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Image Quality Evaluation of 3D CG Images with 8 Viewpoints Lenticular Lens Method

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These days, we have a good chance to watch 3D movies at home or movie theater. However, there is various form of stereogram or display style, and these haven’t ever unified officially yet. In addition, there is no standardization for evaluating objective video quality of 3D movies. For constructing a better viewing environment, a suitable viewing condition about the presence, depth, and nature of the stereoscopic vision should be known. In this paper, in order to find out the viewing conditions better, we experimented image quality evaluation of 3D CG images with 8 viewpoints lenticular lens method, and considered in detail.
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The Fifth International Workshop on Image Media Quality and its Applications, IMQA2011
October 4-5, 2011, Kyoto, Japan
Image Quality Evaluation of 3D CG Images with 8 Viewpoints Lenticular Lens Method
Norifumi Kawabata, Keiji Shibata, Yasuhiro Inazumi and Yuukou Horita
Graduate School of Science and Engineering, University of Toyama
Gofuku 3190, Toyama-city, Toyama, 930-8555 Japan
E-mail: horita@eng.u-toyama.ac.jp
ABSTRACT
These days, we have a good chance to watch 3D movies
at home or movie theater. However, there is various
form of stereogram or display style, and these havent
ever unified officially yet. In addition, there is no
standardization for evaluating objective video quality of
3D movies. For constructing a better viewing
environment, a suitable viewing condition about the
presence, depth, and nature of the stereoscopic vision
should be known. In this paper, in order to find out the
viewing conditions better, we experimented image
quality evaluation of 3D CG images with 8 viewpoints
lenticular lens method, and considered in detail.
Index Terms 3D Images, Computer Graphics, 8
viewpoints lenticular lens method, No Compression,
Image Quality Assessment, Subjective Evaluation,
Absolute Category Rating Method
1. INTRODUCTION
These days, we are in the news that are wide-band
optical communication, super reality system as if we
stayed at home or a movie theater [1], and digital
signage that send much information by using electrical
apparatus, such as display [2].
Now, its not decided for 3D method or display
method on the screen because of many kinds of
difference. When we are watching video, it isnt also
decided definite indicator at official how often
permitting for depth or crosstalk. Until now, they are
studied for many experiments and models of image
quality evaluation, using 3D nature video. However,
there are few research models that require for evaluation
category in 3D CG video [3].
In this paper, we experimented evaluation of 3D
CG images with 8 viewpoints lenticular lens method,
and considered relation of each evaluation category to
hold to what extent presence, depth, nature, and
crosstalk that viewers can permit.
Figure 1 Using image (Wonder World)
Table 1 Main specification of this experiment of
image quality evaluation [3]
Display
Alioscopy 42V model
Image Size
1920x1080(FULL HD)
(pixel)
Image extension
Windows Bitmap
Length of Contents
15 seconds per one pattern
Visual distance
3H
illumination
None
View method
Naked eyes
3D method
Lenticular lens
Evaluation method
ACR method (that is
Single Stimulus
Method[4])
Evaluation people
15 people of University
Students and Graduate
Students
2. PREPARATION OF EXPERIMENT
2.1. Using images and specification
We used images of figure 1 in this experiment that
NICT (National Institute of Information and
Communications Technology) is distributing without
charge [5]. We worked rendering by using Autodesk
Maya 2011 created by Autodesk Corporation, and
changed parameter of 3D to 2D. This parameter was
changing 8 viewpoints of camera what is called
alioscopy camera (on 3D CG in display used in this
experiment). Yet, if value of parameter is , ranges are 7
types of 0, 0.15, 0.25, 0.35, 0.50, 0.75, and 1.00. Also,
specification of this evaluation experiment is shown
table 1. 8 viewpoints lenticular lens method is used to
display 3D image. This method is prepared 8 viewpoints
sliding constantly (for example, if equals 0.00, space
between two cameras is , and if equals 1.00, space
between two cameras is ) of two images, what is
called left or right eye image included binocular
parallax on the back of lenticular lens, and arranged
mutually by vertical 1 line on a display, and is used lens
which forms half cylinder.
2.2. Evaluation Method
Presenting order of image was repeating of image (15s)
and vote (15s). For evaluation method, we used single
stimulus method prescribed by ITU-T
RECOMMENDATION P.910 to experiment.
