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E
lect
rica
l a
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Co
m
p
ute
r
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ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
7
,
No
.
6
,
Dec
em
b
er
201
7
,
p
p
.
3
5
2
9
~
3
5
3
5
I
SS
N:
2088
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v
7
i
6
.
pp
3
5
2
9
-
3535
3529
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2088
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
6
,
Dec
em
b
er
201
7
:
3
5
2
9
–
3
5
3
5
3530
p
r
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in
s
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2
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Sectio
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le
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er
s
to
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la
y
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eo
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b
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w
ith
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t
h
a
v
i
n
g
to
o
b
tain
th
e
w
h
o
le
d
ata
f
ir
s
t
[
2
]
.
A
lth
o
u
g
h
it
h
a
s
m
a
n
y
ad
v
an
ta
g
es,
q
u
ali
t
y
o
f
s
er
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ice
(
Qo
S)
s
o
m
et
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m
e
s
b
ein
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q
u
e
s
tio
n
ed
.
L
ac
k
o
f
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S
e
x
p
er
ien
ce
d
b
y
e
n
d
u
s
er
s
p
ar
ticu
lar
l
y
o
cc
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r
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n
m
e
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h
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et
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k
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u
c
h
as
th
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n
ter
n
et.
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asicall
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o
f
v
id
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tr
ea
m
i
n
g
i
s
in
f
l
u
en
ce
d
b
y
b
an
d
w
id
t
h
li
m
it,
l
o
s
s
o
f
d
ata
an
d
j
itter
[
5
]
[
6
]
[
7
]
.
T
o
tack
le
th
e
is
s
u
e,
v
ar
io
u
s
i
n
tellig
e
n
t
v
id
eo
s
tr
ea
m
in
g
al
g
o
r
ith
m
s
h
av
e
b
ee
n
i
n
tr
o
d
u
ce
d
.
T
h
e
m
ain
ai
m
o
f
th
e
alg
o
r
it
h
m
is
to
m
a
k
e
v
id
eo
p
ac
k
ets
i
n
telli
g
e
n
t
e
n
o
u
g
h
a
n
d
ad
ap
tiv
e
w
i
th
th
e
u
n
f
r
ie
n
d
l
y
n
et
w
o
r
k
co
n
d
itio
n
s
.
At
t
h
e
e
n
d
b
y
ap
p
ly
in
g
t
h
e
in
te
llig
e
n
t
alg
o
r
it
h
m
,
q
u
al
it
y
o
f
v
id
eo
s
tr
ea
m
i
n
g
ca
n
b
e
e
n
h
a
n
ce
d
.
P
r
ev
io
u
s
s
t
u
d
y
[
4
]
s
u
g
g
e
s
ted
th
at
in
te
lli
g
en
t
v
id
eo
s
tr
ea
m
in
g
al
g
o
r
ith
m
s
ca
n
b
e
class
i
f
ied
in
to
f
o
u
r
m
a
i
n
g
r
o
u
p
s
as
f
o
llo
w
s
.
2
.
1
.
Video
Ada
pta
t
io
n Alg
o
r
it
h
m
Vid
eo
A
d
ap
tatio
n
al
g
o
r
ith
m
is
co
n
s
id
er
ed
as
t
h
e
b
as
ic
tech
n
iq
u
e
u
s
ed
f
o
r
v
id
eo
s
tr
ea
m
i
n
g
to
m
ai
n
tai
n
v
id
eo
q
u
ali
t
y
ac
co
r
d
in
g
to
t
h
e
ca
p
ab
ilit
y
o
f
d
ata
s
e
n
d
er
an
d
it
d
ea
ls
p
r
i
m
ar
il
y
w
it
h
i
n
s
tab
le
n
et
w
o
r
k
co
n
d
itio
n
.
T
h
is
ad
ap
tiv
e
s
ch
e
m
e
d
ev
elo
p
s
f
le
x
ib
le
m
ed
ia
s
tr
e
a
m
i
n
g
to
ad
d
r
ess
th
e
p
r
o
b
lem
o
f
s
er
v
in
g
h
eter
o
g
e
n
eo
u
s
clie
n
t
s
w
it
h
ad
ap
tiv
e
v
id
eo
q
u
alit
y
.
Si
m
u
lca
s
t
[
8
]
is
a
m
o
n
g
t
h
e
ea
r
lies
t
ap
p
r
o
ac
h
o
f
v
id
eo
ad
ap
tatio
n
.
I
t
en
co
d
es
s
i
n
g
le
v
id
eo
s
o
u
r
ce
in
to
m
u
ltip
le
in
d
ep
en
d
e
n
t
s
tr
ea
m
s
d
if
f
er
en
t
l
y
a
n
d
at
clien
t
s
id
e,
p
a
r
ticu
lar
b
itra
te
o
f
en
co
d
ed
v
id
eo
is
ch
o
s
en
ac
co
r
d
in
g
to
its
ac
ce
s
s
b
an
d
w
id
t
h
[
9
]
.
Vid
eo
tr
an
s
co
d
in
g
[
1
0
]
is
an
o
th
er
in
telli
g
e
n
t
s
ch
e
m
e
th
at
ad
ap
ts
th
e
v
id
eo
s
tr
ea
m
in
g
ac
co
r
d
in
g
to
t
h
e
f
lo
w
r
ate
co
n
s
tr
ai
n
t
s
o
f
u
s
e
r
p
r
ef
er
en
ce
s
.
Fo
r
m
at
tin
g
v
id
e
o
co
n
v
er
s
io
n
w
h
i
le
r
ed
u
cin
g
b
it
r
ate
o
f
t
h
e
v
id
eo
o
r
d
r
o
p
p
in
g
v
id
eo
s
ize
to
f
it
th
e
b
an
d
w
id
t
h
o
f
e
n
d
u
s
er
ar
e
m
ain
tech
n
iq
u
e
s
ap
p
lied
in
th
is
al
g
o
r
ith
m
[
1
1
]
.
