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Visual Communication-Based Virtual Reality Design of Imaging Information Collection and Display System

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Imaging image acquisition and display has always been an important application field of visual communication art. A good imaging acquisition and display system can give the audience an excellent visual experience. However, conventional imaging image acquisition equipment is too bulky and expensive. At the same time, the existing imaging acquisition technology is generally only suitable for two-dimensional plane design. It is difficult to construct the three-dimensional and realistic sense of actual objects, which makes the development of the entire imaging system slow. Existing imaging acquisition technologies have difficulty keeping pace with the technological age. In this paper, according to the actual needs, a three-dimensional imaging technology was proposed to improve the efficiency of imaging image acquisition. And the principle of imaging information display was used to enhance the visual communication effect. Then, at the algorithm level, virtual reality technology was combined with convolutional neural network-related algorithms to improve the overall accuracy of the algorithm and control the error well. The experimental results showed that the optimized VR technology leads the whole stage in terms of mean square error. Among them, after testing 400 samples in the two experiments, the error performance is controlled below 0.32, and the best error control performance is 0.07; the unoptimized virtual reality technology error control is not ideal. The minimum error failed to break below 0.2, and the optimized algorithm had a high accuracy of 99.3%, which greatly improved the feasibility of the imaging information acquisition and display system.
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Research Article
Visual Communication-Based Virtual Reality Design of Imaging
Information Collection and Display System
Yichen Qi ,
1,2
Tong Sun,
3
and Yan Li
4
1
Visual Communication Design, PhD Program in Design, Faculty of Decorative Arts, Silpakorn University,
Bangkok 10200, Thailand
2
Shandong Youth University of Political Science, Jinan 250103, Shandong, China
3
Visual Communication Design, Shandong Youth University of Political Science, Jinan 250103, Shandong, China
4
Big Data and Articial Intelligence, Weifang Vocational College, Weifang 262737, Shandong, China
Correspondence should be addressed to Yichen Qi; 160108@sdyu.edu.cn
Received 26 July 2022; Revised 24 August 2022; Accepted 1 September 2022; Published 25 September 2022
Academic Editor: Jun Ye
Copyright © 2022 Yichen Qi et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Imaging image acquisition and display has always been an important application eld of visual communication art. A good
imaging acquisition and display system can give the audience an excellent visual experience. However, conventional imaging
image acquisition equipment is too bulky and expensive. At the same time, the existing imaging acquisition technology is
generally only suitable for two-dimensional plane design. It is dicult to construct the three-dimensional and realistic sense of
actual objects, which makes the development of the entire imaging system slow. Existing imaging acquisition technologies have
diculty keeping pace with the technological age. In this paper, according to the actual needs, a three-dimensional imaging
technology was proposed to improve the eciency of imaging image acquisition. And the principle of imaging information
display was used to enhance the visual communication eect. Then, at the algorithm level, virtual reality technology was
combined with convolutional neural network-related algorithms to improve the overall accuracy of the algorithm and control
the error well. The experimental results showed that the optimized VR technology leads the whole stage in terms of mean
square error. Among them, after testing 400 samples in the two experiments, the error performance is controlled below 0.32,
and the best error control performance is 0.07; the unoptimized virtual reality technology error control is not ideal. The
minimum error failed to break below 0.2, and the optimized algorithm had a high accuracy of 99.3%, which greatly improved
the feasibility of the imaging information acquisition and display system.
1. Introduction
Visual communication is an aesthetic design that is pre-
sented to the audience after beautifying and modifying the
original objects through intermediate media such as visual
media. A good visual communication design can reect the
graphic depiction with the characteristics of the times and
stunning visual eects. In the entire complex visual commu-
nication design process, the quality of imaging image acqui-
sition plays an important role in the visual eect presented
by the nal visual communication design, because the acqui-
sition of imaging images has a great impact on the material
preparation of visual communication design,and the image
presented in the nal visual communication design is also
based on the display results of imaging image acquisition.
Therefore, how to design an excellent imaging information
acquisition and display system is the key to produce the best
visual communication eect. Although the traditional image
acquisition equipment and technology are relatively com-
plete in function, they have been gradually unable to adapt
to the high demand in this eld in the Internet era due to
the disadvantages of high price and heavy equipment. How
to choose the imaging image acquisition equipment and
related implementation methods suitable for the Internet
era has become a problem that has attracted much attention
in this eld. In addition, the nal displayed image of the
entire imaging information acquisition and display system
is limited by the performance of traditional methods, and
Hindawi
Wireless Communications and Mobile Computing
Volume 2022, Article ID 1929596, 12 pages
