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[
3
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u
a
n
tizer
i
s
m
an
y
to
o
n
e
ali
g
n
in
g
,
w
h
ic
h
i
s
t
h
e
lo
s
s
y
p
r
o
ce
d
u
r
e
an
d
it
is
t
h
e
m
aj
o
r
s
o
u
r
ce
o
f
co
m
p
r
es
s
io
n
a
n
d
E
n
co
d
in
g
w
il
l
co
m
p
r
ess
th
e
q
u
an
t
ized
v
alu
e
s
lo
s
s
les
s
l
y
to
p
r
o
v
id
e
u
s
th
e
i
m
p
r
o
v
ed
co
m
p
r
e
s
s
io
n
.
T
h
e
en
tr
o
p
y
co
d
in
g
is
g
en
er
all
y
u
s
ed
f
o
r
d
is
ca
r
d
in
g
th
e
co
d
in
g
r
ep
etitio
n
in
th
e
i
m
a
g
es.
T
h
e
m
o
s
t
co
m
m
o
n
l
y
u
s
ed
en
tr
o
p
y
co
d
in
g
ar
e
th
e
H
u
f
f
m
an
E
n
co
d
er
(
HE
)
,
A
r
it
h
m
etic
E
n
co
d
er
(
A
E
)
a
n
d
R
u
n
-
L
en
g
t
h
E
n
co
d
i
n
g
(
R
L
E
)
.
T
o
p
r
o
v
id
e
th
e
g
o
o
d
co
m
p
r
ess
io
n
a
w
ell
-
o
r
g
a
n
ized
q
u
a
n
tizer
an
d
en
co
d
in
g
i
s
v
er
y
m
u
c
h
i
m
p
o
r
t
an
t.
Hu
f
f
m
a
n
co
d
in
g
an
d
A
r
it
h
m
etic
co
d
in
g
ar
e
v
ar
iab
le
-
l
en
g
th
e
n
co
d
in
g
m
e
th
o
d
an
d
ap
p
ly
th
e
p
r
o
b
ab
ilit
y
d
ata
o
f
t
h
e
s
y
m
b
o
l
s
.
T
h
e
s
i
m
p
les
t
f
o
r
m
o
f
q
u
an
t
izatio
n
is
co
m
m
o
n
l
y
u
s
ed
in
co
m
p
r
ess
io
n
s
tan
d
ar
d
is
“
s
ca
lar
q
u
an
t
izati
o
n
”
w
h
er
e
ea
c
h
s
i
g
n
al
v
a
lu
e
o
f
s
ca
lar
q
u
a
n
tizatio
n
is
i
n
d
i
v
id
u
all
y
q
u
a
n
tized
.
Hu
f
f
m
a
n
co
d
i
n
g
is
ea
s
y
a
n
d
f
aster
th
a
n
t
h
e
A
r
ith
m
et
ic
co
d
in
g
.
I
t
d
ep
en
d
s
o
n
p
r
o
b
ab
ilit
y
s
tatis
tic
s
.
A
r
ith
m
eti
c
w
o
r
k
s
b
y
e
v
al
u
ati
n
g
a
s
tr
ea
m
o
f
d
ata
s
y
m
b
o
ls
a
n
d
d
o
es
n
o
t
r
esto
r
e
i
n
d
iv
id
u
al
d
at
a
s
y
m
b
o
ls
w
it
h
a
co
m
p
r
es
s
ed
v
er
s
io
n
o
f
lar
g
e
d
ata;
a
n
u
m
b
er
o
f
b
its
ar
e
n
e
ed
e
d
in
th
e
o
u
tp
u
t
co
d
e.
Gen
er
all
y
,
it
d
o
es
n
o
t
p
er
f
o
r
m
b
etter
t
h
an
H
u
f
f
m
a
n
co
d
in
g
w
h
e
n
i
n
co
r
r
ec
t
p
r
o
b
ab
ilit
ies
ar
e
f
ed
to
th
e
co
d
er
[
4
]
.
R
L
E
r
em
o
v
es
lin
ea
r
co
r
r
elatio
n
in
th
e
d
ata
s
o
u
r
ce
,
b
u
t
it
is
m
o
r
e
s
u
itab
l
e
w
h
e
n
lar
g
e
r
u
n
s
o
f
t
h
e
s
a
m
e
s
y
m
b
o
l
o
cc
u
r
in
th
e
d
ata
s
tr
ea
m
.
I
t is
s
i
m
p
le
to
i
m
p
le
m
en
t a
n
d
th
e
p
r
o
ce
s
s
is
f
ast.
T
h
e
i
m
p
le
m
en
ta
tio
n
o
f
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
co
m
p
ar
ed
w
i
th
th
r
ee
e
n
co
d
in
g
m
et
h
o
d
s
an
d
th
e
p
er
f
o
r
m
an
ce
lik
e
P
ea
k
S
ig
n
al
to
No
i
s
e
R
atio
(
P
SNR
)
,
C
o
m
p
r
es
s
io
n
R
atio
(
C
R
)
a
n
d
S
p
ac
e
Sav
i
n
g
(
SS
)
i
s
e
v
alu
a
ted
to
d
ec
id
e
t
h
e
q
u
a
lit
y
o
f
t
h
e
co
m
p
r
ess
ed
i
m
a
g
e.
C
o
m
p
r
e
s
s
io
n
r
atio
m
o
s
tl
y
r
a
n
g
es
f
r
o
m
2
:1
to
4
:1
f
o
r
n
atu
r
al
i
m
a
g
es,
b
u
t i
t c
an
b
e
m
u
c
h
lar
g
er
f
o
r
th
e
d
o
cu
m
en
t
i
m
a
g
es.
2.
M
AT
E
RIAL
S AN
D
M
E
T
H
O
DS
An
i
m
a
g
e
p
r
o
ce
s
s
in
g
ap
p
licat
io
n
is
p
er
f
o
r
m
ed
in
a
tr
a
n
s
f
o
r
m
d
o
m
ai
n
r
ath
er
t
h
a
n
t
h
e
p
ix
el
d
o
m
a
in
,
o
f
ten
b
y
m
ea
n
s
o
f
a
n
in
v
er
t
ib
le
d
ef
in
i
te
tr
an
s
f
o
r
m
atio
n
.