I
n
m
es
h
n
et
w
o
r
k
en
v
ir
o
n
m
e
n
t
s
u
ch
as
w
ir
eles
s
[
1
2
]
,
an
o
th
er
ad
ap
tatio
n
alg
o
r
ith
m
ca
lled
tr
an
s
co
d
in
g
tech
n
iq
u
e
o
f
f
er
s
f
lex
ib
il
it
y
to
ca
r
ef
u
ll
y
tr
ad
eo
f
f
s
p
atial
a
n
d
t
e
m
p
o
r
al
d
is
to
r
tio
n
s
to
en
ab
le
g
o
o
d
v
id
eo
q
u
alit
y
to
th
e
e
n
d
u
s
er
s
[
1
2
]
[
1
3
]
.
Ho
w
e
v
er
,
th
is
s
ch
e
m
e
h
as
s
er
io
u
s
li
m
itatio
n
f
o
r
lar
g
e
v
ar
iet
y
o
f
clie
n
ts
in
n
et
w
o
r
k
[
1
4
]
.
T
h
en
,
Yu
a
n
,
et.
al
[
1
5
]
i
n
tr
o
d
u
ce
th
e
i
n
telli
g
e
n
t
P
r
io
r
itized
A
d
ap
tiv
e
Sc
h
e
m
e
(
iP
AS)
as
an
ad
v
a
n
ce
d
alg
o
r
ith
m
f
o
r
ad
ap
tin
g
th
e
en
co
d
in
g
a
n
d
tr
an
s
m
is
s
io
n
b
y
es
ti
m
ati
n
g
b
an
d
w
id
t
h
u
s
a
g
e
w
i
th
i
n
d
i
f
f
er
en
t
n
et
w
o
r
k
s
itu
at
io
n
s
.
2
.
2
.
S
ca
la
ble St
re
a
m
i
ng
Alg
o
rit
h
m
I
n
a
b
r
o
ad
ca
s
t
o
r
m
u
lticas
t
en
v
ir
o
n
m
en
t,
s
in
ce
t
h
er
e
ar
e
lar
g
e
v
ar
iatio
n
s
i
n
ad
ap
tatio
n
n
e
ed
am
o
n
g
r
ec
eiv
er
s
,
p
er
f
o
r
m
i
n
g
co
d
in
g
at
ev
er
y
ed
g
e
is
n
o
t
e
f
f
ec
ti
v
e
s
o
lu
tio
n
,
t
h
u
s
s
ca
lab
le
s
tr
ea
m
i
n
g
s
c
h
e
m
e
i
s
m
o
r
e
ap
p
r
o
p
r
iate
th
an
s
o
u
r
ce
ad
ap
tatio
n
s
c
h
e
m
e.
Fi
n
e
Gr
an
u
lar
it
y
Scalab
ilit
y
o
r
F
GS
f
o
r
s
p
ati
al
q
u
alit
y
ad
ap
tatio
n
is
a
m
o
n
g
t
h
e
ea
r
lie
s
t
a
lg
o
r
it
h
m
to
s
ca
lab
le
v
id
eo
s
tr
ea
m
i
n
g
[
1
2
]
[
1
6
]
.
T
h
e
alg
o
r
ith
m
w
a
s
th
e
n
i
m
p
r
o
v
ed
b
y
Oh
m
[
1
7
]
w
h
o
i
n
tr
o
d
u
ce
d
M
o
tio
n
C
o
m
p
e
n
s
ated
T
e
m
p
o
r
al
Fil
ter
in
g
(
M
C
T
F)
alg
o
r
ith
m
.
An
o
th
er
ad
v
a
n
ta
g
e
o
f
th
i
s
alg
o
r
it
h
m
is
t
h
at
tr
u
n
ca
tin
g
b
it st
r
ea
m
ca
n
b
e
d
o
n
e
at
al
m
o
s
t e
v
er
y
p
o
in
t [
1
8
]
.
Self
-
t
u
n
in
g
Neu
r
o
-
F
u
zz
y
(
SN
F)
is
p
r
o
p
o
s
ed
in
[
1
9
]
t
o
en
ab
le
MP
E
G
v
id
eo
d
ata
o
v
er
th
e
B
lu
eto
o
th
ch
an
n
el.
L
ik
e
w
i
s
e,
Kaz
e
m
ian
[
6
]
d
em
o
n
s
tr
ates
th
is
s
c
h
e
m
e
co
m
b
i
n
ed
w
it
h
tr
af
f
ic
-
s
h
ap
i
n
g
b
u
f
f
er
b
ased
o
n
Neu
r
al
-
F
u
zz
y
alg
o
r
it
h
m
to
e
n
ab
le
v
id
eo
tr
an
s
m
is
s
io
n
w
it
h
lo
w
p
o
w
er
,
lo
w
co
s
t,
lo
w
co
m
p
le
x
it
y
w
ir
eles
s
s
tan
d
ar
d
s
,
an
d
v
er
y
li
m
ited
b
an
d
w
id
t
h
s
u
p
p
o
r
t.
I
n
ad
d
iti
o
n
,
Mu
ltip
le
Descr
ip
tio
n
C
o
d
in
g
(
MD
C
)
[
2
0
]
is
an
o
th
er
al
g
o
r
ith
m
w
h
ich
e
n
co
d
e
v
id
eo
in
t
o
t
w
o
o
r
m
o
r
e
in
d
ep
en
d
en
tl
y
d
ec
o
d
ab
le
la
y
er
s
.
2
.
3
.
Video
Su
mm
a
riza
t
io
n A
lg
o
rit
h
m
Vid
eo
Su
m
m
ar
iza
tio
n
al
g
o
r
it
h
m
is
p
r
o
p
o
s
ed
as
a
s
o
l
u
tio
n
to
th
e
w
ea
k
n
ess
e
s
o
f
th
e
p
r
ev
io
u
s
t
w
o
alg
o
r
ith
m
s
[
2
1
]
.
T
h
is
s
ch
e
m
e
d
ea
ls
w
ith
t
h
e
i
s
s
u
e
o
n
h
o
w
to
m
an
ip
u
late
t
h
e
lar
g
e
q
u
an
tit
y
o
f
v
id
eo
s
tr
ea
m
in
g
d
ata
p
ar
ticu
lar
l
y
in
n
et
w
o
r
k
e
n
v
ir
o
n
m
e
n
t.
