P. B ox e al.: Local Pic u e- epe i ion Mode De ec o o Video De-in e lacing
Con ibu ed Pape
Manusc ip ecei ed Sep embe 21, 2007 0098 3063/07/$20.00 © 2007 IEEE
1647
Local Pic u e- epe i ion Mode De ec o
o Video De-in e lacing
Piedad B ox, Leon Woes enbe g, and Ge a d de Haan, Senio Membe , IEEE
Abs ac
—
The de-in e lacing o ideo ma e ial con e e
d
om ilm can be pe ec , p o ided i is possible o
ecognize he ield-pai s ha o igina e om he same ilm
image. Va ious so-called ilm-de ec o s ha e been
p
oposed o his pu pose, mainly in he pa en
-li e a u e.
Typically, hese de ec o s ail in cases whe e ideo
o e lays a e me ged wi h ilm ma e ial, o when non-
s
anda d epe i ion pa e ns a e used. Bo h p oblems occu
equen ly in ele ision b oadcas . Fo hese hyb id and/o
i egula cases, we p opose a de ec o ha can de ec
di e en pic u e- epe i ion pa e ns locally in he image.
This de ec o combines uzzy logic ules and spa io-
empo al p edic ion o a i e a a highly obus decision
signal, sui able o pixel- accu a e de-in e lacing o
hyb id and i egula ideo ma e ial. In addi ion o an
e alua ion o he pe o mance, he pape also p o ides a
complexi y analysis.
Index
T
e ms
—
Pic u e- epe i ion Mode De ec ion, Video
De- in e lacin
g
, Pull Down, Video Si
g
nal P ocessin
g
,
Fuzzy In e ence Sys ems.
I. INTRODUCTION
Knowledge o he pic u e epe i ion pa e n is highly
ele an o se e al ideo signal p ocessing asks, like ideo
comp ession, pic u e- a e con e sion and de-in e lacing. As
his in o ma ion is usually no included in he ansmission,
he de ec ion o pic u e epe i ion om he ideo da a is
necessa y. We shall ocus on he de-in e lacing applica ion
[1], which is pa icula ly ele an , since i he ield-
p
ai s
ha a e o igina ed om he same image a e ecognize
d
hen he de-in e lacing o ideo ma e ial can be pe ec .
As a la ge pe cen age, o en he majo i y, o b oadcas
ideo ma e ial has been con e ed om ilm, me hods o
ealize ilm- mode de ec ion a e cu en ly in demand. In his
con e sion pic u e epe i ion is equi ed, since ideo
signals o igina ing om a ideo came a p o ide a pic u e
a e o 50 Hz, o 60 Hz, whe eas i he ma e ial was
egis e ed wi h a cine-came a he pic u e a e is only 24
images pe second. In o de o adap ilm o bo h s anda
d
ansmissions, a p ocess called ’pull-down’ is pe o med.
Basically, i consis s o epea edly scanning a ilm image un il
i is ime o show he nex . Fo 25 Hz ilm shown in a 50 Hz
P
.
B ox
is
wi h
he
Ins i u o
de
Mic oelec
´
onica
de
Se
illa
(CNM-CSIC)
and
he
Uni
e si y
o
Se
ille,
Spain
(e-mail:[email p o ec ed]).
L.M.P
.
W
oes enbe
g
is
wi h
he
Axon
Digi al
Desing,
Udenhou ,
he
Ne he lands
(e-
mail:leon.w
oes enbe
g@axon. ).
G.
de
Haan
is
wi h
he
Philips
Resea ch
Eindho
en,
Eindho
en,
he
Ne he lands.
He
is
also
p o esso
a
he
In o ma ion
and
Communica ion
G oup,
T
echnology
Uni
e si y
o
Eindho
en,
he
Ne he lands (e-
mail:[email p o ec ed]om).
b oadcas , e e y image is shown wice and he con e sion is
e e ed o as 2:2 pull-down. Fo 24 Hz ilm shown on a
60 Hz ele ision, ilm images a e shown al e na ingly 2
and 3 imes, which is he so-called 3:2 pull-down p ocess.
Independen o he ype o came a and epe i ion pa e n,
in e laced ideo signals ansmi only he odd lines o od
d
images and he e en lines o e en images.
Di e en de ec o s ha e been p oposed o iden i y he ield-
p
ai s o igina ing om he same ilm image o enable pe ec
de-in e lacing, o p ope pic u e- a e con e sion. Among he
m
ze o- ec o ma ching de ec o s ha e widely been employe
d
by he majo i y o cu en ilm de ec o s [2]. They y o
ma ch he ze o mo ion ec o s on a p e ious ield. To pe o
m
i , hey no mally use wo kinds o signals: a i s o
de ec he ame simila i y and a second one o measu e
he ield simila i y. Based on he analysis o bo h
simila i y me ics, con ol signals a e gene a ed. They
indica e he mode o he ideo signal, i.e. ideo o ilm, an
d
he ype and phase o he ilm mode, o de e mine he image’s
posi ion in he 3:2 o 2:2 pull-down pa e n.
O he app oaches y o iden i y jagged edges in ames.
This undesi able phenomenon appea s when wo ields
wi h mo ing objec s, sampled a di e en momen s a
ime, a e me ged in o a single image. Se e al p oposals o
his kind o de ec o s ha e been p esen ed in he li e a u e [3],
[4].
Ano he de ec o based on edge-de ec ion was p oposed in
[5]. I analyzes he posi ion o edges in he image since
i he e is a pic u e epe i ion o he ields, edges should be
a he same spa ial posi ion.
Finally, a mo ion ec o based app oach has been p oposed
in [6]. The sum o he leng h o he mo ion ec o s is
e alua ed o decide i wo ields a e iden ical o no .
Recen ad ances in he a ea o ilm-de ec ion can be
di ided in o wo ca ego ies. The i s ones epo on he
inc eased obus ness o he algo i hms, whe eas he second
ones ocus on he de ec ion o he local ideo mode in
hyb id ideo sequences.
An imp o ed obus ness is especially ele an , as an in-
co ec mode decision p oduces highly annoying a e ac s
in he de-in e laced ideo signal. The app oach desc ibe
d
in [7] educes he numbe o w ong decisions due o
e ical de ails using a new di e ence me ic, whe eas he
p
oposed me hod in [8] uses a laye ed s uc u e o achie e a
obus ness imp o emen .
