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u
n
d
a
n
t
d
ata.
T
h
e
m
o
s
t
cr
u
cial
o
b
je
ctiv
e
is
to
id
e
n
tify
t
h
e
im
ag
e
r
ep
r
esen
tatio
n
th
at
is
least
co
r
r
elate
d
[
4
]
.
T
h
e
im
ag
e
is
co
m
p
r
ess
ed
an
d
its
s
ize
i
s
d
ec
r
ea
s
ed
b
y
r
em
o
v
in
g
r
ed
u
n
d
an
t
b
its
th
r
o
u
g
h
th
e
u
s
e
o
f
co
m
p
r
ess
io
n
alg
o
r
it
h
m
s
[
5
]
.
R
ed
u
n
d
an
cy
r
ed
u
ctio
n
an
d
ir
r
elev
a
n
cy
r
e
d
u
ctio
n
a
r
e
two
f
u
n
d
am
e
n
tal
co
m
p
o
n
en
t
s
o
f
co
m
p
r
ess
io
n
.
Ma
in
tain
in
g
th
e
r
ec
o
n
s
tr
u
ct
ed
im
ag
e
’
s
v
is
u
al
q
u
ality
an
d
r
eso
lu
tio
n
wh
ile
p
r
o
tectin
g
it
is
s
im
ilar
to
th
e
o
r
ig
in
al
im
ag
e,
it
m
u
s
t
d
escr
ib
e
an
im
ag
e
b
y
elim
in
atin
g
s
p
ec
tr
al
an
d
s
p
atial
r
ed
u
n
d
an
cy
as
m
u
ch
as
n
ec
e
s
s
ar
y
.
T
h
e
co
m
p
r
ess
ed
d
ata
a
r
e
ap
p
lied
t
o
an
in
v
er
s
e
p
r
o
ce
d
u
r
e
k
n
o
w
n
as
d
ec
o
m
p
r
ess
io
n
(
d
ec
o
d
in
g
)
in
o
r
d
er
to
p
r
o
d
u
ce
th
e
r
ec
o
n
s
tr
u
cted
im
ag
e
[
6
]
.
T
h
e
lo
s
s
y
an
d
lo
s
s
les
s
co
m
p
r
ess
io
n
s
ar
e
th
e
two
ty
p
e
s
o
f
im
ag
e
co
m
p
r
ess
io
n
tech
n
iq
u
es
[
7
]
.
W
h
en
an
im
a
g
e
is
co
m
p
r
ess
ed
u
tili
zin
g
a
lo
s
s
y
c
o
m
p
r
ess
io
n
,
all
r
ed
u
n
d
an
t
d
ata
is
r
em
o
v
ed
co
m
p
letely
,
h
en
ce
th
e
r
ec
o
n
s
tr
u
c
tio
n
im
ag
es
h
as
a
r
ed
u
ctio
n
in
co
m
p
a
r
is
o
n
t
o
th
e
o
r
i
g
in
al.
Sin
ce
,
lo
s
s
y
te
ch
n
iq
u
es
ca
n
ac
h
iev
e
f
ar
h
i
g
h
er
co
m
p
r
ess
io
n
ca
p
ab
ilit
ies.
Vis
u
ally
lo
s
s
les
s
v
iewin
g
in
d
icate
s
th
at
th
er
e
is
n
o
v
is
ib
le
lo
s
s
[
8
]
.
Mu
lti
m
ed
ia
in
f
o
r
m
atio
n
in
clu
d
in
g
au
d
io
,
v
id
e
o
,
an
d
s
till
im
ag
es a
r
e
m
o
s
t f
r
eq
u
en
tly
co
m
p
r
ess
ed
u
s
in
g
lo
s
s
y
co
m
p
r
ess
io
n
,
p
ar
ticu
lar
ly
in
ap
p
licatio
n
s
i
n
clu
d
in
g
s
tr
e
am
in
g
m
e
d
ia
wh
er
e
a
s
m
all
f
id
elity
r
ed
u
ctio
n
is
ac
ce
p
te
d
i
n
o
r
d
er
t
o
o
b
t
ain
a
s
u
b
s
tan
tial
d
ec
r
ea
s
e
in
b
it
r
ate.
T
h
is
ap
p
r
o
ac
h
is
n
o
t
a
d
v
is
ab
le
f
o
r
m
an
y
ap
p
licatio
n
s
,
b
e
ca
u
s
e
u
s
in
g
lo
w
b
it
r
ates a
llo
ws th
e
in
tr
o
d
u
ctio
n
o
f
co
m
p
r
ess
io
n
ar
tifa
cts.
L
o
s
s
less
co
m
p
r
ess
io
n
p
r
o
tects
in
f
o
r
m
atio
n
;
f
o
r
ar
tific
ial
im
ag
es
lik
e
tech
n
ical
d
r
aw
in
g
s
,
co
m
ics,
o
r
ico
n
s
,
as
well
a
s
h
ig
h
-
v
alu
e
co
n
ten
t
lik
e
m
ed
ical
im
ag
es
o
r
ar
ch
iv
al
im
ag
e
s
ca
n
s
,
th
e
r
ec
o
n
s
tr
u
cted
im
ag
e
is
an
id
en
tical
co
p
y
o
f
th
e
o
r
i
g
in
al
[
9
]
.
Nu
m
er
o
u
s
lo
s
s
less
c
o
m
p
r
ess
io
n
m
et
h
o
d
s
a
r
e
a
v
ialab
le,
in
clu
d
i
n
g
t
h
e
ar
ea
,
r
u
n
len
g
th
,
lem
p
el
-
ziv
-
welc
h
(
L
Z
W
)
en
co
d
in
g
,
Hu
f
f
m
an
en
co
d
i
n
g
[
1
0
]
.
Usi
n
g
a
m
ed
ical
p
ictu
r
e
co
m
p
r
ess
io
n
ap
p
r
o
ac
h
b
ased
o
n
R
OI
co
d
in
g
,
a
n
im
ag
e
is
f
i
r
s
t
s
p
lit
in
to
a
R
OI
an
d
a
b
ac
k
g
r
o
u
n
d
(
B
G)
[
1
1
]
.
W
h
ile
th
e
B
G
ty
p
ically
u
tili
ze
s
a
lo
t
o
f
lo
s
s
y
c
o
m
p
r
ess
io
n
,
t
h
e
R
OI
ty
p
ical
ly
u
s
es
lo
s
s
less
o
r
n
ea
r
ly
lo
s
s
less
co
m
p
r
ess
io
n
.
L
o
w
-
b
itra
te
im
ag
e
co
m
p
r
ess
io
n
h
as
b
ee
n
co
n
s
id
er
ed
to
b
e
a
d
if
f
icu
lt
ar
ea
o
f
s
tu
d
y
d
u
e
to
th
e
u
n
d
is
cip
lin
ar
ity
o
f
th
e
tr
an
s
m
is
s
io
n
ch
an
n
el
an
d
th
e
co
m
p
a
tib
ilit
y
o
f
d
is
p
lay
s
ca
lab
ilit
y
ac
r
o
s
s
d
ev
ices
th
at
h
av
e
v
ar
io
u
s
r
eso
lu
tio
n
s
.
