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w
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[
1
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w
h
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cr
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p
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r
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ess
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n
tia
l
f
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[
2
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Steg
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v
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id
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to
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co
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ce
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m
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[
3
]
.
I
m
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ar
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co
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m
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s
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in
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[
4
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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N:
2252
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8938
A
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…
(
Mo
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Htiti
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2211
C
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[
5
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1
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e
t
h
e
s
ec
r
et
b
it
s
ar
e
g
e
n
er
all
y
h
id
d
en
i
n
t
h
e
L
SB
o
f
t
h
e
co
v
er
p
i
x
els.
A
n
o
t
h
er
tech
n
iq
u
e
ca
l
led
p
ix
el
v
al
u
e
d
if
f
er
en
ci
n
g
(
P
VD
)
in
v
o
lv
e
s
d
is
cr
ee
tl
y
alter
i
n
g
th
e
p
ix
el
v
al
u
es
o
f
a
n
i
m
a
g
e
to
en
co
d
e
th
e
h
id
d
en
in
f
o
r
m
atio
n
.
T
h
is
i
s
ac
h
iev
ed
b
y
m
a
k
in
g
s
m
al
l
m
o
d
i
f
icati
o
n
s
to
th
e
d
is
p
ar
it
y
v
al
u
e
s
o
f
ad
j
ac
en
t
p
ix
els
to
en
co
d
e
th
e
d
esire
d
d
ata
b
its
[
1
1
]
–
[
1
4
]
.
A
n
o
th
er
i
m
p
o
r
tan
t t
y
p
e
o
f
class
ical
tech
n
iq
u
e
is
t
h
e
t
r
an
s
f
o
r
m
d
o
m
ai
n
.
T
h
e
in
itial
s
tep
is
to
tr
an
s
f
o
r
m
th
e
co
v
er
i
m
ag
e
i
n
to
th
e
f
r
eq
u
en
c
y
d
o
m
ai
n
,
an
d
th
e
s
ec
r
et
in
f
o
r
m
atio
n
i
s
th
e
n
h
id
d
en
in
t
h
e
w
a
v
elet.
Se
v
er
a
l
r
esear
ch
p
r
o
j
ec
ts
h
av
e
b
ee
n
ca
r
r
ied
o
u
t
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
is
ap
p
r
o
ac
h
.
T
h
e
d
is
cr
ete
co
s
i
n
e
tr
an
s
f
o
r
m
(
DC
T
)
tech
n
i
q
u
e
h
as
u
n
d
er
g
o
n
e
s
ev
er
al
i
m
p
r
o
v
e
m
e
n
ts
a
n
d
i
m
p
le
m
en
ta
tio
n
s
in
th
i
s
w
o
r
k
,
[
1
5
]
–
[
1
8
]
.
Si
m
ilar
l
y
,
t
h
e
d
is
c
r
ete
w
a
v
elet
tr
a
n
s
f
o
r
m
(
DW
T
)
tech
n
iq
u
e
h
as
al
s
o
b
ee
n
ex
p
lo
r
ed
[
1
9
]
.
I
n
th
e
s
ec
o
n
d
ca
teg
o
r
y
,
w
h
ic
h
u
tili
z
es
ar
t
if
icial
i
n
telli
g
e
n
ce
,
ex
is
tin
g
r
esear
ch
s
h
o
w
s
p
r
o
m
i
s
e
f
o
r
in
f
o
r
m
a
tio
n
s
ec
u
r
it
y
b
ec
a
u
s
e
it
p
r
eser
v
e
s
t
h
e
v
is
u
al
an
d
s
tati
s
tical
p
r
o
p
er
ti
es
o
f
co
v
er
i
m
a
g
es
w
it
h
ef
f
ec
ti
v
e
h
id
i
n
g
o
f
s
ec
r
et
in
f
o
r
m
at
io
n
.