Furthermore, evaluation standard were evaluated 5 steps,
those are very good, good, fair, bad, and very
bad, and calculated MOS (Mean Opinion Score). Also,
evaluation items were 5 types of Is it presence?, Is it
depth?, Is it nature?, Isnt it seeing crosstalk?, and
How is it general evaluation? In case of Presense,
we evaluates as if we stay in display. In case of Depth,
we evaluates if we can see 3D about images. In case of
Nature, we evaluates if we see natural condition about
substances in an image. In case of Not crosstalk, we
evaluate if we dont see double images. We added up
results that in advance, we made evaluation members
write questionnaire of Which evaluation items do you
attach importance watching 3D video by rank?
Weighting parameter is following equation (1). Still,
weighting of questionnaires rank are that first rank is
10 points, forth rank is 1 point. Second rank and third
rank are that difference of weighting parameter become
equal each rank. So, second rank is 7 points, and third
rank is 4 points. From this, we calculated sum of 15
members, and decided weighting of average in sum of 4
evaluation items, that is 1.00. From weighting
percentage of average in sum of 4 evaluation items, I
weighted other ranks.

 󰇝󰇞
󰇛󰇜
By the way, is the number of people of rank in each
evaluation items, number of under bar is rank. Also,
 is average in sum of 4 evaluation items. shows
about each evaluation items. 4 evaluation items, those
are Presence, Depth, Nature, and Not Crosstalk
correspond and . The following equation (2)
calculated general evaluation considered weighting
gained from equation (1).

󰇡  

  󰇢
󰇛󰇜
Table 2 Questionnaire result voted before
experiment by rank
Evaluation
items
1st
2nd
3rd
4th
Sum
Weight
coefficient
(10)
(7)
(4)
(1)
Presence
5
6
2
2
102
1.24
Nature
6
3
3
3
96
1.16
Depth
3
3
5
4
75
0.91
Crosstalk
1
3
5
6
57
0.69
Average
82.5
1.00
(normal)
Figure 2  graph
Figure 3 Weighting coefficient graph
By the way,  were parameters of weighting,
was  of evaluation items, and were each
evaluation people.
3. EXPERIMENTAL RESULT
3.1 Results of questionnaire of importance for
evaluation category
In advance, results of questionnaire conducted before
experiment, were shown from top, Presence, Nature,
Depth, and Crosstalk. Detailed results showed table
2. nature is selected the best of all items by evaluation
people, but as a result, Presence that second rank is
the best of all, indeed, there are 11 people, was the best
score. On the contrary to this, Depth and Crosstalk
occupied less than third rank in the majority of all.
Particularly, from table 2, if more than second rank is
important, it found that crosstalk isnt importance for
people of 70% of all.
3.2 Results of evaluation experiment
In this experiment, if  was better than 2.50, we
thought that its value could permit. It showed figure 2
that value of  gained in experiment by evaluation
items. Error bar, extended top and bottom from plot
points in figure 2 and 3, showed 95% confidential
interval. In Crosstalk, as value of parameter came to
increase, it came to decrease. Presence shifted within
permitting between 2.5 and 3.0. In Depth,  got
only lower when value of parameter was 0.00, and after
this, it got without change. In Nature, as value of
parameter came to higher,  came to lower. In
General evaluation, because  was less than 2.5
when value of parameter was 0.75 and 1.00, it cannot
permit.
Also, it showed figure 3 that result added up
weighting coefficient showed table 2 in figure 2 of
experimental result. From figure 3, if value of parameter
was more than 0.75, it found that value of general
evaluation converged nearby 2.5. And, from value of
general evaluation, these can classify three types of
good (parameter: 0.00, 0.15, : nearby 4.0), fair
(parameter: 0.25, 0.35, 0.50, : between 3.0 and 3.5),
and bad (parameter: 0.75, 1.00, : nearby 2.5).
4. CONSIDERATION
Seeing From experimental result of figure 2,  of
Crosstalk decreased more than value of parameter was
0.50. However, it found that other evaluation items
converged in fixed line. We thought that judgment of
other evaluation items was affected because of
increasing crosstalk as parameter came to increase. Also,
although value of parameter approached 1.00,  of
Presence and Depth didnt increase. We thought
that Presence and Depth come to offend because of
decreasing  of Crosstalk and Nature. And,
when value of parameter of figure 2 was 0.25 and 0.35,
its different to  among evaluation items, but
seeing value of general evaluation of figure 3, when
value of parameter are 0.25 and 0.35, it found that 
was without change. We thought that when  was
0.25 in value of parameter, difference of max and
minimum is small, when  was 0.35 in value of
parameter, difference of max and minimum is large,
however, General evaluation in  was without
change.