E
v
e
n
y
et,
it
wo
u
ld
b
e
u
n
ac
ce
p
tab
l
e
to
h
av
e
v
is
u
al
co
m
p
r
ess
io
n
ar
tifa
cts
ca
u
s
ed
b
y
a
s
m
all
n
u
m
b
er
o
f
b
its
ea
ch
p
ix
el
[
1
2
]
.
A
s
am
p
lin
g
-
b
ased
m
et
h
o
d
o
f
im
a
g
e
co
m
p
r
ess
io
n
h
as
b
ee
n
p
r
esen
ted
f
o
r
lo
w
b
it
r
ate
co
d
in
g
.
I
n
th
is
m
eth
o
d
,
a
n
im
ag
e
is
d
o
wn
s
am
p
led
p
r
io
r
to
co
m
p
r
ess
io
n
to
r
ed
u
ce
s
p
atial
r
ed
u
n
d
an
cies
am
o
n
g
n
eig
h
b
o
r
in
g
p
ix
els
an
d
m
ak
e
th
e
c
o
m
p
r
ess
io
n
co
d
ec
-
f
r
ien
d
ly
.
W
h
en
ev
er
d
is
p
lay
e
d
o
n
s
cr
ee
n
s
with
lo
w
r
e
s
o
lu
tio
n
,
s
u
ch
as
th
o
s
e
s
h
o
wn
in
m
o
b
ile
d
ev
ices,
lo
wer
r
eso
lu
tio
n
m
ig
h
t
p
r
o
v
id
e
f
lex
ib
ilit
y
.
Ho
wev
e
r
,
i
n
f
o
r
m
a
tio
n
lo
s
s
d
u
r
in
g
s
im
p
le
s
am
p
lin
g
m
ay
also
e
f
f
ec
ts
im
ag
e
q
u
ality
.
T
h
er
ef
o
r
e,
ex
ten
s
iv
e
alg
o
r
ith
m
s
f
o
r
im
p
r
o
v
in
g
co
m
p
r
ess
ed
im
ag
e
q
u
al
ity
h
av
e
b
ee
n
p
r
o
p
o
s
ed
.
T
h
ese
alg
o
r
ith
m
s
ca
n
b
e
d
iv
id
ed
in
t
o
th
r
ee
m
ain
ca
t
eg
o
r
ies:
d
ee
p
lear
n
in
g
a
p
p
r
o
ac
h
es,
d
ictio
n
ar
y
lear
n
i
n
g
,
an
d
iter
atio
n
-
b
ased
alg
o
r
ith
m
s
.
A
n
ew
m
eth
o
d
f
o
r
lo
s
s
less
i
m
ag
e
co
m
p
r
ess
io
n
with
em
b
ed
d
ed
en
co
d
in
g
is
aim
o
f
th
is
an
aly
s
is
.
Flo
atin
g
p
o
in
t
co
ef
f
icien
ts
ar
e
g
en
er
ally
p
r
o
d
u
ce
d
b
y
th
e
wav
elet
tr
an
s
f
o
r
m
(
W
T
)
.
W
h
ile
u
tili
zi
n
g
f
in
ite
p
r
ec
is
io
n
q
u
an
tizatio
n
an
d
ar
ith
m
etic
lead
s
to
a
lo
s
s
y
m
e
th
o
d
,
th
ese
co
ef
f
icien
ts
ar
e
u
s
ed
f
r
eq
u
en
tl
y
to
r
eb
u
ild
th
e
o
r
ig
in
al
im
ag
e.
T
o
o
v
er
c
o
m
e
th
is
d
is
ad
v
an
ta
g
e,
i
n
teg
er
W
T
(
I
W
T
)
was
r
ec
en
tly
in
tr
o
d
u
ce
d
.
T
h
e
f
o
llo
win
g
is
th
e
s
tr
u
ctu
r
e
o
f
t
h
e
r
em
ai
n
d
er
o
f
th
e
a
n
aly
s
is
:
l
iter
atu
r
e
r
ev
iew
is
in
clu
d
ed
in
s
ec
tio
n
2
,
s
ec
tio
n
3
p
r
o
v
id
es
a
d
etailed
d
escr
ip
tio
n
o
f
r
e
g
io
n
-
b
ased
lo
s
s
les
s
co
m
p
r
ess
io
n
;
in
s
ec
tio
n
4
,
th
e
ex
p
e
r
im
en
t
r
esu
lts
ar
e
s
h
o
wn
; in
s
ec
tio
n
5
,
th
e
co
n
cl
u
s
io
n
an
d
s
u
m
m
ar
y
ar
e
p
r
esen
ted
.
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
Z
h
ao
et
a
l.
[
1
3
]
p
r
o
v
id
es a
n
o
v
el
tech
n
iq
u
e
f
o
r
im
a
g
e
d
e
-
b
lo
ck
in
g
th
at
r
e
d
u
ce
s
b
lo
ck
i
n
g
ar
tifa
cts an
d
p
r
o
d
u
ce
s
h
ig
h
er
-
q
u
ality
im
a
g
es
b
y
u
tili
zin
g
q
u
an
tizatio
n
co
n
s
tr
ain
t
(
QC
)
p
r
io
r
an
d
s
tr
u
ctu
r
al
s
p
ar
s
e
r
ep
r
esen
tatio
n
(
SS
R
)
p
r
io
r
.
I
n
th
e
f
r
a
m
ewo
r
k
o
f
m
ax
im
u
m
,
a
p
o
s
ter
io
r
i,
th
e
o
p
tim
izatio
n
p
r
o
b
lem
o
f
im
ag
e
de
-
b
lo
ck
i
n
g
is
p
r
esen
ted
.
W
h
i
le
QC
is
ex
p
r
ess
ly
in
clu
d
ed
to
en
ab
le
a
m
o
r
e
r
o
b
u
s
t
an
d
ac
c
u
r
ate
esti
m
atin
g
,
in
o
r
d
er
to
s
u
cc
ess
f
u
lly
en
f
o
r
ce
th
e
n
o
n
lo
ca
l
s
elf
-
s
im
ilar
ity
a
n
d
in
h
er
e
n
t
lo
ca
l
s
p
ar
s
ity
o
f
n
atu
r
al
im
ag
es,
t
h
e
SS
R
p
r
io
r
is
u
s
ed
.
Usi
n
g
an
ad
ap
tiv
ely
m
o
d
if
ied
p
a
r
am
eter
v
alu
e,
th
e
o
p
tim
izatio
n
p
r
o
b
le
m
is
ad
d
r
ess
ed
with
a
n
ew
s
p
lit
B
r
eg
m
an
iter
atio
n
-
b
ased
tech
n
iq
u
e,
wh
ich
im
p
r
o
v
es
th
e
p
r
ac
ticality
o
f
th
e
alg
o
r
ith
m
in
its
en
tire
ty
.
E
x
p
e
r
im
en
ts
s
h
o
w
th
at
th
e
d
escr
i
b
ed
im
ag
e
-
d
eb
lo
c
k
in
g
alg
o
r
ith
m
s
,
wh
ich
co
m
b
i
n
es
SS
R
an
d
QC
,
wo
r
k
s
b
etter
in
ter
m
s
o
f
p
e
ak
s
ig
n
al
-
to
-
n
o
is
e
r
atio
an
d
v
is
u
al
p
er
ce
p
tio
n
th
a
n
cu
r
r
en
t
s
tate
-
of
-
th
e
-
ar
t
tech
n
iq
u
es.