I
n
tr
an
s
f
o
r
m
b
a
s
ed
s
ch
e
m
e
s
th
e
i
m
ag
e
is
f
ir
s
t
tr
an
s
f
o
r
m
ed
b
ef
o
r
e
th
e
h
id
i
n
g
m
et
h
o
d
an
d
th
e
w
a
ter
m
ar
k
i
s
co
n
ce
aled
in
t
h
e
co
ef
f
icien
ts
w
h
ich
ar
e
r
ep
r
esen
ted
b
y
t
h
e
i
m
ag
e.
T
h
e
h
id
in
g
i
m
a
g
e
i
s
ac
h
iev
ed
b
y
u
s
i
n
g
a
r
ev
er
s
e
tr
an
s
f
o
r
m
a
t
io
n
.
T
h
e
p
r
o
p
o
s
e
d
m
et
h
o
d
is
i
m
p
le
m
e
n
ted
b
y
u
s
i
n
g
t
h
e
C
o
m
p
lex
C
o
n
to
u
r
let
T
r
an
s
f
o
r
m
(
C
C
T
)
f
o
r
d
ata
h
id
in
g
.
Or
ig
i
n
o
f
C
C
T
:
I
n
itiall
y
,
W
av
elet
T
r
an
s
f
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r
m
(
W
T
)
p
lay
ed
a
v
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r
o
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in
t
h
e
tele
m
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f
ield
,
d
u
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to
its
b
etter
p
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m
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h
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d
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v
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s
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if
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v
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f
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an
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tio
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alit
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o
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v
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co
m
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w
b
ac
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th
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ap
p
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li
k
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r
id
g
elet,
cu
r
v
elet,
b
ea
m
let,
b
an
d
elet,
co
n
to
u
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let
etc
h
a
v
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b
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n
tr
o
d
u
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d
.
C
o
m
p
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in
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to
all
t
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o
f
‘
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let
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v
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th
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m
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m
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l
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ac
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p
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eth
o
d
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s
C
o
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t
o
u
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let
T
r
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s
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(
C
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,
b
ec
au
s
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o
f
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s
g
o
o
d
d
ir
ec
tio
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alit
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an
d
clea
r
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p
l
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m
en
tatio
n
.
C
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n
to
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let
tr
a
n
s
f
o
r
m
w
as
p
r
o
p
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s
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b
y
DO
(
2
0
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2
)
an
d
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m
o
d
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f
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a
v
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b
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tr
o
d
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d
to
f
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m
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e
w
f
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m
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s
li
k
e
No
n
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Su
b
Sa
m
p
led
C
o
n
to
u
r
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r
an
s
f
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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NSC
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d
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v
er
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NSC
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w
as
p
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s
ed
b
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u
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h
a
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2
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h
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s
a
g
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m
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t
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f
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tr
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s
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v
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n
tag
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is
it
h
a
s
h
ig
h
er
r
ep
etitio
n
[
5
]
w
h
ic
h
is
g
iv
e
n
b
y
t
h
e
f
o
llo
w
i
n
g
(
1
)
,
(
i.e
.
)
=
1
+
∑
2
=
1
(
1
)
W
h
er
e,
S is
s
ca
le
n
u
m
b
er
o
f
t
h
e
tr
an
s
f
o
r
m
.
Du
e
to
th
is
d
is
ad
v
a
n
ta
g
e,
N
SC
T
is
co
m
p
lex
a
n
d
ti
m
e
-
co
n
s
u
m
in
g
.
T
h
er
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o
r
e,
i
t
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eq
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ir
es
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lar
g
er
m
e
m
o
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y
.
Hen
ce
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e
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as c
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:
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s
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a
C
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h
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h
e
DT
-
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W
T
an
d
NSC
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.
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C
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h
as
b
asicall
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t
w
o
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a
m
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v
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tr
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s
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DT
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f
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h
ich
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s
ix
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ir
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b
an
d
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ch
s
c
ale
2
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d
etail
co
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t
s
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b
-
s
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ac
e
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ch
s
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b
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b
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d
h
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s
t
w
o
w
av
ele
ts
,
d
escr
ib
e
b
y
{
1
,
2
}
as
r
ea
l
an
d
an
i
m
a
g
in
ar
y
p
ar
t
o
f
th
e
w
a
v
el
et
co
ef
f
icie
n
t
s
.
(
2
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Dir
ec
tio
n
al
f
ilter
b
an
k
(
DFB
)
f
o
r
m
u
lti
-
d
ir
ec
tio
n
al
d
e
co
m
p
o
s
i
tio
n
to
s
et
th
e
lo
ca
ll
y
co
r
r
elate
d
co
ef
f
icien
ts
w
h
ich
ar
e
ev
alu
ated
b
y
DT
-
C
W
T
w
e
ap
p
ly
lev
els
o
f
Dir
ec
tio
n
al
Fil
ter
B
an
k
(
DFB
)
to
ea
ch
m
u
l
ti
-
s
c
ale
d
etail
s
p
ac
e
an
d
th
e
s
u
b
-
b
an
d
s
ar
e
ex
p
an
d
ed
to
th
e
n
u
m
b
er
o
f
2
[6
]
.
T
h
e
d
ec
o
m
p
o
s
itio
n
eq
u
at
io
n
i
s
g
i
v
en
b
y
,
,
,
,
,
(
)
=
∑
[
−
∈
2
]
,
,
(
)
,
=
1
,
2
,
3
(
2
)
W
h
er
e,
,
,
,
,
(
.
)
is
a
g
r
o
u
p
o
f
d
ir
ec
tio
n
al
s
u
b
s
p
ac
e
,
at
s
ca
le
2
an
d
d
e
co
m
p
o
s
i
tio
n
d
ir
ec
tio
n
k
=
1
…
2
,
(
.
)
is
i
m
p
u
l
s
e
r
esp
o
n
s
e
o
f
th
e
s
y
n
th
e
s
i
s
f
il
ter
,
is
th
e
d
ec
o
m
p
o
s
itio
n
lev
e
l o
f
t
h
e
DFB
.