Vid
eo
s
u
m
m
ar
iz
atio
n
s
ch
e
m
e
ap
p
lies
i
n
telli
g
e
n
t
s
m
ar
t
alg
o
r
it
h
m
f
o
r
an
al
y
s
i
s
,
s
tr
u
ct
u
r
in
g
,
a
n
d
s
u
m
m
ar
izi
n
g
v
id
eo
co
n
ten
t
ac
c
o
r
d
in
g
to
v
ar
io
u
s
u
s
er
p
r
e
f
er
en
ce
s
i
n
v
ie
w
i
n
g
t
h
e
v
id
eo
[
2
2
]
.
T
h
e
m
o
s
t
p
o
p
u
lar
t
y
p
e
o
f
v
id
e
o
s
u
m
m
ar
y
i
s
t
h
e
p
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r
ial
s
u
m
m
ar
y
.
I
t
h
as
th
r
ee
ac
ce
s
s
lev
els
m
ak
i
n
g
ea
s
ier
th
e
s
ea
r
c
h
f
o
r
v
id
eo
s
e
q
u
en
ce
s
.
T
h
e
f
ir
s
t
ac
ce
s
s
lev
e
l
en
ab
les
u
s
er
s
to
o
b
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f
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s
s
f
o
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e
w
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lev
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s
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te
m
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8708
Dec
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3531
w
h
ic
h
o
p
er
ates
o
n
v
id
eo
s
u
m
m
ar
ies
[
2
3
]
.
I
t
is
w
id
el
y
d
ep
lo
y
ed
w
it
h
p
er
s
o
n
aliza
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m
e
s
[
2
4
]
.
Oth
er
t
y
p
e
o
f
in
t
ellig
e
n
t
v
id
eo
s
u
m
m
ar
izatio
n
i
s
in
tr
o
d
u
ce
d
b
y
L
i,
et.
al
[
1
8
]
.
I
t
en
h
an
ce
s
m
u
lti
-
u
s
er
v
id
eo
co
m
m
u
n
icatio
n
s
o
l
u
tio
n
w
it
h
b
etter
ef
f
icie
n
c
y
in
r
eso
u
r
ce
u
tili
za
tio
n
an
d
p
r
o
m
i
s
es b
et
ter
o
v
er
all
r
ec
eiv
ed
v
id
eo
q
u
alit
y
.
2
.
4
. S
ec
ure
M
edia
Str
ea
m
i
n
g
Alg
o
rit
h
m
Un
li
k
e
p
r
ev
io
u
s
g
r
o
u
p
s
,
th
i
s
t
y
p
e
o
f
alg
o
r
it
m
f
o
cu
s
es o
n
ad
d
in
g
s
ec
u
r
it
y
p
ar
a
m
eter
s
to
en
h
a
n
ce
s
m
ar
t
v
id
eo
s
tr
ea
m
i
n
g
.
T
h
e
Secu
r
e
s
ca
lab
le
s
tr
ea
m
i
n
g
(
SS
S
Fra
m
e
w
o
r
k
)
is
co
n
s
id
er
ed
as
th
e
f
i
r
s
t
s
ec
u
r
it
y
s
c
h
e
m
e
p
r
o
p
o
s
ed
b
y
W
ee
an
d
A
p
o
s
to
l
o
p
o
u
lo
s
[
2
5
]
.
T
h
e
f
r
a
m
e
w
o
r
k
s
u
p
p
o
r
ts
en
d
-
to
-
e
n
d
d
eliv
er
y
o
f
en
cr
y
p
ted
m
ed
ia
co
n
ten
t
w
h
ile
en
ab
li
n
g
ad
ap
tiv
e
s
tr
ea
m
in
g
an
d
tr
an
s
co
d
in
g
to
b
e
p
er
f
o
r
m
ed
at
in
ter
m
ed
iate,
p
o
s
s
ib
l
y
u
n
tr
u
s
ted
,
n
o
d
es
w
it
h
o
u
t
r
e
q
u
i
r
in
g
d
ec
r
y
p
tio
n
an
d
t
h
er
ef
o
r
e
p
r
eser
v
in
g
t
h
e
en
d
to
e
n
d
s
ec
u
r
it
y
.
Ho
w
ev
er
,
t
h
i
s
m
et
h
o
d
d
o
es
n
o
t
p
r
o
v
id
e
au
th
en
t
icatio
n
m
ec
h
a
n
i
s
m
at
s
e
n
d
er
s
id
e,
th
u
s
it
v
u
ln
er
ab
le
t
o
m
alic
io
u
s
attac
k
s
[
1
4
]
.
An
o
th
er
ap
p
r
o
ac
h
is
ca
lled
th
e
AR
MS
s
y
s
te
m
p
r
o
p
o
s
ed
b
y
Ven
k
atr
a
m
a
n
i,
et.
al
[
2
6
]
.
T
h
is
ap
p
r
o
ac
h
en
ab
les
s
ec
u
r
e
an
d
ad
ap
tiv
e
r
ich
m
ed
ia
s
tr
ea
m
in
g
to
a
lar
g
e
-
s
ca
le,
h
e
ter
o
g
en
eo
u
s
clie
n
t
p
o
p
u
latio
n
w
it
h
i
n
u
n
tr
u
s
ted
s
er
v
er
s
.
I
n
2
0
0
4
,
Secu
r
e
R
ea
l
T
i
m
e
T
r
an
s
p
o
r
t
P
r
o
to
co
l
(
SR
T
P
)
w
as
d
ev
elo
p
ed
to
p
r
o
v
id
e
co
n
f
id
e
n
tialit
y
,
m
es
s
a
g
e
a
u
th
en
ticatio
n
,
a
n
d
r
ep
la
y
p
r
o
tecti
o
n
as
b
asic
s
ec
u
r
it
y
s
er
v
ice
s
r
eq
u
ir
ed
f
o
r
s
ec
u
r
e
v
id
eo
s
tr
ea
m
i
n
g
[
4
]
[
1
4
]
.
C
h
iar
ig
lio
n
e,
et.
al
[
2
7
]
p
r
o
p
o
s
e
a
MP
E
G
s
tan
d
ar
d
aim
in
g
a
t
s
tan
d
ar
d
izin
g
t
h
e
f
o
r
m
at
f
o
r
d
is
tr
ib
u
tio
n
o
f
g
o
v
er
n
ed
d
i
g
ital
co
n
ten
t.
