d2 |
δj0 =
d2
d1 + |d2 |
(10)
Where δi and δj are the disparities of the two visal sensors
that are selected against the desired direction of view.
C. Virtual View Generation
There are three stages to generate a virtual image view
items, namely, interpolation, pre-wrap, and post-wrap. In
addition, interpolation is the step to generate intermediate
image by using pixel correspondence between two images
from two selected sensors. A method of correspondence
pixel pairs search along epipolar line from both images is
used to make a fast computation. Moreover, epipolar line
correspondence is:
⇢
l = F T p;
(11)
l0 = F p0 .
Where l and l0 are a pair of epipolar line, and F is
fundamental matrix [10] as a result from this equation:
pT = F p0 = 0
Position of point P on image plane that produced by
visual sensor C can be located on image plane produced
by visual sensor C 0 by substituting coordinate equation
between visual sensor C and visual sensor C 0 into projection equation:
0
− sin β
f
Whenever FoV of visual sensor C 0 is field of view of
desired virtual view, this geometry of two visual sensors
model can be used to get scene for virtual view image.
We can get disparity value from reference point on
geometry of two visual sensors. For visual sensor i
and visual sensor j in 2, the disparity, δ, between two
images at camera i and camera j with position parameters
(δi ,ri ,θi ) and (δj ,rj ,θj ) are calculated as [6], [18]:
Figure 2. Geometry of two visual sensor.
x0 = f
Y0
=
Z0
T
(12)
0
Where P and P are pair of point from two-dimensional
coordinate that corresponds from two images. F is fundamental matrix that has redundancy value. The parameter
F can be solved by least square method by minimizing
value from equation below:
n
X
,
i=1
pTi F p0i
-2
(13)
JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
389
Interpolation process suited to disparity viewpoint of two
selected visual sensors. Furthermore, interpolation process
to find new image points is defined below:
pv = d01 p + d01 p0
(14)
To estimate epipolar lines coordinate on virtual view and
warps interpolated image by mean of placing those lines
on correct position on virtual view post-wrap process
aims. By then, virtual view is produced.
D. Energy Consumption
Since energy source of WVSN solely from battery
with limited power, thus energy consumption becomes
a critical criteria on implementation [19]. This means
that the main priority of one particular model or any
implementation method of image virtual view generator
is to produce low energy consumption. In general, there
are three tasks that affect energy consumption of WVSN,
i.e.: 1) energy for images capturing, 2) energy for images
processing, and 3) energy for images transmitting. Those
three energy consumptions are formulated as below:
EW V SN = Ecapt + Eproc + Etransmit
(15)
Ecapt is images capturing energy, Eproc is images processing energy consumption, and Etransmit is images
transmitting energy consumption. Furthermore, we can
manually compute energy consumption by measuring
voltage (V ), current (I), and time of the whole process
i.e. images capturing time (tcapt ), images processing time
(tproc ), and images transmitting time (ttransmit ), with or
without any compression methods, as:
E = V × I × (tcapt + tproc + ttransmit )
(16)
Energy consumption of JPEG 2000 image compression
can be estimated from DWT processes. In this research,
OpenJPEG library used as JPEG 2000 representation
using Le Gall filter model 5 − 3 [15], [20]. Equations
regarding the filter are:
1
(−z 2 + 2Z + 6 + 2z −1 − z −2 )
(17)
8
1
H0 (z) = (z + 2 + z −1 )
(18)
2
H0 (z) defines low frequency sub band of DWT filter and
G0 (z) defines high frequency subband of DWT filter. The
impulse responses of the filter are:
L0 (z) =
−x[2n − 2] + 2x[2n − 1]
4
6x[2n] + 2x[2n + 1] − x[2n + 2]
+
4
L[2n] =
© 2015 ACADEMY PUBLISHER
CDW T (M, N, L) = M N (10S + 12A)
L
X
1
l−1
4
i=1
(21)
Apart from arithmetic operations, on transformation steps,
energy is significantly consumed from memory access.
Within memory access processes, each pixel goes through
read and write processes 2 times, thus memory access for
M x N image is:
CRead (M, N, L) = CW rite (M, N, L) = 2M N
L
X
1
l−1
4
i=1
(22)
Total processing load is computed as sum of computational load (CDW T ) and memory access load, (Cread )
and (Cwrite ), from Eq. (21) and Eq. (22) and becomes:
Cproc = CDW T + Cread + Cwrite
Cproc (M, N, L) = M N (10S + 12A)
2M N
L
X
1
+
l−1
4
i=1
L
L
X
X
1
1
+
2M
N
l−1
l−1
4
4
i=1
i=1
(23)
Furthermore, if Eshif t denote energy consumption for
shift (S) operation, Eadd denote energy consumption for
add (A) operation, Eread denote energy consumption for
read memory access, and Ewrite denote energy consumption for write memory access, then the overall energy
consumption for processing becomes:
Eproc = EDW T + Eread Cread + Ewrite Cwrite
Eproc (M, N, L) = M N (10Eshif t + 12Eadd )
(19)
−x[2n] + 2x[2n + 1] − x[2n + 2]
(20)
2
Analyzing energy consumption of DWT image compression requires size and type of some basic operations
of the given process. Basic operations such as shifting
H[2n + 1] =
and adding can derive from (19) and (20). In (19), filter
decomposition requires 8 shifts and 8 adds operations to
convert 1 pixel of an image into low pass coefficient,
meanwhile (20) requires 2 shifts and 4 adds operations to
convert into high pass coefficient.
Overall the computation of an image on DWT decomposition is acquired by counting the whole operations. The
computation of one image of size M xN with L DWT
level is estimated step by step as follows. Assume that
the first step is horizontal decomposition. Since the whole
pixels at even position are decomposed into low pass
coefficient and pixels at odd positions are decomposed
into high pass, which total weight at horizontal is 14
M N (10S+12A). The vertical decomposition has similar
weight. Image’s size will reduce with factor of 4 from
initial size for every transformation level. Then, the total
computation cost becomes:
Ewrite 2M N
L
X
1
+
l−1
4
i=1
L
L
X
X
1
1
+
E
2M
N
read
l−1
l−1
4
4
i=1
i=1
Eproc (M, N, L) =
M N (10Eshif t + 12Eadd + 2Ewrite + 2Eread )
L
X
1
l−1
4
i=1
(24)
390
JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
Image transmission is generally in the form of data
packets. If the number of packets is m, average packets
size is t, and energy required to transmit 1 bit data is Eb ,
transmission energy is defined as:
Etransmit = m · t · Eb
(25)
The IEEE 802.15.4 communication system on Linux
embedded OS uses the tosmac structure data packet
[1], with the payload of each packet being 28 bytes.
