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g
o
o
d
co
d
in
g
tech
n
iq
u
e
w
ill
aid
in
r
ed
u
cin
g
co
s
t
o
f
s
to
r
ag
e
p
er
b
it a
n
d
i
m
p
r
o
v
e
u
b
iq
u
ito
u
s
d
ata
ac
ce
s
s
o
n
s
m
ar
t
d
ev
ices [
2
]
.
Data
tr
an
s
m
i
s
s
io
n
o
v
er
d
ata
co
m
m
u
n
icatio
n
c
h
a
n
n
e
l
ca
n
in
d
u
ce
co
s
t
o
v
er
h
ea
d
(
e.
g
.
i
n
cr
ea
s
e
i
n
p
h
o
n
e
b
ills
d
u
e
to
u
s
ag
e
o
f
m
o
d
em
,
s
a
tellite
d
ata
tr
a
n
s
m
i
s
s
i
o
n
,
etc.
)
.
T
h
er
ef
o
r
e
m
i
n
i
m
izi
n
g
t
h
e
to
tal
n
u
m
b
er
o
f
b
its
to
b
e
s
e
n
t
is
m
o
s
t
d
esire
d
.
T
h
is
w
ill
aid
i
n
i
m
p
r
o
v
in
g
t
h
e
s
p
ee
d
o
f
d
ata
tr
an
s
m
i
s
s
io
n
e
f
f
icien
c
y
(
i.e
.
r
ea
l
-
ti
m
e)
.
T
h
e
m
u
lti
m
ed
ia
d
ata
s
u
ch
as
i
m
a
g
es,
v
id
eo
s
,
s
o
u
n
d
etc.
r
eq
u
ir
e
s
lar
g
e
a
m
o
u
n
t
o
f
b
an
d
w
id
t
h
w
h
ic
h
ca
n
b
e
r
ed
u
ce
d
b
y
u
s
i
n
g
th
e
r
ig
h
t
co
d
in
g
m
et
h
o
d
s
.
A
f
ix
ed
len
g
t
h
co
d
e
d
o
es
n
o
t
g
u
ar
an
tee
o
f
r
ed
u
cin
g
th
e
to
tal
co
d
e
s
ize
to
b
e
s
en
t.
Sin
ce
s
o
m
e
c
h
ar
ac
ter
s
in
i
m
a
g
e
d
ata
o
cc
u
r
s
m
o
r
e
f
r
eq
u
en
t
th
an
o
t
h
er
s
,
b
u
t
s
til
l
it r
eq
u
ir
es th
e
s
a
m
e
s
ize
(
b
its
)
as f
r
eq
u
e
n
tl
y
o
cc
u
r
r
en
ce
c
h
ar
ac
ter
.
I
m
ag
e
co
m
p
r
es
s
io
n
tech
n
iq
u
e
h
as
b
ee
n
p
r
o
v
en
to
b
e
an
ef
f
ec
tiv
e
s
o
lu
tio
n
to
s
o
lv
e
b
an
d
w
id
t
h
an
d
s
to
r
ag
e
is
s
u
es.
Di
g
ital
i
m
ag
e
s
ar
e
m
aj
o
r
ly
co
m
p
o
s
ed
o
f
s
p
ec
tr
al
a
n
d
s
p
atial
r
ed
u
n
d
an
c
y
.
Sp
ec
tr
al
r
ed
u
n
d
an
c
ies
e
x
is
t
d
u
e
to
r
elatio
n
s
h
ip
a
m
o
n
g
d
if
f
er
en
t
co
lo
r
p
lan
es
a
n
d
s
p
atial
r
ed
u
n
d
an
cie
s
e
x
is
t
d
u
e
to
r
elatio
n
s
h
ip
a
m
o
n
g
n
ei
g
h
b
o
r
h
o
o
d
p
ix
el
p
ar
a
m
eter
.
B
y
ta
k
i
n
g
t
h
e
b
en
e
f
it
s
f
r
o
m
th
e
s
e
r
ed
u
n
d
an
cies
t
h
e
co
d
in
g
(
i
m
ag
e
co
m
p
r
e
s
s
io
n
)
tech
n
i
q
u
e
allo
w
s
i
n
r
ed
u
ci
n
g
t
h
e
n
u
m
b
er
o
f
b
its
n
ee
d
e
d
in
r
ep
r
esen
ti
n
g
a
m
u
lti
m
ed
ia
d
ata.
T
h
e
r
ec
o
n
s
tr
u
ctio
n
p
r
o
ce
s
s
o
f
an
e
n
co
d
ed
d
ata
is
k
n
o
w
n
as
d
ec
o
d
in
g
.
T
h
e
d
ec
o
d
in
g
tech
n
iq
u
e
i
s
t
h
e
r
e
v
er
s
e
p
r
o
ce
s
s
o
f
e
n
co
d
in
g
.
T
h
e
g
o
al
o
f
an
y
co
m
p
r
es
s
i
n
g
tech
n
iq
u
e
is
to
m
i
n
i
m
ize
t
h
e
n
u
m
b
er
o
f
b
its
as p
r
o
b
ab
le,
w
i
th
o
u
t a
f
f
ec
ti
n
g
t
h
e
p
icto
r
ial
q
u
alit
y
o
f
r
ec
o
n
s
tr
u
cted
m
u
lti
m
e
d
ia
d
ata.