I
t
h
as
t
w
o
m
a
in
o
b
j
ec
tiv
es,
f
ir
s
tl
y
to
p
r
o
tect
r
ig
h
t
s
o
f
h
o
ld
er
s
an
d
s
ec
o
n
d
l
y
s
o
lv
e
th
e
in
ter
o
p
er
ab
ilit
y
i
s
s
u
e
t
h
at
i
s
w
o
r
s
e
n
ed
b
y
t
h
e
m
a
n
y
e
x
i
s
ti
n
g
p
r
o
p
r
ietar
y
D
R
M
s
y
s
te
m
s
.
T
h
e
s
tan
d
ar
d
g
o
v
er
n
s
h
o
w
to
d
eliv
er
e
n
cr
y
p
ted
co
n
ten
t
an
d
p
er
f
o
r
m
i
n
g
m
u
tu
al
au
th
e
n
ticatio
n
be
t
w
ee
n
d
ev
ice
s
i
n
v
o
l
v
ed
an
d
in
te
g
r
it
y
au
t
h
e
n
ticatio
n
o
f
g
o
v
er
n
ed
co
n
te
n
t.
Ye
t,
ad
ap
tatio
n
an
d
o
t
h
er
f
lex
ib
le
h
an
d
li
n
g
s
o
f
m
u
lti
m
ed
ia
ar
e
s
ac
r
if
iced
w
h
ic
h
m
ak
e
s
it d
if
f
ic
u
lt
f
o
r
w
id
e
ad
o
p
tio
n
.
3.
RE
S
E
ARCH
M
E
T
H
O
D
I
n
o
r
d
er
to
an
s
w
er
th
e
q
u
es
t
io
n
o
f
h
o
w
to
s
elec
t
t
h
e
b
es
t
a
m
o
n
g
e
x
i
s
tin
g
al
g
o
r
it
h
m
s
o
f
v
id
eo
s
tr
ea
m
i
n
g
an
d
th
e
n
ap
p
l
y
it
in
a
p
r
iv
ate
clo
u
d
co
m
p
u
t
in
g
en
v
ir
o
n
m
e
n
t
,
m
a
n
y
p
er
s
p
ec
ti
v
es,
an
d
cr
iter
ia
s
h
o
u
ld
b
e
in
v
o
lv
ed
an
d
co
n
s
id
er
ed
ap
p
r
o
p
r
iately
.
S
u
ch
ca
s
e
f
all
s
in
to
m
u
lti
cr
iter
ia
d
ec
is
io
n
m
ak
i
n
g
(
MCDM
)
p
r
o
b
lem
.
T
h
is
s
t
u
d
y
f
o
llo
w
s
t
h
e
ap
p
r
o
ac
h
u
s
ed
in
[
3
]
th
at
co
m
b
in
e
s
th
e
An
al
y
tic
H
ier
ar
ch
y
P
r
o
ce
s
s
[
2
8
]
an
d
I
SO
9
1
2
6
So
f
t
w
ar
e
E
n
g
i
n
ee
r
i
n
g
[
2
9
]
to
estab
lis
h
d
ec
is
io
n
f
r
a
m
e
w
o
r
k
,
w
h
ic
h
w
i
ll
b
e
u
s
ed
to
an
s
w
er
r
esear
ch
q
u
esti
o
n
i
n
t
h
is
s
tu
d
y
.
A
HP
’
s
s
i
m
p
licit
y
a
n
d
r
o
b
u
s
t
n
es
s
m
a
k
es
it
w
id
el
y
u
s
ed
t
o
s
o
lv
e
d
ec
is
io
n
an
al
y
s
i
s
p
r
o
b
lem
s
in
v
ar
io
u
s
f
ield
s
[
3
0
]
.
A
ls
o
,
i
t
o
f
f
er
s
f
le
x
ib
lit
y
to
co
m
b
in
e
ta
n
g
ib
le
an
d
in
tan
g
ib
le
f
ac
to
r
s
in
to
a
q
u
a
n
titati
v
e
d
ec
is
io
n
an
al
y
s
i
s
s
tr
u
c
tu
r
ed
w
it
h
i
n
th
r
ee
b
asic
la
y
er
s
n
a
m
el
y
,
g
o
al,
cr
iter
ia
an
d
alter
n
ati
v
e
[
3
1
]
.
T
h
e
f
ir
s
t
la
y
er
is
ca
lled
g
o
al
to
b
e
s
o
lv
e
d
w
h
ic
h
is
to
s
elec
t
th
e
b
es
t
v
i
d
eo
s
tr
ea
m
in
g
al
g
o
r
it
h
m
to
b
e
ap
p
lied
in
p
r
iv
ate
clo
u
d
co
m
p
u
ti
n
g
.
T
h
en
,
I
SO
9
1
2
6
is
in
ter
n
atio
n
al
s
o
f
t
w
ar
e
ev
alu
a
tio
n
s
ta
n
d
ar
d
th
at
co
n
s
i
s
ts
o
f
s
i
x
asp
ec
t
s
o
f
s
o
f
t
w
ar
e
en
g
i
n
ee
r
in
g
n
a
m
el
y
f
u
n
ct
io
n
alit
y
,
r
eliab
ilit
y
,
u
s
ab
il
it
y
,
ef
f
icie
n
c
y
,
m
ain
tain
ab
le
a
n
d
p
o
r
tab
ilit
y
[
2
9
]
.
I
t
is
w
id
el
y
ac
ce
p
ted
th
at
t
h
e
s
tan
d
ar
d
is
ap
p
licab
le
to
r
ev
iew
all
asp
ec
ts
o
f
s
o
f
t
w
ar
e
q
u
ali
t
y
f
r
o
m
p
r
ep
ar
atio
n
an
d
d
ev
elo
p
m
e
n
t
u
n
til
e
v
alu
a
tio
n
s
tag
e
s
[
3
2
]
.
T
h
e
s
ta
n
d
ar
d
is
ap
p
lied
o
n
t
h
e
s
ec
o
n
d
la
y
er
o
f
t
h
e
d
ec
is
io
n
h
ier
ar
ch
y
a
s
cr
iter
ia.