From this model and substituting (24) and (25) into
(15), the energy consumption for transmitting a JPEG
2000 compressed image becomes:
EW V SN = Ecapt + Eproc + Etransmit
EW V SN (M, N, L) = Ecapt + M N (10Eshif t +
12Eadd + 2Eread + 2Ewrite )
L
X
1
+ m · t · Eb
l−1
4
i=1
(26)
According to (26), the captured energy remains fixed
for the same devices. Therefore total consumption
energy depends on both processing energy (Eproc )
and transmission energy (Etransmit ). The transmission
energy (Etransmit ) is affected by image size in bits.
3)
4)
5)
6)
E. Peak Signal to Noise Ratio (PSNR)
Error metrics often used to compare various image
compression techniques are the Mean Square Error (MSE)
and the Peak Signal to Noise Ratio (PSNR). The MSE is
the cumulative squared error between the compressed and
the original image, whereas PSNR represents a measure
of the peak error. The mathematical formula for PSNR
and MSE are:
m−1 n−1
1 XX
2
[I(i, j) − K(i, j)]
m · n i=0 j=0
✓
◆
M AXl
P SN R = 20 log10 √
M SE
M SE =
(27)
(28)
Where I represents matrix of original images, K is
compressed image, m is number of pixel row of image,
i is index of that row, n represents number of image’s
pixel column and j is index of the column.
F. Procedures
The following flowchart of virtual view image generation procedures in accordance with 3, is described as
follows:
1) A number of visual sensors are installed and deployed in the desired area of coverage. These visual sensors send visual information to the central
processing. The central processing unit is in charge
of collecting information from each of the visual
sensor and received a request from a user for a FoV.
2) Calibration to determine the position and direction
of view and visual coverage of each sensor or point
of reference to sensor node is done before any
© 2015 ACADEMY PUBLISHER
7)
8)
other task because the direction of visual sensor is
assumed not to be changed for energy savings. The
disparity between cameras is obtained by determining the direction of view of each sensor. An initial
image of each visual sensor is distributively stored
in each sensor node and also in a central processing
to be used ingenerating virtual image.
JPEG 2000 image compression method is implemented on all visual sensors to compress the captured image. The purpose of image compression
is to save energy in the image delivery as the
transmission of data requires much higher energy
than computing the data.
After the system recognizes the position of each
sensor node in accordance to reference point, request of each user to any desired FoV can be
performed.
Desired FoV will be compared with the position /
direction of view of any visual sensors. Based on
mutual information criteria and the disparity, the
system decides which camera to be used.
Since environmental conditions might change at
any time, due to weather, new additional object,
changing the position of objects and others, then on
every FoV request, a new image will be captured
from selected visual sensors. The new image will
be compared with the initial image to find their
differences.
The system calculates mutual information of images. Furthermore, only two images with the largest
mutual information are transmitted to a central
processing to save energy for transmitting.
Image generation process is done at central processing. The Image generation steps are 1) Interpolation
of correspondence points, 2) pre-wrapping process,
3) post-wrapping process.
III.
RESULTS AND DISCUSSION
Initial stage is begun when all visual sensor nodes
capture and store images. Then the nodes send the
compressed images to central processing. Each node has
calculated and stored mutual information of the images.
When particular FoV is requested, the central processing
will select two visual sensor nodes based on disparity
method as seen in Fig. 4.
In the initial stage, the mutual information computation
for the first image and the second image to themselves
applies (2), that are H(I11 , I11 ) = 7.0800 and H(I21 , I21 )
= 5.3526 (See TABLE III). Condition 2 is the condition
when images are captured with no differences to the
initial condition, Fig. 4c and Fig. 4d, so the mutual information of images is the same. The mutual information
are H(I11 , I12 )=7.0800 and H(I21 , I22 )= 5.3536. On the
other hand, the mutual information, as seen in TABLE
III is obtained from calculation of image in Fig 4c to the
image in Fig. 4a, and image in Fig 4d to theimage in Fig
4b.
JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
391
TABLE III.
M UTUAL I NFORMATION COMPUTATION .
Condition
1 (initial)
2
3
Mutual Information of image 1
7.0800
7.0800
1.3599
Mutual Information of image 2
5.3526
5.3526
1.1110
from (26). For condition 2 and condition 3, the processing energy consumption (Eproc ) and transmission energy
consumption (Etransmit ) are change as follows:
EW V SN = m · t · Eb + M N
L
X
1
l−1
4
i=1
(10Eshif t + 12Eadd + 2Eread + 2Ewrite )
Assumed that active processor is a condition when signal
processing takes place, then Eshif t , Eadd , Eread , and
Ewrite can be substituted with the energy consumption
when the processor is active, as shown in Table I. According to this table, the energy consumption of active
processor is 192.3 mW. Then by encoding time duration,
the EDW T , the proccesing energy consumption (Eproc )
becomes:
L
X
1
EP roc = M N
4l−1
l=1
(10Eshif t + 12Eadd + 2Eread + 2Ewrite )
= Pproc · t
where t is the time required for encoding processes. Based
on JPEG 2000 parameters in TABLE II, the average
encoding time for 0.1 bpp and DWT level 3 is 18.5 s
and the energy consumption becomes:
EW V SN = 0.04J + 192.3mW × 18.5s + 864 × 3, 291.5
= 0.04J + 3.558J + 131, 9J = 135.52J
Figure 3. Image Generation Flowchart based on desired FoV request.
Whenever any differences occurred, as in condition
3, the mutual information between initialized image and
captured image at that time, becomes smaller, that are
H(I11 ; I13 )= 1.3599 and H(I21 ; I23 )= 1.1110. These are
the criteria that are used to determine whether it is
necessary to send new image to the central processing.
When mutual information is relatively the same, the visual
sensor node only sends one bit data, 0, to inform the
central processing that there is no new image available.