https://doi.org/10.1155/2022/1929596
its imaging accuracy and resolution are not ideal. A good
simulation environment is lacking to better display the
imaging images. In order to solve the above problems, it is
necessary to optimize the original image acquisition device
and acquisition method and to improve the overall restora-
tion eect of the imaging image. At the same time, in order
to reect the scientic and professional nature of the con-
structed system, science and professionalism are important
factors in building a system, and this paper will adopt virtual
reality related technologies for system design and research.
In this paper, the virtual reality technology is improved,
and the convolutional neural network model related algorithm
is introduced to improve the performanceof the whole system.
The practical application is combined to provide scientic
support for the imaging information acquisition and display
system constructed in this paper to make the whole system
more ecient. The innovations of this paper are as follows:
(1) the three-dimensional imaging technology is used to real-
ize the three-dimensional design of the information acquisi-
tion process of the imaging image, and the operation of
imaging information acquisition is simplied. (2) The virtual
reality technology is optimized in combination with the con-
volutional neural network-related algorithms, so that the main
performances such as the accuracy and control error of the
optimized algorithm are signicantly improved.
2. Related Work
Imaging image acquisition and display has always been a hot
research topic in the eld of visual communication design,
and many scholars have done a lot of research to improve a
more ecient imaging system. Among them, scholar Ding
et al. analyzed the current situation and exposed problems of
existing imaging image acquisition equipment and technology.
A strategy to optimize the eld of view problem in the acquisi-
tion device was proposed. Then, by starting from the control
condition of minimum resolution, the quality of the whole
imaging image was optimized, and nally, the feasibility of this
strategy in real environment operation was discussed [1].
Gupta and Choi proposed a novel design strategy for imaging
information acquisition and display systems. The acquisition
of imaging images was simplied. At the same time, the e-
ciency of the acquisition process was guaranteed. Compressed
sensing techniques were then used to optimize image accuracy
after imaging image acquisition [2]. Lockwood and his team
described the zebrash imaging image acquisition process in
detail and discussed how to ensure the smooth progress of
the image acquisition process without aecting the survivability
of the target object in practical situations. Finally, the com-
monly used imaging techniques were combined and optimized
accordingly to solve the above problems [3]. Aiming at the
problems encountered in the process of image acquisition of
human body structure imaging, Koga et al. proposed an inverse
kinematics method to reconstruct human body posture and
shape. Finally, the eectiveness of the improved imaging image
acquisition was veried by the driving experiment of the car
[4]. Lee and his team reviewed and analyzed the existing real-
time imaging image information acquisition methods. Imaging
image evaluation modes were constructed to optimize the
acquisition process. Then, the improved imaging image acqui-
sition method was applied to the thermal imaging eld to better
acquire NIR images. Finally, the precision and accuracy level of
the method were veried by experiments [5]. The above studies
on imaging image acquisition and display provided a lot of
theoretical knowledge for the development of related systems.
The algorithm framework of image acquisition technology
was greatly enriched. However, the above research did not
adopt a scientic and reliable technology to construct the
system and lacked sucient real data support.
In view of the lack of scientic and reliable technology to
build the entire imaging image acquisition and display sys-
tem, the virtual reality technology can be used to solve the
problem. Virtual reality technology is a booming frontier
technology, which has also attracted many scholars to study
this technology. Among them, the scholar Maples-Keller
and his team briey explained the development process of
virtual reality technology. The related research of virtual real-
ity technology in the eld of psychotherapy was deeply
reviewed and discussed. The achievements of virtual reality
technology in this eld were listed. Finally, the future work
of virtual reality technology in psychotherapy was prospected
[6]. Hyun and Lee analyzed the diculties faced in re pre-
vention and control and research in the past combined with
practical problems. The feasibility of virtual reality technol-
ogy application in this eld was analyzed. Finally, through
relevant experiments, the reliability of the designed re
research model was proved [7]. Zhang et al. applied virtual
reality technology to the university asset management system
and analyzed in detail the role of the technology in optimiz-
ing the visualization capability of the system. Finally, the
designed system was simulated by a real case to verify the
overall performance [8]. Mai et al. combined somatosensory
equipment with virtual reality technology to explore the
impact of the implementation of this technology on the lives
of cerebrovascular patients. The experimental results demon-
strated the eectiveness of the method [9]. Ding et al.
expounded the current situation of physical education in col-
leges and universities in actual teaching. The reasons for the
unsatisfactory eect of the teaching process were summa-
rized. A specic plan to implement virtual reality technology
to construct a physical education system was proposed.
Finally, the actual eect of the system was tested through
an example [10]. The above-related researches on virtual
reality technology have well demonstrated the comprehen-
siveness and good development prospects of the technology,