Dig
ital
I
m
a
g
i
n
g
a
n
d
C
o
m
m
u
n
icatio
n
s
i
n
Me
d
icin
e
(
DI
C
O
M)
,
a
m
ed
ical
i
m
a
g
i
n
g
s
ta
n
d
ar
d
[
1
]
is
cu
r
r
en
tl
y
u
s
ed
m
ec
h
a
n
is
m
f
o
r
s
h
ar
i
n
g
d
ata
ac
r
o
s
s
s
er
v
ice
p
latf
o
r
m
s
.
A
d
ig
i
tal
m
u
lt
i
m
ed
i
a
d
ata
is
g
e
n
er
all
y
co
m
p
o
s
ed
o
f
p
i
x
els
o
f
t
w
o
d
i
m
e
n
s
io
n
al
ar
r
a
y
s
.
T
h
ese
d
i
g
i
tal
i
m
a
g
es
ar
e
f
u
r
t
h
er
clas
s
i
f
i
ed
in
to
b
i
-
to
n
al
o
r
bi
-
lev
e
l
w
h
ic
h
co
n
s
i
s
t
o
f
t
w
o
in
ten
s
it
y
lev
el
s
(
b
lack
an
d
w
h
ite)
,
g
r
a
y
s
ca
le
i
m
a
g
es
w
h
ich
is
also
ca
lled
as
co
n
tin
u
o
u
s
-
to
n
e
i
m
a
g
es
w
h
er
e
th
e
p
ix
e
l
v
al
u
e
i
s
i
n
r
an
g
e
o
f
[
0
-
2
5
5
]
,
h
alf
to
n
e
i
m
a
g
e
s
c
o
m
p
r
i
s
e
o
f
b
l
u
r
r
ed
d
is
cr
ete
d
o
ts
an
d
th
e
co
lo
r
im
ag
e
s
w
h
ich
co
n
s
i
s
t
o
f
p
ix
e
l
s
co
r
r
esp
o
n
d
in
g
to
R
GB
(
R
e
d
,
Gr
ee
n
an
d
B
lu
e)
co
m
p
o
n
e
n
t
s
.
T
h
e
im
a
g
e
co
m
p
r
ess
io
n
m
et
h
o
d
s
ar
e
g
en
er
al
l
y
class
if
ied
i
n
to
t
w
o
t
y
p
e’
s
lo
s
s
y
an
d
lo
s
s
le
s
s
tech
n
iq
u
es a
s
s
h
o
w
n
i
n
Fi
g
u
r
e
1.
L
o
s
s
less
co
m
p
r
es
s
io
n
tec
h
n
iq
u
e
h
as
b
ee
n
ad
o
p
ted
in
ap
p
lic
atio
n
w
h
er
e
h
i
g
h
-
q
u
a
lit
y
i
s
r
e
q
u
ir
ed
s
u
ch
as
m
ed
ical
i
m
a
g
i
n
g
,
P
r
o
p
er
ty
r
eg
is
tr
atio
n
ce
r
tif
ica
te
e
tc.
L
o
s
s
y
tech
n
iq
u
e
s
p
r
o
v
id
e
m
u
c
h
h
ig
h
er
co
m
p
r
es
s
io
n
r
atio
th
a
n
L
o
s
s
les
s
b
u
t
t
h
e
q
u
alit
y
i
s
n
o
t
a
s
g
o
o
d
as
lo
s
s
l
ess
co
m
p
r
e
s
s
io
n
m
et
h
o
d
o
lo
g
ies
ac
h
iev
e.
I
n
[
3
]
ev
alu
a
ted
th
e
p
er
f
o
r
m
a
n
ce
o
f
lo
s
s
y
a
n
d
lo
s
s
les
s
co
m
p
r
ess
io
n
s
ch
e
m
e
f
o
r
m
ed
ical
d
ata
co
m
p
r
ess
io
n
.
T
h
eir
m
o
d
el
m
i
n
i
m
ized
t
h
e
b
an
d
w
id
th
,
s
to
r
ag
e
a
n
d
tr
an
s
m
is
s
io
n
co
s
t.
Ho
w
e
v
er
it
in
d
u
c
es
h
ig
h
co
m
p
u
ti
n
g
o
v
er
h
ea
d
.
T
o
ad
d
r
ess
in
[
4
]
p
r
esen
ted
a
HE
VC
co
d
in
g
tec
h
n
iq
u
e
to
m
i
n
i
m
ize
co
m
p
u
tatio
n
al
co
m
p
lex
it
y
a
n
d
r
ed
u
ce
f
ile
s
ize.
Ho
w
e
v
er
th
ei
r
m
o
d
el
s
u
p
p
o
r
ts
lo
s
s
les
s
co
m
p
r
e
s
s
io
n
o
n
l
y
u
p
to
co
d
in
g
u
n
i
t
lev
el
[
5
]
an
d
to
en
ab
le
lo
s
s
le
s
s
co
m
p
r
es
s
io
n
r
e
q
u
ir
es h
u
g
e
co
m
p
u
ti
n
g
p
latf
o
r
m
.