When the object or scene has experienced any modification, visual sensor node sends another bit, i.e. bit 1,
followed by new captured image.
Energy consumption as seen in TABLE IV is obtained
© 2015 ACADEMY PUBLISHER
At condition 2, when two selected sensor nodes send
two images, then total energy consumption of sending
two compressed images by JPEG 2000 as base of virtual
image construction is 271.04 J. Meanwhile, at condition
3, when the mutual information value is the same as
the value at initial stage, only information bit is sent.
Therefore the energy is required for transmission process
is:
EW V SN = Ecapt + Eproc + Etransmit
= 0.04J + 1.10J + 0.02J = 1.16J
Hence total energy is required for transmitting information from two selected sensors of 2.32J.
On the contrary, transmitting image without compression makes file size of 900 KB. This large file is split
into 32,915 packets with average of 28 byte payload per
packet. By the use of Imote2 parameter on TABLE II and
computed using (26), energy consumption becomes:
EW V SN = Ecapt + m · k · Eb = 0.04J+
32, 915 · 864 · 46.39µJ = 1, 319.35J
392
JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
TABLE IV.
E NERGY CONSUMPTION COMPARISON
Method
(a)
(b)
Image Size
(KB)
Ecapt (J)
Ecomp (J)
Etrans (J)
Etotal (J)
P SN R
Send
Compessed
Image
90
Send information bit
Send Raw
Image [10]
0
900
0.04
3.35
131.93
135.52
25.65
0.04
1.10
0.02
1.16
36.65
0.04
0
1319.30
1319.35
36.6
IV. C ONCLUSIONS
(c)
(d)
(e)
(f)
Figure 4. Images captured by visual sensors. a). Initial image captured
by visual sensor I. b). Initial image captured by visual sensor II. c).
Image created by visual sensor I for one FoV without any alteration on
the scene. d). Image created by visual sensor II for one FoV without
any alteration on the scene. e). Image created by visual sensor I for one
FoV with alteration on the scene. f). Image created by visual sensor II
for one FoV with alteration on the scene.
Virtual view image from one particular direction that
is demanded by user requires at least two images from
visual sensors. This application becomes a challenge in
WVSN environment due to its limited resources. One
method to save energy consumption is by applying image
compression, such as JPEG 2000 on each visual sensor.
In this research, we conclude that by applying such
compression method, energy consumption on each sensor
node increases as much as 3.35 J. This cost is compromised by significant reduction of energy consumption on
image transmission. Total energy required to send one
compressed image reduces to only 10.27% of energy
consumption of uncompressed image.
The mutual information computation as detector for
scene modification also increases energy consumption on
each node. However when mutual information shows no
change on the scene, the total energy required is down to
1.16 J, or 0.9% of energy of uncompressed image.
ACKNOWLEDGMENT
(a)
(b)
Figure 5. a) Virtual View image on disparity 0.5; b) Image Created by
visual sensor on the same FoV with the same disparity
Finally, the energy consumption to send one compressed
image is reduced to only 10.27% compared to energy
consumption without compression (see Table 4). When
there is no modification of object or scene, i.e. condition
2, the energy consumption reduces to 3% when comparred
with the whole compressed image is transmitted.
The total energy consumption of sending two compressed images by JPEG 2000 as base of virtual image
construction is 271.04 J, and 2639.70 J for uncompressed images. Virtual image of disparity of 0.5 is shown
in Fig. 5a and the captured image from visual sensor for
FoV as requested by user is shown in Fig. 5b.
© 2015 ACADEMY PUBLISHER
The authors are grateful to the anonymous referees for their valuable comments and suggest̃ions
to improve the presentation of this paper. This
work is supported by Indonesian Ministry of Education and Culture through the BPPS scholarship
and 2013 Doctoral Research Grant, contract number:
175.45/ŨN14.2/PNL.01.03.00/2013, May 16, 2013.
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© 2015 ACADEMY PUBLISHER
393
Nyoman Putra Sastra received the B.Eng.
and M.Eng. degree in electrical engineering
from Institut Teknologi Bandung (ITB), Indonesia, in 1998 and 2001, respectively. Since
2001, he joined with Universitas Udayana, Bali
as lecturer. He is currently working toward
the Ph.D.degree in electrical engineering from
ITS. His research interests include His research
interests include wireless multimedia sensor
networks and multimedia signal processing.
He is a Student Member of the IEEE.
Wirawan received the B.E. degree in
telecommunication engineering from Institut
Teknologi Sepuluh Nopember (ITS), Surabaya,
Indonesia, in 1987, the DEA in signal and
image processing from Ecole Suprieure en Sciences Informatiques, Sophia Antipolis, and the
Dr. degree in image processing from Telecom
ParisTech (previously Ecole Nationale Suprieure des Tlcommunications), Paris, France,
in 1996 and 2003, respectively. Since 1989 he
has been with ITS as lecturer in the Electrical
Engineering Department. His research interest lie in the general area
of image and video signal processing for mo
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Zhicai Shi, Fei Wu, Yongxiang Xia, Yihan Wang, Jian Dai, and Changzhi Wang
369
A Multi-Constrained Routing Algorithm for Software Defined Network Based on Nonlinear
Annealing
Lijie Sheng, Zhikun Song, and Jianhua Yang
376
Energy Efficiency of Image Compression for Virtual View Image over Wireless Visual Sensor
Network
Nyoman Putra Sastra, Wirawan, and Gamantyo Hendrantoro
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JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
385
Energy Efficiency of Image Compression for
Virtual View Image over Wireless Visual Sensor
Network
Nyoman Putra Sastraa,b , Wirawana , and Gamantyo Hendrantoroa
a
Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia
Email: {wirawan, gamantyo}@ee,its.ac.id
b
Department of Electrical Engineering, Universitas Udayana, Bali, 80236 Indonesia
Email: [email protected]
Abstract— Many applications use virtual view to see objects
or sceneries in certain directions where there is no angle
of visual sensor viewing the directions. Generating indirect
virtual view images also save visual sensor node in a
network. In addition visual sensors have abilities to give
more information related to virtual view images. In fact,
generating virtual view images using Wireless Visual Sensor
Network is a challenging task, due to the limitation of
energy, computation, and bandwidth. Therefore this paper
proposes using combination of camera selection method
with parameters optimization of JPEG 2000 image compression and image transmission strategy applying mutual
information criteria. The camera selection method based
on the smallest value of disparity was adopted to reduce
transmission of number of images. In each node of visual
sensor, the optimization of JPEG 2000 image compression
using parameters selection such as DWT level and bit rate
was applied to reduce the size of the images. However image
quality in PSNR has to be preserved. By implementing the
sensor selection, the image compression, and the mutual
information strategic, the study results that the total energy
required to send one compressed image can be reduced
to 10.27% of energy consumption of uncompressed image.