I
n
th
e
p
er
f
o
r
m
ed
tr
an
s
f
o
r
m
,
C
C
T
co
m
b
i
n
es
t
h
e
p
r
o
p
er
ties
o
f
NSC
T
(
m
u
lti
-
r
eso
l
u
tio
n
,
l
o
ca
lizatio
n
,
d
ir
ec
tio
n
alit
y
,
a
n
d
a
n
is
o
tr
o
p
y
)
an
d
DT
-
C
W
T
(
tr
an
s
lati
o
n
in
v
ar
ia
n
t,
d
ir
ec
tio
n
alit
y
)
.
T
h
er
ef
o
r
e,
it
is
co
m
p
u
tatio
n
all
y
m
o
r
e
ef
f
icie
n
t
th
an
t
h
e
o
th
er
tr
an
s
f
o
r
m
tec
h
n
iq
u
e.
T
h
e
lev
el
1
s
tag
e
d
ec
o
m
p
o
s
i
tio
n
o
f
C
C
T
is
s
h
o
w
n
i
n
Fi
g
u
r
e
2
.
Fig
u
r
e
2
.
Dec
o
m
p
o
s
i
tio
n
Sta
g
e
f
o
r
L
ev
e
l 1
C
C
T
3.
P
RO
P
O
SE
D
M
E
T
H
O
D
I
n
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
an
a
lg
o
r
ith
m
f
o
r
d
ata
h
id
i
n
g
is
ex
p
lain
ed
w
it
h
a
n
e
x
a
m
p
le.
Dat
a
Hid
in
g
:
T
h
e
p
r
o
ce
s
s
o
f
d
ata
h
id
in
g
m
eth
o
d
is
s
h
o
w
n
i
n
Fi
g
u
r
e
3
.
I
n
th
e
n
e
w
d
ata
h
id
in
g
m
et
h
o
d
s
ca
lin
g
f
ac
to
r
/th
r
e
s
h
o
ld
v
al
u
e
is
s
e
t
in
s
u
c
h
a
w
a
y
t
h
at
n
o
v
io
lato
r
s
ca
n
p
ir
ate
th
e
p
atien
t
d
ata.
I
f
t
h
e
s
ca
li
n
g
f
ac
to
r
is
v
er
y
les
s
in
v
al
u
e
th
e
n
,
it
w
ill
g
iv
e
a
b
etter
in
v
i
s
ib
le
h
id
in
g
al
g
o
r
ith
m
.
T
o
p
r
o
v
e
th
e
s
ec
u
r
it
y
s
y
s
te
m
o
f
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
it
is
v
a
lid
ated
w
i
th
v
a
r
io
u
s
attac
k
s
.
T
h
e
attac
k
s
ap
p
lied
i
n
t
h
e
m
e
th
o
d
s
ar
e
Ga
u
s
s
ian
No
is
e,
Salt &
P
ep
p
er
No
is
e,
Sh
ar
p
en
i
n
g
,
R
o
tat
in
g
,
His
to
g
r
a
m
E
q
u
aliza
tio
n
,
an
d
Me
d
ian
F
ilter
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
6
,
Dec
em
b
er
2
0
1
9
:
4
7
1
3
-
4
7
2
0
4716
Fig
u
r
e
3
.
B
lo
ck
Diag
r
a
m
-
Dat
a
Hid
in
g
Gen
era
tio
n
o
f
P
N
S
eq
u
e
n
ce
:
Sp
r
ea
d
s
p
ec
tr
u
m
is
o
n
e
o
f
t
h
e
i
m
p
o
r
tan
t
tec
h
n
iq
u
es
f
o
r
tr
an
s
m
is
s
io
n
i
n
w
h
ic
h
t
h
e
d
ata
s
eq
u
en
ce
ap
p
ly
t
h
e
b
an
d
w
id
t
h
w
it
h
lea
s
t
r
e
q
u
ir
e
m
e
n
t.
I
t
h
as
t
h
e
m
aj
o
r
ad
v
an
ta
g
e
s
li
k
e
s
tr
o
n
g
an
ti
-
i
n
ter
f
er
e
n
ce
ab
ilit
y
,
lo
w
b
it
er
r
o
r
r
ate,
b
etter
h
id
in
g
c
ap
ac
it
y
,
lo
w
i
n
ter
ce
p
t,
an
d
h
i
g
h
co
n
f
id
en
tial
it
y
.
I
t
h
as
t
w
o
m
aj
o
r
p
r
o
p
er
ties
(
1
)
b
an
d
w
id
t
h
o
f
t
h
e
s
ig
n
a
l
tr
an
s
m
i
s
s
io
n
is
h
ig
h
er
t
h
a
n
th
e
b
a
n
d
w
id
th
o
f
th
e
o
r
ig
in
al
d
ata
s
ig
n
al,
(
2
)
tr
an
s
m
i
s
s
io
n
s
ig
n
al
b
an
d
w
i
d
th
is
d
ec
id
ed
b
y
t
h
e
p
s
e
u
d
o
-
r
an
d
o
m
co
d
e
[7
]
.
P
s
eu
d
o
No
is
e
s
e
q
u
e
n
ce
g
e
n
er
ates
a
s
eq
u
e
n
ce
o
f
p
s
e
u
d
o
r
an
d
o
m
b
i
n
ar
y
n
u
m
b
er
s
b
y
u
s
in
g
th
e
s
h
if
t
r
eg
i
s
ter
.
T
h
er
e
ar
e
‘
r
’
r
eg
is
ter
s
in
t
h
e
g
en
er
ato
r
w
h
ic
h
b
o
o
s
t
th
eir
v
alu
es
at
ea
ch
i
n
s
ta
n
t
d
ep
en
d
i
n
g
o
n
t
h
e
v
al
u
e
o
f
th
e
i
n
co
m
i
n
g
p
o
in
ter
to
th
e
s
h
if
t
r
eg
is
ter
.