I
n
[
6
]
p
r
esen
ted
a
p
ar
allel
i
m
p
le
m
e
n
tatio
n
o
f
B
it p
la
n
e
co
d
in
g
to
ac
h
iev
e
e
f
f
icie
n
t c
o
m
p
r
es
s
io
n
r
atio
.
T
h
is
m
o
d
el
ac
h
ie
v
es
f
in
e
g
r
ai
n
ed
p
ar
allelis
m
a
n
d
s
p
ee
d
u
p
s
,
h
o
w
e
v
er
th
e
s
e
m
o
d
el
m
a
y
n
o
t
b
e
ap
p
licab
le
f
o
r
u
b
iq
u
ito
u
s
co
m
p
u
ti
n
g
o
n
r
es
o
u
r
ce
s
tar
v
ed
s
m
ar
t
d
e
v
ices.
T
o
s
u
p
p
o
r
t
d
ata
co
m
p
r
ess
io
n
o
n
s
u
c
h
r
e
s
o
u
r
ce
s
tar
v
ed
p
latf
o
r
m
a
s
i
m
p
le
c
o
m
p
r
es
s
io
n
tec
h
n
iq
u
e
m
u
s
t
b
e
d
ev
elo
p
ed
.
I
n
[
7
]
p
r
esen
ted
co
n
tex
t
a
w
ar
e
p
r
o
b
a
b
ilit
y
b
ased
f
ix
ed
co
d
e
len
g
th
e
n
co
d
in
g
tec
h
n
iq
u
e
to
ad
d
r
ess
th
e
i
s
s
u
e
o
f
tr
ad
itio
n
al
ar
ith
m
etic
co
d
er
tech
n
iq
u
e
w
h
ic
h
r
eq
u
ir
es
p
r
io
r
i
k
n
o
w
led
g
e
o
f
co
d
e
w
o
r
d
f
o
r
r
en
o
r
m
a
lizatio
n
p
r
o
ce
s
s
.
T
h
e
r
en
o
r
m
aliza
tio
n
p
r
o
ce
s
s
is
eli
m
i
n
ated
i
n
f
i
x
e
d
len
g
th
co
d
i
n
g
,
h
o
w
e
v
er
it
ca
n
n
o
t
ass
u
r
e
r
ed
u
ct
io
n
i
n
n
u
m
b
er
o
f
b
it
s
f
o
r
en
co
d
in
g
.
A
f
ix
ed
le
n
g
th
co
d
e
d
o
es
n
o
t
g
u
ar
a
n
tee
o
f
r
ed
u
ci
n
g
t
h
e
t
o
tal
co
d
e
s
ize
to
b
e
s
en
t.
Sin
ce
s
o
m
e
ch
ar
ac
ter
s
i
n
i
m
a
g
e
d
ata
o
cc
u
r
s
m
o
r
e
f
r
eq
u
e
n
t
t
h
a
n
o
t
h
er
s
,
b
u
t
s
ti
ll
i
t
r
eq
u
ir
es
t
h
e
s
a
m
e
s
ize
o
f
b
it
s
a
s
f
r
eq
u
en
tl
y
o
cc
u
r
r
en
ce
c
h
ar
ac
t
er
.
T
h
er
ef
o
r
e
p
r
ef
ix
-
f
r
ee
co
d
e
f
o
r
co
m
p
r
ess
io
n
ca
n
b
e
e
f
f
ec
tiv
e
s
o
l
u
tio
n
.
I
n
[
8
]
p
r
esen
ted
an
Var
iab
le
-
L
e
n
g
t
h
C
o
d
es
(
V
L
C
s
)
an
d
[
9
]
p
r
esen
ted
an
h
y
b
r
id
co
m
p
r
ess
io
n
a
lg
o
r
ith
m
,
b
o
th
[
8
]
an
d
[
9
]
h
a
v
e
ad
o
p
ted
Hu
f
f
m
an
co
d
e
[
1
0
]
to
r
ed
u
ce
to
tal
b
it
le
n
g
t
h
a
n
d
to
s
u
p
p
o
r
t
ef
f
icie
n
t
lo
s
s
less
co
m
p
r
es
s
io
n
o
v
er
w
ir
ele
s
s
c
h
a
n
n
el.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
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f
f
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e
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p
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t d
ev
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h
e
p
a
p
er
o
r
g
an
izatio
n
is
as
f
o
llo
w
s
:
T
h
e
liter
atu
r
e
s
u
r
v
e
y
i
s
p
r
esen
ted
in
s
ec
tio
n
t
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.
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h
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p
r
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p
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s
ed
m
o
d
el
s
ar
e
p
r
ese
n
ted
i
n
Sect
i
o
n
t
h
r
ee
.
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h
e
s
i
m
u
latio
n
r
esu
l
ts
a
n
d
th
e
ex
p
er
i
m
en
tal
s
tu
d
y
ar
e
p
r
esen
ted
i
n
t
h
e
s
ec
tio
n
f
o
u
r
.