J
u
liet
et
a
l.
[
1
4
]
p
r
esen
ted
a
r
ip
p
let
-
b
ased
m
eth
o
d
f
o
r
co
m
p
r
ess
in
g
m
ed
ical
im
ag
es.
T
h
e
te
ch
n
iq
u
e
’
s
p
r
im
ar
y
co
ef
f
icien
ts
ar
e
en
co
d
ed
u
s
in
g
th
e
s
et
p
ar
titi
o
n
in
g
in
h
ier
ar
ch
ical
tr
ee
s
(
SP
I
HT
)
alg
o
r
ith
m
,
s
in
g
le
p
o
in
ts
o
n
an
y
s
h
ap
e
cu
r
v
e
ar
e
ch
ar
ac
ter
ized
u
s
in
g
an
an
is
o
tr
o
p
ic
r
ip
p
let
tr
a
n
s
f
o
r
m
atio
n
.
T
h
e
in
ter
p
r
etatio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
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:
2
5
0
2
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4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
3
8
,
No
.
3
,
J
u
n
e
2
0
2
5
: 1
8
7
0
-
1
8
7
9
1872
an
d
an
al
y
s
is
o
f
m
a
g
n
etic
r
eso
n
an
ce
im
a
g
in
g
(
MRI)
im
ag
es
d
ep
en
d
s
im
p
o
r
tan
t
o
n
an
au
to
m
atic
an
d
ac
cu
r
at
e
class
if
icatio
n
s
y
s
tem
.
R
o
th
e
et
a
l.
[
1
5
]
u
s
in
g
th
e
im
ag
e
s
u
p
er
-
r
eso
lu
tio
n
,
t
h
e
n
o
v
el
ar
tifa
ct
r
ed
u
ctio
n
m
et
h
o
d
o
lo
g
y
th
at
p
er
f
o
r
m
s
well
is
d
escr
ib
ed
b
y
th
e
ad
ju
s
ted
an
ch
o
r
ed
n
ei
g
h
b
o
r
h
o
o
d
r
eg
r
ess
io
n
(
A+
)
m
eth
o
d
.
I
m
p
r
ess
iv
e
r
esu
lts
h
av
e
b
ee
n
o
b
tain
ed
wi
th
a
r
ec
en
tly
d
ev
elo
p
ed
lear
n
ed
s
em
i
-
lo
ca
l
g
au
s
s
ian
p
r
o
ce
s
s
es
-
b
ased
s
o
lu
tio
n
(
SLGP)
.
T
h
e
im
p
r
o
v
em
e
n
t
is
less
s
ig
n
if
ican
t
wh
en
u
s
ed
with
b
etter
co
m
p
r
ess
io
n
alg
o
r
ith
m
s
in
cl
u
d
in
g
J
PEG
2
0
0
0
.
L
ar
g
e
s
to
r
ag
e
r
ed
u
ctio
n
s
ar
e
p
o
s
s
ib
le
with
lo
s
s
y
im
ag
e
co
m
p
r
ess
io
n
,
b
u
t
th
e
co
m
p
r
ess
ed
im
ag
es
p
o
s
s
ess
r
ed
u
ce
d
im
ag
e
f
id
elity
.
T
h
er
e
is
a
lo
t
o
f
r
esear
ch
th
at
aim
s
to
r
esto
r
e
b
y
r
em
o
v
in
g
co
m
p
r
ess
io
n
ar
tifa
cts.
W
h
en
co
m
p
ar
e
d
to
s
tate
-
of
-
th
e
-
ar
t
tech
n
iq
u
es
lik
e
SLGP,
th
ey
o
b
tain
o
r
d
e
r
s
o
f
m
ag
n
itu
d
e
f
aster
p
er
f
o
r
m
an
ce
s
wh
ile
d
o
u
b
le
th
e
r
elativ
e
g
ain
s
in
p
ea
k
s
ig
n
al
-
to
-
n
o
is
e
r
atio
(
PS
NR
)
.
L
o
g
an
ath
a
n
an
d
Ku
m
ar
aswa
m
y
[
1
6
]
s
u
g
g
ested
in
v
esti
g
at
in
g
in
to
v
ar
io
u
s
c
o
m
p
r
ess
io
n
m
eth
o
d
s
d
ep
en
d
i
n
g
o
n
r
eg
io
n
o
f
in
ter
est
(
R
OI
)
.
T
h
e
p
ap
er
p
r
o
p
o
s
es
a
n
o
v
el
ad
ap
tiv
e
ac
tiv
e
c
o
n
to
u
r
m
eth
o
d
t
h
at
with
o
u
t
ed
g
es
to
m
ar
k
th
e
R
OI
.
L
o
s
s
less
co
m
p
r
ess
io
n
is
u
s
ed
to
co
m
p
r
ess
th
e
s
p
ec
if
ic
R
OI
ar
ea
,
wh
ile
l
o
s
s
y
wav
elet
co
m
p
r
ess
io
n
is
u
s
ed
to
co
m
p
r
ess
th
e
o
th
er
p
ar
ts
o
f
th
e
im
ag
e.
Usi
n
g
a
n
u
m
b
er
o
f
MRI
im
ag
es,
th
e
s
u
g
g
ested
m
eth
o
d
p
r
o
d
u
ce
d
a
n
o
v
er
all
c
o
m
p
r
ess
io
n
r
atio
o
f
6
9
-
8
1
% wh
ile
m
ain
tain
in
g
t
h
e
R
OI
’
s
o
r
ig
in
ality
.
L
iu
et
a
l.
[
1
7
]
t
h
is
r
esear
ch
p
r
o
p
o
s
es
a
f
ast
f
r
ac
tal
-
b
ase
d
MRI
im
ag
e
co
m
p
r
ess
io
n
t
ec
h
n
iq
u
e.
T
o
m
ak
e
th
e
f
r
ac
tal
co
m
p
r
ess
io
n
-
b
ased
im
ag
e
s
eq
u
en
ce
o
p
er
atin
g
,
a
two
-
d
im
en
s
io
n
al
(
2
D)
im
ag
e
s
eq
u
en
ce
n
ee
d
s
to
b
e
cr
ea
ted
f
r
o
m
th
r
ee
-
d
im
en
s
io
n
al
(
3
D)
MRI
im
ag
es.
Acc
o
r
d
in
g
to
th
e
ex
p
er
im
e
n
tal
f
in
d
in
g
s
,
th
er
e
is
a
ten
o
r
m
o
r
e
in
cr
ea
s
e
in
P
SNR
an
d
a
two
-
to
-
th
r
ee
-
f
o
ld
in
cr
ea
s
e
in
co
m
p
r
ess
io
n
s
p
ee
d
.
I
t
s
u
g
g
ests
th
at
th
e
s
u
g
g
ested
ap
p
r
o
ac
h
wo
r
k
s
well
an
d
r
eso
lv
es
th
e
co
n
tr
a
d
ictio
n
b
etwe
en
h
i
g
h
q
u
ality
MRI
im
ag
es
an
d
a
h
ig
h
co
m
p
r
ess
io
n
r
atio
.