5. CONCLUSION
In this paper, we experimented of image quality
evaluation of 3D CG images with 8 viewpoints
lenticular lens method, and considered. From
experimental result, when parameter of each interval of
8 cameras is changed, we could grasp the best  was
between 0.15 and 0.25 in value of parameter. Also, we
could catch a feature of graph better by calculating
generate evaluation, and could judge certainly. In this
case, there were two types of method, generate
evaluation voting in evaluation experiment and
experiment result, using weighting coefficient decided
by questionnaire method before experiment. Either can
predict drawing the same lines from graph.
However, in this case, we couldnt verify about
crosstalk and double image. Also, we couldnt verify
about different images to videos in detail. Included these
points, from now on, we experiment for evaluation of
videos, besides, we advance research.
6. REFERENCES
[1] H. Harajima ITE Ultra Presece System, 2010.
[2] K. Muramoto, ITE bulletin Vol.65, No.2, pp.119-120,
Tendency of Digital Signage, 2011.
[3] N. Kawabata, K.Shibata, Y.Inazumi, Y.Horita,
Technical report of IEICE, Video Quality Evaluation of 3D
CG Movies with Active Shutter Glasses, 2011.
[4]
http://www.ntt.co.jp/qos/qoe/technology/visual/01_5_1.
html, August, 2011.
[5] 3D CG Contents of National Institute of Information
and Communications Technology,
http://3d-contents.nict.go.jp/
... On the other hand, About the Camera's interval and the number of Viewpoints in the 3D Image and Video Contents (2) (C) NORIFUMI KAWABATA, YUUKOU HORITA There are also problems associated with discomfiture in using multi-view glassless 3D system. ...
Presentation
Recently, we are able to watch 3D videos or movies increasingly without glasses. However, they are various stereological and evaluation methods for multi-view 3D with no glasses for image quality, and their display methods are not unified. In this paper, we showed 3D CG images with 8 viewpoints lenticular lens method by ACR and DSIS methods, when we analyzed the results statistically with subjective evaluation. The experiment examined whether or not assessor were able to comfortable view the images by degree of camera’s interval and viewpoints, and whether or not they perceive or annoy degree of coded degradation at certain viewpoints.
Article
Recently, the use of 3D video systems without glasses has increased, and therefore 3D image quality and presence evaluation is important. There are various stereo-logical image quality evaluation methods for multi-view 3D systems without glasses. However, there is no uniform method for evaluating 3D video systems. In this study, we focus on camera interval and JPEG coding degradation with a multi-view 3D system. Previously, many studies have examined camera interval or JPEG coding degradation with 3D glasses or the binocular method. In such systems, viewers perceive stereoscopic and depth effects. Moreover, they can see from different angles, increasing viewpoints with multi-view 3D systems. However, viewers feel discomfort when changing their viewpoint. Hence, we consider, in particular, the accommodation of the camera interval and JPEG coding degradation while changing viewpoints. We have performed subjective evaluations using the absolute category rating system to assess the effects of changing the camera interval of 3D CG images or video content using an 8 viewpoint lenticular lens method. We measure assessors' ability to identify the degree of the camera interval. We analyze the results of our subjective evaluations statistically and discuss the results. Using the optimal camera interval, we perform a subjective quality evaluation employing the double stimulus impairment scale to determine assessors' ability to identify JPEG coding degradation by degree. The experimental results of this subjective evaluation are also statistically analyzed.
Ultra Presece System
  • H Harajima
H. Harajima ITE "Ultra Presece System," 2010.
Tendency of Digital Signage
  • K Muramoto
K. Muramoto, ITE bulletin Vol.65, No.2, pp.119-120, "Tendency of Digital Signage," 2011.
Video Quality Evaluation of 3D CG Movies with Active Shutter Glasses
  • N Kawabata
  • K Shibata
  • Y Inazumi
  • Y Horita
N. Kawabata, K.Shibata, Y.Inazumi, Y.Horita, Technical report of IEICE, "Video Quality Evaluation of 3D CG Movies with Active Shutter Glasses," 2011.