general orthonormal mra ref: rao & bopardikar, ch. 3
TRANSCRIPT
General Orthonormal MRA
Ref: Rao & Bopardikar, Ch. 3
Outline
• MRA characteristics– Nestedness, translation, dilation, …
• Properties of scaling functions
• Properties of wavelets
• Digital filter implementations
Recall Formal Definition of an MRA
An MRA consists of the nested linear vector space such that
• There exists a function (t) (called scaling function) such that is a basis for V0
• If and vice versa•
• Remarks:– Does not require the set of (t) and its integer translates
to be orthogonal (in general)– No mention of wavelet
2101 VVVV
integer:)( kkt
1)2( then )( kk VtfVtf
)(lim 2 RLV jj
}0{jV
Properties of Scaling Functions
1)( dtt
1)(
2dtt
0for basis
tindependenlinearly :)(
)()(),(
V
Zkkt
nntt
)(t
)1( t
Explained using Haar basis
Dilation of Scaling Functions
)(2)2
1(),
2
1(
)(2
1)2(),2(
nntt
nntt
)(2)2(),2( nntt kkk
)(t
)2( t
)12( t
)2/(t
1
2
1t
k
k
V
Zllt
for basistindependenlinearly
:)2(
Nested Spaces
• Every vector in V0 belongs to V1 as well
– In particular (t)
• Possible to express (t) as a linear combination of the basis for V1
ZkktV
ZkktV
VV
:)2(: of basis
:)(: of basis
1
0
10
Haar may be misleading …
• One can translate an arbitrary function by integers and compress it by 2; BUT there is no reason to think that the spaces Vj created by the function and its translates and dilates will necessarily be nested in each other
V0
V1
10 VV 10 VV
Remark
Two-Scale Relations(Scaling Fns)
n
ntnct )2()()(
2
1)(
)2()()(1
:sidesboth gintegratin
n
n
nc
dtntncdtt
n
nc 2)(
n
ntnct )2()()(
nn
lntncnltnclt )22()())(2()()(
)()2()(2
1)(),( llmcmcltt
m
Constraints on c(n)
n
kk ntnkatf )2(),()(
)(),()(),(
0)(),()(
)()()( :fn detail
01
000
010
nttfnttf
nttgVtg
tftftg
),0()(),(0 nanttf )2(),(),1()(),(1 mtntmanttfm
Orthogonal Projection in Subspaces
n
ntnatf )(),0()(0
n
ntnatf )2(),1()(1 Finer approx
Coarser approx
See next page
)(2
1
)2(),2()()2(),(
mc
mtntncmttn
)2(2
1)2(),(
)2(2
1
)2(),22()(
)2(),)1(2()()2(),1(
nmcmtnt
mc
mtntnc
mtntncmtt
n
n
m
nmcmana
2
)2(),1(),0(
From previous pageFiner coefficients and coarser ones are related by c(n)
0)( dtt
1)(
2dtt
0for basist independenlinearly
)()(),(
W
nntt
0)(),( ntt
Properties of Wavelets
Orthogonality
Two-Scale Relations(wavelet)
0)(
)(2
1)2()()(0
:sidesboth gintegratin
)2()()()( 1
n
nn
n
nd
nddtntnddtt
ntndtVt
)()2()(2
1)(),( llmcmcltt
m
We showed :
Similarly :
)()2()(2
1)(),( llmdmdltt
m
0)2()(2
1)(),(
m
lmcmdltt
Constraints on c(n) and d(n)
),0()(),(
)(),0()(),0(
)()()(
1
001
nbnttf
ntnbntna
tgtftf
nn
Function Reconstruction
m
m
mtntmanttf
mtmatf
)2(),(),1()(),(
)2(),1()(
1
1
See next page
m
nmdmanb
2
)2(),1(),0(
)(2
1