T
h
e
m
ai
n
r
eq
u
ir
e
m
en
ts
o
f
th
e
p
s
e
u
d
o
n
o
is
e
co
d
e
ar
e
g
o
o
d
s
p
ec
tr
al
ch
ar
ac
ter
is
tic
s
an
d
s
ec
u
r
it
y
[8
]
.
A
P
N
s
eq
u
e
n
ce
is
a
b
it
s
tr
ea
m
o
f
1
’
s
an
d
0
’
s
w
it
h
s
o
m
e
id
le
p
r
o
p
er
ties
.
I
n
a
P
N
s
eq
u
en
ce
th
e
n
u
m
b
er
o
f
1
’
s
an
d
0
’
s
o
f
a
n
y
a
m
o
u
n
t
o
f
len
g
t
h
d
if
f
er
o
n
l
y
b
y
o
n
e
(
i.e
.
)
th
e
n
u
m
b
er
o
f
1
’
s
is
j
u
s
t
o
n
e
m
o
r
e
th
an
t
h
e
n
o
o
f
0
’
s
(
Fo
r
e.
g
.
)
th
e
P
N
s
eq
u
en
ce
o
f
le
n
g
th
1
5
is
g
i
v
en
b
y
(
2
4
−
1
)
(
i.e
.
)
(
2
−
1
)
,
it
co
n
tain
s
eig
h
t 1
’
s
an
d
s
ev
e
n
0
’
s
(
Mu
ta
g
i,
1
9
9
6
)
.
T
h
er
ef
o
r
e,
a
s
er
ies o
f
1
’
s
a
n
d
0
’
s
is
ca
lled
‘
r
u
n
’
a
n
d
th
e
a
m
o
u
n
t o
f
1
’
s
an
d
0
’
s
is
ca
lled
‘
r
u
n
len
g
t
h
’
.
A
P
N
s
eq
u
en
ce
o
f
s
p
an
2
−
1
in
clu
d
es
o
n
e
r
u
n
o
f
N
1
’
s
an
d
o
n
e
r
u
n
o
f
N
-
1
0
’
s
.
T
h
e
s
er
ies o
f
o
th
er
r
u
n
s
,
N
-
2
to
1
,
o
f
1
’
s
an
d
r
is
es a
s
th
e
p
o
w
er
o
f
2
[9
]
w
h
ich
i
s
s
h
o
w
n
i
n
T
ab
le
1
.
T
ab
le
1
.
Nu
m
b
er
o
f
r
u
n
s
1
’
s
a
n
d
0
’
s
o
f
v
ar
io
u
s
le
n
g
t
h
s
i
n
a
P
N
s
eq
u
en
ce
o
f
le
n
g
th
2
−
1
Ru
n
L
e
n
g
t
h
1
’
s
0
’
s
N
1
0
N
-
1
0
1
N
-
2
1
1
N
-
3
2
2
N
-
4
4
4
….
….
….
2
2
−
4
2
−
4
1
2
−
3
2
−
3
Da
t
a
H
idi
ng
Alg
o
rit
h
m
:
1.
C
h
o
o
s
e
t
h
e
m
ed
ical
co
v
er
i
m
a
g
e
an
d
d
ec
o
m
p
o
s
e
it b
y
C
o
m
p
lex
C
o
n
to
u
r
let
T
r
an
s
f
o
r
m
(
C
C
T
)
2.
Select
t
h
e
s
ca
li
n
g
f
ac
to
r
o
r
s
im
p
l
y
ca
lled
th
r
e
s
h
o
ld
v
a
lu
e
f
o
r
a
b
etter
tr
an
s
m
i
s
s
io
n
p
u
r
p
o
s
e
an
d
al
s
o
f
o
r
d
ata
s
ec
u
r
it
y
3.
I
m
p
le
m
e
n
t
t
h
e
Sp
r
ea
d
s
p
ec
tr
u
m
h
id
i
n
g
p
r
o
ce
s
s
b
y
g
e
n
er
atin
g
t
h
e
P
s
eu
d
o
No
is
e
Seq
u
e
n
ce
to
e
m
b
ed
th
e
m
e
s
s
a
g
e
b
it in
t
h
e
C
o
ef
f
ici
en
t o
b
tain
e
d
b
y
C
C
T
d
ec
o
m
p
o
s
itio
n
p
r
o
ce
s
s
f
o
r
m
ed
ical
co
v
er
i
m
ag
e
4.
Hid
in
g
Data
=
C
C
T
C
o
ef
f
icie
n
t +
Scali
n
g
Facto
r
*
Me
s
s
a
g
e
b
it to
b
e
e
m
b
ed
5.
R
ep
ea
t th
e
s
tep
s
3
an
d
4
u
n
til
all
th
e
m
ess
a
g
e
b
it is
e
m
b
ed
d
ed
6.
A
p
p
l
y
R
ev
er
s
e
C
C
T
to
o
b
tain
th
e
f
in
al
E
m
b
ed
d
ed
I
m
a
g
e
E
x
a
m
p
le
f
o
r
P
ro
po
s
ed
M
et
ho
d:
L
et
u
s
co
n
s
id
er
t
h
e
f
o
llo
w
i
n
g
ex
a
m
p
le
f
o
r
b
etter
u
n
d
er
s
tan
d
i
n
g
ab
o
u
t
th
e
p
r
o
p
o
s
ed
d
ata
h
id
i
n
g
.
I
n
i
tiall
y
,
af
ter
th
e
d
ec
o
m
p
o
s
itio
n
p
r
o
ce
s
s
ch
o
o
s
e
a
n
y
t
w
o
co
ef
f
icien
ts
f
r
o
m
a
n
y
d
ir
ec
tio
n
an
d
f
o
llo
w
i
n
g
p
r
o
ce
s
s
is
p
er
f
o
r
m
ed
to
e
m
b
ed
th
e
e
n
tir
e
m
e
s
s
a
g
e
d
ata.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
efficien
t d
a
ta
h
id
in
g
meth
o
d
in
ima
g
es
(
S
a
r
a
n
ya
G
)
4717
E
m
be
dd
i
ng
1.
L
et,
A
is
t
h
e
m
ax
i
m
u
m
d
ata,
B
is
th
e
m
i
n
i
m
u
m
d
ata
a
n
d
C
i
s
th
e
to
tal
m
es
s
ag
e
b
it to
b
e
em
b
ed
d
ed
.