I
n
d
ee
d
,
u
n
li
k
e
tr
ad
itio
n
al
s
teg
a
n
o
g
r
ap
h
y
,
w
h
ic
h
o
f
ten
f
o
c
u
s
e
s
o
n
m
o
d
i
f
y
i
n
g
t
h
e
ch
ar
ac
ter
is
tic
s
o
r
p
ix
els
o
f
th
e
co
v
er
im
a
g
e
to
in
co
r
p
o
r
ate
s
ec
r
et
d
ata,
d
ee
p
s
teg
a
n
o
g
r
ap
h
y
u
s
e
s
n
eu
r
al
n
et
w
o
r
k
s
to
g
e
n
er
ate
i
m
ag
e
s
co
n
tai
n
i
n
g
h
id
d
en
i
n
f
o
r
m
atio
n
.
T
h
is
o
f
f
er
s
p
o
ten
tiall
y
m
o
r
e
r
o
b
u
s
t
an
d
s
o
p
h
is
ticated
co
n
ce
al
m
en
t
ca
p
ab
ilit
ies,
as
n
e
u
r
al
n
e
t
w
o
r
k
s
ca
n
lear
n
co
m
p
le
x
p
atter
n
s
an
d
r
ep
r
esen
tatio
n
s
.
Dee
p
s
teg
a
n
o
g
r
ap
h
y
ta
k
es
ad
v
an
tag
e
o
f
t
h
e
s
tr
en
g
t
h
s
o
f
m
ac
h
in
e
lear
n
i
n
g
,
en
ab
li
n
g
t
h
e
p
r
o
ce
s
s
o
f
h
id
i
n
g
a
n
d
ex
tr
ac
ti
n
g
co
n
f
id
e
n
tial d
ata
to
b
e
lear
n
ed
au
to
m
atica
ll
y
.
Sev
er
al
ar
tif
i
c
ial
in
tel
lig
e
n
ce
to
o
ls
h
av
e
b
ee
n
e
m
p
lo
y
ed
b
y
r
esear
ch
er
s
i
n
th
e
f
ield
o
f
d
ee
p
s
teg
a
n
o
g
r
ap
h
y
,
s
u
c
h
as
g
e
n
e
tic
alg
o
r
it
h
m
s
[
2
0
]
,
g
en
er
a
ti
v
e
ad
v
er
s
ar
ial
n
et
w
o
r
k
s
[
2
1
]
,
an
d
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
et
w
o
r
k
(
C
NN
)
a
u
to
en
co
d
er
;
B
alu
j
a
[
2
2
]
w
h
o
u
s
e
d
ee
p
n
eu
r
al
n
et
w
o
r
k
s
to
h
id
e
a
co
lo
r
im
a
g
e
i
n
s
id
e
an
o
th
er
i
m
a
g
e
o
f
t
h
e
s
a
m
e
s
ize,
d
em
o
n
s
tr
ati
n
g
t
h
e
ab
ilit
y
o
f
th
e
d
ee
p
lear
n
in
g
m
eth
o
d
in
i
m
ag
e
h
id
in
g
.
B
alu
j
a'
s
p
r
o
b
le
m
i
s
t
h
at
it
is
p
o
s
s
ib
le
f
o
r
a
n
attac
k
er
to
o
b
tain
th
e
o
r
ig
i
n
al
co
v
er
i
m
a
g
e
(
C
)
w
i
th
o
u
t
t
h
e
e
m
b
ed
d
ed
s
ec
r
et
i
m
a
g
e.
I
n
t
h
is
ca
s
e,
it
b
ec
o
m
es
p
o
s
s
ib
le
t
o
p
ar
tially
r
ev
ea
l
t
h
e
s
ec
r
et
i
m
ag
e
b
y
co
m
p
ar
i
n
g
th
e
o
r
ig
i
n
al
co
v
er
i
m
a
g
e
(
C
)
w
it
h
t
h
e
s
en
t
i
m
a
g
e
(
C
'
)
b
y
p
lo
ttin
g
th
eir
d
if
f
er
en
ce
|C
-
C
’
|
.
B
alu
j
a
[
2
3
]
,
w
h
il
e
s
u
g
g
e
s
t
i
n
g
co
n
ce
ali
n
g
m
u
ltip
l
e
i
m
a
g
es
w
it
h
i
n
a
s
i
n
g
le
i
m
ag
e,
in
itia
ll
y
tr
ain
ed
h
i
s
m
o
d
el
t
o
e
m
b
ed
o
n
e
i
m
a
g
e
in
o
n
e
co
v
er
.