which played an important role in expanding the application
eld of the technology. However, the experimental accuracy
of the above studies is not ideal, which is dicult to meet
the high-precision requirements of imaging images.
3. Construction Method of Visual
Communication Art Imaging Information
Collection and Display System
3.1. Construction of Imaging Information Acquisition and
Display System. Under the background of the rapid develop-
ment of Internet technology, it is dicult for traditional
2 Wireless Communications and Mobile Computing
image imaging information acquisition equipment to meet
the high standards and technical requirements of imaging
images. Therefore, this paper adopts the thriving virtual real-
ity technology to optimize the whole imaging process. At the
same time, when constructing an imaging information acqui-
sition and display system, the traditional method is based on
two-dimensional plane design, which makes the proportion
and visual eects of the acquired imaging images not close to
reality and the visual communication is too simple. Based on
this, this paper proposes a three-dimensional imaging technol-
ogy to improve the information acquisition process of imaging
images, so that viewers can perceive the acquired imaging
images more intuitively and clearly. This is an attempt to
embody realism. The information acquisition process under
the 3D imaging technology is shown in Figure 1.
It can be seen from Figure 1 that the information acqui-
sition structure under the three-dimensional imaging tech-
nology abandons the shortcomings of the traditional
method, which have low utilization of each component of
the target object and insucient acquisition angles. At the
same time, the imaging image acquisition device not only
collects visual information in the vertical direction of the
optical axis but also covers the central area of the optical axis
with an acquisition camera. In the specic operation process,
the acquisition device moves unidirectionally along the opti-
mal direction with a xed angle from the optical axis, which
makes the entire acquisition process simpler than the tradi-
tional method and improves the acquisition eciency [11].
After the acquisition of imaging image information is
completed, data processing and structural reconstruction of
the acquired 3D imaging information are also required,
which is an important step in expressing virtual reality of
3D actual objects in reality. There are two main application
methods for 3D reconstruction, namely, 3D reconstruction
based on RGB-D depth camera and depth estimation and
structure reconstruction based on deep learning. Since the
imaging image operation process proposed in this paper
mainly uses the technology in the eld of deep-level infor-
mation processing, this paper adopts the computer three-
dimensional reconstruction method for processing, and the
specic process is shown in Figure 2.
In the three-dimensional reconstruction process shown in
Figure 2, there are a total of nacquired element imaging
images. The imaging image captured by the acquisition device
with the farthest relative distance fromthe target objectis used
as the comparison image. Then, all the imaging images are
mapped from the unreal aperture to the 3D virtual space by
back-projection, and nally, the nal 3D reconstructed imag-
ing image is obtained by scaling, translation, and overlapping.
Finally, the reconstructed imaging images are visualized,
and the corresponding 3D stereo images are displayed in real
time. In the process of image display, it mainly performs
operations such as retouching, cropping, line optimization,
light and shadow eect processing, and zooming on the
image. The scaling operation is one of the most important
operation links. In this paper, the zoom function of the
imaging image display part combines the principle of image
zoom to optimize the overall visual eect of the image
display. The specic principle is shown in Figure 3.
Figure 3 shows that the optimization method for the
zoom operation in the imaging information display theory
proposed in this paper based on the principle of image zoom
is intuitive, simple, and eective. The reduced image based
on the original imaging image is only reduced as a whole,
and the proportion of three-dimensional objects in the entire
image will not be changed. At the same time, the higher pre-
cision of the imaging image can also be guaranteed, and the
operation principle of magnication is similar. The size is
changed, but the scale will not change. Although the preci-
sion is relatively small, the visual communication eect will
also be enhanced [12]. The two operations satisfy dierent
needs, respectively, but the nal display eect is obviously
enhanced compared with the traditional display method.
3.2. Virtual Reality Technology. Virtual reality technology is
an important research direction of emerging simulation
technology in recent years. It is a comprehensive and e-
cient practical technology that combines a variety of tech-
nologies. The specic structure is shown in Figure 4.
As can be seen from Figure 4, the virtual reality technol-
ogy is formed by the combination of seven technologies.
Among them, three-dimensional computer graphics tech-
nology and wide-angle stereoscopic display technology are
very suitable for the imaging image processing system of this
paper. The following is a specic introduction to the virtual
reality technology algorithm [13, 14]. In the design of 5G
network virtual reality technology, Formula (1) species
the direction setting of the action space. C1indicates that
there are closely associated conversion types in all objects
in the specied declaration. The weights of directed action
sequences reect the enormous diversity of data products
in the statement:
Ntn =12γmγt+C1
ðÞ
2φmt +C2
ðÞ
γ2
m+γ2
t+C1
ðÞ
φ2
m+φ2
t+C2
ðÞ
:ð1Þ
The impact of giperformance is described by introduc-
ing articial intelligence modeling techniques. The relevant
code elements are shown in the following formulas:
Kg
i,lh
ðÞ
=Kg
i
ðÞ
Klh
gi