)2(),2()()2(),(
md
mtntndmttn
)2(2
1
)2(),22()()2(),1(
md
mtntndmttn
)2(2
1)2(),( nmdmtnt
Detail coefficients and finer representation are related by d(n)
jj
kj
kjk WVWV
WWWV
WWVWVV
WVV
WVV
1
1011
100112
110
001
Nested Space
VN
VN-1 WN-1
VN-2 WN-2
VN-3 WN-3
Digital Filter Implementation
Use existing methodology in signal processing for discrete wavelet comput
ation
Digital Filter Implementation
)()(~2
)()(
)()(~
2
)()(
Define
ngngnd
ng
nhnhnc
nh
m
nmcmana
2
)2(),1(),0(
m
nmdmanb
2
)2(),1(),0(
Recall
m
mnhmana )2(~
),1(),0(
m
mngmanb )2(~),1(),0(
Then
n
nh 1)(
n
n
n
kngnh
kkngng
kknhnh
0)2()(
)(2
1)2()(
)(2
1)2()(
n
nc 2)(
)()2()(2
1llmdmd
m
0)2()(2
1
m
lmcmd
)()2()(2
1llmcmc
m
n
ng 0)(
n
nd 0)(
)0(
~)2,1()1(
~)1,1()2(
~)0,1()3(
~)1,1(
)2(~
),1()1,0(
hahahaha
mhmaam
)0(
~)0,1()1(
~)1,1()2(
~)2,1()3(
~)3,1(
)(~
),1()0,0(
hahahaha
mhmaam
)0,0(a
)0(~h)1(
~h)2(
~h)3(
~h
)2,1( a )1,1( a )0,1(a )1,1(a)3,1( a )2,1(a )3,1(a
)1,0(a
)0(~h)1(
~h)2(
~h)3(
~h
)2,1( a )1,1( a )0,1(a )1,1(a)3,1( a )2,1(a )3,1(a
two)offactor aby n (decimatio
samples indexed retain and ~
with ),1( Convolve
:),0( getting ...
evenhma
naCoarsening
Similarly, …
two)offactor aby n (decimatio
samples indexed retain and ~ with ),1( Convolve
:),0( getting ...
evengma
nbencethe differComputing
)0,0(b
)0(~g)1(~g)2(~g)3(~g
)2,1( a )1,1( a )0,1(a )1,1(a)3,1( a )2,1(a )3,1(a
)1,0(b
)0(~g)1(~g)2(~g)3(~g
)2,1( a )1,1( a )0,1(a )1,1(a)3,1( a )2,1(a )3,1(a
Signal Reconstruction
m lm l
ll
mltmdlbmltmcla
ltlbltla
tgtftf
)22()(),0()22()(),0(
)(),0()(),0(
)()()( 001
m
mtmct )2()()(
m
mtmdt )2()()(
n ln l
ntlndlbntlnclatf
mln
)2()2(),0()2()2(),0( )(
2 ngSubstituti
1
)2()1,0()1(0)0()0,0()1(0)2()1,0(
)2()1,0()0()0,0()2()1,0()2(),0(
caccacca
cacacalclal
)1(c)0(c)1(c)2(c
0 )0,0(a 0 )1,0(a)1,0( a
)2(c
Subdivision … getting a(1,n):Zero insertion (upsampling) and convolve with 2H
n=0
)2(),1( )(1 ntnatfn
l l
l l
lnglblnhla
lndlblnclana
)2(),0(2)2(),0(2
)2(),0()2(),0(),1(
Hence
)2()1,0()1(0)0()0,0()1(0)2()1,0(
)2()1,0()0()0,0()2()1,0()2(),0(
dbddbddb
dbdbdbldlbl
)1(d)0(d)1(d)2(d
0 )0,0(b 0 )1,0(b)1,0( b
)2(d
Detail part: … getting a(1,n):upsampling and convolve with 2Gn=0
Similarly, …
Notations of Digital Filters
Interpolator and Decimator
nnMxny for )()(
otherwise0
,for )('
kkMnM
ny
nx
H~ H
G~ G
analysis filter bankperfect reconstruction pair:
Whatever goes into analysis bank isrecovered perfectly by the synthesisbank
synthesis filter bank
H~
H
G~
G
Haar Revisited
3,2,1,05379)( nnx
Analysis Filters
0
9
7
3
5 0
8
5
4
2.5
2
0
1
2
-1
2.5
2
-1 0
h(-n)
0.5 0.5
0
-1g(-n)0.5
-0.5
2
1)1()0( hh
2
1)1(,
2
1)0( gg
Haar:
Haar Revisited
Synthesis Filters
0 1
2 h(n)
1
0
2 g(n)
0
9
7
3
50
8
4
2
0
1
-1
2
-1
1
11