2.
No
w
,
ca
lc
u
late
th
e
d
i
f
f
er
en
ce
(
D)
b
et
w
ee
n
t
h
e
A
a
n
d
B
,
C
o
n
s
id
er
th
e
s
ca
li
n
g
f
ac
to
r
o
r
th
e
t
h
r
es
h
o
ld
v
alu
e
w
h
ic
h
is
m
i
n
i
m
u
m
3.
T
h
er
ef
o
r
e
,
th
e
d
if
f
er
en
ce
s
h
o
u
ld
b
e
g
r
ea
ter
th
an
t
h
e
s
ca
li
n
g
f
ac
to
r
.
Hen
ce
,
in
t
h
is
ca
s
e,
D
>
s
ca
lin
g
f
ac
to
r
4.
T
o
em
b
ed
th
e
m
e
s
s
a
g
e
b
it
n
e
w
p
ar
a
m
eter
(
E
)
is
in
tr
o
d
u
ce
d
b
y
tak
in
g
d
if
f
er
en
ce
b
et
w
ee
n
to
tal
m
e
s
s
a
g
e
b
it a
n
d
D,
th
en
it i
s
d
iv
id
ed
b
y
2
5.
T
o
o
b
tain
th
e
e
m
b
ed
d
i
n
g
co
ef
f
icie
n
t
A’
an
d
B
’
is
ca
lc
u
lated
,
A’
=
A
+
E
B
’
=
B
–
E
4.
E
XP
E
R
I
M
E
NT
A
L
RE
SUL
T
S
I
n
th
i
s
p
ap
er
,
p
e
r
f
o
r
m
a
n
ce
ev
alu
atio
n
f
o
r
d
ata
h
id
in
g
is
m
ea
s
u
r
ed
b
y
u
s
in
g
P
ea
k
Si
g
n
al
to
No
is
e
R
atio
(
P
SNR
)
,
Sp
ac
e
Sav
i
n
g
(
SS
)
,
Me
an
Sq
u
ar
e
E
r
r
o
r
(
MSE
)
an
d
C
o
m
p
r
ess
io
n
R
atio
(
C
R
)
[
1
0
].
a.
Me
an
Sq
u
ar
e
E
r
r
o
r
(
MSE
)
I
t is u
s
ed
to
m
ea
s
u
r
e
t
h
e
d
is
to
r
tio
n
p
r
o
d
u
ce
d
af
ter
h
id
in
g
t
h
e
d
ata
=
1
∑
∑
(
−
)
²
−
1
=
0
−
1
=
0
W
h
er
e,
M
an
d
N
is
th
e
n
u
m
b
e
r
o
f
an
i
m
a
g
e
p
ix
el
s
.
b.
P
SNR
:
I
t is u
s
ed
to
m
ea
s
u
r
e
t
h
e
o
b
j
ec
tiv
e
eq
u
ali
t
y
o
f
an
i
m
ag
e
w
h
ic
h
is
g
iv
e
n
b
y
,
=
10
l
og
10
²
W
h
er
e,
Ma
x
is
a
m
a
x
i
m
u
m
g
r
a
y
s
ca
le
o
f
a
n
i
m
ag
e.
B
etter
h
i
g
h
P
SN
R
h
a
v
e
h
i
g
h
er
q
u
alit
y
i
m
ag
e
(
i.e
.
)
it is
clo
s
e
to
th
e
co
v
er
i
m
ag
e.
c.
C
o
m
p
r
ess
io
n
R
at
io
(
C
R
)
I
t is g
i
v
en
b
y
,
=
⁄
d.
Sp
ac
e
Sav
i
n
g
(
S
S)
I
t is g
i
v
en
b
y
,
=
1
−
5.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
s
h
o
w
s
t
h
e
p
er
f
o
r
m
an
ce
e
v
alu
a
tio
n
f
o
r
t
h
e
d
ata
e
m
b
ed
d
in
g
s
ch
e
m
e
b
ased
o
n
tr
an
s
f
o
r
m
d
o
m
ai
n
.
I
n
tiall
y
,
th
e
m
ed
ical
co
v
er
i
m
ag
e
is
c
h
o
s
en
a
n
d
it
is
d
ec
o
m
p
o
s
ed
b
y
th
e
C
C
T
.
T
h
e
m
ed
ical
co
v
er
i
m
a
g
e
is
s
h
o
w
n
in
Fi
g
u
r
e
4
.
Af
ter
t
h
e
p
r
o
p
er
s
elec
tio
n
o
f
a
n
in
p
u
t
i
m
ag
e,
d
ec
o
m
p
o
s
i
tio
n
p
r
o
ce
s
s
is
p
er
f
o
r
m
ed
f
o
r
3
lev
els.
C
o
m
p
le
x
C
o
ef
f
ici
en
ts
o
f
C
C
T
is
s
h
o
w
n
i
n
Fi
g
u
r
e
5
an
d
Fig
u
r
e
6
.
Fig
u
r
e
4
.
Me
d
ical
co
v
er
i
m
ag
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
6
,
Dec
em
b
er
2
0
1
9
:
4
7
1
3
-
4
7
2
0
4718
Fig
u
r
e
5
.
Dec
o
m
p
o
s
i
tio
n
o
f
C
C
T
–
(
a
)
L
ev
el
1
C
o
m
p
le
x
co
n
to
u
r
let
co
ef
f
icien
t
(
b
)
L
ev
el
2
C
o
m
p
lex
co
n
to
u
r
l
et
co
ef
f
icie
n
t
Fig
u
r
e
6
.
L
ev
el
3
C
o
m
p
lex
co
n
to
u
r
let
co
ef
f
ic
ien
t
f
o
r
d
if
f
er
e
n
t d
ir
ec
tio
n
T
h
e
m
ed
ical
co
v
er
i
m
a
g
e
is
d
ec
o
m
p
o
s
ed
f
o
r
3
lev
els,
w
h
er
e
lo
w
co
ef
f
icien
ts
ar
e
d
is
p
la
y
ed
in
b
lack
an
d
h
ig
h
co
e
f
f
icie
n
ts
ar
e
d
is
p
l
a
y
ed
i
n
w
h
ite.