Fin
all
y
,
o
n
t
h
e
th
ir
d
la
y
er
o
f
A
HP
h
ier
ar
ch
y
i
s
ca
lled
th
e
al
ter
n
ati
v
e
.
T
h
e
alter
n
ativ
e
i
n
th
is
ca
s
e
ar
e
th
e
f
o
u
r
t
y
p
es
o
f
i
n
te
ll
ig
e
n
t
v
i
d
eo
s
tr
ea
m
i
n
g
alg
o
r
it
h
m
s
as
m
en
tio
n
ed
p
r
ev
io
u
s
l
y
i
n
s
ec
t
i
o
n
2
.
T
h
er
e
ar
e
f
o
u
r
altar
n
ati
v
es
s
et
in
t
h
is
s
t
u
d
y
,
n
a
m
el
y
Vid
eo
A
d
ap
tatio
n
a
lg
o
r
ith
m
,
Scalab
le
S
tr
ea
m
i
n
g
alg
o
r
it
h
m
,
Vid
eo
Su
m
m
ar
izatio
n
al
g
o
r
ith
m
,
a
n
d
Secu
r
e
Me
d
ia
Stre
a
m
in
g
a
lg
o
r
ith
m
.
Tt
h
e
co
m
p
lete
h
ier
ar
ch
y
o
f
d
ec
is
io
n
f
r
a
m
e
w
o
r
k
co
n
s
i
s
ts
o
f
th
r
ee
la
y
er
s
is
s
h
o
w
n
in
t
h
e
f
o
llo
w
in
g
F
ig
u
r
e
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2088
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
6
,
Dec
em
b
er
201
7
:
3
5
2
9
–
3
5
3
5
3532
Fig
u
r
e
1
.
T
h
e
d
ec
is
io
n
an
al
y
s
i
s
f
r
a
m
e
w
o
r
k
A
cc
o
r
d
in
g
to
Saa
t
y
[
2
8
]
,
A
HP
an
al
y
s
i
s
ca
n
b
e
co
m
p
leted
b
y
tak
i
n
g
th
e
f
o
llo
w
i
n
g
f
iv
e
s
i
m
p
le
s
tep
s
as
f
o
lo
w
s
,
S
tep
1
:
Dec
o
m
p
o
s
in
g
d
ec
is
io
n
f
r
a
m
e
w
o
r
k
in
t
h
e
f
o
r
m
o
f
a
h
ier
ar
ch
y
co
n
s
is
t
s
o
f
g
o
al,
cr
iter
ia,
a
n
d
alter
n
ati
v
es.
Fig
u
r
e
1
d
ep
icts
th
e
f
r
a
m
e
w
o
r
k
h
ier
ar
ch
y
ac
co
r
d
in
g
to
A
HP
m
et
h
o
d
w
h
i
ch
co
n
s
is
t
s
o
f
t
h
r
ee
la
y
er
s
.
Firs
t
la
y
er
is
th
e
g
o
al
o
f
Selecti
n
g
V
S
C
(
Vid
eo
St
r
ea
m
in
g
C
lo
u
d
)
,
th
e
s
ec
o
n
d
la
y
er
is
cr
iter
ia
o
f
s
elec
tio
n
b
ased
o
n
I
SO 9
1
2
6
,
an
d
f
i
n
all
y
t
h
e
la
s
t la
y
er
r
ep
r
esen
t
s
alter
n
ati
v
es o
f
f
o
u
r
t
y
p
e
s
o
f
v
id
eo
s
tr
ea
m
i
n
g
alg
o
r
ith
m
s
.
Step
2
:
C
o
llecti
n
g
in
p
u
t
f
r
o
m
e
x
p
er
ts
th
r
o
u
g
h
s
u
r
v
e
y
b
ased
o
n
th
e
d
ec
is
i
o
n
an
al
y
s
i
s
h
ier
ar
ch
y
i
n
th
e
p
air
w
i
s
e
co
m
p
ar
is
o
n
o
f
alter
n
ati
v
es
o
n
a
q
u
alita
tiv
e
s
ca
le
o
f
f
r
o
m
1
to
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an
d
o
r
g
a
n
izi
n
g
t
h
e
m
in
to
a
s
q
u
ar
e
m
atr
i
x
.
Step
3
:
C
alcu
l
atin
g
p
r
in
cip
al
eig
e
n
v
al
u
e
an
d
th
e
co
r
r
esp
o
n
d
in
g
n
o
r
m
aliz
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r
ig
h
t
eig
e
n
v
ec
to
r
o
f
th
e
co
m
p
ar
is
o
n
m
atr
i
x
.
Ste
p
4
:
E
v
alu
ati
n
g
t
h
e
co
n
s
is
te
n
c
y
r
atio
(
C
R
)
o
f
t
h
e
m
a
tr
ix
o
f
ex
p
er
t’
s
j
u
d
g
m
e
n
t.
I
n
ca
s
e
th
e
v
al
u
e
o
f
C
R
is
les
s
th
a
n
0
.
1
,
j
u
d
g
m
e
n
t
p
r
o
ce
s
s
m
u
s
t
b
e
r
ev
i
s
ed
.
Step
5
:
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g
r
eg
atin
g
g
lo
b
al
w
ei
g
h
t
o
f
cr
i
ter
ia
to
o
b
tain
th
e
f
in
al
r
a
n
k
i
n
g
.
4.
RE
SU
L
T
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A
ND
AN
AL
Y
SI
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x
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t
C
h
o
ice
2
0
0
0
A
ca
d
em
ic
E
d
itio
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as
u
s
ed
to
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er
f
o
r
m
th
e
w
h
o
le
p
r
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ce
s
s
es
s
tar
t
in
g
f
r
o
m
co
n
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tr
u
ct
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g
t
h
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is
io
n
a
n
al
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s
i
s
h
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ch
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cr
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g
t
h
e
AHP
s
u
r
v
e
y
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n
til
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n
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ir
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E
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0
0
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o
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ig
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2
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ased
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Fig
u
r
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elin
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u
r
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3
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Fu
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x
[
3
3
]
,
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it
s
ap
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[
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3
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tates
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Vo
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7
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6
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Dec
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201
7
:
3
5
2
9
–
3
5
3
5
3534
Sin
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tech
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RE
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NC
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S
[1
]
A
.
V
ij
a
y
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d
V
.