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h
e
co
n
clu
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in
g
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m
ar
k
i
s
d
is
cu
s
s
ed
i
n
th
e
la
s
t s
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tio
n
.
2.
L
I
T
T
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RA
T
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SURV
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Y
T
h
er
e
h
av
e
b
ee
n
s
ev
er
al
m
e
th
o
d
o
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ies
th
at
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e
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ee
n
p
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ed
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t
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m
p
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th
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m
a
n
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f
co
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n
,
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ce
th
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n
u
m
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er
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en
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d
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p
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x
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o
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b
o
th
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n
d
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les
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p
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ess
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w
h
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ar
e
s
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v
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y
ed
b
elo
w
.
I
n
[
7
]
p
r
esen
ted
a
co
n
te
x
t a
w
a
r
e
b
in
ar
y
ar
it
h
m
e
tic
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g
co
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x
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g
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h
.
Her
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y
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e
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th
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u
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p
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tain
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en
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p
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k
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n
len
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is
m
o
d
el
i
s
ad
ap
tiv
e
w
it
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r
esp
ec
t
to
v
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ab
le
s
ize
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e.
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h
e
m
o
d
el
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ts
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ar
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lid
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x
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ter
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ate
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d
P
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to
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h
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m
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ac
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t
o
v
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MQ
co
d
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an
d
M
co
d
er
.
I
n
[
1
1
]
d
ev
elo
p
ed
a
m
o
d
el
to
co
n
s
tr
u
ct
d
ict
io
n
ar
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f
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s
p
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s
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m
u
lti
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ed
ia
d
ata
r
ep
r
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tat
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n
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T
h
ey
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o
p
ted
d
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n
ar
y
b
ase
d
lear
n
in
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tec
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n
iq
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e
to
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u
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co
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f
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m
u
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T
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o
o
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ased
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p
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tatio
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.
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w
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o
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ter
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f
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ain
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d
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n
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ies.
B
ased
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th
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d
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y
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th
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y
d
ev
elo
p
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e
w
co
m
p
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n
tech
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iq
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e.
E
x
p
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m
e
n
t
s
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r
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co
n
d
u
cted
an
d
o
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h
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w
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b
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p
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n
J
P
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G
-
2
0
0
0
in
ter
m
s
o
f
P
SNR
an
d
B
it
-
er
r
o
r
r
ate.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
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J
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&
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Sci,
Vo
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12
,
No
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2
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No
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m
b
er
2
0
1
8
:
7
6
5
–
7
7
4
76
8
I
n
[
6
]
estab
lis
h
ed
an
e
f
f
icie
n
t
co
m
p
r
es
s
io
n
tech
n
iq
u
e
to
i
m
p
r
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v
e
t
h
e
s
p
ee
d
u
p
o
f
co
m
p
r
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s
s
io
n
.
T
h
ey
p
r
esen
ted
a
w
a
v
elet
-
b
as
ed
i
m
a
g
e
co
d
in
g
m
et
h
o
d
b
y
a
d
o
p
tin
g
p
ar
alleliza
tio
n
tec
h
n
i
q
u
e.
T
h
is
m
o
d
el
is
p
ar
allelize
d
w
it
h
GP
U
to
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h
iev
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f
i
n
e
g
r
ai
n
ed
p
ar
allelis
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b
y
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g
B
it
p
la
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e
co
d
in
g
.
T
h
e
m
o
d
el
also
p
r
esen
ted
s
m
ar
t
m
e
m
o
r
y
m
a
n
ag
e
m
e
n
t
a
n
d
th
r
ea
d
to
d
ata
m
ap
p
in
g
m
ec
h
a
n
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s
m
f
o
r
b
et
ter
co
m
m
u
n
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n
a
m
o
n
g
in
te
r
s
th
r
ea
d
s
.
E
x
p
er
i
m
en
t
ar
e
co
n
d
u
cted
f
o
r
h
ig
h
r
eso
lu
tio
n
i
m
a
g
es
a
n
d
o
u
tco
m
es
s
h
o
w
s
it
ac
h
ie
v
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b
etter
s
p
ee
d
u
p
s
an
d
it a
ls
o
co
n
s
u
m
e
les
s
p
o
w
er
o
r
en
er
g
y
.
I
n
[
1
2
]
co
n
s
tr
u
cted
a
co
d
eb
o
o
k
an
d
p
r
esen
ted
v
ec
to
r
q
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tio
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tech
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e
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d
eter
m
in
e
th
e
q
u
alit
y
o
f
co
m
p
r
es
s
ed
d
ata.
On
e
o
f
th
e
w
id
el
y
u
s
ed
co
d
eb
o
o
k
d
esig
n
L
i
n
d
e
–
B
u
zo
–
Gr
ay
(
L
B
G)
ten
d
s
to
s
u
f
f
er
f
r
o
m
lo
ca
l
m
i
n
i
m
a
p
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le
m
an
d
g
et
tr
ap
to
lo
ca
l
m
i
n
i
m
a
f
o
r
in
itial
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o
o
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itial c
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k
d
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m
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el.