,
Klh
gi

=
p
p=1
Klh
wp
!
Kwp
gi

,ð2Þ
kp=2p
p+1 +1
2+1
2p

c2c1
3
hi
2
+2c2c1
ðÞ
3:ð3Þ
In order to achieve the eect of c2c1evolution from
rule formulation to actual implementation of specic virtual
reality technology [15], the static analyzer method can be
used for specic analysis and processes:
Cha =ð
0
gFht
ðÞ
ðt
0
tm
ðÞ
gFam
ðÞ
:ð4Þ
3Wireless Communications and Mobile Computing
Camera optical axis
direction
Three-dimensional
object
……
……
Camera movement
direction
Camera initial
position
Figure 1: Imaging information acquisition structure under 3D imaging technology.
Reconstructed
image plane
Camera
optical axis
First element
image
Virtual
pinhole
n-th element
image
……
Figure 2: 3D reconstruction process of imaging information acquisition.
4 Wireless Communications and Mobile Computing
As can be seen from Formula (4), in this process, the t
msingle-program report generates a set of data attributes
used in the input process for its own behavior gFh. This col-
lection has been digested and processed accordingly. Finally,
a digital signal object Chasis formed:
ln Niv
Niv 1

=β+χln Niv 1, ð5Þ
ε
m=1
Xm ×k=ε
m=1 L+ZI/y
1Lθ

/C+Z+Zc
χ+βln Niv +K/C
ðÞ :ð6Þ
The technical information in Formula (6) is denoted by
Niv, the same set of audio data items β+χis denoted by ε,
and the series of digital signal objects is also denoted by Lθ.
Xm ×kis the claim granted by the input object k, which
conforms to the process Zinformation and data objects.
And according to the formula, the statement is expanded
Row original size
Reduced row size
Row original size
Enlarged row size
Column original size
Column original size
Reduced column size
Enlarged column size
w
origina
l
size
E
n
l
a
r
g
e
r
o
w
s
Figure 3: Principle of imaging information display.
Virtual
reality
technology
Real-time 3D
computer graphics
technology
Wide-angle
stereoscopic
display technology
Shape tracking
technology
Haptic/Force
Feedback
Technology
Stereo
technology
Network
transmission
technology
Voice input
and output
technology
V
i
r
t
u
a
l
r
eality
hl
Figure 4: Composition of virtual reality technology.
5Wireless Communications and Mobile Computing
even if the data item uis equal to the process ε
m=1Xm ×k
output data object.
3.3. Convolutional Neural Network Optimization Virtual
Reality Technology. In order to improve the accuracy and
error control of the entire imaging information acquisition
and display system, this paper introduces a convolutional
neural network-related algorithm to optimize virtual reality
technology [16, 17]. The specic derivation process of the
algorithm is as follows. When the imaged image is input,
the image data is input to the central area of the optical axis
node. Therefore, the output value in the connection point in
the input process is set to the same value as the input value.
At the same time, the base station, which is an intermediate
hub, plays an important role in the connection point in the
output process. Only one of all base stations is selected as
the active base station. And the transmission values of all
the connection points of the base station of the intermediate
hub will be sent to the base station and outputted by it.
The connection point in the input process is represented
as ni,i=f1, 2, ,yg, and there are yexperimental samples
in total. Equation Formula (7) represents the input represen-
tation of the connection point Gin the model:
Gh=
y
h=1
ηihni:ð7Þ
The imaging image information obtained from the base
station to the connection point is shown in Formula (8):
Sh=
y
h=1
1
1+ y
i=1ηhSh