Ar
tu
ǧ
er
a
n
d
Öz
k
ay
n
a
k
[
1
8
]
a
s
u
g
g
ested
tech
n
iq
u
e
f
o
r
co
m
p
r
ess
in
g
im
ag
es
is
f
r
a
ctal.
J
PEG
co
m
p
r
ess
io
n
p
er
f
o
r
m
s
well
f
o
r
s
m
all
-
s
ized
im
ag
es
b
u
t
p
o
o
r
ly
f
o
r
lar
g
e
-
s
ized
im
ag
es.
An
im
ag
e
’
s
p
r
o
ce
s
s
in
g
tim
e
in
cr
ea
s
es
alo
n
g
with
t
h
e
im
ag
e
’
s
s
ize,
ac
cu
r
ac
y
,
an
d
c
o
m
p
r
ess
io
n
r
atio
d
ec
r
e
asin
g
.
T
h
e
f
r
ac
tal
co
m
p
r
ess
io
n
tech
n
iq
u
e,
h
o
we
v
er
,
elim
in
ates
th
ese
is
s
u
es.
Ma
tlab
is
u
s
ed
f
o
r
d
e
v
elo
p
in
g
th
e
p
r
o
g
r
am
s
,
an
d
th
e
r
esu
lts
ar
e
ev
alu
ated
.
I
t
is
f
o
u
n
d
th
at
th
e
f
r
ac
tal
co
m
p
r
ess
io
n
ap
p
r
o
ac
h
wo
r
k
s
b
ett
er
as
th
e
im
ag
e
s
izes
g
et
lar
g
er
.
A
m
ir
p
o
u
r
et
a
l.
[
1
9
]
in
t
h
is
p
a
p
er
,
a
r
a
n
d
o
m
ac
ce
s
s
-
o
r
ien
ted
lig
h
t
f
ield
im
ag
e
e
n
co
d
i
n
g
te
ch
n
iq
u
e
is
p
r
o
p
o
s
ed
.
T
h
e
s
u
g
g
ested
tech
n
iq
u
e
d
iv
i
d
es
1
5
×1
5
v
iew
im
ag
es
in
to
2
5
s
ep
ar
ate
3
×
3
v
iew
im
ag
es,
wh
ich
ar
e
r
ef
er
r
ed
to
as
ma
cr
o
v
iew
im
ag
es
(
MV
I
)
.
Usi
n
g
a
h
ier
ar
c
h
ical
r
ef
er
en
ce
s
tr
u
ctu
r
e
,
th
e
ce
n
ter
v
iew
im
ag
e
co
m
p
r
ess
es
th
e
v
iew
im
ag
es
th
at
ar
e
n
ea
r
est
to
it
to
en
c
o
d
e
MV
I
s
.
Usi
n
g
th
e
ce
n
ter
o
f
at
m
o
s
t
th
r
ee
MV
I
s
an
d
th
e
m
o
s
t
ce
n
tr
al
v
iew
im
ag
e
as
r
ef
er
en
ce
im
ag
es,
th
e
d
is
p
ar
i
ty
esti
m
atio
n
p
r
o
ce
s
s
en
co
d
es
th
e
ce
n
tr
al
v
iew
o
f
ea
ch
MV
I
.
Fu
r
th
er
m
o
r
e,
b
y
u
s
in
g
p
ar
allel
co
m
p
u
tatio
n
,
th
e
s
u
g
g
ested
m
eth
o
d
ca
n
im
p
r
o
v
e
th
e
en
co
d
in
g
/d
ec
o
d
i
n
g
tim
e
co
m
p
lex
ity
.
HE
VC
tile
p
ar
titi
o
n
in
g
is
u
tili
ze
d
to
m
in
im
ize
m
em
o
r
y
f
o
o
tp
r
in
t
in
th
e
ev
en
t th
a
t a
R
OI
is
r
eq
u
ir
e
d
.
Par
ik
h
et
a
l.
[
2
0
]
f
o
c
u
s
es
co
m
p
r
ess
io
n
ef
f
icien
cy
in
c
o
m
p
ar
is
o
n
to
J
PEG
2
0
0
0
an
d
d
is
cu
s
s
es
th
e
u
s
ag
e
o
f
HE
VC
f
o
r
m
ed
ical
i
m
ag
e
co
m
p
r
ess
io
n
th
at
is
d
iag
n
o
s
tically
ac
ce
p
tab
le.
Acc
o
r
d
i
n
g
to
th
e
r
esu
lts
o
f
ex
p
er
im
en
ts
,
c
o
m
p
ar
in
g
HE
V
C
to
J
P
E
G
2
0
0
0
,
c
o
m
p
r
ess
io
n
ef
f
icien
c
y
ca
n
b
e
in
c
r
ea
s
ed
b
y
a
p
p
r
o
x
im
ately
5
4
%.
C
o
n
cu
r
r
en
tly
,
a
n
o
v
el
te
ch
n
iq
u
e
is
p
r
o
p
o
s
ed
f
o
r
lo
wer
in
g
th
e
HE
VC
en
co
d
in
g
’
s
co
m
p
u
tatio
n
al
co
s
t
f
o
r
m
ed
ical
im
ag
es.
T
h
e
r
esu
lts
in
d
icate
th
at
a
s
ig
n
if
ican
t
i
n
cr
ea
s
e
in
f
ile
s
ize
ca
n
b
e
ac
h
iev
ed
wh
ile
r
ed
u
cin
g
th
e
HE
VC
in
tr
a
-
en
co
d
in
g
co
m
p
le
x
ity
b
y
m
o
r
e
th
a
n
5
5
%.
Su
n
et
a
l.
[
2
1
]
p
r
o
v
id
ed
a
C
NN
-
b
ased
tech
n
iq
u
e
th
at
d
iv
i
d
ed
im
ag
e
p
atch
es
i
n
to
a
clo
s
ed
class
o
f
b
lu
r
k
er
n
el
ty
p
es
to
r
em
o
v
e
n
o
n
-
u
n
if
o
r
m
m
o
tio
n
b
lu
r
.
A
Ma
r
k
o
v
r
a
n
d
o
m
f
ield
m
o
d
el
u
s
ed
th
e
lo
ca
l
class
if
icatio
n
o
u
tp
u
ts
as in
p
u
t
to
esti
m
ate
th
e
d
en
s
e
n
o
n
-
u
n
i
f
o
r
m
m
o
tio
n
b
lu
r
f
ield
ac
r
o
s
s
th
e
en
tire
im
ag
e.
Me
s
r
a
et
a
l.
[
2
2
]
t
h
e
p
u
r
p
o
s
e
o
f
th
e
c
o
m
p
r
ess
io
n
tech
n
iq
u
e
is
to
less
en
d
ata
r
ed
u
n
d
an
c
y
.
I
n
t
h
is
wo
r
k
,
a
r
e
v
er
s
ib
le
lo
w
co
n
tr
ast
m
ap
p
in
g
(
R
L
C
M)
tr
an
s
f
o
r
m
atio
n
is
u
s
ed
in
a
d
if
f
er
e
n
t
m
an
n
er
to
em
b
ed
s
p
ec
if
ic
d
atu
m
b
its
in
to
o
th
er
d
atu
m
in
im
ag
e
d
ata.