T
h
e
m
o
d
el
id
en
tif
ie
s
t
h
e
o
p
ti
m
al
lo
ca
tio
n
s
f
o
r
e
m
b
ed
d
in
g
t
h
e
h
id
d
en
i
n
f
o
r
m
atio
n
an
d
d
ec
id
es
h
o
w
to
co
m
p
r
ess
a
n
d
r
ep
r
esen
t
it.
Su
b
s
eq
u
en
tl
y
,
t
h
e
au
t
h
o
r
p
r
o
p
o
s
ed
u
tili
zin
g
th
e
r
esu
lt
in
g
m
o
d
el
to
co
m
p
r
es
s
an
d
co
n
ce
al
t
w
o
i
m
a
g
es
w
it
h
i
n
o
n
e
co
v
er
.
I
n
th
is
p
ap
er
,
w
e
p
r
o
p
o
s
e
a
n
e
w
ar
c
h
itect
u
r
e
t
h
at
h
id
es
t
w
o
s
ec
r
et
i
m
a
g
e
s
r
ath
er
t
h
a
n
j
u
s
t
o
n
e
in
th
e
co
v
er
i
m
a
g
e.
A
f
ter
tr
ain
i
n
g
o
u
r
m
o
d
el,
it
s
h
o
u
ld
b
e
ab
le
t
o
h
id
e
t
w
o
s
ec
r
et
i
m
a
g
es
w
it
h
in
a
s
i
n
g
le
co
v
er
i
m
a
g
e.
B
ef
o
r
e
i
m
p
le
m
e
n
ti
n
g
t
h
is
,
w
e
s
u
g
g
e
s
t
a
tech
n
iq
u
e
to
en
h
a
n
ce
th
e
s
o
lu
tio
n
'
s
p
er
f
o
r
m
an
ce
b
y
r
a
n
d
o
m
l
y
g
en
er
ati
n
g
a
f
ak
e
s
ec
r
et
i
m
ag
e
an
d
e
m
b
ed
d
in
g
it
alo
n
g
s
id
e
th
e
r
ea
l
s
ec
r
et
i
n
t
h
e
co
v
er
i
m
ag
e.
T
h
e
r
est
o
f
t
h
e
p
ap
er
is
s
tr
u
ct
u
r
ed
as
f
o
llo
w
s
:
s
ec
tio
n
2
d
escr
ib
es
t
h
e
p
r
o
b
le
m
atic
a
s
p
ec
ts
r
elate
d
to
t
h
e
t
o
p
ic
an
d
h
i
g
h
lig
h
t
s
o
u
r
s
p
ec
if
ic
co
n
tr
ib
u
tio
n
s
.
R
e
s
u
lt
s
an
d
d
is
cu
s
s
io
n
s
ar
e
p
r
es
en
ted
in
s
ec
tio
n
3
.
Fin
all
y
,
in
s
ec
tio
n
4
,
w
e
g
iv
e
th
e
co
n
cl
u
s
io
n
an
d
p
er
s
p
ec
tiv
es o
f
o
u
r
s
t
u
d
y
.
2.
P
RO
P
O
SE
D
M
E
T
H
O
D
B
alu
j
a
[
2
3
]
h
as
s
u
cc
ee
d
ed
i
n
h
id
in
g
a
n
o
r
ig
in
al
co
lo
r
i
m
a
g
e
(
S)
in
s
id
e
an
o
t
h
er
co
v
er
i
m
a
g
e
(
C
)
,
t
h
e
r
esu
lt
b
ein
g
t
h
e
s
te
g
an
o
i
m
a
g
e
(C
'
)
.
Ho
w
ev
er
,
it
i
s
p
o
s
s
ib
le
f
o
r
a
n
attac
k
er
to
o
b
tai
n
t
h
e
o
r
ig
in
a
l
co
v
er
i
m
a
g
e
(
C
)
w
it
h
o
u
t
th
e
e
m
b
ed
d
ed
s
ec
r
et
i
m
ag
e.