Ne
e
lan
a
ra
y
a
n
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M
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Driv
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ti
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o
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2
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p
p
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1
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,
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0
1
6
.
[2
]
D.
A
u
ste
rb
e
rr
y
,
“
T
h
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T
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h
n
o
lo
g
y
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d
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”
,
T
a
y
lo
r
&
Fra
n
c
is
,
2
0
0
5
.
[3
]
I.
S
y
a
m
su
d
d
in
,
“
P
r
o
b
lem
Ba
se
d
L
e
a
rn
in
g
o
n
Clo
u
d
Eco
n
o
m
ics
A
n
a
l
y
sis
Us
in
g
Op
e
n
S
o
u
rc
e
S
im
u
latio
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”
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In
ter
n
a
t
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a
l
J
o
u
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o
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li
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1
2
,
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o
.
6
,
p
p
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4
-
9
,
2
0
1
6
.
[4
]
I.
S
y
a
m
su
d
d
in
,
“
A
No
v
e
l
F
ra
m
e
w
o
rk
to
S
e
lec
t
In
telli
g
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n
t
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h
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e
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S
o
f
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re
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s
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e
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”
,
IAE
S
2
0
1
4
In
ter
n
a
ti
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l
C
o
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fer
e
n
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o
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lec
trica
l
E
n
g
i
n
e
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g
,
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o
mp
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ter
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c
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c
e
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n
fo
rm
a
t
ics
,
p
p
.
91
-
9
5
,
2
0
1
4
.
[5
]
C.
Hu
a
n
g
,
e
t.
a
l.
,
“
A
n
in
telli
g
e
n
t
stre
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m
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m
e
d
ia
v
id
e
o
s
e
rv
ice
s
y
ste
m
”
,
Pro
c
.
O
f
IEE
E
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fer
e
n
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e
o
n
Co
mp
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ter
s,
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o
mm
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n
ica
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tro
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g
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e
rin
g
,
p
p
.
1
-
5
,
2
0
0
2
.
[6
]
H.B.
Ka
z
e
m
ian
,
“
A
n
in
telli
g
e
n
t
v
id
e
o
stre
a
m
in
g
tec
h
n
iq
u
e
in
z
ig
b
e
e
w
irele
ss
”
,
IEE
E
I
n
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
Fu
zz
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S
y
ste
ms
,
p
p
.
1
2
1
-
1
2
6
,
2
0
0
9
.
[7
]
J.G
.
A
p
o
sto
lo
p
o
u
lo
s
,
e
t.
a
l.
,
“
V
i
d
e
o
stre
a
m
in
g
:
Co
n
c
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p
ts,
A
lg
o
rit
h
m
s,
a
n
d
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y
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m
s”
,
HP
T
e
c
h
n
ica
l
R
e
p
o
rt,
2
0
0
2
.
[8
]
B.
F
u
r
h
t,
e
t.
a
l.
,
“
M
u
l
ti
m
e
d
ia
Bro
a
d
c
a
stin
g
Ov
e
r
th
e
In
te
rn
e
t:
P
a
rtI
I
–
V
id
e
o
Co
m
p
re
ss
io
n
”
,
IEE
E
M
u
lt
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a
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v
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l
.
6
,
n
o
.
1
,
p
p
.
8
5
–
8
9
,
1
9
9
9
.
[9
]
A
.
L
ip
p
m
a
n
,
“
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id
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g
f
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T
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rg
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t
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e
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PI
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Vi
s
u
a
l
Co
mm
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s
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n
d
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g
e
Pro
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e
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in
g
,
p
p
.
7
8
0
–
7
8
2
,
1
9
9
9
.
[1
0
]
A
.
V
e
tro
,
e
t.
a
l.
,
“
Vid
e
o
T
ra
n
sc
o
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in
g
A
rc
h
it
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c
tu
re
s
a
n
d
T
e
c
h
n
iq
u
e
s:
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n
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e
r
v
ie
w
”
,
IEE
E
S
ig
n
a
l
Pro
c
e
ss
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g
M
a
g
a
zin
e
,
v
o
l.
2
0
,
n
o
.
2
,
p
p
.
1
8
–
2
9
,
2
0
0
3
.
[1
1
]
J.
X
i
n
,
e
t.
a
l.
,
“
Dig
it
a
l
V
i
d
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o
T
ra
n
sc
o
d
in
g
”
,
Pro
c
e
e
d
i
n
g
s
o
f
IEE
E
,
v
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l
9
3
,
n
o
.
1
,
p
p
.
8
4
-
9
7
,
2
0
0
5
.
[1
2
]
Z.
L
i,
e
t.
a
l.
,
“
Ra
te
-
Disto
rti
o
n
Op
t
i
m
a
l
V
id
e
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S
u
m
m
a
r
y
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e
n
e
ra
ti
o
n
”
,
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E
T
r
a
n
s.
o
n
Ima
g
e
Pro
c
e
ss
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g
,
v
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l
1
4
,
n
o
.
1
0
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p
p
.
1
5
5
0
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1
5
6
0
,
2
0
0
5
.
[1
3
]
S
.
L
iu
,
a
n
d
C.
J.Ku
o
,
“
Jo
i
n
t
tem
p
o
ra
l
-
sp
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ti
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l
b
i
t
a
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d
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n
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w
it
h
d
e
p
e
n
d
e
n
c
y
”
,
IEE
E
T
ra
n
s.
o
n
Circ
u
it
s
&
S
y
ste
m f
o
r V
id
e
o
T
e
c
h
,
v
o
l.
1
5
,
n
o
.
1
,
p
p
.
1
5
-
2
6
,
2
0
0
5
.
[1
4
]
L
.
M
o
u
,
e
t.
a
l
.
,
“
A
S
e
c
u
re
M
e
d
ia
S
trea
m
in
g
M
e
c
h
a
n
is
m
Co
m
b
in
in
g
En
c
r
y
p
ti
o
n
,
A
u
th
e
n
ti
c
a
ti
o
n
a
n
d
T
ra
n
sc
o
d
in
g
”
,
S
ig
n
a
l
Pro
c
e
ss
in
g
:
Ima
g
e
C
o
mm
u
n
ica
ti
o
n
,
v
o
l
.
2
4
,
p
p
.