T
h
e
d
ata
is
en
co
d
ed
b
y
th
e
alg
o
r
ith
m
in
a
cy
clic
-
lik
e
m
an
n
er
.
T
h
e
s
u
g
g
ested
ap
p
r
o
ac
h
u
s
es
Hu
f
f
m
an
co
d
in
g
,
wh
ich
is
d
if
f
er
en
t
f
r
o
m
R
L
C
M.
T
h
is
s
tu
d
y
in
v
esti
g
ates
th
e
s
u
g
g
ested
tech
n
iq
u
e
u
s
in
g
im
a
g
e
d
ata
th
at
is
ac
ce
s
s
ib
le
to
th
e
p
u
b
lic.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
h
as
a
h
i
g
h
er
co
m
p
r
ess
io
n
r
atio
th
an
Hu
f
f
m
an
co
d
i
n
g
f
o
r
all
test
im
ag
es.
B
h
av
an
i
an
d
T
h
an
u
s
h
k
o
d
i
[
2
3
]
ev
alu
ated
th
e
well
v
ar
io
u
s
f
r
a
ctal
co
d
in
g
tech
n
iq
u
es
co
m
p
r
ess
ed
MRI
d
ata,
s
u
ch
as
im
p
r
o
v
ed
q
u
asi
-
lo
s
s
less
f
r
ac
tal
co
d
in
g
,
f
r
ac
tal
co
d
i
n
g
s
tan
d
ar
d
s
,
an
d
f
r
ac
tal
co
d
in
g
.
T
h
is
led
t
o
th
e
d
e
v
elo
p
m
e
n
t
o
f
a
n
ew
q
u
asi
-
lo
s
s
les
s
f
r
ac
tal
co
m
p
r
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io
n
te
ch
n
iq
u
e
th
at
s
u
cc
ess
f
u
lly
p
r
eser
v
ed
im
p
o
r
tan
t
im
ag
e
f
ea
tu
r
e
s
.
T
h
e
alg
o
r
ith
m
’
s
en
co
d
in
g
tim
e
was r
ed
u
ce
d
an
d
co
m
p
r
ess
io
n
p
er
f
o
r
m
an
ce
was im
p
r
o
v
ed
u
s
in
g
a
m
ac
h
in
e
l
ea
r
n
in
g
tech
n
iq
u
e.
Xin
p
en
g
[
2
4
]
p
r
esen
ts
an
n
o
v
el
s
ch
em
e
f
o
r
lo
s
s
y
co
m
p
r
e
s
s
io
n
o
f
an
en
cr
y
p
ted
im
ag
e
th
at
h
as
an
ac
ce
s
s
ib
le
co
m
p
r
ess
io
n
r
atio
.
T
o
en
cr
y
p
t
an
o
r
ig
in
al
im
a
g
e,
a
p
s
eu
d
o
r
an
d
o
m
p
e
r
m
u
t
atio
n
is
u
s
ed
,
an
d
th
r
o
u
g
h
elim
in
atin
g
t
h
e
o
r
th
o
g
o
n
al
t
r
an
s
f
o
r
m
’
s
co
e
f
f
icien
t
s
o
f
e
x
tr
em
ely
f
in
e
an
d
r
o
u
g
h
in
f
o
r
m
atio
n
,
th
e
en
cr
y
p
ted
d
ata
a
r
e
ef
f
icie
n
tly
co
m
p
r
ess
ed
.
O
n
ce
th
e
co
m
p
r
ess
ed
d
ata
is
r
ec
eiv
ed
,
with
th
e
u
s
e
o
f
s
p
atial
co
r
r
elatio
n
in
a
n
atu
r
al
im
ag
e
,
a
r
ec
eiv
er
ca
n
iter
ativ
ely
u
p
d
ate
th
e
co
ef
f
icien
t
v
alu
es
to
r
ec
o
n
s
tr
u
ct
th
e
k
ey
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
R
eg
io
n
b
a
s
ed
lo
s
s
less
co
mp
r
ess
io
n
fo
r
d
ig
ita
l ima
g
es u
s
in
g
en
tr
o
p
y
co
d
in
g
(
Ma
n
g
a
la
p
a
lli V
a
msi
kris
h
n
a
)
1873
elem
en
ts
o
f
th
e
o
r
ig
in
al
im
a
g
e.
I
n
th
is
way
,
t
h
e
q
u
ality
o
f
th
e
r
ec
o
n
s
tr
u
cted
im
ag
e
b
ett
er
with
a
s
m
o
o
th
er
o
r
ig
in
al
im
ag
e
a
n
d
a
h
ig
h
e
r
co
m
p
r
ess
io
n
r
atio
.
Wu
e
t
a
l.
[
2
5
]
i
n
o
r
d
er
to
f
u
r
th
er
r
ed
u
ce
th
e
s
ize
o
f
a
g
r
o
u
p
o
f
jo
in
t
p
h
o
t
o
g
r
a
p
h
ic
ex
p
er
ts
’
g
r
o
u
p
(
J
PEG
)
co
d
ed
lin
k
ed
im
ag
es
with
o
u
t
lo
s
in
g
an
y
in
f
o
r
m
at
io
n
,
p
r
o
v
id
e
a
n
in
n
o
v
ativ
e
lo
s
s
les
s
co
m
p
r
ess
io
n
tech
n
iq
u
e.
I
n
th
e
f
ea
tu
r
e,
s
p
ati
al,
an
d
f
r
eq
u
e
n
cy
d
o
m
ain
s
,
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
jo
in
tly
elim
in
ates
in
ter
-
an
d
in
tr
a
-
im
ag
e
r
ed
u
n
d
an
c
y
.
T
h
e
y
in
itially
m
in
im
ize
t
h
e
g
lo
b
al
p
r
ed
ictio
n
co
s
t
in
t
h
e
f
ea
t
u
r
e
d
o
m
ain
f
o
r
ea
ch
co
llectio
n
in
o
r
d
e
r
to
ar
r
an
g
e
th
e
im
ag
es
in
to
a
p
s
eu
d
o
-
v
id
eo
.
Nex
t,
we
in
tr
o
d
u
ce
a
h
y
b
r
id
d
is
p
ar
it
y
co
m
p
en
s
atio
n
tech
n
iq
u
e
th
at
e
n
h
an
ce
s
th
e
u
tili
za
tio
n
o
f
b
o
th
lo
ca
l a
n
d
g
lo
b
al
im
a
g
e
co
r
r
el
atio
n
s
in
th
e
s
p
atial
d
o
m
ain
.
T
h
e
s
u
g
g
ested
lo
s
s
le
s
s
co
m
p
r
ess
io
n
tech
n
iq
u
e
is
ef
f
ec
tiv
e,
as
s
h
o
wn
b
y
th
e
ex
p
er
im
en
tal
r
esu
lts
.
Ou
r
tech
n
i
q
u
e
ac
h
iev
es
m
o
r
e
th
an
3
1
%
o
n
av
e
r
ag
e
b
it
s
av
in
g
s
wh
en
co
m
p
ar
ed
to
th
e
J
PEG
co
d
ed
im
ag
e
co
llectio
n
s
.
3.