Thus the image will be faster delivered.
Index Terms— Wireless Sensor Network, virtual view images, image transmission, image compression, Linux embedded
I. I NTRODUCTION
W
IRELESS Sensor Network (WSN) is a wireless
communication system with limited resources,
especially on energy, computation, and bandwidth [1], [2].
The use of WSN platform in visual data transmission,
such as image and video, is known as WVSN [3]. Visual
sensor or camera generates information in the form of
images, or video, to be further processed for various visual
applications at a distributed processing unit or a central
processing. WVSN is widely used in many applications
such as environmental observation and surveillance system, which is designed with limited resources to provide
optimal information to users. This limitation of resources,
mainly on energy source of WVSN, creates a challenge in
system design since the size of data is larger than scalar
data.
© 2015 ACADEMY PUBLISHER
doi:10.4304/jnw.10.6.385-393
Visual applications require various angle of view in
order to observe an object. Even though visual sensors
are distributed in many places, certain FoV may not
be available. Moreover, observation of one particular
direction is changing in time because of the movement or
displacement of objects and weather condition. To address
user demand of viewing an object from certain angle,
more than one camera is required.
More visual sensor means better view or scenery reconstruction, but limitation of energy, computation, and
bandwidth become issues to be addressed. Thus, an image
compression technique is required to save those resources.
A visual sensor selection method is also needed to generate virtual image with the lowest size of data transmission.
In wireless network, energy required to transmit information is larger than energy to process data [3]. To save
energy, there are some issues to be considered in this
paper: 1) selecting images to be transmitted which has
differences of the information from predefine image of
each visual sensor, 2) Applying distributed compression
to the captured images, 3) Reducing number of images or
visual sensors as basic image to generate image on certain
FoV
The first issue will be addressed in Section 2.A.4 that
discuss implementation of mutual information strategy.
Section 2.A.1.up to Section 2.A.3 will meet the second
issue by optimizing JPEG 2000 parameters, which is
adapted in Imote2 with its OS Linux Embedded. Last
issue will be responded in Section 2.C and Section 2.D,
which will discuss selection technique of visual sensor, or
camera who provide maximal information of images, in
other words, from all available visual sensors, only few
are selected to provide maximal information.
The method of visual sensor selection is required to
reduce number of visual sensors. There are many visual
selection methods that had been developed by some
researchers. In [4]–[6], cameras are communicating to find
number of active cameras to create space that is desired in
scene. They developed distributed processing and centered
algorithms. These three researches require three processes
on sensor nodes and these processes require extended time
that lead to significant energy consumption. None of them
is aimed to directly reconstruct virtual image in WVSN
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platform.
In [7] and [8], researches of multiview image from
overlapping images captured by a group of visual sensors
in WVSN environment have been conducted to broaden
the view. In [7], visual sensor selection is based on
spatial correlation and entropy between images. The
implementation of image joining in WVSN or WMSN
in [8] is mainly about the effort of resources utilization reduction, especially on memory usage, by reducing
less important information and transmitting only image’s
portion of a visual sensor that overlapped with images
from other sensor visual. There is no explanation of
energy consumption in that research. In [8] also nothing
specifically addresses the selection of visual sensors to
evoke the image of a particular FoV. Ref. [9] is research
of generating duplicates of foot ball play scene. In this
method, image is constructed from two and three visual
sensors located near the FoV without explicitly describing
the criteria of visual sensor selection.
In our previous research [10], we have developed a
method of image construction in WVSN. This method
has successfully generated virtual image with low PSNR
compared to capture image by visual sensor in the FoV.
The research had not implemented image compression
after capturing using visual sensor. This causes the image
file size becomes larger, such that sending one image to
central processing requires up to 10 minutes of time, with
the cost of high energy consumption.Additionally, the research did not make effort of reducing data transmission.
Moreover, In this study did not implement the mutual
information criterion for the selection of images to be
sent.
Based on the discussion of the above studies, the
main contribution of this research is to give solution for
energy saving in distributed processing. The distributed
processing by means of image compression and image
selection which have different information to the information already stored in each visual sensor node. The
use of energy on each process is measured. The quality of
virtual view image is improved and compared to the result
in [10]. WVSN platform XScale PXA 271 processor,
visual sensor SOC OV 7670, and sensor board with
Linux Embedded OS [11]–[13] are used in this research.
This paper is organized as follows. Section II describes
proposed scheme and system design. Section III presents
testing, measurement and analysis of the system. Finally,
section IV concludes the research.
II. T HE PROPOSED S CHEME
Multivisual sensors are used in WVSN to provide
multiview and multi resolution services, and also for environment surveillance. With the availability of resources,
image can be constructed by sending all image captured
by visual sensor. In WVSN application, visual sensor
node processes the image or video before transmission.
Virtual view image is image on peculiar FoV where
no camera is directly available to serve users. Many
applications have taken advantages of the virtual view
© 2015 ACADEMY PUBLISHER
Figure 1. System Schema.
image, for examples, (1) object recognition as a result
of captured object from different point of view, (2) view
widen of specific scenery, and (3) virtual reality such as
to see player and ball position in foot ball game.
WVSN inherently has limited resources; as a result a
method is required to save resources by sending the image
that was selected based on some defined criteria. Fig. 1
shows the platform of generating virtual view image in
WVSN. Initially, system is setup whereas sensor node
position is calibrated. Each sensor node will send its
captured image to central processor as a referenced image.
When an image is requested based on FoV, the system will
calculate and calibrate the requested FoV according to all
images in the central processor. A sensor node, then, is
selected to send the image which is employed as a virtual
view image foundation. Next step, the sensor node will
capture and compressed new images. The new images are
compared to the referenced image. Mutual information is
used as decision basis in sending the image. Finally the
image will be foundation in building virtual view image.