T
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e
m
o
d
el
d
iv
id
es th
e
tr
ain
i
n
g
v
ec
to
r
i
n
to
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t
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n
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m
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c
h
ar
ac
ter
is
t
ics
p
ar
a
m
eter
o
f
tr
ain
in
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v
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to
r
s
.
T
h
en
th
e
in
itial
co
d
eb
o
o
k
is
g
en
er
ated
b
y
c
h
o
o
s
i
n
g
co
d
e
wo
r
d
f
r
o
m
ea
c
h
g
r
o
u
p
.
E
x
p
er
i
m
en
ts
ar
e
co
n
d
u
cted
to
ev
al
u
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
eir
in
itial c
o
d
eb
o
o
k
g
en
er
ati
o
n
o
v
er
ex
is
t
in
g
tec
h
n
iq
u
e
s
h
o
w
s
b
etter
p
er
f
o
r
m
a
n
ce
i
n
ter
m
o
f
P
SNR
.
I
n
[
1
3
]
p
r
esen
ted
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m
o
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el
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p
ee
d
u
p
th
e
en
co
d
in
g
b
y
a
d
o
p
tin
g
Vec
to
r
q
u
an
tizatio
n
t
ec
h
n
iq
u
e.
A
d
o
p
tin
g
Vec
to
r
q
u
an
tizatio
n
f
o
r
en
co
d
in
g
is
g
en
er
a
ll
y
e
x
p
en
s
i
v
e
i
n
ter
m
o
f
co
m
p
u
t
in
g
ti
m
e.
Si
n
ce
,
it
h
a
s
to
f
i
n
d
clo
s
est
co
d
e
w
o
r
d
f
o
r
ea
ch
in
p
u
t
v
ec
to
r
f
r
o
m
e
n
tir
e
co
d
eb
o
o
k
.
T
o
ad
d
r
ess
th
is
ti
m
e
co
m
p
le
x
it
y
,
th
e
y
p
r
esen
ted
a
f
as
t
s
ea
r
ch
in
g
s
tr
ate
g
y
b
y
ad
o
p
tin
g
m
u
ltil
e
v
el
e
li
m
in
a
tio
n
cr
i
ter
ia.
T
h
e
m
u
ltil
e
v
e
l
eli
m
i
n
atio
n
is
d
er
iv
ed
f
r
o
m
f
ea
tu
r
e
o
f
n
o
r
m
,
m
ea
n
a
n
d
v
a
r
ian
ce
.
T
h
e
m
u
ltil
e
v
el
f
ea
t
u
r
e
s
ar
e
o
p
ti
m
ized
to
s
p
ee
d
u
p
th
e
en
co
d
in
g
p
r
o
ce
s
s
.
T
h
e
elim
i
n
atio
n
co
n
d
itio
n
o
f
lev
el
o
n
e
is
f
i
x
ed
to
m
ea
n
in
eq
u
alit
y
b
o
u
n
d
d
u
e
to
its
n
ar
r
o
w
s
ea
r
ch
w
id
th
,
an
d
s
elec
tiv
it
y
o
r
d
er
at
lev
el
t
w
o
an
d
in
lev
el
t
h
r
ee
o
f
n
o
r
m
an
d
v
ar
ia
n
ce
in
eq
u
ali
ties
b
o
u
n
d
s
is
o
p
ti
m
iz
ed
b
ased
o
n
th
e
lo
ca
tio
n
o
f
in
p
u
t
v
ec
to
r
s
an
d
d
is
tr
ib
u
tio
n
o
f
co
d
e
w
o
r
d
in
ter
m
o
f
th
e
f
ea
tu
r
e
s
co
n
s
id
er
ed
.
E
x
p
er
im
e
n
t o
u
tco
m
e
s
h
o
w
s
e
f
f
ec
tiv
e
n
ess
o
f
th
e
ir
m
o
d
el
o
v
er
ex
is
t
in
g
tec
h
n
iq
u
es.
T
h
e
w
id
e
r
esear
ch
s
u
r
v
e
y
ca
r
r
ied
o
u
t
in
t
h
i
s
w
o
r
k
s
h
o
w
s
th
a
t
r
ed
u
cin
g
t
h
e
b
it
s
ize
f
o
r
en
c
o
d
in
g
a
n
d
tr
an
s
m
is
s
io
n
o
v
er
d
ata
ch
a
n
n
e
l
is
m
o
s
t
d
esire
d
.
I
m
a
g
e
co
m
p
r
ess
io
n
tec
h
n
iq
u
e
p
la
y
s
a
cr
it
i
ca
l
r
o
le
in
r
ed
u
ci
n
g
s
ize
o
f
a
m
u
lti
m
ed
ia
d
ata.
T
h
e
m
aj
o
r
ch
allen
g
es
o
f
th
e
a
ll
t
h
ese
s
tate
o
f
ar
t
tec
h
n
iq
u
e
i
s
t
o
r
ed
u
ce
d
ata
s
ize
an
d
co
m
p
u
ti
n
g
co
m
p
le
x
it
y
.