Local de ec ion has been mo i a ed by he inc ease o TV
ma e ial ha combines images om di e en o igins in a
IEEE T ansac ions on Consume Elec onics, Vol. 53, No. 4, NOVEMBER 2007
1648
single ield. None o he echniques p e iously ci ed can
locally de ec di e en modes in a single ield, as hei ou pu
is a single lag o he en i e ield. They usually compa e he
sum o absolu e alues o ame and ield di e ences o e he
en i e ield wi h a h eshold alue [2], [7]. This s a egy is a
om op imal, since he bes h eshold alue s ongly depends
on he amoun o mo ion and he le el o noise in he pic u e.
Mo eo e , i lea es no op ions o dis inguish he di e en
modes in a single ield ha occu in hyb id ma e ial. To sol e
his p oblem, a me hod o de ec ing he ilm mode o
indi idually mo ing objec s wi hin ields is desc ibed in [9].
The iden i ica ion o hese objec s is pe o med using
segmen a ion.
Ou p oposal combines uzzy logic and spa io- empo al
p
edic ion o inc ease he obus ness o he inal decision, an
d
also o ake a decision locally on a pixel-by-
p
ixel basis. Due o
he capaci y o he uzzy logic-
b
ased models o pe o m
a
non-linea mapping be ween he inpu and ou pu space,
hey a e well-known as good in e pola o s [10]. One example
is he me hod de eloped in [11], which uses an adap i e
de-in e lacing p ocess by weigh ing be ween ’ ield
inse ion’ and ’a spa ial in e pola ion algo i hm’. The
weigh ing ac o s a e ob ained analyzing, as inpu s o he
uzzy sys em, he in a and in e - ield signal di e ences
o he cu en pixel along a se o p e-de e mined di ec ions.
In his pape , we p opose a se o uzzy IF-THEN ules o
ake a decision ins ead o ealizing a weigh ed in e pola ion. I
n
his no el app oach, each ule models heu is ic knowledge o
iden i y one o he possible pic u e- epe i ion modes on
a
pixel-by-pixel basis. To make his pixel-accu a e de ec o
obus , a se o p oposals a e p esen ed. Among hem, he main
no el y is he inclusion o a spa io- empo al p edic ion scheme
inspi ed on ecu si e mo ion es ima ion [12]. He e, p edic io
n
implies ha he inal decision no only co esponds o he
cu en pixel, bu also he decisions in a spa io- empo al
neighbo hood o he cu en pixel a e conside ed.
Since he decision is made on a pixel-by-
p
ixel basis,
ou me hod can deal success ully wi h hyb id ideo
ma e ial. Mo eo e , ou p oposal is no limi ed o he
ecogni ion o he s anda d epe i ions pa e ns, like he
popula 2:2 o 3:2 pull-down pa e ns o ilm. This ex ends i s
applicabili y o any i egula , pic u e- epe i ion sequence.
This pape is o ganized as ollows. The p oposed algo i h
m
is desc ibed in Sec ion II. We p esen se e al p oposals om a
b
asic one o a mo e sophis ica ed one in he di e en
subsec ions o Sec ion II. The pe o mance o he app oach is
p o en by ex ensi e simula ions o ideo sequences applying
he mode de ec ion o pe o m di e en de-in e lacing
echniques. These esul s a e p esen ed in Sec ion III. This
sec ion also includes a complexi y analysis o he algo i hm.
Finally, we d aw ou conclusions in Sec ion IV.
II.
LOCAL PICTURE-REPETITION MODE DETECTOR
The p oposed mode de ec o is a decision-making
sys em based on a se o ules. Each single ule models
heu is ic knowledge o iden i y locally di e en modes. As
men ioned in he in oduc ion, ou p oposal, o de-
in e lacing, o e s mo e han jus he unc ionali y o a
ilm-de ec o , as i s ules deal wi h all possible pic u e-
epe i ion pa e ns. To help app ecia e he backg ound o he
ules, we shall i s b ie ly desc ibe he con e sion
be ween ilm and ideo, which is s ill he mos common
cause o pic u e- epe i ion in b oadcas ideo.
The 3:2 pull-down p ocess is common o ans e 24
Hz ilm o 60 Hz ideo. To achie e his, e e y odd ilm image
is scanned wice, while e e y e en ilm image is scanned h ee
imes as shown in Fig. 1(a). The ea e , he signal is in e laced.
The 2:2 pull-down p ocess is common o ans e 24
Hz ilm o 50 Hz ideo. Ini ially, he pic u e- a e o he
ilm is inc eased o 25 images pe second by unning he
ilm 4% as e . Then, each ilm image is scanned wice and
in e laced, gene a ing wo ideo ields as shown in Fig. 1(b).
To a i e a pic u e- epe i ion de ec ion, we calcula e h ee
di e ence signals, he ame di e ence signal (δ ame) be ween
he nex and p e ious ield a he same spa ial posi ion (x, y),
and he wo ield di e ences o he cu en pixel: wi h he
p e ious ield (δ ield1) and wi h he nex ield (δ ield2). In o de o
inc ease obus ness agains noise he median alue o each
di e ence a h ee e ical posi ions is used (see Fig. 2). They
a e de ined by he ollowing exp essions:
δ
ame
(
x
,
y
,
n
)
=
med
(
δ
ame
(
−
2
)
,
δ
am
e
(
0
)
,
δ
am
e
(
2
)
)
(
1
)
Film
24 Hz
A
B
Video
60 Hz
A
odd
A
e en
A
odd
B
e en
Film
25 Hz
A
B
Video
50 Hz
A
odd
A
e en
B
odd
Line
y-2
δ
ame
(
-
2
)
T ansmi ed pixel
In e pola ed pixel
B
odd
C
e en
B
e en
Codd
y
C
D
C
odd
C
e en
C
D
Ce en
Dodd
y
+1
D
odd
D
e en
De en
y
+2
(
a
)
(
b
)
n-1
n
n+1
Fiel
d
Fig. 1. S anda d con e sion be ween ideo and ilm o ma s: (a) 3:2
Pull- down.
(
b
)
2:2 Pull-down.