A. Image Processing in Visual Sensor Node
1) Wireless Sensor Network Platform: In this research
we use Imote2 as WSN platform. Imote2 has modules of
radio board and visual sensor board. The radio board uses
Intel Xscale PXA271 processor with 32 MB SDRAM,
32 MB flash, IO port as GPIO, 2x SPI, 3x UART, PC,
SDIO, USM host, USB client, and integrated wireless
communication IEEE 802.15.4 (CC2420). The Visual
sensor board provides miniature speaker and line output,
color image and video camera chip, omni Vision OV7670,
audio capture and playback CODEC WM890, onboard
microphone and line input, and PIR (Passive Infrared)
motion sensor. Table I displays the Imote2 consumption
energy parameters.
2) Embedded Linux Operating System: Linux embedded has been successfully developed in Imote2 board [12],
[13]. With this Linux embedded system, development of
JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
TABLE I.
PARAMETERS OF I MOTE 2.
Initial Energy
Active Camera withdraws
PXA Processor Active
Image Capture (including Sensing)
Radio Active
Read from SD Flash
Write in SD Flash
Image Dimension
Image Size
200 Joules
40 mW
192.3 mW
0.04 J
46.39 µJ/bit
5.32 nJ/bit
7.65 nJ/bit
640x480 pixel
900 KB
Algorithm 1 Installation step of Linux embedded implementation
1: Linux Embedded Implementation
a. Compiling Linux Kernel for JSVN Implementation
b. Flashing Bootloader, Linux Kernel, and File system
to JSN Platform
2: Visual Sensor Implementation
c. Modified Visual Sensor for Linux Implementation
d. Cross compiling and upload Linux OV7670 driver
e. Cross compiling and upload binary coding to
capture image
3: JPEG 2000 Compression Implementation in JSN
f. OpenJPEG Library modification
g. OpenJPEG Cross compiling for XScale Processor
f. Upload binary code from Linux Host to JSN with
Embedded Linux
applications for scalar and multimedia data becomes easier and more dynamic. The advantage of Linux embedded
OS is the ability of replacing the TinyOS bootloader,
which support images size up to 28 MB and independent
configuration and data storage. The communication model
is technology independent (programming via wireless
may be possible with same communication protocol), and
the flexibility of Linux to be modified on working or
running system.
The main steps of the implementation of Linux embedded are installing or uploading bootloader, kernel, and
file system. Bootloader or bootloader object (Blop) is
the basic system start-up and used to initialize system
components, i.e. system memory initialization, system
clock management, and GPIO initialization. When Blob
finishes system initialization, system will automatically
run Linux kernel. Kernel is generally read in system file,
known as JFFS2. The JFFS2 itself is a system file that had
been activated by bootloader. Linux Embedded system
that is installed in flash memory puts the system file in
the end of memory. The implementation steps are shown
below.
3) Image Compression on Visual Sensor: The DWT
processes of JPEG 2000 compression method are performed after preprocessing, including tiling, level offset, and Irreversible Colour Transform (ICT) [14]. The
tiling process is the process of dividing source image
into uniform size of non-overlappingsquared tiles, except
for tiles on image boundaries. Each tile is compressed
© 2015 ACADEMY PUBLISHER
387
TABLE II.
PARAMETERS OF JPEG 2000.
Image Dimesion
DWT Level
Compression
Bit Rate
Size of Block code (bc)
Tile size
640 x 480 x 3
3
Lossless
0.01
64x64
640 x 480 (1 tile)
independently using its own parameters. The aim of tiling
is to reduce memory utilization.
Preprocessed image source becomes input for blocks
of wavelet transform. DWT is a two dimensions filter
separable along its columnand row. The filter works by
applying low pass filter (L) and high pass filter (H) on
each sample, row by row, and columnby column. The
output is then applied again to the same filter. As the
result, image is divided into sub-bands: low-low (LL1)
low-high (LH1), high-low (HL1) and high-high (HH1)
[15]. High pass sub-band refers to the residues of original
image, which is required in the perfect reconstruction of
the image from its low resolution version. DWT level
can be raised to create transformation level two with sub
bands LL2, LH2, HL2, HH2, and so on.
The implementation of JPEG 2000 in this research
is using library OpenJPEG [16] on WSN platform. The
JPEG 2000 parameters are altered for system optimization
that is reducing energy consumption while keeping the
image quality. From our preliminary experiments, as
shown in installation step of Linux embedded implementation, the parameters of JPEG 2000 being used in this
implementation is as in II. These parameters have low
energy consumption with adequate PSNR.
4) Mutual Information to Determine Image Transmission: Joint histogram of two images can be used to
estimate joint probability distribution from grey values of
image by dividing each value of histogram with its total
value. Shannon entropy for joint distribution is defined
as:
X
p(i, j) log2 p(i, j)
(1)
H=−
i,J
The value of joint entropy of two images defines their
degree of correlation. In registry image, low joint entropy
means that both images are similar. Indifferent to registry
image, for camera selection, large value of joint entropy
is preferable to get more information.
Another technique to see information contain within
two or more images is by computing mutual information.
Mutual information is one of many quantities to state how
much one random variable provides information about
another random variable. The mutual information of two
images, I and J, is defined as:
M (i, j) = H(i) + H(j) − H(i, j);
(2)
Where H(i, j) is joint entropy of two images I and J. The
equation shows that low joint entropy means high mutual
information. In other words, whenever mutual information
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y0 = f 0
of two images taken from visual sensors tends to be
similar, there is no information is required to be sent from
sensor node to central processing. The sensor only sends
image, after being compressed, when there is a significant
change in mutual information value.
B. Visual Sensor Selection
Disparity is difference of depth of a point P , on the
same scenery subjected to different field of view, which is
shown in 2. Disparity changes as rotation and translation
difference of one field of view to other field of view [17].
To simplify the implementation, virtual view movement
in this study only the horizontal rotation, which are in the
left and right point P .