T
h
e
p
r
o
p
o
s
ed
d
ata
h
id
i
n
g
m
eth
o
d
i
s
ap
p
lied
in
t
h
e
d
ec
o
m
p
o
s
ed
co
v
er
i
m
a
g
e
b
y
s
elec
ti
n
g
a
n
y
t
w
o
co
ef
f
ic
ien
t
s
.
H
id
d
en
i
m
ag
e
a
n
d
t
h
e
r
ec
o
n
s
tr
u
cte
d
i
m
ag
e
i
s
s
h
o
w
n
in
Fi
g
u
r
e
7
(
a)
an
d
(
b
)
.
Fig
u
r
e
7
.
(
a)
W
ater
m
ar
k
ed
i
m
ag
e
(
b
)
R
ec
o
n
s
tr
u
c
te
d
i
m
a
g
e
T
h
e
r
ec
o
n
s
tr
u
ctio
n
o
f
t
h
e
i
m
ag
e
i
s
ac
h
iev
ed
b
y
t
h
e
in
v
er
s
e
C
C
T
.
T
h
e
p
er
f
o
r
m
a
n
ce
e
v
a
lu
atio
n
f
o
r
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
is
a
n
al
y
s
ed
b
y
e
s
ti
m
ati
n
g
th
e
M
SE
,
P
SNR
,
C
R
a
n
d
SS
f
o
r
w
it
h
co
m
p
r
ess
io
n
a
n
d
w
it
h
o
u
t c
o
m
p
r
ess
io
n
.
T
h
e
ev
alu
at
ed
r
esu
l
ts
ar
e
s
h
o
w
n
i
n
T
a
b
le
2
an
d
T
ab
le
3
.
T
ab
le
2
.
P
r
o
p
o
s
ed
m
et
h
o
d
w
it
h
o
u
t c
o
m
p
r
es
s
io
n
P
a
r
a
me
t
e
r
P
r
o
p
o
se
d
M
e
t
h
o
d
W
i
t
h
o
u
t
c
o
m
p
r
e
ssi
o
n
M
S
E
0
.
0
0
0
0
1
1
P
S
N
R
i
n
d
B
9
7
.
7
5
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
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8708
A
n
efficien
t d
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ta
h
id
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meth
o
d
in
ima
g
es
(
S
a
r
a
n
ya
G
)
4719
T
ab
le
3
.
P
r
o
p
o
s
ed
m
et
h
o
d
w
it
h
o
u
t c
o
m
p
r
es
s
io
n
a
n
d
w
ith
at
t
ac
k
s
D
i
f
f
e
r
e
n
t
A
t
t
a
c
k
s
A
p
p
l
i
e
d
f
o
r
P
r
o
p
o
se
d
M
e
t
h
o
d
w
i
t
h
o
u
t
C
o
m
p
r
e
ssi
o
n
P
a
r
a
me
t
e
r
M
S
E
P
S
N
R
i
n
d
B
G
a
u
ssi
a
n
N
o
i
se
0
.
0
0
9
1
2
6
8
.
5
6
S
a
l
t
&
P
e
p
p
e
r
N
o
i
se
0
.
0
0
0
0
7
0
8
9
.
6
5
S
h
a
r
p
e
n
i
n
g
0
.
0
0
0
0
7
4
8
9
.
4
7
R
o
t
a
t
i
n
g
0
.
0
0
0
0
9
9
8
8
.
2
0
H
i
st
o
g
r
a
m E
q
u
a
l
i
z
a
t
i
o
n
0
.
0
1
2
6
7
.
3
7
M
e
d
i
a
n
F
i
l
t
e
r
0
.
0
0
0
8
8
9
7
8
.
6
7
Fro
m
th
e
T
ab
le
2
an
d
T
ab
le
3
it
is
clea
r
th
at
th
e
MSE
an
d
P
SNR
h
a
v
e
b
etter
v
al
u
e
f
o
r
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
.
T
ab
le
4
clea
r
ly
s
h
o
w
s
h
o
w
m
u
ch
th
e
o
r
i
g
i
n
al
d
ata
is
co
m
p
r
ess
ed
i
n
s
ize.
I
t
clea
r
l
y
s
h
o
w
s
th
a
t
th
e
co
m
p
r
e
s
s
io
n
r
atio
a
n
d
s
p
ac
e
s
a
v
i
n
g
is
h
ig
h
f
o
r
Hu
f
f
m
an
en
co
d
er
b
ased
C
o
m
p
le
x
C
o
n
to
u
r
le
t
T
r
an
s
f
o
r
m
(
C
C
T
)
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
i
s
also
co
m
p
ar
ed
w
it
h
o
th
er
tr
a
n
s
f
o
r
m
s
lik
e
W
av
elet
T
r
an
s
f
o
r
m
(
W
T
)
,
an
d
No
n
-
Su
b
Sa
m
p
led
C
o
n
to
u
r
let
T
r
an
s
f
o
r
m
(
NS
C
T
)
to
ev
al
u
ate
th
e
i
m
p
r
o
v
ed
v
al
u
es
f
o
r
C
C
T
w
h
ic
h
is
s
h
o
w
n
i
n
Fi
g
u
r
e
8
,
9
,
1
0
an
d
Fig
u
r
e
11
.
Ta
b
le
4
.
P
r
o
p
o
s
ed
m
et
h
o
d
w
it
h
en
co
d
in
g
C
o
mp
l
e
x
C
o
n
t
o
u
r
l
e
t
T
r
a
n
sf
o
r
m (CC
T
)
P
r
o
p
o
se
d
M
e
t
h
o
d
W
i
t
h
C
o
mp
r
e
ssi
o
n
R
L
E
A
R
I
TH
M
ETI
C
H
U
F
F
M
A
N
D
i
me
n
si
o
n
5
1
2
*
5
1
2
5
1
2
*
5
1
2
5
1
2
*
5
1
2
U
n
c
o
mp
r
e
sse
d
S
i
z
e
3
2
K
B
3
2
K
B
3
2
K
B
C
o
mp
r
e
sse
d
S
i
z
e
0
.