Fig. 2. Pic u e- epe i ion mode de ec o ape u e. The shown pixels a e
used o calcula e he local di e ences.
y
-1
ame
(
0)
ame
(
2
)
ield1
(
-
1)
ield
2
(
-
1)
ield1
(
1)
ield1
(
0)
δ
δ
δ
δ
δ
δ
δ
δ
ield
2
(
1)
ield
2
(0
)
P. B ox e al.: Local Pic u e- epe i ion Mode De ec o o Video De-in e lacing 1649
Video
60 Hz
A
odd
A
e en
A
odd
B
e en
B
odd
C
e en
C
odd
C
e en
D
odd
D
e en
δ
ield
S
S
L
S
L
S
S
L
S
δ
ame
S
L
L
L
L
S
L
L
δ
ield1
S
S
L
S
L
S
S
L
δ
ield2
S
L
S
L
S
S
L
S
δ
ame
S
L
L
L
L
S
L
L
Video
50 Hz
A
odd
A
e en
B
odd
B
e en
C
odd
C
e en
D
odd
D
e en
S
L
S
L
S
L
S
L
L
L
L
L
L
δ
ield1
S
L
S
L
S
L
S
L
δ
ield2
L
S
L
S
L
S
L
S
δ
ame
L
L
L
L
L
L
L
L
(a) 3:2 Pull-down
(
b
)
2:2 Pull-down
Fig. 3. Tempo al di e ence pa e ns o s anda d con e sions.
δ ame (x, y, n) =med (δ ame(−2) , δ ame(0) , δ ame(2) ) (1)
δ ield1 (x, y, n) =med (δ ield1(−1) , δ ield1(0) , δ ield1(1) ) (2)
δ ield2 (x, y, n) =med (δ ield2(−1) , δ ield2(0) , δ ield2(1) ) (3)
whe e n deno es he ield numbe in he sequence o de :
δ ame(i) (x, y, n) = |F (x, y + i, n + 1) − F (x, y + i, n − 1)|
δ ield1(i) (x, y, n) = |Fd (x, y + i, n) − F (x, y + i, n − 1)|
δ ield2(i) (x, y, n) = |Fd (x, y + i, n) − F (x, y + i, n + 1)|
To calcula e he di e ences, a simple ini ial de-in e lacing
algo i hm is used o gene a e p og essi e ames (Fd).
Typically, a e ical- empo al median o a e ical- empo al
linea il e is p oposed [1]. I he ini ial de-in e lacing p ocess
would be pe ec , he di e ence be ween ields om he same
sou ce image, as i occu s wi h ilm, should be equal o ze o.
Wi h a simple and ealis ic ini ial de-in e lacing algo i h
m
alias and e ical de ails in he ield may in oduce alse
de ec ions o mo ion. In o de o educe his p oblem, he
ield di e ences shown in exp essions (2) and (3) a e
no malized by e ical in a- ields di e ences.
The di e en ypes o empo al di e ences pa e ns a e
shown in Fig. 3(a) and 3(b) o he pull-down 3:2 and 2:2
p ocess, whe e ’L’ means a la ge di e ence and ’S’ a
small di e ence. Conside ing hese empo al di e ence
p
a e ns, he ollowing knowledge can be applied o de ec he
di e en modes:
1)
I he ame di e ence is la ge and bo h iel
d
di e ences a e la ge oo, hen he pixel co esponds
o a mo ing objec in a ideo sequence, whe e all
ields a e di e en .
2)
I he ame di e ence is small and bo h ield
di e ences a e also small, hen he pixel
co esponds o an a ea wi hou mo ion du ing
hese wo ield-pe iods. The e- o e, i mus be,
ei he a s a iona y a ea, o a mo ing a ea i he
3 ields o igina e om a 3 imes epea ed image,
e.g. as i occu s wi h 3:2 pull-down.
3)
I he ame di e ence is la ge, bu one o he
ield di e ences is small while he o he is la ge, he
n
he cu en pixel belongs o a sequence wi h pic u e-
epe i ion, whe e a leas one ield is epea ed, as i
occu s e.g. in 2:2 pull-down mode.
4)
O he wise, none o he modes is iden i ied. This he
may occu when he ini ial de-in e lacing p ocess,
hen necessa y o calcula e he ield di e ences,
su e s, om alias, because he signals a e
co up ed by noise, o because he image has a la
a ea.
This heu is ic knowledge can be modelled using a sys e
m
wi h uzzy IF-THEN ules, since he concep s la ge and small
a e unde s ood as uzzy de ini ions ins ead o h eshold alues.
Using uzzy logic, he concep s o ’SMALL’ an
d
’LARGE’ a e ep esen ed by uzzy se s, he membe ship
alues o which change con inuously be ween 0 and 1, as
shown Fig. 4(a) and Fig. 4(b).
Each uzzy IF-THEN ule in ou sys em has
an eceden 1 linguis ic alues and a single consequen
mode as shown in Table I. The minimum/maximum2
ope a o s a e selec ed as connec i es ’and’/’o ’ o he
an eceden s, espec i ely.
1
0
Membe ship deg ee
o
δ
o LARGE
µ(δ)
Membe ship deg ee
o
δ
o SMALL
0
D0 D1
δ
D2 D3
δ
1
An eceden is he common e m o a condi ion in he uzzy logic domain
[13]
(a) (b)
Fig.
4.
Membe ship
unc ions
o
he
uzzy
se s
(a)
LARGE,
(b)
SMALL.
2 Minimum and maximum ope a o s a e usually de ined as ’and’ and ’o ’
ope a o s in he uzzy logic domain espec i ely [13]
µ(δ)
δ
ield
δ
ame
1
IEEE T ansac ions on Consume Elec onics, Vol. 53, No. 4, NOVEMBER 2007
1650
TABLE I
FU Z Z Y RU L E SE T
i
an eceden
hen
consequen
1) δ ame (x,y,n) is LARGE and δ ield1 (x,y,n) is LARGE and δ ield2 (x,y,n) is LARGE MODE is ideo
2) δ ame (x,y,n) is SMALL and δ ield1 (x,y,n) is SMALL and δ ield2 (x,y,n) is SMALL MODE is s a iona y
3) (δ ame (x,y,n) is LARGE and δ ield1 (x,y,n) is SMALL and δ ield2 (x,y,n) is LARGE) o MODE is epe i ion
(δ ame (x,y,n) is LARGE and δ ield1 (x,y,n) is LARGE and δ ield2 (x,y,n) is SMALL)
4
)
o he wise MODE is unde e mined
The use o he ope a o ’and’ o ces he sys em o
analyze he ield di e ences signals only i he
co esponding ame di e ence signal is la ge. This s a egy
inc eases he obus ness o he de ec ion, since he ame
di e ence signal is mo e eliable han he o he di e ences
ha a e based on impe ec ini ial de-in e lacing esul s (Fd).