Two visual sensors used to generate images with the
same scenery, as in 2, and according to [17], one point in
the first camera C has projection point below:
y
y
x=f
y=f
(3)
z
z
0
Projection that point on the second camera C , is stated
by below equation:
y0
y0
y‘ = f 0
0
z
z
Coordinate system C 0 can be expressed
system C rotated by R and followed by
[tx ty tz ]
2 0 3
2
3 2
X
X
tx
4 Y 0 5 = R̄ 4 Y 5 + 4 ty
Z0
Z
tz
From this system model, there is no
axes X and Z (rotation on horizontal
Rotation equation becomes [15]:
0
cos β 0 sin β
0
1
0
R̄ = @
− sin β 0 cos β
(4)
as coordinate
translation T ,
3
5
(5)
rotation around
direction only).
1
(6)
A
0
x =f
0X
0
Z0
=
0
cos β ff x + sin β ff
© 2015 ACADEMY PUBLISHER
− sin β
f
+ cos β
1
+ cos β
(8)
*
*
d sin β **
1 ** d sin β
+
4 * d + cosβ
d − cosβ *
*
*
*
−d cos β
1 ** d cos β
−1+
+ 1**
+ *
4 d + sinβ
d − sinβ
δ=
(7)
(9)
First visual sensor is selected by searching smallest
rotation angle β that has minimum disparity between
visual sensors C and desired FoV. Second visual sensors
selected by smallest disparity with opposite direction to
β, seen from virtual view. As example, let position of
first visual sensor be on the right side of virtual view, so
the position of second visual sensor is on the left side
of virtual view. From this two points, the FoV disparity
when seen from both visual sensors position is defined
by equation below:
δi0 =
d1
d1 + |d2 |
δj0 =
d2
d1 + |d2 |
(10)
Where δi and δj are the disparities of the two visal sensors
that are selected against the desired direction of view.
C. Virtual View Generation
There are three stages to generate a virtual image view
items, namely, interpolation, pre-wrap, and post-wrap. In
addition, interpolation is the step to generate intermediate
image by using pixel correspondence between two images
from two selected sensors. A method of correspondence
pixel pairs search along epipolar line from both images is
used to make a fast computation. Moreover, epipolar line
correspondence is:
⇢
l = F T p;
(11)
l0 = F p0 .
Where l and l0 are a pair of epipolar line, and F is
fundamental matrix [10] as a result from this equation:
pT = F p0 = 0
Position of point P on image plane that produced by
visual sensor C can be located on image plane produced
by visual sensor C 0 by substituting coordinate equation
between visual sensor C and visual sensor C 0 into projection equation:
0
− sin β
f
Whenever FoV of visual sensor C 0 is field of view of
desired virtual view, this geometry of two visual sensors
model can be used to get scene for virtual view image.
We can get disparity value from reference point on
geometry of two visual sensors. For visual sensor i
and visual sensor j in 2, the disparity, δ, between two
images at camera i and camera j with position parameters
(δi ,ri ,θi ) and (δj ,rj ,θj ) are calculated as [6], [18]:
Figure 2. Geometry of two visual sensor.
x0 = f
Y0
=
Z0
T
(12)
0
Where P and P are pair of point from two-dimensional
coordinate that corresponds from two images. F is fundamental matrix that has redundancy value. The parameter
F can be solved by least square method by minimizing
value from equation below:
n
X
,
i=1
pTi F p0i
-2
(13)
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389
Interpolation process suited to disparity viewpoint of two
selected visual sensors. Furthermore, interpolation process
to find new image points is defined below:
pv = d01 p + d01 p0
(14)
To estimate epipolar lines coordinate on virtual view and
warps interpolated image by mean of placing those lines
on correct position on virtual view post-wrap process
aims. By then, virtual view is produced.
D. Energy Consumption
Since energy source of WVSN solely from battery
with limited power, thus energy consumption becomes
a critical criteria on implementation [19]. This means
that the main priority of one particular model or any
implementation method of image virtual view generator
is to produce low energy consumption. In general, there
are three tasks that affect energy consumption of WVSN,
i.e.: 1) energy for images capturing, 2) energy for images
processing, and 3) energy for images transmitting. Those
three energy consumptions are formulated as below:
EW V SN = Ecapt + Eproc + Etransmit
(15)
Ecapt is images capturing energy, Eproc is images processing energy consumption, and Etransmit is images
transmitting energy consumption. Furthermore, we can
manually compute energy consumption by measuring
voltage (V ), current (I), and time of the whole process
i.e. images capturing time (tcapt ), images processing time
(tproc ), and images transmitting time (ttransmit ), with or
without any compression methods, as:
E = V × I × (tcapt + tproc + ttransmit )
(16)
Energy consumption of JPEG 2000 image compression
can be estimated from DWT processes. In this research,
OpenJPEG library used as JPEG 2000 representation
using Le Gall filter model 5 − 3 [15], [20]. Equations
regarding the filter are:
1
(−z 2 + 2Z + 6 + 2z −1 − z −2 )
(17)
8
1
H0 (z) = (z + 2 + z −1 )
(18)
2
H0 (z) defines low frequency sub band of DWT filter and
G0 (z) defines high frequency subband of DWT filter. The
impulse responses of the filter are:
L0 (z) =
−x[2n − 2] + 2x[2n − 1]
4
6x[2n] + 2x[2n + 1] − x[2n + 2]
+
4
L[2n] =
© 2015 ACADEMY PUBLISHER
CDW T (M, N, L) = M N (10S + 12A)
L
X
1
l−1
4
i=1
(21)
Apart from arithmetic operations, on transformation steps,
energy is significantly consumed from memory access.
Within memory access processes, each pixel goes through
read and write processes 2 times, thus memory access for
M x N image is:
CRead (M, N, L) = CW rite (M, N, L) = 2M N
L
X
1
l−1
4
i=1
(22)
Total processing load is computed as sum of computational load (CDW T ) and memory access load, (Cread )
and (Cwrite ), from Eq. (21) and Eq. (22) and becomes:
Cproc = CDW T + Cread + Cwrite
Cproc (M, N, L) = M N (10S + 12A)
2M N
L
X
1
+
l−1
4
i=1
L
L
X
X
1
1
+
2M
N
l−1
l−1
4
4
i=1
i=1
(23)
Furthermore, if Eshif t denote energy consumption for
shift (S) operation, Eadd denote energy consumption for
add (A) operation, Eread denote energy consumption for
read memory access, and Ewrite denote energy consumption for write memory access, then the overall energy
consumption for processing becomes:
Eproc = EDW T + Eread Cread + Ewrite Cwrite
Eproc (M, N, L) = M N (10Eshif t + 12Eadd )
(19)
−x[2n] + 2x[2n + 1] − x[2n + 2]
(20)
2
Analyzing energy consumption of DWT image compression requires size and type of some basic operations
of the given process. Basic operations such as shifting
H[2n + 1] =
and adding can derive from (19) and (20). In (19), filter
decomposition requires 8 shifts and 8 adds operations to
convert 1 pixel of an image into low pass coefficient,
meanwhile (20) requires 2 shifts and 4 adds operations to
convert into high pass coefficient.