RE
G
I
O
N
B
AS
E
D
L
O
S
SL
E
SS
CO
M
P
R
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S
SI
O
N
T
h
e
b
lo
ck
d
iag
r
am
o
f
r
e
g
io
n
b
ased
lo
s
s
les
s
co
m
p
r
ess
io
n
f
o
r
d
ig
ital
im
ag
es
u
s
in
g
en
tr
o
p
y
co
d
in
g
is
r
ep
r
esen
ted
in
b
elo
w
Fig
u
r
e
1
.
Usi
n
g
en
tr
o
p
y
co
d
i
n
g
o
n
a
n
u
m
b
er
o
f
im
ag
es,
th
e
p
r
o
p
o
s
e
d
ap
p
r
o
ac
h
aim
s
to
im
p
r
o
v
e
im
ag
e
c
o
m
p
r
ess
io
n
.
T
h
e
im
ag
e
s
p
lits
u
p
in
to
v
ar
io
u
s
b
lo
ck
s
.
B
o
th
e
n
co
d
in
g
a
n
d
d
ec
o
d
in
g
m
ak
e
u
s
e
o
f
en
tr
o
p
y
co
d
i
n
g
.
I
t
is
u
tili
ze
d
to
a
co
m
p
r
ess
ed
im
ag
e
to
e
n
h
an
ce
c
o
m
p
r
ess
io
n
p
er
f
o
r
m
a
n
ce
an
d
r
ed
u
ce
th
e
en
co
d
in
g
p
r
o
ce
s
s
.
T
h
e
in
p
u
t
im
ag
e
is
co
n
v
er
ted
to
a
g
r
ay
im
ag
e
d
esp
ite
its
co
lo
r
.
R
ec
o
g
n
ize
t
h
e
im
ag
e
’
s
d
im
en
s
io
n
s
; if
t
h
ey
a
r
e
n
o
t sq
u
ar
es,
m
o
d
if
y
th
em
t
o
th
e
n
ea
r
est s
q
u
ar
e
s
izes.
Qu
an
tizatio
n
is
th
e
m
eth
o
d
o
f
esti
m
atin
g
th
e
c
o
n
tin
u
o
u
s
s
et
o
f
v
alu
es
in
th
e
im
ag
e
d
at
a
u
s
in
g
th
e
f
in
ite
n
ea
r
est
s
et
o
f
allo
wed
v
alu
es.
T
h
e
o
u
tp
u
t
o
f
th
e
q
u
an
t
izer
is
alwa
y
s
o
n
e
o
f
a
lim
ite
d
n
u
m
b
e
r
o
f
lev
els,
wh
ile
th
e
in
p
u
t
is
th
e
o
r
ig
in
al
d
ata.
T
o
r
ed
u
ce
e
n
co
d
i
n
g
tim
e,
a
g
o
o
d
q
u
an
tizer
r
e
p
r
esen
ts
th
e
o
r
ig
in
al
s
ig
n
al
with
th
e
least
am
o
u
n
t
o
f
lo
s
s
o
r
d
is
to
r
tio
n
.
T
o
ac
h
iev
e
b
etter
o
v
er
all
c
o
m
p
r
ess
io
n
,
a
lo
s
s
less
en
tr
o
p
y
en
c
o
d
er
f
u
r
th
er
co
m
p
r
ess
es
th
e
q
u
an
ti
ze
d
d
ata.
Fo
r
ev
er
y
q
u
an
tized
v
alu
e,
it
g
en
er
ates
an
o
u
t
p
u
t
co
d
e
s
tr
ea
m
th
at
is
s
m
aller
th
an
t
h
e
in
p
u
t
c
o
d
e
s
tr
ea
m
b
y
u
s
in
g
a
m
o
d
el
to
c
alcu
late
th
e
p
r
o
b
a
b
ilit
ies.
Do
m
ain
s
ize,
lo
ca
tio
n
,
d
o
m
ain
n
u
m
b
er
a
n
d
d
o
m
ain
c
lass
in
f
o
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m
atio
n
ar
e
co
m
p
u
ted
as
d
o
m
ain
d
ata.
T
h
e
class
if
icatio
n
o
f
d
o
m
ain
s
is
ca
r
r
ied
th
r
o
u
g
h
a
m
eth
o
d
th
at
co
n
s
id
er
s
th
e
b
r
i
g
h
tn
ess
v
alu
e
s
o
f
th
e
d
o
m
ain
s
.
Fig
u
r
e
1
.
B
lo
ck
d
iag
r
am
o
f
r
e
g
io
n
b
ased
l
o
s
s
less
co
m
p
r
ess
i
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
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n
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&
C
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p
Sci,
Vo
l.
3
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,
No
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3
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n
e
2
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1874
T
h
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co
m
p
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at
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ak
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p
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ag
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o
f
ten
k
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wn
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as b
lo
ck
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y
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e
p
r
o
ce
s
s
o
f
im
ag
e
s
eg
m
en
tatio
n
.
T
h
e
p
r
im
ar
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o
b
jectiv
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o
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s
eg
m
en
tat
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n
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to
tr
an
s
f
o
r
m
an
im
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g
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s
r
ep
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tatio
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o
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u
l
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e
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o
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is
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I
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w
as
also
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tili
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f
o
r
o
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ject
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d
im
ag
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b
o
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ar
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t
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in
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im
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m
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im
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e
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ly
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im
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at
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el
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av
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h
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n
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o
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s
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f
th
e
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h
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alg
o
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ith
m
f
o
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d
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cl
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.
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v
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i
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atch
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m
ain
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atch
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e
s
am
e
ca
teg
o
r
izatio
n
s
ch
em
e
as
d
o
m
ain
s
.
T
h
e
d
o
m
ain
s
an
d
r
an
g
es
th
at
ca
n
b
e
co
m
p
ar
ed
m
u
s
t
all
to
th
e
s
am
e
class
,
an
d
th
e
th
r
esh
o
ld
is
d
eter
m
in
ed
b
y
c
o
m
p
u
tin
g
th
e
d
if
f
er
en
ce
in
er
r
o
r
b
etwe
en
th
em
.
Mo
r
e
s
eg
m
en
tatio
n
o
f
th
e
im
ag
e
h
as
b
ee
n
p
er
f
o
r
m
e
d
.
C
alcu
latio
n
o
f
a
tr
an
s
f
o
r
m
ati
o
n
is
d
o
n
e
if
th
is
er
r
o
r
v
alu
e
e
x
ce
ed
s
th
e
d
ef
in
ed
th
r
esh
o
ld
.
Usi
n
g
th
e
s
eg
m
en
tatio
n
alg
o
r
i
th
m
,
th
e
b
ac
k
g
r
o
u
n
d
o
f
R
OI
was
r
ed
u
ce
d
to
ze
r
o
.
Fo
llo
wi
n
g
th
at,
th
e
R
OI
p
o
r
tio
n
o
f
ea
c
h
im
ag
e
is
id
en
tifie
d
.
An
au
to
m
ated
p
r
o
ce
d
u
r
e
is
u
s
ed
to
id
en
tif
y
th
e
R
OI
f
o
r
th
e
co
m
p
r
ess
io
n
.