T
h
e
o
v
er
all
s
u
r
v
e
y
s
h
o
w
s
th
a
t
n
o
n
e
o
f
th
e
w
o
r
k
ca
r
r
ied
o
u
t
s
o
f
ar
h
ad
co
n
s
id
er
ed
co
m
p
r
es
s
io
n
o
n
r
eso
u
r
ce
s
tar
v
ed
d
ev
ice
s
u
c
h
as
s
m
ar
t
p
h
o
n
e.
T
h
e
d
ig
ital
d
ata
ac
ce
s
s
th
r
o
u
g
h
s
m
ar
t
p
h
o
n
e
d
ev
ice
en
ab
les
u
s
to
ac
h
iev
e
u
b
iq
u
ito
u
s
co
m
p
u
ti
n
g
.
T
o
ac
h
iev
e
u
b
iq
u
ito
u
s
co
m
p
u
tin
g
t
h
e
co
m
p
r
ess
io
n
tech
n
iq
u
e
s
h
o
u
ld
u
s
e
m
in
i
m
u
m
r
eso
u
r
ce
a
n
d
m
ax
i
m
ize
co
m
p
r
e
s
s
io
n
r
atio
w
i
th
o
u
t
a
f
f
ec
tin
g
t
h
e
q
u
alit
y
o
f
r
ec
o
n
s
tr
u
ct
ed
i
m
ag
e
s
.
I
n
n
e
x
t
s
ec
tio
n
w
e
p
r
esen
t a
n
I
d
ea
l H
u
f
f
m
a
n
en
co
d
in
g
tec
h
n
iq
u
e.
3.
P
RO
P
O
SE
D
I
D
E
A
L
H
UF
F
M
AN
E
NCO
DING
T
E
CH
N
I
Q
U
E
Her
e
w
e
p
r
ese
n
t
an
ef
f
icie
n
t
i
m
a
g
e
co
m
p
r
es
s
io
n
tec
h
n
i
q
u
e
b
y
o
p
ti
m
izi
n
g
H
u
f
f
m
a
n
en
co
d
in
g
tech
n
iq
u
es.
T
h
e
co
d
e
w
o
r
d
s
ize
is
r
ed
u
ce
d
w
it
h
o
u
t
af
f
ec
t
i
n
g
t
h
e
q
u
alit
y
o
f
i
m
ag
e.
An
o
p
tim
a
lit
y
p
r
o
o
f
o
f
p
r
o
p
o
s
ed
I
d
ea
l H
u
f
f
m
a
n
co
d
ew
o
r
d
o
v
er
ex
is
t
in
g
H
u
f
f
m
a
n
co
d
e
is
p
r
esen
ted
.
T
h
e
d
r
aw
b
ac
k
o
f
ex
i
s
ti
n
g
m
o
d
if
ied
Hu
f
f
m
an
co
m
p
r
ess
io
n
t
ec
h
n
iq
u
e
i
s
t
h
at
it
r
eq
u
ir
es
lar
g
e
m
e
m
o
r
y
s
ize
(
s
i
n
ce
t
h
e
co
d
e
len
g
t
h
is
ar
o
u
n
d
1
8
3
an
d
m
a
x
i
m
u
m
len
g
th
o
f
co
d
e
is
1
3
)
an
d
co
m
p
r
ess
io
n
r
atio
s
ar
e
lo
w
[
1
4
]
.
I
t
also
in
d
u
ce
s
co
m
p
u
tat
io
n
o
v
er
h
ea
d
d
u
e
to
lar
g
er
co
d
e
len
g
th
.
Mi
n
i
m
izi
n
g
n
u
m
b
e
r
o
f
co
d
e
w
o
r
d
s
a
n
d
th
eir
co
r
r
esp
o
n
d
in
g
le
n
g
th
s
,
with
o
u
t o
u
t e
f
f
ec
ti
n
g
d
ata
lo
s
s
i
s
th
e
m
aj
o
r
o
b
j
ec
tiv
e
o
f
p
r
o
p
o
s
ed
tech
n
iq
u
e.
T
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
m
i
n
i
m
ize
s
th
e
co
d
e
w
o
r
d
s
ize
f
o
r
b
etter
ef
f
ic
ien
c
y
a
n
d
ac
h
ie
v
ed
b
etter
co
m
p
r
es
s
io
n
r
atio
s
a
n
d
less
e
n
co
d
in
g
an
d
d
ec
o
d
in
g
ti
m
e.
P
r
o
p
o
s
ed
co
d
e
w
o
r
d
s
ar
e
s
eg
m
en
ted
in
to
th
r
ee
s
e
g
m
en
t
s
.
First
s
e
g
m
e
n
t
co
m
p
r
is
e
s
o
f
m
u
ltip
les
o
f
1
4
4
u
p
to
1
7
2
8
an
d
c
o
n
tain
s
a
to
tal
o
f
2
4
c
o
d
es
(
1
2
ea
ch
f
o
r
b
lack
an
d
w
h
ite)
.
Ne
x
t
s
e
g
m
en
t
co
m
p
r
is
es
o
f
m
u
ltip
le
o
f
1
3
u
p
to
1
4
3
an
d
c
o
n
tain
s
a
to
tal
o
f
2
2
co
d
es
(
1
1
ea
ch
f
o
r
b
lack
an
d
w
h
ite)
.
