ART Iwan Setyawan Perceptual quality Full text
PERCF.PTV4L QH4L TTY OF OEOMETRJC4.H Y I>TSTORTEJ> IMAGES PART II: HUMAN
PERCEPTION OF THE QUAUrY OF (,'EOMETRil ALLY DTSTORTEl> IMA(IES
/wan Setyawan
PERCEPTUAL QUALITY OF GEOMETRICALLY DI.\'TORTEI)
EYAGES
PART IT:
HUMAN PERCEPTION OF THE QUALITY OF GEOMETRICALLY
DISTORTEIJ TMA GES
120JU4l''
lwan Setyawan
(iwan.setyawan(Q)ieee.org)
Abstract
This paper presents a
ｳｴｵ､ｾ＠
of how human perceire the
ｱｵ｡ｬｩｴｾ＠
of geometrically
distot1ed images by presenting the design and analysis of a sulJ.jectiYe-test experiment
The results of this experiment ts then used to eYaluate the performance HPQM
(Homogeneity-based Perceptual Quality Measurement) method presented in the first
paper of this series.
Keyworfls:Geometric
distortion.
human
Yisual
system.
perceptual
ｱｵ｡ｬｩｴＮｾ＠
measurenwnt of images. paired-comparison experiment
1. Introduction
Research on human perception of image quality has been widely performed.
Aspects of images considered in such research are. for example. color. granularity or
sharpness. Another example is to test specific artifacts of a compression algorithm
(e.g .. the blocking artifact of JPEG compression) or watermarking system (e.g.. the
random noise artifact of noise-based watermark-ing systems). Some examples of the
image quality assessment for these disto11ions can be found in [I]. As a result "e
already haYe a good understanding of how these aspects influence human perception
of quality and we are able to quantify these perceptual aspects in cases "·here the
distot1ion is near the Yisibility threshold. We can use the result. tor example. to build a
system to objectiYely measure image quality based on these aspects \Yhich
corresponds quite well to subJectiYe
ｱｵ｡ｬｩｴｾ＠
perception. We can also use the result of
this research to improYe the performance of mrious applications dealing "·ith images
by designing the systems such that most changes or distortions to the images occur in
the areas that hm e small perceptlJal impact for human obsen ers. The examples
13
Techne Jurnal llnuah Elektrotekmka Vol. . \\llich depends on the Yalue of the
is lower or equal to a certain Yalue
significance leYel a. In other \Yords. ne want to find K· such that the probabilit) P[R 2::
K] is lo\Yer or equal to the chosen sigtlificallce le\'el a. We declare the
ｯｾｪ･｣ｴｳ＠
''ithin
each group to be not significalltly different. while those from different groups are
declared to be significantly different. By ad.iusting the Yalue of a. we call ad.iust the
size of the groups. Th.is in turn controls the probability of false positiYes (declaring 2
objects to be significantly different when they are not) and false negatiYes. The larger
the groups. the higher the probability of false negatiYes On the otl1er hand. the
smaller the groups. the higher the probabilitY of false positiYes.
21
l
Tcchne Jurnalllmiah Elektrolekmka Vol tJ No I Apnl 20!0 Hal 11- :;lJ
The distribution of the rangeR is asymptotically the same as the distribution of
Yariance-normaJized range. W,, of a set of normal random Yariables with yariance = 1
and t samples
l4l
Therefore. "e can use the folio" ing relation to approXimate f'LR :::_
Rl
2R
Pl ｵｾ＠
'
l
2
. ·, . ----J;i
J
( 14)
In Equation ( 14 ). W,., is the yaJue of the upper percentage point of W, at significance
point u. The 'alues of Wr.u are tabulated in statistics books for example the one
prO\ ided in [g J.
The significance test for the differences bet\\ een scores proceeds as folio\\ s.
1. Choose the desired significance leYel u...
2. Compute the critical Yalue R using the following relation
1
r: tl
R.= -W,,-vnt+-l
'
2 .
4
1
3
(15)
Am difference bet\Yeen 2 scores that is 10\Yer than R is declared to be
insignificant. OthenYise. the score difference is declared significant.
3. Test procedure
User test mechanism to measure the impact of geometric distortion on the
human perception of image quality is not ''idely discussed in the literature. Therefore.
"e ha' e proposed a ne" user test system that is specifica11y designed for tllis purpose.
In this section\\ e shall describe in more detail the design of a suitable test set and user
interface for such user test system.
3.1. Test set
J
We used the same test sets as the ones "e used in the preYious paper in tllis
series. The t" o images used as a basis to build our test set. i e. the Bird and Kremlin
images (shom1 in Figure 2). are g-bit grayscale images with 512 ' 512 pixels
resolution. The images are chosen prunarily due to theu content. The first image.
22
PERl'FPJ'C:4L ｑｬｾＴｌＯｔｙ＠
OF UEOMETR/('ALLY J)JSTORTED IMAOES PART 11: HUMAN
PERCEPTION OF THE QUALITY OF GEOMETRICALLY D/.\'TORTE/) IMA(JES
bmn .\'e ryawan
Bird. does not haYe many stmctures such as straight lines. Furthermore. not eYery test
subject is Yery familiar ''"ith the shape of a bird (in particular the species of bird
depicted in the image). So in this case. a subject should haYe little (if any) .. mental
picture·· of \\hat things should look like. On the other hand. the Kremlin picture has a
lot of structures and eYen though a test subject may not be familiar "ith the Kremlin.
h"' dw dwnld
hr the Bird image
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A1
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j
PERCEPTION OF THE QUAUrY OF (,'EOMETRil ALLY DTSTORTEl> IMA(IES
/wan Setyawan
PERCEPTUAL QUALITY OF GEOMETRICALLY DI.\'TORTEI)
EYAGES
PART IT:
HUMAN PERCEPTION OF THE QUALITY OF GEOMETRICALLY
DISTORTEIJ TMA GES
120JU4l''
lwan Setyawan
(iwan.setyawan(Q)ieee.org)
Abstract
This paper presents a
ｳｴｵ､ｾ＠
of how human perceire the
ｱｵ｡ｬｩｴｾ＠
of geometrically
distot1ed images by presenting the design and analysis of a sulJ.jectiYe-test experiment
The results of this experiment ts then used to eYaluate the performance HPQM
(Homogeneity-based Perceptual Quality Measurement) method presented in the first
paper of this series.