An
R
OI
-
m
ask
i
s
cr
ea
ted
s
o
th
at
th
e
im
ag
e
’
s
f
o
r
eg
r
o
u
n
d
is
co
m
p
letely
c
o
v
e
r
ed
f
o
r
th
e
p
u
r
p
o
s
e
o
f
R
OI
d
etec
tio
n
.
An
R
OI
-
m
a
s
k
ca
n
b
e
ef
f
ec
tiv
ely
p
r
o
d
u
c
e
d
u
s
in
g
m
o
r
p
h
o
lo
g
ical
o
p
e
r
atio
n
s
.
T
h
is
in
d
icate
s
th
at
th
e
v
alu
e
“
1
”
ca
n
b
e
f
o
u
n
d
in
th
e
im
ag
e
’
s
f
o
r
eg
r
o
u
n
d
wh
ile
th
e
v
alu
e
“
0
”
ca
n
b
e
f
o
u
n
d
in
t
h
e
im
a
g
e
’
s
b
ac
k
g
r
o
u
n
d
.
Af
ter
th
at,
th
e
i
m
ag
e
an
d
th
e
m
ask
ar
e
lo
g
ica
lly
ANDe
d
to
s
ep
ar
ate
th
e
R
OI
p
ar
t
f
r
o
m
th
e
n
o
n
-
R
OI
p
o
r
tio
n
.
Fo
r
b
it
s
tr
ea
m
co
m
p
r
ess
io
n
an
d
r
e
d
u
cin
g
co
d
i
n
g
r
ed
u
n
d
a
n
cy
,
th
e
m
o
s
t
u
s
ed
lo
s
s
less
m
eth
o
d
is
Hu
f
f
m
an
co
d
i
n
g
.
Fo
r
s
o
u
r
ce
s
y
m
b
o
ls
with
v
ar
io
u
s
p
r
o
b
a
b
ilit
ies,
it
o
f
f
er
s
a
e
f
f
ec
tiv
e
s
o
u
r
c
e
co
d
in
g
al
g
o
r
ith
m
.
T
h
e
s
tatis
tica
l
ch
ar
ac
ter
is
tics
o
f
t
h
e
ch
ar
ac
ter
s
in
th
e
s
o
u
r
ce
s
tr
ea
m
in
g
ar
e
u
s
ed
b
y
Hu
f
f
m
an
co
d
in
g
to
p
r
o
d
u
ce
eq
u
iv
ale
n
t
co
d
es
f
o
r
s
u
ch
s
y
m
b
o
ls
.
T
h
r
o
u
g
h
co
m
p
ar
es
to
s
y
m
b
o
l
co
d
i
n
g
with
l
o
wer
p
r
o
b
ab
ilit
ies,
g
r
ea
ter
p
r
o
b
ab
ilit
y
s
y
m
b
o
l
c
o
d
es
ar
e
s
m
aller
.
T
h
e
f
r
eq
u
e
n
cy
with
wh
ich
an
ess
en
tial
r
eq
u
ir
em
en
t
is
th
e
d
ev
elo
p
m
e
n
t
o
f
a
p
ar
ticu
lar
d
ata
item
(
p
ix
els
o
r
s
m
all
b
lo
c
k
s
o
f
p
ix
els
in
im
ag
es).
An
au
th
o
r
ized
co
d
e
b
o
o
k
co
n
tain
s
th
e
co
d
es.
E
v
er
y
im
a
g
e
o
r
c
o
llectio
n
o
f
im
ag
es h
as
its
o
wn
co
d
e
b
o
o
k
.
Fo
r
m
ed
ical
im
ag
es,
l
o
s
s
less
c
o
m
p
r
ess
io
n
is
o
f
f
er
ed
b
y
t
h
e
I
W
T
.
Fo
r
th
e
p
r
o
v
id
e
d
s
ig
n
als
o
r
p
ix
els,
tr
ad
itio
n
al
W
T
u
s
u
ally
p
r
o
v
i
d
es
th
e
f
lo
atin
g
-
p
o
in
t
co
ef
f
i
cien
ts
.
I
n
teg
er
co
ef
f
icien
ts
a
r
e
p
r
o
d
u
ce
d
b
y
it.
T
h
er
ef
o
r
e,
its
co
m
p
u
tatio
n
al
co
m
p
lex
ity
is
lo
wer
th
an
t
h
at
o
f
o
th
er
W
T
s
.
A
m
eth
o
d
o
f
l
o
s
s
less
d
ata
co
m
p
r
ess
io
n
is
en
tr
o
p
y
en
c
o
d
in
g
.
On
e
o
f
th
e
p
r
im
ar
y
m
eth
o
d
s
o
f
en
tr
o
p
y
co
d
in
g
is
u
s
ed
to
ass
ig
n
a
u
n
iq
u
e
p
r
ef
ix
c
o
d
e
to
ea
ch
d
if
f
er
en
t
s
y
m
b
o
l
in
th
e
in
p
u
t.
T
h
ese
en
tr
o
p
y
e
n
co
d
er
s
th
e
n
co
m
p
r
ess
th
e
in
f
o
r
m
atio
n
i
n
to
wo
r
d
s
b
y
r
ep
lacin
g
a
co
r
r
esp
o
n
d
in
g
v
a
r
iab
le
-
len
g
th
p
r
ef
i
x
c
o
d
e
f
o
r
ea
c
h
f
ix
ed
-
len
g
th
i
n
p
u
t
s
y
m
b
o
l.
Sin
ce
th
e
len
g
th
o
f
ea
ch
co
d
e
wo
r
d
is
r
o
u
g
h
ly
p
r
o
p
o
r
tio
n
al
to
t
h
e
lo
g
ar
ith
m
o
f
th
e
p
r
o
b
ab
ilit
y
,
th
e
m
o
s
t
f
r
eq
u
e
n
t
s
y
m
b
o
ls
ar
e
r
e
p
r
esen
ted
b
y
th
e
s
m
allest
co
d
in
g
.
I
n
t
h
is
ca
s
e,
th
e
im
ag
e
is
ef
f
ec
tiv
el
y
co
m
p
r
ess
ed
with
Hu
f
f
m
an
c
o
d
in
g
.
4.
E
XP
E
R
I
M
E
N
T
A
L
RE
SUL
T
ANA
L
YS
I
S
Usi
n
g
a
n
u
m
b
er
o
f
p
e
r
f
o
r
m
a
n
ce
p
ar
am
eter
s
,
t
h
ey
e
v
alu
ate
th
e
r
eg
io
n
b
ased
lo
s
s
less
co
m
p
r
ess
io
n
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
in
th
is
s
ec
tio
n
.
On
th
e
co
lo
r
to
g
r
ay
-
s
ca
le
lev
el
o
f
im
ag
e
d
ata,
e
x
p
e
r
im
en
ts
h
av
e
b
ee
n
ca
r
r
ied
s
u
cc
ess
f
u
lly
.
Dif
f
e
r
en
t
p
er
f
o
r
m
an
ce
m
etr
ics
ar
e
u
s
ed
to
c
o
m
p
ar
e
th
e
r
esu
lts
.
T
h
e
ex
p
e
r
im
en
t
was
co
n
d
u
cte
d
u
s
in
g
th
e
3
0
0
im
ag
es
in
th
is
d
ata
s
et.