6
9
K
B
1
.
4
7
K
B
0
.
1
1
K
B
M
S
E
8
.
7
8
8
.
5
0
5
.
6
5
P
S
N
R
i
n
d
B
8
7
.
5
3
8
8
.
0
8
4
9
2
.
7
2
C
o
mp
r
e
ssi
o
n
R
a
t
i
o
4
6
:
1
1
8
:
1
1
8
8
:
1
S
p
a
c
e
S
a
v
i
n
g
9
8
%
9
5
%
9
9
%
Fig
u
r
e
8
.
C
o
m
p
ar
is
o
n
o
f
C
o
m
p
r
e
s
s
io
n
R
atio
Fig
u
r
e
9
.
C
o
m
p
ar
is
o
n
o
f
Sp
ac
e
Sav
i
n
g
Fig
u
r
e
1
0
.
C
o
m
p
ar
is
o
n
o
f
P
S
NR
Fig
u
r
e
1
1
.
C
o
m
p
ar
is
o
n
o
f
MS
E
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
6
,
Dec
em
b
er
2
0
1
9
:
4
7
1
3
-
4
7
2
0
4720
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
is
ev
alu
ated
also
f
o
r
co
m
p
r
ess
io
n
an
d
w
it
h
attac
k
s
w
h
ich
is
s
h
o
w
n
i
n
th
e
Fi
g
u
r
e
1
2
an
d
Fig
u
r
e
13
.
T
h
e
r
esu
lts
o
f
en
co
d
er
ar
e
r
ec
o
r
d
e
d
o
n
th
e
b
asis
o
f
p
ar
am
eter
s
s
u
c
h
as
C
o
m
p
r
ess
io
n
R
atio
(
C
R
)
,
Sp
ac
e
Sav
in
g
(
SS
)
,
P
ea
k
Sig
n
al
to
No
is
e
R
atio
(
P
SNR
)
an
d
Me
an
Sq
u
ar
e
E
r
r
o
r
(
MSE
)
is
al
s
o
co
m
p
ar
ed
w
it
h
C
C
T
,
NSC
T
an
d
W
T
.
I
t
is
test
ed
w
i
th
v
ar
io
u
s
at
tack
s
b
y
ev
al
u
ati
n
g
P
ea
k
Sig
n
al
to
No
is
e
R
atio
(
P
SN
R
)
an
d
Me
a
n
Sq
u
ar
e
E
r
r
o
r
(
MSE
)
an
d
f
r
o
m
th
e
a
n
al
y
s
is
it
is
co
n
cl
u
d
ed
th
a
t
Hu
f
f
m
a
n
b
ased
C
C
T
g
i
v
es i
m
p
r
o
v
ed
v
alu
es t
h
an
o
t
h
er
m
et
h
o
d
s
.
Fig
u
r
e
12
.
P
r
o
p
o
s
ed
Me
th
o
d
w
it
h
C
o
m
p
r
ess
io
n
an
d
W
ith
A
ttac
k
s
f
o
r
MSE
Fig
u
r
e
13
.
P
r
o
p
o
s
ed
Me
th
o
d
w
it
h
C
o
m
p
r
ess
io
n
an
d
W
ith
A
ttac
k
s
f
o
r
P
SNR
6.
CO
NCLU
SI
O
N
P
r
o
v
id
e
I
n
th
is
p
ap
er
,
r
ev
er
s
ib
le
d
ata
h
id
i
n
g
m
et
h
o
d
is
u
s
ed
f
o
r
tele
m
ed
ici
n
e
e
h
ea
lt
h
ca
r
e
ap
p
licatio
n
u
s
i
n
g
C
C
T
.
I
t
h
as
t
h
e
m
aj
o
r
p
r
o
p
e
r
ties
o
f
h
ig
h
ca
p
ac
it
y
to
e
m
b
ed
t
h
e
d
ata;
tr
a
n
s
p
ar
en
c
y
,
i
n
v
is
ib
ili
t
y
,
an
d
r
o
b
u
s
t
n
es
s
to
attac
k
s
ar
e
less
w
h
e
n
i
t
is
tr
an
s
m
itted
f
o
r
d
iag
n
o
s
is
.
Fro
m
t
h
e
e
v
al
u
ati
o
n
,
it
i
s
co
n
c
lu
d
ed
th
at
th
e
C
C
T
b
ased
d
ata
h
id
i
n
g
is
b
etter
t
h
an
o
th
er
tr
an
s
f
o
r
m
s
.
T
h
er
ef
o
r
e,
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
r
etr
ie
v
e
s
th
e
o
r
ig
i
n
al
m
ed
ical
i
m
a
g
e
an
d
th
e
ex
p
er
i
m
en
ta
l r
esu
lts
s
u
g
g
est t
h
at
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ca
n
b
e
ef
f
icie
n
tl
y
u
s
ed
f
o
r
d
ata
h
id
i
n
g
p
r
o
ce
s
s
.
T
h
e
m
aj
o
r
ad
v
an
ta
g
es
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ar
e
b
etter
P
SNR
,
m
i
n
i
m
u
m
MSE
an
d
lar
g
e
h
id
in
g
ca
p
ac
it
y
.
RE
F
E
R
E
NC
E
S
[1
]
S
a
ra
n
y
a
,
G
.
a
n
d
S
.
Nirm
a
la
De
v
i,
“
A
n
a
p
p
ro
a
c
h
f
o
r
e
m
b
e
d
d
in
g
a
n
d
re
tri
e
v
in
g
th
e
d
a
ta
in
m
e
d
ica
l
ima
g
e
u
sin
g
c
o
n
to
u
rlet
tra
n
sf
o
rm
”
In
t.
J
.
S
c
ien
ti
fi
c
a
n
d
En
g
in
e
e
rin
g
Res
e
a
rc
h
,
3
:
7
6
9
-
7
7
5
,
2
0
1
2
.