T
h
e
last
s
e
g
m
e
n
t
h
as
th
e
r
e
m
ai
n
in
g
2
4
t
er
m
i
n
ati
n
g
co
d
es
(
1
2
ea
ch
f
o
r
w
h
ite
a
n
d
b
lack
)
.
A
d
d
itio
n
al
d
etai
ls
o
f
p
r
o
p
o
s
ed
co
d
e
w
o
r
d
s
is
av
ai
lab
le
in
[
1
5
]
,
f
e
w
co
d
ew
o
r
d
s
ar
e
ch
a
n
g
ed
co
m
p
ar
ed
to
[
1
5
]
.
A
ll
s
eg
m
e
n
ts
p
u
t
to
g
eth
er
w
e
co
n
s
id
er
a
to
tal
o
f
7
1
c
o
d
e
w
o
r
d
s
an
d
th
e
m
a
x
i
m
u
m
le
n
g
t
h
is
o
b
s
er
v
ed
t
o
b
e
1
2
b
its
an
d
v
er
y
f
e
w
n
u
m
b
er
o
f
1
2
-
b
it
co
d
es.
T
h
e
p
r
o
p
o
s
ed
co
d
e
w
o
r
d
s
ca
n
b
e
u
n
iq
u
el
y
d
ec
o
d
ed
;
r
ec
o
n
s
tr
u
cted
d
ata
is
lo
s
s
less
an
d
th
e
y
s
ati
s
f
y
Kr
af
t
Mc
Mi
llan
in
eq
u
alit
y
co
n
d
itio
n
s
.
T
h
e
m
e
m
o
r
y
co
n
s
u
m
p
tio
n
is
r
ed
u
ce
d
a
n
d
th
e
en
co
d
in
g
a
n
d
d
ec
o
d
in
g
is
also
r
ed
u
ce
d
.
T
h
e
p
r
o
p
o
s
ed
co
d
ew
o
r
d
r
u
n
len
g
t
h
(
R
L
)
is
co
m
p
u
ted
an
d
is
r
ep
r
esen
ted
as
,
w
h
er
e
t is ter
m
i
n
ati
n
g
co
d
e,
m
ak
e
u
p
co
d
es,
is
m
a
k
e
u
p
co
d
e.
3
.
1
.
P
r
o
po
s
ed
H
uff
m
a
n Co
d
e
O
pti
m
a
lity
J
us
t
if
ica
t
io
n
:
L
et
co
n
s
id
er
a
s
e
t
o
f
co
d
e
w
o
r
d
s
r
ep
r
esen
ted
u
s
in
g
a
b
i
n
ar
y
tr
ee
,
w
h
er
e
r
ig
h
t
an
d
le
f
t
d
i
v
i
s
io
n
s
m
ap
to
eith
er
a
1
o
r
0
b
it
en
co
d
in
g
o
p
er
atio
n
.
T
h
e
ch
ar
ac
ter
th
at
is
en
co
d
ed
w
i
ll
b
e
a
n
o
d
e
o
n
th
e
tr
ee
,
r
ea
ch
ab
le
in
a
s
eq
u
en
ce
o
f
r
i
g
h
t
-
lef
t (
i.e
.
1
o
r
0
b
it)
ch
an
g
e
s
f
r
o
m
r
o
o
t to
th
e
ch
ar
ac
ter
’
s
n
o
d
es.
I
n
p
r
o
p
o
s
ed
tech
n
iq
u
e,
e
v
er
y
ch
ar
ac
ter
th
at
is
e
n
co
d
ed
is
c
o
n
s
id
er
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to
b
e
a
leaf
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o
d
e
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n
d
th
e
tr
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f
o
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m
ed
co
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tai
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en
c
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f
ch
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ac
ter
s
to
b
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en
co
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d
.
I
n
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th
er
w
o
r
d
s
ev
er
y
c
h
ar
ac
ter
to
b
e
en
co
d
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,
p
p
.
7
4
0
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7
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2
[5
]
M
e
n
g
m
e
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a
n
g
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1
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Jia
n
f
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n
g
Qu
1
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i
h
u
i
Ba
i2
,
”
F
a
st
In
tra
P
re
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ictio
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M
o
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e
De
c
isio
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lg
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th
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C”
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EL
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NIK
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o
l.
1
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2
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0
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6
[6
]
P
.
En
f
e
d
a
q
u
e
;
F
.
A
u
li
-
L
li
n
a
s;
J.
C.
M
o
u
re
,
"
G
P
U
Im
p
le
m
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tatio
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o
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d
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a
r
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t
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r
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ss
in
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r
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h
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e
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o
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m
a
n
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e
I
m
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g
e
Co
m
p
re
ss
io
n
,
"
in
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
P
a
ra
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e
l
a
n
d
Distrib
u
ted
S
y
ste
m
s
,
v
o
l.
P
P
,
n
o
.
9
9
,
p
p
.
1
-
1
.
2
0
1
7
.
[7
]
F
.