The main ad an age o he uzzy-logic based app oach is ha
i p o ides a smoo h ansi ion be ween one decision and
ano he . The ac i a ion deg ee o a ule (αi) indica es he
compa ibili y g ade o he (i h) IF-THEN ule, which is
calcula ed by compu ing he membe ship alues o he
an eceden s:
α
1 (x, y, n)= min(µLARGE(
δ
ame ),µLARGE (
δ
ield1 ),µLARGE (
δ
ield2)) (4)
α
2 (x, y, n)= min(µSMALL(
δ
ame ),µSMALL (
δ
ield1 ),µSMALL (
δ
ield2)) (5)
α
3 (x, y, n)= max((α3a , α3b )) (6)
α
4 (x, y, n)= 1-
α
1 -
α
2 -
α
3 (7)
whe e:
α
3a (x, y, n)= min(µLARGE(
δ
ame ),µLARGE (
δ
ield1 ),µSMALL (
δ
ield2)) (8)
α
3b (x, y, n)= min(µLARGE(
δ
ame ),µSMALL (
δ
ield1 ),µLARGE (
δ
ield2)) (9)
Fo each pixel, he alues α{1,2,3,4} a e he ou pu signals o
he uzzy sys em. Each signal co esponds o he
ac i a ion deg ee o an indi idual ule and anges om 0
o 1. Since ou p oposed de ec o aims a a pixel-by-
p
ixel
mode decision, al e na i e obus ness measu es a e necessa y.
These a e desc ibed in he ollowing subsec ions.
A. Inc ease o he obus ness o he uzzy sys em decision
The p oposed easoning me hod is based on a
single winne ule. The winne is he uzzy IF-THEN ule
ha has he maximum ac i a ion deg ee, ha is, he
maximum compa ibili y g ade wi h one o he pa e ns
des e y low.
Missing line
T ansmi ed line
3x3 Spa ial-
desc ibed by he an eceden s. Howe e i mul iple ac i a io
n
deg ees o con a y ules a e ac i a ed, choosing he
maximally ac i a ed mode easily esul s in w ong decisions.
To imp o e his, a decision is adop ed when i s
co esponding ule is he mos ac i a ed and also he
ac i a ion deg ee o he con a y ule is e y low.
B. Spa io- empo al P edic ion
In ou p oposal, he inal decision o he cu en pixel is
no only based on he decision o he sys em o he cu en
pixel, bu applies ’spa io- empo al’ p edic ions, aking in o
accoun also he decisions o he pixels in a 3x3
neighbo hood.
The idea is o make a decision only when one mode
is ac ually clea , and p opaga e he decision un il a new
clea decision is aken. To educe e o p opaga ion
a
meande ed scanning is p oposed (see Fig. 5). The de ec o
p
ocesses he e en ields in a s eaming ashion, ha is, o
m
he op-le pixel o he bo om- igh pixel, whe eas he
odd ields a e p ocessed om he bo om- igh pixel o
he op- le pixel . Fou pixels in he 3x3 window a e spa ial
neighbo s and belong o he cu en ield, and ou a e
empo al neighbo s o he p e ious ield as shown Fig. 5.
C.
Tempo al o wa d p edic ion p ocess
The empo al p edic ions a e mo e complex han he spa ial
p edic ion. To illus a e he p oblem, le us conside
he empo al di e ence pa e n o pull-down 3:2 p ocess
shown in Fig. 3(a). Analysis o his pa e n shows ha no
only he hi d ule is ac i a ed bu also he second one.
To be exac , he second ule is ac i a ed e e y i e ields
o he ideo sequence. This means ha he MODE o a pixel
wi h he same spa ial coo dina es in he p e ious ield no
always has o ag ee wi h he cu en one. Fo any pic u e
epe i ion pa e n, decisions om he p e ious ield can be
ans o med in o new p edic ions as shown in Fig. 6. The
alues o di e ence signals a e ep esen ed using he no a io
n
’LSL’, which means a LARGE di e ence o δ ield1 and
a
SMALL di e ence o δ ield2 and a LARGE alue o δ ame . Fo
each alue o he di e ence signals in he p e ious ield, he
di e en al e na i es o a pixel in he cu en ield a e
shown in Fig. 6. F om he knowledge o he p e ious mode,
only he alue o δ ield1 can be assigned. Fo ins ance, a pixel
om he p e ious ield whe e ideo mode is de ec ed implies
a la ge alue o di e ence signals, ha is ’LLL’. In his case,
he alue o δ ield1 will be su ely ’L’ in he nex ield and hen,
δ ame will be also ’LARGE’. Howe e , he e is no in o ma ion
o p edic he alue o δ
ield2. Analyzing each possibili y he
P e
i
ous
i
e
ld
(
o
dd)
C
u en
i
e
ld
(
e en
)
empo a
l
ape
u
e
Spa ial neigh
b
ou
s Cu en
p
ixel Tempo
al neighbou s
Fig. 5. The decisions a 9 posi ions in a 3x3-ape u e a e
in ol ed in he decision-making p ocess.
P. B ox e al.: Local Pic u e- epe i ion Mode De ec o o Video De-in e lacing 1651
P e ious
di e ence
alues
LLL
SSS
LSL
SLL
???
Upda ed
di e ence
alues
L?L
S??
S??
L?L
???
Mode
o
a pixel
in
he
p e ious
ield
MODE is
ideo
MODE is
s a iona y
MODE is
epe i ion
MODE is
unde e mined
Tempo al
p edic ion o
he Mode
MODE can be
ideo o
epe i ion
MODE can be
s a iona y
o
epe i ion
MODE can be
ideo,
epe i ion
and
s a iona y
MODE is
unde e mined
Fig. 8. I pic u e- epe i ion is assumed inco ec ly, ea he ing (le ) will
show.
Fig. 6. Tempo al p edic ions o he MODE in he p e ious ield.
empo al p edic ions o he modes a e shown in Fig. 6. The
mul iple modes o he empo al p edic ions ha e an equal bias.
D.
Rein o cemen o he inal decision
The lowcha o he p ocess is shown in Fig. 7. The inpu
signal is he MODE o he pixels in he 3x3 neighbo hood.
Only i he occu ence o modes exceeds a se o alues
C{1,2,3,4} and he e a e no unde e mined decisions,
a
con ol signal called PATTERN is ac i a ed. I no , i.e. he
majo i y o decisions a e unde e mined, he con ol signal will
be gene a ed o code he PATTERN signal o ’unde e mined’
decisions. Finally, in pixels whe e none o he ules is
su icien ly ac i a ed, he decision o he p e ious pixel in he
scanning di ec ions is assigned.