[2
]
Jiri
F
rid
r
ich
,
“
A
p
p
l
ica
ti
o
n
s
o
f
d
a
t
a
h
i
d
i
n
g
in
d
i
g
it
a
l
ima
g
e
s
,”
P
ro
c
e
e
d
in
g
s
o
f
th
e
Co
n
f
e
re
n
c
e
o
n
S
ig
n
a
l
P
ro
c
e
ss
in
g
a
n
d
Its
A
p
p
li
c
a
ti
o
n
s (IS
S
P
A
),
IE
EE
,
A
u
g
2
2
-
2
5
,
Br
isb
a
n
e
,
Ql
d
,
1
,
1
9
9
9
.
[3
]
S
u
n
e
sh
a
n
d
Ha
rish
Ku
m
a
r,
“
W
a
ter
ma
rk
a
tt
a
c
k
s
a
n
d
a
p
p
li
c
a
ti
o
n
s
in
wa
ter
ma
rk
in
g
,”
Na
ti
o
n
a
l
W
o
rk
sh
o
p
-
c
u
m
-
Co
n
f
.
Re
c
e
n
t
T
re
n
d
s in
M
a
th
e
m
a
t
ics
a
n
d
Co
m
p
u
ti
n
g
(RT
M
C),
p
p
:
8
-
1
0
,
2
0
1
1
.
[4
]
M
e
g
h
n
a
S
h
a
rm
a
a
n
d
Ra
jee
v
M
a
th
u
r,
“
A
m
a
tl
a
b
to
g
e
n
e
ra
ti
o
n
a
n
d
sim
u
latio
n
o
f
P
N
se
q
u
e
n
c
e
in
m
a
tl
a
b
,”
In
t.
J
.
Eme
rg
in
g
T
e
c
h
n
o
l
o
g
y
a
n
d
Ad
v
a
n
c
e
d
En
g
in
e
e
rin
g
,
2
:
1
4
3
-
1
4
6
,
2
0
1
2
.
[5
]
Li
-
Wei
L
iu
,
X
in
-
W
u
Ch
e
n
a
n
d
Zh
i
-
W
e
i
Yin
g
,
“
T
e
x
tu
re
ima
g
e
re
trie
v
a
l
a
lg
o
rit
h
m
wit
h
d
u
a
l
tre
e
c
o
mp
lex
c
o
n
to
u
rle
t
a
n
d
t
h
re
e
sta
ti
stica
l
fea
tu
re
s
,
”
In
t.
C
o
n
f
.
Co
m
m
u
n
ica
ti
o
n
S
o
f
twa
re
a
n
d
n
e
tw
o
rk
s
(I
CCS
N),
IEE
E,
M
a
y
2
7
-
2
9
,
X
i’an
,
p
p
:
3
4
9
-
3
5
2
,
2
0
1
1
.
[6
]
S
h
a
o
-
W
e
i
Da
i,
Ya
n
-
Ku
i
S
u
n
,
Xia
o
-
L
in
T
ian
a
n
d
Ze
-
S
h
e
n
g
T
a
n
g
,
2
0
0
7
,
“
Ima
g
e
d
e
n
o
isin
g
b
a
se
d
o
n
c
o
m
p
lex
c
o
n
to
u
rle
t
tra
n
sfo
rm
,
”
In
t.
C
o
n
f
.
W
a
v
e
let
A
n
a
l
y
sis
a
n
d
P
a
tee
rn
Re
c
o
g
n
it
io
n
,
IE
EE
,
N
o
v
2
-
4
,
Be
ij
in
g
,
Ch
i
n
a
,
4
,
p
p
:
1
7
4
2
-
1
7
4
7
,
2
0
0
7
.
[7
]
M
u
tag
i,
R.
N.,
“
P
se
u
d
o
No
ise
se
q
u
e
n
c
e
s
f
o
r
e
n
g
in
e
e
rs
,”
IET
T
ra
n
s.,
J
.
El
e
c
tro
n
ics
&
Co
mm
u
n
ica
ti
o
n
En
g
in
e
e
ri
ng
,
8
:
7
9
-
8
7
,
1
9
9
6
.
[8
]
S
a
if
A
lz
a
h
ir
a
n
d
A
rb
e
r
Bo
rici,
“
A
n
in
n
o
v
a
ti
v
e
lo
ss
les
s
c
o
m
p
re
ss
io
n
m
e
th
o
d
f
o
r
d
isc
re
te
-
c
o
lo
r
im
a
g
e
s
,”
Ima
g
e
Pro
c
.
IEE
E
T
ra
n
s
.
,
2
:
4
4
-
5
6
,
2
0
1
5
.
[9
]
Ra
m
e
sh
M
u
th
iy
a
,
“
G
o
m
a
th
y
Ba
las
u
b
ra
m
a
n
ian
,
a
n
d
S
u
n
d
a
ra
ra
jan
P
a
ra
m
a
si
v
a
m
,
P
se
u
d
o
ra
n
d
o
m
No
ise
S
e
q
u
e
n
c
e
o
f
Dig
it
a
l
W
a
ter
m
a
r
k
in
g
A
lg
o
rit
h
m
b
a
se
d
o
n
Disc
re
te
W
a
v
e
let
T
ra
n
sf
o
rm
u
sin
g
M
e
d
ica
l
I
m
a
g
e
,
”
T
h
e
In
ter
n
a
ti
o
n
a
l
Ara
b
J
o
u
rn
a
l
o
f
I
n
fo
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
1
5
,
No
.
4
,
2
0
1
8
.
[1
0
]
Ro
h
it
T
h
a
n
k
i,
V
e
d
V
y
a
s
D
w
iv
e
d
i,
a
n
d
Ko
m
a
l
Bo
risa
g
a
r,
“
Ro
b
u
st
W
a
ter
m
a
r
k
in
g
Tec
h
n
iq
u
e
Us
in
g
Diffe
re
n
t
W
a
v
e
let
De
c
o
m
p
o
siti
o
n
L
e
v
e
ls F
o
r
S
ig
n
a
t
u
re
Im
a
g
e
P
ro
tec
ti
o
n
,
”
J
o
u
rn
a
l
o
f
ICT
,
1
6
,
N
o
.
1
,
p
p
:
1
5
7
-
1
7
4
,
2
0
1
7
.
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