T
h
e
d
ata
s
et
co
n
tain
s
X
-
r
a
y
im
ag
es
o
f
t
h
e
s
p
in
al
co
r
d
,
lu
n
g
s
,
s
k
u
ll,
an
d
o
th
er
p
a
r
ts
.
T
h
e
im
ag
es
ar
e
ab
o
u
t
5
1
2
×
5
1
2
p
i
x
els
in
s
ize.
Ap
p
ly
in
g
th
e
th
r
esh
o
ld
s
eg
m
en
tatio
n
tech
n
iq
u
e
d
i
v
id
es
th
e
b
ac
k
g
r
o
u
n
d
s
o
f
th
e
i
n
p
u
t
im
a
g
es.
Af
ter
s
ep
ar
atin
g
th
e
R
OI
p
ar
ts
o
f
t
h
e
im
ag
es
f
r
o
m
t
h
e
r
est
o
f
th
e
im
ag
e,
th
e
R
OI
p
ar
ts
o
f
th
e
im
ag
es
co
u
ld
th
en
b
e
co
m
p
r
ess
ed
.
I
n
o
r
d
er
to
o
b
tain
th
e
o
r
ig
in
al
im
ag
e,
th
e
co
m
p
r
ess
io
n
alg
o
r
ith
m
’
s
r
ev
er
s
e
p
a
r
t
is
th
en
u
tili
ze
d
.
T
h
er
e
ar
e
a
n
u
m
b
er
o
f
m
et
r
ics
f
o
r
o
b
jectiv
ely
ev
alu
atin
g
im
ag
es
p
er
ce
p
tu
al
q
u
ality
.
T
h
ey
u
til
ize
th
e
MSE
,
PS
N
R
,
SS
I
M,
an
d
en
co
d
in
g
tim
e
p
ar
am
eter
s
.
Me
an
s
q
u
ar
e
er
r
o
r
(
MSE
)
:
t
h
e
d
if
f
er
en
ce
b
etwe
en
th
e
c
o
m
p
r
ess
ed
an
d
o
r
ig
in
al
im
ag
es
i
s
ex
am
in
ed
u
s
in
g
th
e
MSE
as
s
h
o
wn
in
(
1
)
.
B
etwe
en
PS
N
R
an
d
MSE
,
th
er
e
is
an
in
v
er
s
e
r
elatio
n
s
h
ip
:
a
lo
wer
v
alu
e
in
d
icate
s
less
er
r
o
r
.
(
1
,
2
)
=
1
∑
∑
[
1
(
,
)
−
2
(
,
)
]
2
=
1
=
1
(
1
)
T
h
e
o
r
ig
in
al
im
ag
e
is
r
ep
r
esen
ted
b
y
i_
1
(
x
,
y
)
,
th
e
r
ec
o
n
s
tr
u
cted
im
ag
e
b
y
i_
2
(
x
,
y
)
,
an
d
t
h
e
im
ag
e
’
s
d
im
en
s
io
n
s
ar
e
M
an
d
N.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
R
eg
io
n
b
a
s
ed
lo
s
s
less
co
mp
r
ess
io
n
fo
r
d
ig
ita
l ima
g
es u
s
in
g
en
tr
o
p
y
co
d
in
g
(
Ma
n
g
a
la
p
a
lli V
a
msi
kris
h
n
a
)
1875
Peak
s
ig
n
al
-
to
-
n
o
is
e
r
atio
(
P
SNR
)
:
PS
N
R
is
u
s
ed
to
e
v
alu
ate
th
e
l
o
s
s
y
im
ag
e
r
ec
o
n
s
tr
u
ctio
n
p
er
f
o
r
m
ed
.
A
r
ep
r
esen
tatio
n
’
s
f
id
elity
is
d
eter
m
in
e
d
b
y
th
e
r
atio
o
f
th
e
m
ax
im
u
m
p
o
wer
o
f
th
e
s
ig
n
al
t
o
th
e
p
o
wer
o
f
co
r
r
u
p
tin
g
n
o
is
e
as
s
h
o
wn
in
(
2
)
.
B
etwe
en
th
e
o
r
ig
in
al
an
d
co
m
p
r
ess
ed
im
a
g
es
an
d
th
is
r
atio
is
f
r
eq
u
e
n
tly
u
s
ed
to
m
ea
s
u
r
e
q
u
ality
.
An
im
a
g
e
th
at
h
as
b
ee
n
co
m
p
r
ess
ed
o
r
r
ec
o
n
s
tr
u
cte
d
will
h
av
e
a
h
ig
h
er
PS
NR
,
en
h
an
cin
g
im
ag
e
q
u
ality
.
=
10
.
10
(
2
)
(
2
)
w
h
er
e,
B
is
th
e
s
ig
n
al
with
th
e
g
r
ea
test
am
p
litu
d
e
:
E
n
co
d
in
g
t
im
e
(ET):
f
o
r
s
u
ch
p
u
r
p
o
s
es
o
f
s
to
r
ag
e
o
r
tr
an
s
m
is
s
io
n
,
th
e
u
s
e
o
f
a
s
p
ec
if
ic
f
o
r
m
at
is
u
s
ed
to
en
co
d
e
a
s
eq
u
e
n
ce
o
f
ch
ar
ac
ter
s
.
Stru
ctu
r
al
s
im
ilar
ity
in
d
ex
(
SS
I
M)
:
t
h
e
SS
I
M
is
a
p
er
ce
p
tu
al
m
ea
s
u
r
e
th
at
lo
o
k
s
at
h
o
w
d
ata
co
m
p
r
ess
io
n
o
r
p
r
o
ce
s
s
in
g
o
r
d
ata
tr
an
s
m
is
s
io
n
lo
s
s
es
af
f
ec
t
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5
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AUTH
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RS
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
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ra
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h
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g
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il
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m
.
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.
As
il
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ti
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u
n
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K
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m
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is wo
rk
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n
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s a
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ro
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t
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Ra
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n
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ra
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is
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ifetime
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TE
.
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c
a
n
b
e
c
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tac
ted
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t
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:
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n
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las
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m
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il
.
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o
m
.
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.
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sy
Th
a
n
k
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c
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u
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ter
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ti
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Un
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v
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,
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i
p
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h
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h
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s
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tern
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ls.
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ter sc
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.
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s to
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tern
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h
e
c
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n
tac
ted
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t
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:
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les
sy
t2
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g
m
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.
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.
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sy
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s.
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h
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p
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iew
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rn
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re
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d
2
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2
P
h
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.
sc
h
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lars
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re
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o
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r
s
y
ste
m
s
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
k
a
p
p
a
g
a
n
tu
las
u
b
b
u
@g
m
a
i
l.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
R
eg
io
n
b
a
s
ed
lo
s
s
less
co
mp
r
ess
io
n
fo
r
d
ig
ita
l ima
g
es u
s
in
g
en
tr
o
p
y
co
d
in
g
(
Ma
n
g
a
la
p
a
lli V
a
msi
kris
h
n
a
)
1879
Na
th
a
De
e
p
th
i
is
a
h
i
g
h
ly
m
o
ti
v
a
ted
a
ss
istan
t
p
r
o
fe
ss
o
r
wi
th
a
p
a
ss
io
n
f
o
r
tea
c
h
in
g
a
n
d
re
se
a
rc
h
.
Wi
th
8
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