Some e oneous mode decisions a e mo e c i ical han
o he s. Fo ou de-in e lacing applica ion, epe i ion mode
leads o pe ec esul s h ough me ging he lines o ields ha
b
elong o he same ilm image. Howe e , when his
mode is e oneously de ec ed, e.g. in ideo came a ma e ial,
annoying ea he ing a e ac s appea in he de-in e lace
d
p
ic u e, as shown in Fig. 8. The consequences o he ideo
mode decision is less c i ical, since in his case a obus
de-in e lacing echnique is employed ha pe o ms sub-
op imal on ilm ma e ial, bu gi es no e y objec ionable
a e ac s. Gi en his asymme ical beha iou , p io i y is gi en
o he ideo-mode, i.e. he mode o which we assume he e is
no pic u e epe i ion. The e o e, his mode is co obo a ed
i s ly and i equi es a lowe numbe o modes in he
ape u e o ake a decision: C1 <C2≈C3≈C4 (see Fig. 7).
Th ough analysis o he wo pa s o he hi d ule, a secon
d
con ol signal is gene a ed named ‘PHASE’ o iden i y
which wo o he h ee ields a e iden ical in he case
ha a pic u e epe i ion, e.g. a epe i ion mode, is
de ec ed. Fig. 9 shows he placemen o he uzzy mode
de ec o in he ideo p ocessing chain o ou de-
in e lacing applica ion. Bo h signals gene a ed by he
uzzy sys em a e used as con ol signals o de e mine he
de-in e lacing s a egy. In case ha a epe i ion mode is
de ec ed, he de-in e lacing p ocess becomes pe ec by
wea ing wo ields oge he . This is also alid in he case
ha he pixel is classi ied as belonging o a s a iona y
a ea. Howe e , i he i s o he ou h ule a e he mos
ac i a ed, a con en ional ideo de-in e lacing app oach
[1]
has o be used.
E.
Imp o ing he pe o mance o he uzzy sys em by
membe ship unc ion lea ning
F om heu is ic knowledge, he e is no es ic ion o ix he
pa ame e s o membe ship unc ions. Howe e , some alues
will p o ide be e esul s han o he ones. Ou idea is o selec
he mos sui able alues using a se o inpu /ou pu aining
p
a e ns o image sequences whe e he mode decision is
known by he designe . Fi e ields o wo di e en sequences
we e used. O iginally bo h sequences a e ideo ma e ial bu
he 3:2 and 2:2 pull-down cadences we e gene a ed and used
as aining pa e ns. These sequences a e called Kielp an
d
Bicycle in Sec ion III. Simula ion esul s in Sec ion III p o e
ha he me hod is obus o a wide numbe o es sequences.
coun e
ideo
> C
1
1
PATTERN [00]
ideo
MODE
3X3 nei
g
hbo hood
coun e
s a iona y
coun e
epe i ion
> C
2
> C
3
1
1
A
ND
0
AND
0
1
1
PATTERN [01]
s a iona y
PATTERN [10]
epe i ion
coun e
unde e mined
==0
> C
3
1
1
NOT
A
ND
0
PATTERN [11]
unde e mined
INPUT
SIGNAL DE-INTERLACING
STRATEGY
DE-INTERLACING
TECHNIQUE
FUZZY MODE
PHASE
PREVIOUS DECISION
DETECTO
R
PATTERN
Fig. 7. Flowcha o he decision block o inc ease he obus ness
o he con ol ’MODE’ signal.
Fig. 9. Placemen o
he uzzy ilm-mode de ec o in he ideo chain.
A
ND
0
OUTPUT
SIGNAL

IEEE T ansac ions on Consume Elec onics, Vol. 53, No. 4, NOVEMBER 2007
1652
TABLE II
MEMBERSHIP FUNCTION PARAMETERS AFTER THE LERANING PROCESS
Inpu
Mode
M
ode
Mode
M
ode
Ini ial
a iable
Pa ame e s
ideo
‘
s a iona y’
‘
epe i ion’
‘
unde e mined’
pa ame e s
LSL SLL
D0 1 0 0.5 0 0 0
δ ame D1 8 8 1.5 8 8 8
D2 0 0 0 0 0 0
D3 8 8 8 8 8 8
D0 0 0 0 0 0 0
δ ield2 D1 2 2 2 2 2 2
D2 0 0 0 0 0 0
D3 2 2 2 2 2 2
D0 0 0 0 0 0 0
δ ield1 D1 0.25 2 0.5 2 2 2
D2 0 1 0 1 0 0
D3 2 8.5 2 9 2 2
ha he me hod is obus o a wide numbe o es sequences.
Fo he uning p ocess, we used he de elopmen
en i onmen X uzzy3.0 [14]. This is an en i onmen o
designing uzzy se s ha is composed o a se o CAD ools
co e ing he di e en s ages o desc ip ion, e i ica ion,
simpli ica ion and syn hesis o in e ence sys ems based o
n
uzzy logic. X uzzy3.0 in eg a es a CAD ool, named
x
s
l
[15], o une uzzy sys ems desc ibed in he en i onmen .
We u he applied a se o aining ideo sequences. Onl
y
he alues D0, D1, D2 and D3 ha de ine he
membe ship unc ions ha e been adjus ed in he lea ning
s age. The Le enbe g-Ma qua d algo i hm has been
selec ed as supe ised lea ning algo i hm and he esul s
o he p ocess a e shown in Table II. This able shows he
uned pa ame e s o each one o he modes, and also he
ini ial pa ame e s o he membe ship unc ions ha we e ixe
d
manually. Fo he i s ield o he ideo sequence, he
ini ial pa ame e s a e used. Fo he es o he ields, he
uned pa ame e s o each mode is aken.
F.
Mode il e ing o imp o e obus ness
Since an e oneous ideo de ec ion in he epe i ion a ea is
less se ious han an e oneous epe i ion mode decision,
a
simple spa ial il e ing is pe o med o sp ead he ideo mode
decision. Fig. 10 shows he shape o he spa ial ape u e. As i
can be seen, i con ains mo e pixels in he e ical di ec ion
han in he ho izon al. The eason is ha he image is
p ocessed
p
ocessed in a s eaming di ec ion, so mis akes a e ansmi ed
along ho izon al di ec ion. To a oid his, a highe num
b
e o
pixels in e ical di ec ion a e conside ed. The inal s uc u al
o e iew o he p oposed de ec o is shown in Fig. 11.
III.