Overall the computation of an image on DWT decomposition is acquired by counting the whole operations. The
computation of one image of size M xN with L DWT
level is estimated step by step as follows. Assume that
the first step is horizontal decomposition. Since the whole
pixels at even position are decomposed into low pass
coefficient and pixels at odd positions are decomposed
into high pass, which total weight at horizontal is 14
M N (10S+12A). The vertical decomposition has similar
weight. Image’s size will reduce with factor of 4 from
initial size for every transformation level. Then, the total
computation cost becomes:
Ewrite 2M N
L
X
1
+
l−1
4
i=1
L
L
X
X
1
1
+
E
2M
N
read
l−1
l−1
4
4
i=1
i=1
Eproc (M, N, L) =
M N (10Eshif t + 12Eadd + 2Ewrite + 2Eread )
L
X
1
l−1
4
i=1
(24)
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Image transmission is generally in the form of data
packets. If the number of packets is m, average packets
size is t, and energy required to transmit 1 bit data is Eb ,
transmission energy is defined as:
Etransmit = m · t · Eb
(25)
The IEEE 802.15.4 communication system on Linux
embedded OS uses the tosmac structure data packet
[1], with the payload of each packet being 28 bytes.
From this model and substituting (24) and (25) into
(15), the energy consumption for transmitting a JPEG
2000 compressed image becomes:
EW V SN = Ecapt + Eproc + Etransmit
EW V SN (M, N, L) = Ecapt + M N (10Eshif t +
12Eadd + 2Eread + 2Ewrite )
L
X
1
+ m · t · Eb
l−1
4
i=1
(26)
According to (26), the captured energy remains fixed
for the same devices. Therefore total consumption
energy depends on both processing energy (Eproc )
and transmission energy (Etransmit ). The transmission
energy (Etransmit ) is affected by image size in bits.
3)
4)
5)
6)
E. Peak Signal to Noise Ratio (PSNR)
Error metrics often used to compare various image
compression techniques are the Mean Square Error (MSE)
and the Peak Signal to Noise Ratio (PSNR). The MSE is
the cumulative squared error between the compressed and
the original image, whereas PSNR represents a measure
of the peak error. The mathematical formula for PSNR
and MSE are:
m−1 n−1
1 XX
2
[I(i, j) − K(i, j)]
m · n i=0 j=0
✓
◆
M AXl
P SN R = 20 log10 √
M SE
M SE =
(27)
(28)
Where I represents matrix of original images, K is
compressed image, m is number of pixel row of image,
i is index of that row, n represents number of image’s
pixel column and j is index of the column.
F. Procedures
The following flowchart of virtual view image generation procedures in accordance with 3, is described as
follows:
1) A number of visual sensors are installed and deployed in the desired area of coverage. These visual sensors send visual information to the central
processing. The central processing unit is in charge
of collecting information from each of the visual
sensor and received a request from a user for a FoV.
2) Calibration to determine the position and direction
of view and visual coverage of each sensor or point
of reference to sensor node is done before any
© 2015 ACADEMY PUBLISHER
7)
8)
other task because the direction of visual sensor is
assumed not to be changed for energy savings. The
disparity between cameras is obtained by determining the direction of view of each sensor. An initial
image of each visual sensor is distributively stored
in each sensor node and also in a central processing
to be used ingenerating virtual image.
JPEG 2000 image compression method is implemented on all visual sensors to compress the captured image. The purpose of image compression
is to save energy in the image delivery as the
transmission of data requires much higher energy
than computing the data.
After the system recognizes the position of each
sensor node in accordance to reference point, request of each user to any desired FoV can be
performed.
Desired FoV will be compared with the position /
direction of view of any visual sensors. Based on
mutual information criteria and the disparity, the
system decides which camera to be used.
Since environmental conditions might change at
any time, due to weather, new additional object,
changing the position of objects and others, then on
every FoV request, a new image will be captured
from selected visual sensors. The new image will
be compared with the initial image to find their
differences.
The system calculates mutual information of images. Furthermore, only two images with the largest
mutual information are transmitted to a central
processing to save energy for transmitting.
Image generation process is done at central processing. The Image generation steps are 1) Interpolation
of correspondence points, 2) pre-wrapping process,
3) post-wrapping process.
III.
RESULTS AND DISCUSSION
Initial stage is begun when all visual sensor nodes
capture and store images. Then the nodes send the
compressed images to central processing. Each node has
calculated and stored mutual information of the images.
When particular FoV is requested, the central processing
will select two visual sensor nodes based on disparity
method as seen in Fig. 4.
In the initial stage, the mutual information computation
for the first image and the second image to themselves
applies (2), that are H(I11 , I11 ) = 7.0800 and H(I21 , I21 )
= 5.3526 (See TABLE III). Condition 2 is the condition
when images are captured with no differences to the
initial condition, Fig. 4c and Fig. 4d, so the mutual information of images is the same. The mutual information
are H(I11 , I12 )=7.0800 and H(I21 , I22 )= 5.3536. On the
other hand, the mutual information, as seen in TABLE
III is obtained from calculation of image in Fig 4c to the
image in Fig. 4a, and image in Fig 4d to theimage in Fig
4b.
JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
391
TABLE III.
M UTUAL I NFORMATION COMPUTATION .