8
2
5
–
8
3
3
,
2
0
0
9
.
[1
5
]
Z.
Yu
a
n
,
e
t.
a
l.
,
“
i
P
A
S
A
n
Us
e
r
P
e
rc
e
iv
e
d
Qu
a
li
ty
-
Ba
se
d
In
telli
g
e
n
t
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rio
r
it
ize
d
A
d
a
p
ti
v
e
S
c
h
e
m
e
f
o
r
IP
T
V
i
n
W
irele
ss
Ho
m
e
Ne
t
w
o
rk
s
”
,
IEE
E
In
ter
n
a
ti
o
n
a
l
S
y
mp
o
si
u
m
o
n
B
ro
a
d
b
a
n
d
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u
l
ti
me
d
ia
S
y
ste
ms
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n
d
Bro
a
d
c
a
sti
n
g
,
2
0
1
0
.
[1
6
]
F
.
W
u
,
e
t.
a
l.
,
“
DCT
-
P
re
d
ictio
n
Ba
se
d
P
r
o
g
re
ss
iv
e
F
in
e
G
ra
n
u
larity
S
c
a
lab
le
Co
d
in
g
”
,
Pro
c
e
e
d
i
n
g
o
f
I
EE
E
I
n
t
.
Co
n
fer
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n
c
e
o
n
Ima
g
e
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ss
in
g
,
p
p
.
1
9
0
3
-
1
9
0
6
,
2
0
0
0
.
[1
7
]
J.R.
Oh
m
,
“
A
d
v
a
n
c
e
s in
S
c
a
lab
le V
id
e
o
Co
d
i
n
g
”
,
Pro
c
e
e
d
in
g
o
f
IE
EE
,
v
o
l.
9
3
,
n
.
1
,
p
p
.
4
2
-
5
6
,
2
0
0
5
.
[1
8
]
Z.
L
i,
e
t.
a
l.
,
“
In
tell
ig
e
n
t
W
irele
ss
V
id
e
o
Co
m
m
u
n
ica
ti
o
n
:
S
o
u
rc
e
Ad
a
p
tati
o
n
a
n
d
M
u
lt
i
-
Us
e
r
Co
ll
a
b
o
ra
ti
o
n
”
,
Ch
i
n
a
Co
mm
u
n
ica
ti
o
n
s,
Oc
to
b
e
r
,
p
p
.
5
8
-
7
0
,
2
0
0
6
.
[1
9
]
H.B.
Ka
z
e
m
ian
,
a
n
d
L
.
M
e
n
g
,
“
A
f
u
z
z
y
c
o
n
tro
l
sc
h
e
m
e
f
o
r
v
id
e
o
tran
sm
is
sio
n
in
Blu
e
t
o
o
t
h
w
irele
ss
”
,
In
fo
rm
a
ti
o
n
S
c
ien
c
e
s,
v
o
l.
1
7
6
,
n
o
.
9
,
p
p
.
1
2
6
6
-
1
2
8
9
,
2
0
0
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
-
8708
Dec
is
io
n
Ma
kin
g
A
n
a
lysi
s
o
f
V
id
eo
S
tr
ea
min
g
A
l
g
o
r
ith
m
fo
r
P
r
iva
te
C
lo
u
d
C
o
mp
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tin
g
…
(
I
r
fa
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S
ya
msu
d
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in
)
3535
[2
0
]
A
.
R.
R
e
ib
m
a
n
,
e
t.
a
l.
,
“
M
u
lt
ip
le
-
De
sc
rip
ti
o
n
Vid
e
o
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in
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Us
in
g
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m
p
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m
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o
ra
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re
d
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o
n
”
,
IEE
E
T
ra
n
s.
Circ
u
it
s S
y
st.V
id
e
o
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c
h
n
o
l
.
1
2
,
p
p
.
1
9
3
-
2
0
4
,
2
0
0
2
.
[2
1
]
A.
Ha
n
jalic,
“
S
h
o
t
-
Bo
u
n
d
a
ry
De
t
e
c
ti
o
n
:
Un
ra
v
e
led
a
n
d
Re
so
lv
e
d
?
”
,
IEE
E
T
r
a
n
s
a
c
ti
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n
s
o
n
CS
V
T
,
v
o
l.
12
,
n
.
2
,
p
p
.
90
-
1
0
5
,
2
0
0
2
.
[2
2
]
C.
Co
tsa
c
e
s,
e
t.
a
l.
,
“
V
i
d
e
o
S
h
o
t
De
tec
ti
o
n
A
n
d
Co
n
d
e
n
se
d
Re
p
re
se
n
tatio
n
”
,
IEE
E
S
i
g
n
a
l
Pro
c
e
ss
in
g
M
a
g
a
zin
e
,
v
o
l.
23
,
n
o
.
2
,
p
p
.
2
8
-
37
,
2
0
0
6
.
[2
3
]
H.
Ka
rra
y
,
e
t.
a
l.
,
“
In
d
e
x
in
g
V
id
e
o
S
u
m
m
a
rie
s
f
o
r
Qu
ick
V
id
e
o
Bro
w
sin
g
”
,
Co
mp
u
ter
Co
mm
u
n
ica
ti
o
n
s
a
n
d
Ne
two
rk
s
,
p
p
.
7
7
-
95
,
2
0
1
0
.
[2
4
]
F.
Ch
e
n
,
e
t.
a
l.
,
“
A
n
A
u
to
n
o
m
o
u
s
F
ra
m
e
w
o
rk
T
o
P
ro
d
u
c
e
A
n
d
Distri
b
u
te
P
e
rso
n
a
li
z
e
d
T
e
a
m
-
S
p
o
rt
V
i
d
e
o
S
u
m
m
a
ries
:
A
Ba
sk
et
-
Ba
ll
Ca
s
e
S
tu
d
y
”
,
IEE
E
T
r
a
n
s
a
c
ti
o
n
s
o
n
M
u
lt
ime
d
i
a
,
v
o
l
.
13
,
n
o
.
6
,
p
p
.
1
3
8
1
-
1
3
9
4
,
2
0
1
1
.
[2
5
]
S
.
J.
W
e
e
,
a
n
d
J.G
.