PERFORMANCE OF THE PROPOSED ALGORITHM
The pe o mance o he p oposed algo i hm has been
e alua ed in he de-in e lacing applica ion. We in es iga ed
he image quali y and calcula ed he compu a ional cos o
he de ec o . Subsec ion A desc ibes he cos calcula ions. A
b
ie desc ip ion o he ideo es sequences can be ound in
Subsec ion B and inally, he o e all pe o mance is gi en in
Subsec ion C.
A. Algo i hm Cos
The algo i hmic cos is measu ed using he numbe o
loa ing poin ope a ions (FLOPS) as a(n in e se) igu e
o me i . The algo i hm equi es 543.7 Megas loa ing
poin ope a ions o analyze one ield o a ideo sequence
wi h a esolu ion o 720x576. We ha e conside ed his
measu emen ins ead o compu a ional ime as i is
s ongly depends on he pla o m on which he algo i hm
is implemen ed and
he e icienc o he p og amming
Fig. 10. 3x9-ape u e o he spa ial il e ing. Fig. 11. S uc u al o e iew o he ilm de ec o .
FIELD
MEMOR
Y
FIELD
MEMOR
Y
δ
ield2
FUZZ
Y
LOGIC
SYSTEM
3x3
ape u e
MODE
FILTERING
δ
ield1
Modi ica ion
membe ship
pa ame e s
Upda ed
MODE
MEMOR
Y
δ
ame
MODE
P. B ox e al.: Local Pic u e- epe i ion Mode De ec o o Video De-in e lacing 1653
(
a
)
TMF se
q
uence
(
b
)
Fi e- ose se
q
uence
(
c
)
Rena a h
y
b id se
q
uence
Fig. 12. Snapsho s o eal sequences used o p o e he pe o mance o he p oposed algo i hm.
measu emen ins ead o compu a ional ime as i s ongly
depends on he pla o m on which he algo i hm is
implemen ed and he e iciency o he p og amming.
B. Desc ip ion o he Sequences
Subsec ion C con ains esul s om he analysis o se e al
sequences. Some o hem a e eal sequences om TV
channels o mo ies, and o he s a e es ideo ma e ial.
Fo y ields o each sequence ha e been p ocessed. Th ee o
hese sequences ha e been especially analyzed:
- TMF. This is an o iginal sequence cap u ed om a Du ch
b
oadcas channel called TMF. The sequence is an in e lace
d
ideo clip (2:2 pull-down mode) wi h an o e lay
con aining a icke - ape ideo ex as can be seen in Fig.
12(a). I also con ains s a iona y a eas (a ound he clock an
d
he TMF-logo).
- Fi e- ose. This is an in e laced 2:2 ilm sequence. The
de ec ion o epe i ion mode is di icul due o he ine de ails
in he man’s bea d as shown in Fig. 12(b). Mo eo e his
sequence con ains a e y low le el o mo ion.
- Rena a. This sequence has been used o show he
imp o emen s in oduced by he indi idual obus ness
measu es explained in Sec ion II. I is o iginally a ideo
scene. Howe e , i has been a i icially ans o med in o
an in e laced 2:2 epe i ion mode. The sequence has hen
b
een con e ing in o a hyb id sequence, by adding a
ho izon ally ho izon ally mo ing ideo ex in he middle o
he ields, as shown in Fig. 12(c).
C. Simula ion Resul s
To p o e he pe o mance o he p oposed de ec o , he
h ee- ields VT il e ing app oach [16] is used i he ’ ideo’ o
’unde e mined’ mode is de ec ed. On he o he hand, i one o
he ’ epe i ion’ mode is de ec ed, he de-in e lacing p ocess is
implemen ed by wea ing. Compa ing he de-in e laced wi h
he o iginal p og essi e pic u e o Rena a, a Mean
Squa ed E o can be calcula ed. Fig. 13 shows he ela i e
MSE-sco e as a pe cen age o he MSE-sco e ob ained wi h
he VT il e ing [16]. As can be seen, ou inal p oposal
educes he o al MSE e o wi h almos 60 %. I includes he
imp o emen s ha a e desc ibed in subsec ions {A, B, C, D,
E, F} o Sec ion II. The esul s achie ed by he p oposal wi h
a modi ica ion o membe ship unc ion pa ame e s a e sligh ly
b
e e (column P4 in Fig. 13) han he ob ained wi h ixed
pa ame e s (column P3 in Fig. 13).
The de ec o has also been used o de-in e lace he
eal sequences shown in Tables III and IV. I dec eases
he o al MSE sco e by a high ac o in pe cen age
(almos 100%) in he majo i y o ilm sequences (see
Table III). This no only p oduces a pe ec ly de-in e laced
image, bu also conside ably educes he complexi y as
wea ing is he me hod wi h he lowes compu a ional cos .
Due o he p esence o low mo ion and/o a high numbe o
he de ails, epe i ion mode is no well de ec ed in some o
he ilm sequences and he MSE only alls o 40%. This is no
c ucial o de-in e lacing applica ions because con en ional
de-in e lacing is applied when ac ual epe i ion is
misin e p e ed as ideo.
Finally, he o al MSE is sligh ly educed when he
de ec o is used o ideo sequences (see Table IV). This is
due o he imp o ed de-in e lacing o he ew s a ic a eas.
Al hough he modi ica ion o membe ship unc ion pa ame e s
does no in oduce many ad an ages o he Rena a hyb i
d
Tables III and IV.
RE
NA
T
A
HY
B
RID SEQUENCE
200
180
160
140
120
MSE %
1
00
80
60
40
20
0
VT
P1
P2
P3
P4
P5 P oposal
P1
: Simple Fuzzy
Sys em(SFS)
P2
: SFS+
{A}
P3
: SFS+
{A, B, C, D}
P4
: SFS+
{A, B, C, D, E}
P5
: SFS+
{A, B, C, D, E, F}
Fig. 13. MSE pe cen age o each one o he p oposals.