Condition
1 (initial)
2
3
Mutual Information of image 1
7.0800
7.0800
1.3599
Mutual Information of image 2
5.3526
5.3526
1.1110
from (26). For condition 2 and condition 3, the processing energy consumption (Eproc ) and transmission energy
consumption (Etransmit ) are change as follows:
EW V SN = m · t · Eb + M N
L
X
1
l−1
4
i=1
(10Eshif t + 12Eadd + 2Eread + 2Ewrite )
Assumed that active processor is a condition when signal
processing takes place, then Eshif t , Eadd , Eread , and
Ewrite can be substituted with the energy consumption
when the processor is active, as shown in Table I. According to this table, the energy consumption of active
processor is 192.3 mW. Then by encoding time duration,
the EDW T , the proccesing energy consumption (Eproc )
becomes:
L
X
1
EP roc = M N
4l−1
l=1
(10Eshif t + 12Eadd + 2Eread + 2Ewrite )
= Pproc · t
where t is the time required for encoding processes. Based
on JPEG 2000 parameters in TABLE II, the average
encoding time for 0.1 bpp and DWT level 3 is 18.5 s
and the energy consumption becomes:
EW V SN = 0.04J + 192.3mW × 18.5s + 864 × 3, 291.5
= 0.04J + 3.558J + 131, 9J = 135.52J
Figure 3. Image Generation Flowchart based on desired FoV request.
Whenever any differences occurred, as in condition
3, the mutual information between initialized image and
captured image at that time, becomes smaller, that are
H(I11 ; I13 )= 1.3599 and H(I21 ; I23 )= 1.1110. These are
the criteria that are used to determine whether it is
necessary to send new image to the central processing.
When mutual information is relatively the same, the visual
sensor node only sends one bit data, 0, to inform the
central processing that there is no new image available.
When the object or scene has experienced any modification, visual sensor node sends another bit, i.e. bit 1,
followed by new captured image.
Energy consumption as seen in TABLE IV is obtained
© 2015 ACADEMY PUBLISHER
At condition 2, when two selected sensor nodes send
two images, then total energy consumption of sending
two compressed images by JPEG 2000 as base of virtual
image construction is 271.04 J. Meanwhile, at condition
3, when the mutual information value is the same as
the value at initial stage, only information bit is sent.
Therefore the energy is required for transmission process
is:
EW V SN = Ecapt + Eproc + Etransmit
= 0.04J + 1.10J + 0.02J = 1.16J
Hence total energy is required for transmitting information from two selected sensors of 2.32J.
On the contrary, transmitting image without compression makes file size of 900 KB. This large file is split
into 32,915 packets with average of 28 byte payload per
packet. By the use of Imote2 parameter on TABLE II and
computed using (26), energy consumption becomes:
EW V SN = Ecapt + m · k · Eb = 0.04J+
32, 915 · 864 · 46.39µJ = 1, 319.35J
392
JOURNAL OF NETWORKS, VOL. 10, NO. 6, JUNE 2015
TABLE IV.
E NERGY CONSUMPTION COMPARISON
Method
(a)
(b)
Image Size
(KB)
Ecapt (J)
Ecomp (J)
Etrans (J)
Etotal (J)
P SN R
Send
Compessed
Image
90
Send information bit
Send Raw
Image [10]
0
900
0.04
3.35
131.93
135.52
25.65
0.04
1.10
0.02
1.16
36.65
0.04
0
1319.30
1319.35
36.6
IV. C ONCLUSIONS
(c)
(d)
(e)
(f)
Figure 4. Images captured by visual sensors. a). Initial image captured
by visual sensor I. b). Initial image captured by visual sensor II. c).
Image created by visual sensor I for one FoV without any alteration on
the scene. d). Image created by visual sensor II for one FoV without
any alteration on the scene. e). Image created by visual sensor I for one
FoV with alteration on the scene. f). Image created by visual sensor II
for one FoV with alteration on the scene.
Virtual view image from one particular direction that
is demanded by user requires at least two images from
visual sensors. This application becomes a challenge in
WVSN environment due to its limited resources. One
method to save energy consumption is by applying image
compression, such as JPEG 2000 on each visual sensor.
In this research, we conclude that by applying such
compression method, energy consumption on each sensor
node increases as much as 3.35 J. This cost is compromised by significant reduction of energy consumption on
image transmission. Total energy required to send one
compressed image reduces to only 10.27% of energy
consumption of uncompressed image.
The mutual information computation as detector for
scene modification also increases energy consumption on
each node. However when mutual information shows no
change on the scene, the total energy required is down to
1.16 J, or 0.9% of energy of uncompressed image.
ACKNOWLEDGMENT
(a)
(b)
Figure 5. a) Virtual View image on disparity 0.5; b) Image Created by
visual sensor on the same FoV with the same disparity
Finally, the energy consumption to send one compressed
image is reduced to only 10.27% compared to energy
consumption without compression (see Table 4). When
there is no modification of object or scene, i.e. condition
2, the energy consumption reduces to 3% when comparred
with the whole compressed image is transmitted.
The total energy consumption of sending two compressed images by JPEG 2000 as base of virtual image
construction is 271.04 J, and 2639.70 J for uncompressed images. Virtual image of disparity of 0.5 is shown
in Fig. 5a and the captured image from visual sensor for
FoV as requested by user is shown in Fig. 5b.
© 2015 ACADEMY PUBLISHER
The authors are grateful to the anonymous referees for their valuable comments and suggest̃ions
to improve the presentation of this paper. This
work is supported by Indonesian Ministry of Education and Culture through the BPPS scholarship
and 2013 Doctoral Research Grant, contract number:
175.45/ŨN14.2/PNL.01.03.00/2013, May 16, 2013.
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393
Nyoman Putra Sastra received the B.Eng.
and M.Eng. degree in electrical engineering
from Institut Teknologi Bandung (ITB), Indonesia, in 1998 and 2001, respectively. Since
2001, he joined with Universitas Udayana, Bali
as lecturer. He is currently working toward
the Ph.D.degree in electrical engineering from
ITS. His research interests include His research
interests include wireless multimedia sensor
networks and multimedia signal processing.
He is a Student Member of the IEEE.
Wirawan received the B.E. degree in
telecommunication engineering from Institut
Teknologi Sepuluh Nopember (ITS), Surabaya,
Indonesia, in 1987, the DEA in signal and
image processing from Ecole Suprieure en Sciences Informatiques, Sophia Antipolis, and the
Dr. degree in image processing from Telecom
ParisTech (previously Ecole Nationale Suprieure des Tlcommunications), Paris, France,
in 1996 and 2003, respectively. Since 1989 he
has been with ITS as lecturer in the Electrical
Engineering Department. His research interest lie in the general area
of image and video signal processing for mo