I
n
t
h
i
s
ca
s
e,
it
b
ec
o
m
es
p
o
s
s
ib
le
to
p
ar
tiall
y
r
e
v
e
al
th
e
s
ec
r
et
i
m
ag
e
b
y
co
m
p
ar
in
g
th
e
o
r
i
g
i
n
al
c
o
v
er
p
h
o
to
(
C
)
w
it
h
th
e
s
e
n
t
p
h
o
to
(
C
'
)
b
y
ca
lcu
lati
n
g
t
h
eir
d
if
f
er
e
n
ce
.
An
ex
a
m
p
le
o
f
B
alu
j
a'
s
m
o
d
el
is
s
h
o
w
n
in
F
ig
u
r
e
1
,
w
h
ich
i
llu
s
tr
ates
h
o
w
t
h
e
d
if
f
er
en
ce
b
et
w
ee
n
(
C
)
an
d
(
C
'
)
ca
n
r
ev
ea
l
t
h
e
s
ec
r
et.
C
o
lu
m
n
3
r
ep
r
esen
ts
th
e
Ste
g
an
o
i
m
a
g
e,
w
h
ich
is
s
i
m
p
l
y
t
h
e
s
ec
r
et
i
m
a
g
e
d
is
p
la
y
ed
i
n
co
lu
m
n
2
an
d
e
m
b
ed
d
ed
in
t
h
e
co
v
er
i
m
ag
e
i
n
co
l
u
m
n
1
.
T
h
e
ex
tr
ac
ted
s
ec
r
et
i
s
s
h
o
wn
i
n
co
l
u
m
n
4
.
B
y
co
m
p
ar
i
n
g
t
h
e
co
v
er
i
m
a
g
e
(
C
)
an
d
th
e
s
te
g
a
n
o
i
m
ag
e
(
C
'
)
in
co
lu
m
n
5
,
w
e
ca
n
s
ee
th
at
th
e
s
ec
r
et
h
id
d
en
i
n
th
e
s
te
g
o
i
m
a
g
e
is
v
is
ib
le.
W
h
en
a
s
te
g
an
al
y
s
t
o
b
tain
s
i
m
ag
e
s
C
a
n
d
C
'
,
h
e
ca
n
d
ete
r
m
in
e
t
h
e
s
ec
r
et
b
y
ca
lcu
lat
i
n
g
th
e
d
i
f
f
er
e
n
ce
b
etw
ee
n
th
e
m
|C
-
C
’
|
.
On
e
s
o
l
u
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
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8938
I
n
t J
A
r
ti
f
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tell
,
Vo
l.
1
4
,
No
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3
,
J
u
n
e
2
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5
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1
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2219
2214
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M
)
.
Her
e'
s
a
d
etailed
p
r
esen
tat
io
n
o
f
ea
ch
o
f
t
h
e
s
e
in
d
icato
r
s
:
3
.
1
.
P
e
a
k
s
ig
na
l
-
to
-
no
is
e
ra
t
io
P
SNR
is
an
i
n
d
icato
r
co
m
m
o
n
l
y
u
s
ed
to
ass
es
s
t
h
e
lev
el
o
f
n
o
is
e
p
r
esen
t
in
t
h
e
p
ix
els
o
f
a
s
teg
a
n
o
g
r
ap
h
ic
i
m
ag
e,
i
n
o
r
d
er
to
m
ea
s
u
r
e
its
i
m
p
er
ce
p
tib
ilit
y
.
Steg
a
n
o
g
r
ap
h
ic
i
m
ag
e
s
o
f
ex
ce
l
len
t
q
u
alit
y
h
av
e
a
P
SNR
v
al
u
e
o
f
4
0
d
B
o
r
m
o
r
e,
w
h
ile
t
h
o
s
e
w
i
th
a
v
alu
e
o
f
le
s
s
t
h
a
n
3
0
d
B
ar
e
co
n
s
id
er
ed
to
b
e
o
f
in
f
er
io
r
q
u
alit
y
.
P
SNR
i
s
ca
l
cu
lated
u
s
in
g
lo
g
ar
it
h
m
ic
v
al
u
es
o
f
m
ea
n
s
q
u
ar
e
er
r
o
r
(
MSE
)
[
2
5
]
an
d
2
r
ep
r
esen
ts
th
e
h
i
g
h
e
s
t p
ix
e
l v
a
lu
e
in
t
h
e
i
m
a
g
e
.