11
hi
2

=
y
h=1
1
G1
h1

2
hi
:
ð8Þ
Among them, ηhis used to represent the power required
from the ground base station G1
hof the connection point
with image information to the center position hof the
connection point, and Shrepresents the element information
in the image.
The activation function also needs to be congured in
the whole process, as shown in Formula (9):
T=
y
h=1
1
1+ y
i=1ηhSh

11
hi
2

2:ð9Þ
In Formula (9), the cell in the output process contains S
connection points. The optimal governing formula [18] for
the image can be expressed as the mean of the squared error
of the error, as shown in Formula (10):
R=1
N

y
n=1
tt
½
2=1
N

y
n=1
Rh:ð10Þ
The introduction of convolutional neural networks into
virtual reality technology will correspondingly change the
σRframework. However, the relevant properties of the neu-
ral network can be modied to obtain the minimum value of
ϕ. The function of σdih is to adjust the decoupling. Equation
Formula (11) is the specic expression:
Rih =
ηih =ϕσR
σdih

,
ηh=ϕσR
σηih

:
8
>
>
>
<
>
>
>
:
ð11Þ
In addition, in the rate optimization of the imaging
information acquisition and display process, the total num-
ber of aiηhS2
hoptimization parameters between the neural
network and the access points in the outer layer of the factor
is shown by Formula (12):
ηih =aiηhS2
h1
y
i=1
ηihah
"#
εh:ð12Þ
Formula (13) represents the calculation method of the
total number of connection optimization parameters [19]:
ηh=
s
h=1
t2Sh1
s
h=1
ηhSh
"#
tt
½
2
:ð13Þ
Among them,
tand tbelong to the variables of η1, which
are used to specify the maximum and minimum values of
the variation range, respectively. Then, the optimized algo-
rithm framework can be put into practical application. By
selecting the s
h=1ηhShimprovement strategy of the neural
network that meets the conditions of the system, the purpose
of assigning the interactive weight of the algorithm frame-
work is achieved. This process can also eectively improve
the accuracy of the overall system. After combining the
convolutional neural network technology with the algo-
rithm, the system will get higher performance. The specic
strategies are as follows:
min R
ðÞ
=
s
h=1 ðη1,,ηy

:ð14Þ
In Formula (14), min ðRÞrepresents the relative devia-
tion value of the system, and ðη1,,ηyÞis the xed weight
after the strong joint numbering. It contains the connection
points that store data during the input process and the
weights of the endpoints that are aected. The connection
points located in the central area of the model and the asso-
ciated evaluation test model of the connection point module
during export are also included. mis used to count the sum
of the quantities of various parameters of the system [20].
The optimization algorithm is used to explore and solve
the problem of insucient storage capacity of the whole sys-
tem. Since the coverage area of the algorithm model is
approximately at the level of Ððη1,,ηyÞ, it will be of great
6 Wireless Communications and Mobile Computing
help to the whole system if the algorithm can be perfectly
integrated into the system. Due to the strong correlation
between the steps and methods when WRperforms the
optimization operation, Formula (15) can be used to express
the training method with an intensity of R<W:
ði
=
i=1
WR,R<W,
0, RW:
(ð15Þ
In Formula (15), Rrepresents the accumulated value of
all RUat this stage. In order to distinguish the parameter
Nsin the optimization process to achieve the eect of satis-
fying the integration eciency of Ð1ðWRÞ/W½0, 0:5,
and at the same time, the reduction of the integration e-
ciency caused by the decryption operation can be minimized
as much as possible, and this paper uses the following
formula for further derivation:
Ns=2
Ð1WRR <W
W