IEEE T ansac ions on Consume Elec onics, Vol. 53, No. 4, NOVEMBER 2007
1654
TABLE III
SIMULATION RESULTS OF FILM SEQUENCES
Sequence
Sh ek
Gladia o
S ai s
F
a go
F
a go
Ma ze1
Ma ze2
F
i e
Chop
F
a go
V
anessa
Chop
Fligh
o
ice
e
p
ai
ose
hun
land
T
o al
MSE
De ec o
o
327.607
62.72
93.96
208.54
327.31
745.74
682.17
204.61
52.01
364.13
217.53
220.69
231.65
De ec o
on
1.52
0.098
0
124.77
0.143
419.78
0
0
0.077
0.74
0.21
2.83
0
Reduc ion(%)
99.53
99.84
100
40.16
99.95
43.708
100
100
99.85
99.79
99.901
98.71
100
TABLE IV
SIMULATION
R
ESULTS OF VIDEO
Sequence
Anima ix-a
Anima ix-b
Dieano he day
Bicycle
Kielp
Gi lga e
Wman
Rena a
Xmen2
News eade
T
o al
MSE
De ec o
o
139.71
712.46
446.38
1517.03
5321.83
156.109
71.56
450.425
266.52
1027.992
De ec o
on
52.06
711.91
371.29
1503.52
5319.12
154.71
71.45
449.79
250.81
1027.93
Reduc ion(%)
62.74
0.03
16.83
0.9
0.05
0.8
0.19
0.01
5.89
0.01
sequence, i is necessa y o achie e good esul s o some
sequences in Tables III and IV.
The esponse o he de ec o was also analyzed o he TMF
and Fi e- ose sequences. The ou pu modes when p ocessing
he snapsho in Fig. 12(a) can be isually co obo a ed in Fig.
14(a). In his igu e, whi e colo means epe i ion mode, ligh
g g ey means s a iona y a eas, da k g ey co esponds o ideo
mode and black colo shows zones whe e he decision is no
clea . As can be seen, he c i ical a eas o he ield a e
co ec ly de ec ed. The MSE alue o he TMF sequence
could no be included in Table III since he o iginal
p og essi e ma e ial is no a ailable.
The pe o mance o he o iginal Fi e- ose ( ilm ma e ial)
sequence is pe ec as shown in Table III. I his sequence is
ans o med in o ideo by elimina ing he epea ed ields,
ideo mode is also co ec ly de ec ed despi e he low le el o
mo ion as shown Fig. 14(b).
Finally, a es is p oposed o p o e he ad an ages o using
uzzy de ini ions o he concep s SMALL and LARGE ins ead
o c isp de ini ions. The esul s show a mo e c i ical
dis inc ion among he di e en mode a eas o he ield i c isp
de ini ions a e used. This p oduces se ious mis akes as i can
be seen in Fig. 15(a) o Rena a hyb id sequence and in Fig.
15(b) o TMF sequence.
Un o una ely, he e is no compe i i e de ec o ha
p
e o ms a local pic u e epe i ion mode de ec ion in cu en
scien i ic li e a u e. This is why compa isons wi h o he
p oposals o simila cha ac e is ics a e no included in his
sec ion.
IV. CONCLUSIONS
The de-in e lacing o ideo ma e ial con e ed om il
m
can be pe ec , p o ided i is de ec ed co ec ly.
Typically, howe e , a ailable de ec o s ail in cases whe e
ideo o e lays a e me ged wi h ilm ma e ial, o when non-
s anda d epe i ion pa e ns a e used. Bo h p oblems occu
equen ly in ele ision b oadcas . Fo hese hyb id and/o
i egula cases, we ha e p oposed a de ec o ha is
capable o de ec locally in he image di e en pic u e-
epe i ion pa e ns. By dis inguishing only he ollowing cases:
- S a iona y, i.e. all 3 ields show objec a same posi ion
-
N
o iden ical ields, i.e. all 3 ields show objec a di e en
posi ion
Pai ed iden ical ields case A, i s wo ields show objec
a same posi ion, hi d ield a di e en posi ion
ii hid i ld di ii
Fig. 14. Mode decisions aken by he sys em o (a) TMF and (b)
Rena a sequences. Whi e indica es epe i ion, ligh g ey s a iona y,
da k g ey ideo and black a e unclea a eas.
(a) Rena a sequence (b) TMF sequence
Fig.
15.
Simula ion
esul s
using
c isp
de ini ions
o
LARGE
and
SMALL.
P. B ox e al.: Local Pic u e- epe i ion Mode De ec o o Video De-in e lacing 1655
Pai ed iden ical ields case B, las wo ields show objec
a same posi ion, i s ield a di e en posi ion
- Unclea , i.e. he local da a is ambiguous we designed
a
p
ic u e epe i ion de ec o , sui able o all possible pa e ns
wi hou limi a ion o he common pa e ns, like 2:2 and 3:2
pull-down.
Fo ins ance, a long a bi a y cadence such as 3:2:2:3 can
be de ec ed since he ules an eceden s only compu e
absolu e di e ences among h ee consecu i e ields. The
de ec o combines uzzy logic ules, o deal wi h
unce ain cases, and uses spa io- empo al p edic ion o ge
a obus decision signal e en in unclea a eas. Ou
e alua ion shows a e y a ou able pe o mance and an
a ac i e low compu a ional complexi y.
REFERENCES
[1] G. de Haan and E. B. Belle s, De-in e lacing - an o e iew. P oc. o he
IEEE, ol. 86, Issue 9, pp. 1839-1857, Sep. 1998.
[2] T. C. Lyon and J. J. Campbell, Mo ion sequence pa e n de ec o 1,
1991. Uni ed S a es Pa en O ice US 4,982,290.
[3] C. Co ea and R. Schwee , Film mode de ec ion p ocedu e and de ice.
Assignee: Deu sche Thomson-B and GMBH, Villingen-Schwennigen
(DE), July 1, 1998. Eu opean Pa en O ice 0567072B1.
[4] H. Y. W. Lucas, P og essi e/in e lace and edundan ield de ec ion
o encode . Applican : STMICRO-Elec onics Asia Paci ic PTE LTD,
Singapo e, June, 2000. Wo ld In ellec ual P ope y O ganiza ion,
In e na ional Publica ion Numbe : WO 00/33579.
[5] P. Swan, Sys em and me hod o econs uc ing nonin e laced
cap u ed con en o display on a p og essi e sc een. Assignee: ATE
Technologies, Inc. Tho nhill, anada, Ap . 25, 2000. Uni ed S a es
Pa en O ice US 6,055,018.
[6] G. de Haan, H. Huijgen, P .Biezen, and O. Ojo, Me hod and appa a us o
disc imina ing be ween mo ie ilm and non-mo ie ilm and gene a ing
a
pic u e signal p ocessing mode con ol signal. Assignee: U.S.
Philips Co po a ion, New Yo k, USA, No . 15, 1994. Uni ed S a es
Pa en O ice US 5,365,280.
[7] A. Dommisse, Film de ec ion o ad anced scan a e con e e s, M.
Sc.Thesis, TUE, Eindho en, Aug. 2002.