=
10
l
og
10
(
2
)
(
1
)
3
.
2
.
Univ
er
s
a
l i
m
a
g
e
qu
a
lity
ind
ex
T
h
is
in
d
e
x
i
s
u
s
ed
to
ev
al
u
at
e
th
e
v
ar
iat
io
n
s
p
r
esen
t
i
n
t
h
e
s
te
g
an
o
g
r
ap
h
ic
i
m
a
g
e
co
m
p
a
r
ed
to
th
e
o
r
ig
in
al
i
m
a
g
e.
T
h
is
m
et
h
o
d
d
iv
id
es
t
h
e
i
m
a
g
e
co
m
p
ar
is
o
n
i
n
to
th
r
ee
d
is
ti
n
ct
p
ar
ts
:
i)
l
u
m
in
an
ce
co
m
p
ar
i
s
o
n
(
L
C
)
,
ii)
c
o
n
tr
ast
co
m
p
ar
is
o
n
(
C
C
)
,
an
d
iii)
s
tr
u
ctu
r
al
co
m
p
ar
i
s
o
n
(
SC
)
.
T
h
e
UI
QI
in
d
ex
v
ar
ies
b
et
w
ee
n
-
1
an
d
1
,
th
e
b
est v
al
u
e
b
ein
g
1
[
2
6
]
:
(
,
)
=
2
2
+
2
(
2
)
(
,
)
=
2
2
+
2
(
3
)
(
,
)
=
2
+
(
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
A
n
ew d
ee
p
s
teg
a
n
o
g
r
a
p
h
ic
te
ch
n
iq
u
e
f
o
r
h
id
in
g
s
ev
era
l sec
r
et
ima
g
es in
o
n
e
co
ve
r
…
(
Mo
h
a
med
Htiti
)
2215
UI
QI
(
,
)
=
(
,
)
∗
(
,
)
∗
(
,
)
(
5
)
W
h
er
e
X
r
ep
r
esen
ts
th
e
co
v
er
i
m
ag
e,
Y
r
ep
r
esen
ts
t
h
e
s
te
g
o
im
a
g
e
,
r
ep
r
esen
ts
th
e
m
ea
n
v
al
u
e
o
f
th
e
X
m
atr
i
x
,
Y
r
ep
r
esen
ts
t
h
e
m
ea
n
v
alu
e
o
f
t
h
e
Y
,
X
r
ep
r
esen
ts
th
e
s
ta
n
d
ar
d
d
ev
iatio
n
o
f
t
h
e
X
m
atr
ix
,
Y
r
ep
r
esen
ts
t
h
e
s
ta
n
d
ar
d
d
ev
iat
io
n
o
f
t
h
e
Y
m
atr
i
x
,
an
d
XY
r
ep
r
e
s
en
t
s
t
h
e
co
v
ar
ian
ce
b
et
w
ee
n
t
h
e
X
a
n
d
Y
m
atr
ices.
3
.
3
.
B
lin
d/ref
er
en
ce
less
i
m
a
g
e
s
pa
t
ia
l qua
lity
ev
a
lua
t
o
r
s
co
re
T
h
e
B
R
I
SQUE
s
co
r
e
[
2
7
]
is
a
s
tatis
tica
l
m
ea
s
u
r
e
m
e
n
t
o
f
th
e
n
at
u
r
al
s
ce
n
e,
ex
c
lu
d
i
n
g
r
ef
er
en
c
e
i
m
a
g
es
s
u
c
h
a
s
P
SNR
an
d
SS
I
M.
I
t
r
an
g
e
s
f
r
o
m
0
to
1
0
0
,
w
it
h
th
e
b
e
s
t
s
co
r
e
b
ei
n
g
th
e
l
o
w
est.
T
h
e
s
co
r
e
is
ca
lcu
lated
u
s
in
g
t
h
e
s
u
p
p
o
r
t v
ec
to
r
r
eg
r
ess
io
n
(
SV
R
)
m
o
d
el
an
d
d
if
f
er
e
n
ce
m
ea
n
o
p
in
io
n
s
co
r
e
(
DM
OS
)
.
I
t i
s
w
id
el
y
u
s
ed
in
i
m
a
g
e
s
te
g
an
o
g
r
ap
h
y
r
esear
c
h
.