A
p
o
sto
lo
p
o
u
l
o
s,
“
S
e
c
u
re
sc
a
lab
le
v
id
e
o
stre
a
m
in
g
f
o
r
w
irele
s
s
n
e
tw
o
rk
s
”
,
Pr
o
c
e
e
d
in
g
o
f
t
h
e
IEE
E
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Aco
u
stics
,
S
p
e
e
c
h
,
a
n
d
S
i
g
n
a
l
P
ro
c
e
ss
in
g
,
2
0
0
1
[2
6
]
C.
V
e
n
k
a
tram
a
n
i,
e
t.
a
l.
,
"
S
e
c
u
ri
n
g
M
e
d
ia f
o
r
A
d
a
p
ti
v
e
S
trea
m
in
g
”
,
ACM
Co
n
fer
e
n
c
e
o
n
M
u
lt
ime
d
i
a
,
2
0
0
3
.
[2
7
]
F.
Ch
iarig
li
o
n
e
,
e
t.
a
l.
,
“
S
trea
m
i
n
g
o
f
g
o
v
e
rn
e
d
c
o
n
ten
t
—
T
im
e
f
o
r
a
sta
n
d
a
rd
”
,
Pro
c
e
e
d
i
n
g
o
f
th
e
5
th
IE
EE
Co
n
su
me
r Co
mm
u
n
ica
ti
o
n
s a
n
d
Ne
two
rk
in
g
C
o
n
fer
e
n
c
e
,
2
0
0
8
.
[2
8
]
T
.
L
.
S
a
a
t
y
,
“
T
h
e
An
a
lytic Hier
a
rc
h
y
Pro
c
e
ss
”
,
RW
S
P
u
b
l
ica
ti
o
n
s,
P
i
tt
sb
u
rg
h
,
P
A
,
1
9
9
0
.
[2
9
]
IS
O/IEC
9
1
2
6
-
1
:
2
0
0
1
.
S
o
f
twa
re
En
g
in
e
e
rin
g
[3
0
]
X
.
Z
h
a
n
g
,
a
n
d
S
.
W
e
i,
“
Us
in
g
th
e
A
HP
M
e
th
o
d
t
o
Re
se
a
rc
h
th
e
Ca
rg
o
S
to
w
a
g
e
o
f
V
e
h
icle
s
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
On
l
in
e
E
n
g
i
n
e
e
rin
g
,
v
o
l.
11
,
n
o
.
8
,
p
p
.
4
7
-
5
0
,
2
0
1
5
.
[3
1
]
X.
T
a
n
g
,
“
Re
se
a
rc
h
o
n
P
e
rf
o
rm
a
n
c
e
Ev
a
lu
a
ti
o
n
b
y
IDSS
Ba
se
d
o
n
A
HP
”
,
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
O
n
li
n
e
En
g
i
n
e
e
rin
g
,
v
o
l.
9
,
p
p
.
9
-
1
2
,
2
0
1
3
.
[3
2
]
I.
S
y
a
m
su
d
d
in
,
“
F
u
z
z
y
M
u
lt
i
Crit
e
ria
Ev
a
lu
a
ti
o
n
F
ra
m
e
w
o
rk
f
o
r
E
-
L
e
a
rn
in
g
S
o
f
twa
re
Qu
a
li
ty
”
,
Aca
d
e
mic
Res
e
a
rc
h
In
ter
n
a
ti
o
n
a
l
,
v
o
l.
2
,
n
o
.
1
,
p
p
.
1
3
9
-
1
4
7
,
2
0
1
2
.
[3
3
]
S
.
J.
Be
rm
a
n
,
e
t.
a
l.
,
“
H
o
w
c
lo
u
d
c
o
m
p
u
ti
n
g
e
n
a
b
les
p
ro
c
e
s
s
a
n
d
b
u
si
n
e
ss
m
o
d
e
l
in
n
o
v
a
ti
o
n
”
,
S
tra
te
g
y
&
L
e
a
d
e
rs
h
ip
,
v
o
l.
40
,
n
o
.
4
,
p
p
.
27
-
35
,
2
0
1
2
.
[3
4
]
B.
L
i,
e
t.
a
l
.
,
“
T
w
o
d
e
c
a
d
e
s
o
f
in
t
e
rn
e
t
v
id
e
o
stre
a
m
in
g
:
A
re
tro
sp
e
c
ti
v
e
v
ie
w”
,
ACM
T
ra
n
s
a
c
ti
o
n
s
o
n
M
u
lt
ime
d
i
a
Co
mp
u
t
in
g
,
C
o
mm
u
n
ic
a
ti
o
n
s,
a
n
d
Ap
p
li
c
a
ti
o
n
s (
T
OM
M
)
,
v
o
l.
9
,
n
o
.
1
,
p
p
.
33
-
4
6
,
2
0
1
3
.
[3
5
]
T
.
H.
Hs
u
a
n
d
L
.
Y.
W
u
,
“
A
Clo
u
d
-
a
ss
isted
P
2
P
V
id
e
o
S
trea
m
in
g
A
rc
h
it
e
c
tu
re
f
o
r
S
c
a
l
a
b
le
V
id
e
o
Co
d
i
n
g
”
,
T
h
e
Fo
u
rt
h
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
I
n
fo
rm
a
ti
c
s
&
Ap
p
li
c
a
t
io
n
s (
ICIA2
0
1
5
)
p
.
8
9
,
2
0
1
5
.
[3
6
]
D.
Niu
,
e
t.
a
l.
,
“
Qu
a
li
ty
-
A
ss
u
re
d
Clo
u
d
Ba
n
d
w
id
th
A
u
to
-
S
c
a
li
n
g
f
o
r
V
id
e
o
-
on
-
De
m
a
n
d
A
p
p
li
c
a
ti
o
n
s
”
,
IEE
E
INFOCOM
,
2
0
1
2
.
[3
7
]
Z.
Hu
a
n
g
,
e
t.
a
l.
,
“
Cl
o
u
d
S
trea
m
:
De
li
v
e
rin
g
Hig
h
-
Qu
a
li
t
y
S
trea
m
i
n
g
V
i
d
e
o
s
th
r
o
u
g
h
A
Clo
u
d
-
b
a
se
d
S
V
C
P
r
o
x
y
”
,
IEE
E
INFOCOM
,
2
0
1
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.