A
u
lí
-
L
li
n
à
s,
"
Co
n
tex
t
-
A
d
a
p
ti
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Bin
a
ry
A
rit
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m
e
ti
c
Co
d
in
g
W
it
h
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ix
e
d
-
L
e
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g
th
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d
e
w
o
rd
s,"
i
n
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EE
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ra
n
sa
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ti
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o
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u
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l.
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7
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p
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1
3
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3
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0
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5
.
[8
]
En
y
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n
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h
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n
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p
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,
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g
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ra
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u
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s”
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T
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L
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u
rn
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~
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.
[9
]
H.
C.
Ku
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a
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.
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.
L
in
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H
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ra
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s,"
in
IEE
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ia,
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3
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p
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0
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.
[1
0
]
D.
A
.
Hu
ffm
a
n
,
“
A
m
e
th
o
d
f
o
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th
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n
stru
c
ti
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o
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m
in
im
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m
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d
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a
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y
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s,”
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ro
c
.
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R.
E.
,
v
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l.
4
0
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n
o
.
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,
p
p
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0
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8
–
1
1
0
2
,
S
e
p
.
1
9
5
2
.
[1
1
]
M
.
Ne
jati,
S
.
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a
m
a
v
i,
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Ka
rim
i,
S
.
M
.
R
.
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o
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N
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jaria
n
,
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Bo
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ste
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rn
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f
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r
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a
g
e
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m
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ss
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n
,
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in
IEE
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ti
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s
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g
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ro
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e
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,
v
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l.
2
5
,
n
o
.
1
0
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p
p
.
4
9
0
0
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4
9
1
5
,
2
0
1
6
.
[1
2
]
X
.
M
a
,
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.
P
a
n
,
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L
i
a
n
d
J.
F
a
n
g
,
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Hig
h
-
q
u
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li
ty
in
it
ial
c
o
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e
b
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d
e
sig
n
m
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th
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ro
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,
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in
IET
I
m
a
g
e
P
r
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ss
in
g
,
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o
l.
9
,
n
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.
1
1
,
p
p
.
9
8
6
-
9
9
2
,
2
0
1
5
.
[1
3
]
Y.
F
.
X
ie,
J.
H.
L
iu
,
C.
F
.
Z
h
a
n
g
,
L
.
S
.
Ko
n
g
a
n
d
J.
L
.
Yi,
"
Co
d
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rd
s
Distri
b
u
ti
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-
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se
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ti
m
a
l
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m
b
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ti
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f
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u
a
l
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v
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ra
g
e
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u
a
l
-
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rian
c
e
Eq
u
a
l
-
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rm
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re
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g
h
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r
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st
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h
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l
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m
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a
n
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z
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c
o
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,
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in
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EE
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ra
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ro
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p
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5
8
0
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8
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3
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1
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.
[1
4
]
K.
W
a
k
a
b
a
y
a
sh
i,
"
Re
se
a
r
c
h
a
n
d
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e
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a
t
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e
rm
it
ted
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a
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si
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il
e
Us
e
to
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p
lo
d
e
i
n
Ja
p
a
n
,
"
2
0
0
9
IEE
E
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lo
b
e
c
o
m
W
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rk
sh
o
p
s,
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n
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l
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lu
,
HI,
2
0
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,
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p
.
1
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6.
[1
5
]
T
.
Ka
v
it
h
a
a
n
d
Dr.
K.
Ja
y
a
S
a
n
k
a
r,
“
A
n
Eff
icie
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t
Co
m
p
re
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io
n
T
e
c
h
n
iq
u
e
f
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r
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-
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ro
u
p
3
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d
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d
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a
g
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s
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in
g
V
a
riab
le
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th
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d
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s
w
it
h
Re
d
u
c
e
d
A
v
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ra
g
e
Len
g
th
”
,
2
0
1
6
IEE
E
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
In
d
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tern
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l
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In
f
o
rm
a
ti
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in
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(IICIP
-
2
0
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6
)
,
p
p
.
1
-
6.
[1
6
]
Kh
a
li
d
S
a
y
o
o
d
,
"
In
tr
o
d
u
c
ti
o
n
t
o
Da
ta
c
o
m
p
re
ss
io
n
"
,
M
o
rg
a
n
Ko
f
fm
a
n
p
u
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sh
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rs,
t
h
ird
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it
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o
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,
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a
n
F
ra
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isc
o
,
CA
,
2
0
0
5
.
[1
7
]
Da
v
i
d
sa
l
m
o
n
"
Da
ta co
m
p
re
ss
io
n
:,
T
h
e
c
o
m
p
lete
re
f
e
re
n
c
e
"
C
A
,
U
S
A
,
3
rd
e
d
it
i
o
n
,
2
0
0
4
.
[1
8
]
S
tan
d
a
rd
T
e
st I
m
a
g
e
s.
Co
m
p
il
e
d
b
y
M
ik
e
W
a
k
e
n
,
Un
iv
e
rsity
o
f
M
ich
ig
a
n
--
ww
.
e
c
e
.
rice
.
e
d
u
/~
w
a
k
in
/i
m
a
g
e
s/.
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