Keyworfls:Geometric
distortion.
human
Yisual
system.
perceptual
ｱｵ｡ｬｩｴＮｾ＠
measurenwnt of images. paired-comparison experiment
1. Introduction
Research on human perception of image quality has been widely performed.
Aspects of images considered in such research are. for example. color. granularity or
sharpness. Another example is to test specific artifacts of a compression algorithm
(e.g .. the blocking artifact of JPEG compression) or watermarking system (e.g.. the
random noise artifact of noise-based watermark-ing systems). Some examples of the
image quality assessment for these disto11ions can be found in [I]. As a result "e
already haYe a good understanding of how these aspects influence human perception
of quality and we are able to quantify these perceptual aspects in cases "·here the
distot1ion is near the Yisibility threshold. We can use the result. tor example. to build a
system to objectiYely measure image quality based on these aspects \Yhich
corresponds quite well to subJectiYe
ｱｵ｡ｬｩｴｾ＠
perception. We can also use the result of
this research to improYe the performance of mrious applications dealing "·ith images
by designing the systems such that most changes or distortions to the images occur in
the areas that hm e small perceptlJal impact for human obsen ers. The examples
13
Techne Jurnal llnuah Elektrotekmka Vol. . \\llich depends on the Yalue of the
is lower or equal to a certain Yalue
significance leYel a. In other \Yords. ne want to find K· such that the probabilit) P[R 2::
K] is lo\Yer or equal to the chosen sigtlificallce le\'el a. We declare the
ｯｾｪ･｣ｴｳ＠
''ithin
each group to be not significalltly different. while those from different groups are
declared to be significantly different. By ad.iusting the Yalue of a. we call ad.iust the
size of the groups. Th.is in turn controls the probability of false positiYes (declaring 2
objects to be significantly different when they are not) and false negatiYes. The larger
the groups. the higher the probability of false negatiYes On the otl1er hand. the
smaller the groups. the higher the probabilitY of false positiYes.
21
l
Tcchne Jurnalllmiah Elektrolekmka Vol tJ No I Apnl 20!0 Hal 11- :;lJ
The distribution of the rangeR is asymptotically the same as the distribution of
Yariance-normaJized range. W,, of a set of normal random Yariables with yariance = 1
and t samples
l4l
Therefore. "e can use the folio" ing relation to approXimate f'LR :::_
Rl
2R
Pl ｵｾ＠
'
l
2
. ·, . ----J;i
J
( 14)
In Equation ( 14 ). W,., is the yaJue of the upper percentage point of W, at significance
point u. The 'alues of Wr.u are tabulated in statistics books for example the one
prO\ ided in [g J.
The significance test for the differences bet\\ een scores proceeds as folio\\ s.
1. Choose the desired significance leYel u...
2. Compute the critical Yalue R using the following relation
1
r: tl
R.= -W,,-vnt+-l
'
2 .
4
1
3
(15)
Am difference bet\Yeen 2 scores that is 10\Yer than R is declared to be
insignificant. OthenYise. the score difference is declared significant.
3. Test procedure
User test mechanism to measure the impact of geometric distortion on the
human perception of image quality is not ''idely discussed in the literature. Therefore.
"e ha' e proposed a ne" user test system that is specifica11y designed for tllis purpose.
In this section\\ e shall describe in more detail the design of a suitable test set and user
interface for such user test system.
3.1. Test set
J
We used the same test sets as the ones "e used in the preYious paper in tllis
series. The t" o images used as a basis to build our test set. i e. the Bird and Kremlin
images (shom1 in Figure 2). are g-bit grayscale images with 512 ' 512 pixels
resolution. The images are chosen prunarily due to theu content. The first image.
22
PERl'FPJ'C:4L ｑｬｾＴｌＯｔｙ＠
OF UEOMETR/('ALLY J)JSTORTED IMAOES PART 11: HUMAN
PERCEPTION OF THE QUALITY OF GEOMETRICALLY D/.\'TORTE/) IMA(JES
bmn .\'e ryawan
Bird. does not haYe many stmctures such as straight lines. Furthermore. not eYery test
subject is Yery familiar ''"ith the shape of a bird (in particular the species of bird
depicted in the image). So in this case. a subject should haYe little (if any) .. mental
picture·· of \\hat things should look like. On the other hand. the Kremlin picture has a
lot of structures and eYen though a test subject may not be familiar "ith the Kremlin.
h"' dw dwnld
hr the Bird image
AJ A1
A1
/
Az 37
7
:-:·
A_; A6 A,. As A9 A1o An AI1 Au Au AH An) A17
ｾ＠ .
0
0
4
0
ｾＮＱＨＩ＠
ｾ＠
ｾＧ＠
A-I 44 44 41
A·:> 44 44 43 41
24
2
10
9
1
0
0
30
36
lO
Il
12
5
I
l
41
4::!
""'t;
tR
T7
q
'
i)
ＬｾＮＺ［＠
44 44 42 43
43
43
37
3'J
43
31
15
l
557
44
11
21
19
0
33
28
29 24
!
-C
_.3
j