3
.
4
.
Str
uct
ura
l si
m
ila
rit
y
in
dex
T
h
e
SS
I
M
is
a
m
etr
ic
u
s
ed
to
ev
alu
a
te
th
e
i
m
p
er
ce
p
tib
ilit
y
o
f
s
te
g
a
n
o
g
r
ap
h
ic
i
m
a
g
es.
U
n
li
k
e
P
SNR
,
w
h
ic
h
r
elie
s
o
n
a
s
u
m
m
atio
n
m
et
h
o
d
,
SS
I
M
f
o
cu
s
es
o
n
t
h
r
ee
k
e
y
f
ac
to
r
s
:
lu
m
i
n
a
n
ce
,
co
n
tr
as
t,
a
n
d
s
tr
u
ct
u
r
e,
p
r
o
v
id
in
g
a
m
o
r
e
co
m
p
r
eh
e
n
s
iv
e
as
s
es
s
m
en
t
o
f
i
m
a
g
e
q
u
a
li
t
y
.
I
n
r
ed
,
g
r
ee
n
,
a
n
d
b
lu
e
(
R
GB
)
co
lo
r
im
a
g
es,
th
e
SS
I
M
ca
n
b
e
m
a
th
e
m
ati
ca
ll
y
d
ef
i
n
ed
u
s
i
n
g
(
6
)
.
T
h
e
f
ir
s
t
co
m
p
o
n
en
t,
(
,
im
′
)
,
is
r
esp
o
n
s
ib
le
f
o
r
co
m
p
ar
i
n
g
t
h
e
l
u
m
in
a
n
ce
le
v
els
b
et
w
ee
n
t
w
o
i
m
ag
e
s
,
im
an
d
i
m
’
.
T
h
is
lu
m
i
n
a
n
ce
co
m
p
ar
is
o
n
r
ea
ch
es
its
m
ax
i
m
u
m
v
al
u
e
o
f
1
w
h
e
n
th
e
lu
m
i
n
a
n
ce
o
f
b
o
th
i
m
a
g
es
i
s
id
en
tical
.
T
h
e
m
a
x
i
m
u
m
v
al
u
e
o
f
(
,
′
)
is
1
,
w
h
ic
h
o
cc
u
r
s
w
h
e
n
t
h
e
co
n
tr
asts
o
f
th
e
t
w
o
i
m
ag
e
s
,
ca
lcu
lated
b
ased
o
n
th
eir
s
tan
d
ar
d
d
ev
iatio
n
s
(
σ
)
,
ar
e
eq
u
al.
T
h
e
th
ir
d
co
m
p
o
n
en
t
,
(
,
im
′
)
,
co
m
p
ar
es
th
e
s
tr
u
ct
u
r
al
s
i
m
il
ar
it
y
b
et
w
ee
n
t
w
o
i
m
a
g
es
im
an
d
i
m
’
b
ased
o
n
t
h
eir
co
r
r
elatio
n
co
e
f
f
icien
t.
T
h
e
m
ax
i
m
u
m
p
o
s
s
ib
le
v
al
u
e
f
o
r
S
SIM
is
1
,
w
it
h
t
h
e
o
v
er
all
r
an
g
e
o
f
SS
I
M
v
al
u
e
s
f
alli
n
g
b
et
w
ee
n
0
an
d
1
.
T
o
p
r
ev
en
t
d
iv
i
s
io
n
b
y
ze
r
o
,
co
n
s
ta
n
t
v
al
u
es
C
1
,
C
2
,
C
3
ar
e
i
n
tr
o
d
u
ce
d
in
t
h
e
f
o
r
m
u
la.
I
t
is
r
ec
o
m
m
en
d
ed
to
u
s
e
t
h
e
v
al
u
es
C
1
=(
0
.
0
1
×2
5
5
)
2
,
C
2
=(
0
.
0
3
×2
5
5
)
2
,
an
d
C
3
=C
2
/2
a
s
t
h
e
d
ef
au
lt
v
al
u
e
[
2
8
]
.