[8] C.-C. Ku and R.-K. Liang, Robus Laye ed Film-Mode Sou ce 3:2
Pulldown De ec ion/Co ec ion. IEEE T ans. on Consume
Elec onics, ol.50, no.4, pp. 1190-1193, No . 2004.
[9] G. de Haan and R. B. Wi eb ood, Recognizing ilm and ideo
objec occu ing in pa allel in single ele ision signals ields. Assignee:
Konin- klijke Philips Elec onics N. V., Eindho en, NL, Aug. 30,
2005. Uni ed S a es Pa en O ice US 6,937,655.
[10] J. L. Cas o, Fuzzy logic con olle s a e uni e sal app oxima o s. IEEE
T ans. on ys ems, Man and Cybe ne ics, ol.25, no.4, pp. 629-635, Ap .
1995.
[11] L. He and H. Zhang, Mo ion objec ideo on ilm de ec ion and adap i e
de-in e lace me hod based on uzzy logic. Assignee: nDSP Co po a ion,
Campbell, CA, Sep. 28, 2004. Uni ed S a es Pa en O ice US 6,799,168.
[12] G. de Haan, and P. W. A. C. Biezen, Sub-pixel mo ion es ima ion wi h 3-
D ecu si e sea ch block-ma ching.Signal P ocessing: Image Commu-
nica ion 6, pp.229-239, June 1994.
[13] E. Cox, The Fuzzy Sys ems Handbook: A P ac i ione ’s Guide o Build-
ing, Using, and Main aining Fuzzy Sys ems. AP P o essional
Edi o ial (2nd edi ion).
[14] F.J. Mo eno-Velo, I. Ba u one, S. Sánchez-Solano, and A. Ba iga,
Rapid design o complex uzzy sys ems wi h X uzzy.P oc. IEEE In .
Con . on Fuzzy Sys ems, pp.342-347, S . Louis, USA, May 2003.
[15] F.J. Mo eno-Velo, I. Ba u one, R. Senhadji, and S. Sánchez-Solano,
Tuning complex uzzy sys ems by supe ised lea ning
algo i hms.P oc. IEEE In . Con . on Fuzzy Sys ems, pp.226-231, S .
Louis, USA, May 2003.
[16] M. Wes on, In e pola ing lines o ideo signals. Assignee: B i ish
B oadcas ing Co po a ion, London, GB2, Dec. 6, 1988. Uni e
d
S a es Pa en O ice US 4,789,893.
Piedad B ox was bo n in 1979 in Có doba, Spain.
She ecei ed he deg ee in Elec onic Physics o
m
he Uni e si y o Có doba in 2002, and he
Ad anced S udies Deg ee in Mic oelec onics om
he Uni e si y o Se ille in 2004.
Since 2002, she has been wi h he
Mic oelec onics Ins i u e o Se ille (IMSE),
which belongs o he Spanish Resea ch Council
(CSIC) and he Uni e si y o Se ille. She ecei e
d
a ellowship o In oduc ion o Resea ch o
Unde g adua e S uden s du ing 2002.
Cu en ly, she is a Pos g adua e Re- sea ch Fellow and has a Pos g adua e
Fellowship unde F.P.U. p og am om he Spanish go e nmen . He
esea ch a eas a e uzzy image p ocessing algo i hms and hei ha dwa e
implemen a ion (s a ing om high-le el desc ip ions o p o o yping in
FPGAs). She belongs o he ’Digi al and Mixed Signal In eg a ed Ci cui
Design G oup’ o IMSE.
Leon Woes enbe g ecei ed he M.Sc. deg ee
in Elec ical Enginee ing om he Uni e si y o
Tech- nology Eindho en in 2006. His inal p ojec
in ol ed con en -adap i e ideo de-in e lacing unde
supe ision o Ge a d de Haan.
Since 2000 he has been employed, and pe o med
his M.Sc. inal p ojec a Axon Digi al Design, a
manu ac u e o audio and ideo p ocessing sys ems
o ele ision b oadcas and p oduc ion. As an
embedded so wa e enginee he de eloped and e-
used open-sou ce so wa e in a comme cial
en i onmen .
Since 1992 he is in ol ed in open-sou ce so wa e p ojec s as de elope
and p ojec manage . Cu en ly, since 2004, as a senio sys ems designe he
designs signal p ocessing and con ol sys ems and so wa e and acqui es and
p
epa es newly a ailable echnology o use in u u e designs.
His p o essional in e es s in ol e ope a ing sys ems, ha dwa e/so wa e
codesign, ideo and g aphics, he open-sou ce de elopmen model and he use
o open-sou ce so wa e in comme cial en i onmen s.
Ge a d de Haan (Senio Membe , IEEE) ecei ed
B.Sc., M.Sc., and Ph.D. deg ees om Del Uni-
e si y o Technology in 1977, 1979 an
d
1992 espec i ely. He joined Philips Resea ch in
1979. He has led esea ch p ojec s in he a ea o
ideo p ocessing, and pa icipa ed in Eu opean
p ojec s. He has coached s uden s om a ious
uni e si ies, and eaches since 1988 o he
Philips Cen e o Technical T aining. Cu en ly, he
is a Resea ch Fellow in he Video P ocessing &
Visual Pe cep ion g oup o Philips Resea ch
Eindho en, and a pa - ime ull P o esso a he Eind-
ho en Uni e si y o Technology eaching ”Video P ocessing o Mul imedia
Sys ems”.
He has a pa icula in e es in algo i hms o mo ion es ima ion, ideo a e
con e sion, and image enhancemen . His wo k in hese a eas has
esul ed in se e al books, mo e han 120 pape s, abou 100 pa en s, and
a ious comme cially a ailable ICs. He was he i s place winne in he
1995 and 2002 ICCE Ou s anding Pape Awa ds p og am, he second place
winne in 1997, 1998 and 2003, and he 1998 ecipien o he Gilles Hols
Awa d. In 2002, he ecei ed he Ches e Sall Awa d om he IEEE Consume
Elec onics Socie y.
The Philips ‘Na u al Mo ion Tele ision’ concep , based on his PhD-s udy
ecei ed he Eu opean Video Inno a ion Awa d o he Yea 95 om
he Eu opean Imaging and Sound Associa ion. In 2001, he successo o
his concep ”Digi al Na u al Mo ion Tele ision” ecei ed a ”Business
Inno a ion Awa d” om he Wall S ee Jou nal Eu ope. Ge ad de Haan
is a Senio Membe o he IEEE.