SS
IM
(
,
im
′
)
=
(
,
im
′
)
(
,
im
′
)
(
,
im
′
)
(
6
)
(
,
′
)
=
2
′
+
1
2
+
′
2
+
1
(
7
)
(
,
′
)
=
2
′
+
2
2
+
′
2
+
2
(
8
)
(
,
′
)
=
′
+
3
′
+
3
(
9
)
=
∑
=
1
∑
=
1
∑
=
1
(
1
0
)
2
=
∑
=
1
∑
=
1
∑
=
1
(
−
)
2
(
1
1
)
,
′
=
∑
=
1
∑
=
1
∑
=
1
(
−
)
(
′
−
′
)
(
1
2
)
′
=
∑
=
1
∑
=
1
∑
=
1
′
(
1
3
)
′
2
=
∑
=
1
∑
=
1
∑
=
1
(
′
−
′
)
2
(
1
4
)
W
h
er
e
M
an
d
N
r
ep
r
esen
t th
e
i
m
a
g
e
r
eso
lu
tio
n
an
d
O
d
en
o
te
s
th
e
n
u
m
b
er
o
f
i
m
ag
e
c
h
an
n
e
ls
.
Af
ter
co
n
d
u
cti
n
g
v
alid
a
tio
n
te
s
ts
o
n
b
o
th
o
u
r
m
o
d
el
a
n
d
B
a
lu
j
a
'
s
m
o
d
el,
w
e
co
m
p
i
led
t
h
e
r
esu
l
ts
in
T
ab
le
1
.
T
a
b
le
1
p
r
o
v
id
es
a
d
etailed
co
m
p
ar
is
o
n
b
et
w
ee
n
t
h
e
t
w
o
ap
p
r
o
ac
h
es,
h
i
g
h
li
g
h
ti
n
g
t
h
e
s
tr
e
n
g
t
h
s
an
d
w
ea
k
n
ess
e
s
o
f
ea
c
h
.
I
t
allo
w
s
f
o
r
a
clea
r
er
u
n
d
er
s
ta
n
d
in
g
o
f
h
o
w
th
e
m
o
d
e
ls
p
er
f
o
r
m
ac
r
o
s
s
v
ar
io
u
s
m
etr
ic
s
.
W
e
ca
n
s
ee
t
h
at
o
u
r
m
o
d
el
i
s
m
u
c
h
b
etter
t
h
an
t
h
e
B
alu
j
a
o
n
e.
I
n
d
ee
d
,
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
h
a
s
s
li
g
h
t
l
y
h
ig
h
er
P
SNR
,
SS
I
M
,
an
d
UI
QI
v
al
u
es
t
h
an
t
h
e
B
alu
j
a
m
o
d
el,
in
d
icatin
g
b
etter
p
r
eser
v
at
io
n
o
f
i
m
a
g
e
q
u
alit
y
.
I
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
1
4
,
No
.
3
,
J
u
n
e
2
0
2
5
:
2
2
1
0
-
2219
2216
ad
d
itio
n
,
o
u
r
m
o
d
el
h
a
s
s
ig
n
i
f
ican
tl
y
lo
w
er
B
R
I
SQUE
s
co
r
es,
in
d
icat
in
g
b
etter
s
e
g
o
i
m
a
g
e
q
u
a
lit
y
.
O
v
er
all,
th
e
p
r
o
p
o
s
ed
m
o
d
el
o
f
f
er
s
b
etter
i
m
ag
e
q
u
a
lit
y
p
r
eser
v
atio
n
.
Fig
u
r
e
5
.
R
esu
lts
o
f
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
T
ab
le
1
.
C
o
m
p
ar
ativ
e
a
n
al
y
s
i
s
o
f
o
u
r
m
o
d
el
an
d
B
alu
j
a
'
s
m
o
d
el
b
ased
o
n
v
alid
atio
n
test
s
M
e
t
r
i
c
s
B
a
l
u
j
a
’
s mo
d
e
l
O
u
r
mo
d
e
l
P
S
N
R
8
3
.
6
2
8
4
8
4
.
8
6
9
3
S
S
I
M
0
.
9
2
2
2
4
0
.
9
4
7
1
7
U
I
Q
I
7
8
.
6
2
9
0
1
8
2
.
4
3
3
9
2
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I
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2219
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DATA AV
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telli
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laz
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.
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