,Ð1WR
ðÞ
W0, 0:5
½
:ð16Þ
In order to transform into a dimensionless expression
through the correlation operation pair Ð1ðWRÞ/W½0:5,
1, this paper will use a max-minimization technique that
can eciently process information. The advantage is that it
can keep the original state of the target object unchanged.
At the same time, the occurrence of data duplication can be
eectively avoided. In this regard, the normalization formula
of the data in the input process proposed in this paper is
Ns=121
ðÐ1WRR <W
W

2
"#
,Ð1WR
ðÞ
W0:5, 1
½
:
ð17Þ
By reducing all the key information in the algorithm
model to a representation range of only ½0, 1, the method is
dened as normalization Ð1ðWRÞ/W½0:5, 1s, and its
solution is expressed as follows:
a=
y
i=1
aamin
amax amin
+Ð1WR
ðÞ
W0:5, 1
½
:ð18Þ
The function of the normalization process λis to convert
the information with specication errors in the target data set
into random values between ½0, 1. Formula (19) is an elabo-
ration of the specic conversion process:
a=
y
i=1
aamin
λ+amax amin:ð19Þ
Each site is composed of three layers: a connection layer
that represents the operation of the input process, a connec-
tion layer that stores hidden information, and a connection
layer that is used for convolution operations. The weights
of the three layers are dened as χ,μ, and β, respectively,
and the relational expression is shown in Formula (20):
χk
z=
N
i=1
ηizak
i+
N
z
ηzzpz:ð20Þ
If the transmission task is completed for each data, ηzz
pk1
zwork will continue to be performed in the receiver of
the entire system. Then, only by entering 111, the next nerve
cell can continue to operate as a new transmission informa-
tion system. This will also aect the parameter values of the
subsequent input process as follows:
βk1
z=
z
k1
ηzpk1
z

+ηzzpk1
z:ð21Þ
For all neurons existing in the input process, the orig-
inal information of the connection layer unit that stores
the hidden information and the output variable of the unit
existing in the output process at time step kare shown in
Formula (22):
μk1
z=
N
z=1
ηz0pk
z+ηzpk1
z

:ð22Þ
Virtual reality technology is accelerating its penetration
into production and life. In the production eld, it is mainly
used in the R&D and design of new products to reduce R&D
costs and shorten the R&D cycle. Therefore, it is necessary
to study the optimized virtual reality technology.
4. Application of Virtual Reality Technology to
Imaging Information Collection and
Display System
4.1. Application of Imaging Information Acquisition and
Display System. In order to verify the visual communication
eect of the proposed imaging information acquisition and
display system, this paper will take two methods of question-
naire survey and simulation experiment to comprehensively
investigate the feasibility and professionalism of the system.
This paper interviews four professionals in the eld of visual
communication technology design. The structure and com-
position of each part of the imaging information acquisition
and display system and the specic realization results are
shown. After that, a corresponding questionnaire survey is
conducted to explore the construction eect of the system
proposed in this paper from the perspective of professional
designers. The survey results are shown in Table 1.
In Table 1, each professional scored each section on a
scale of 0 to 5. It can be seen that the four professionals have
a good overall impression of the imaging information acqui-
sition and display system in this paper. The highest score is
the design of the system information collection part and the
system algorithm design, with an average score of 4.75
points. The lowest score was in the imaging information
7Wireless Communications and Mobile Computing
display section, with an average score of 4.25. Although the
score is acceptable, compared with other designs, there are
still some details in this part that need to be rened.
In addition, this paper also selects 50 experimental vol-
unteers who are quite interested in visual communication
design or take it as a hobby. The 50 people spend a month
familiarizing themselves with and using the system in their
daily lives. The purpose is to contrast with the views of pro-
fessionals. The feasibility and practicability of the imaging
information acquisition and display system designed in this
paper are investigated from the perspective of ordinary peo-
ple. One month later, the 50 people are given a satisfaction
survey. The questions in the questionnaire are all related to
the design of the system in this paper. Each person can eval-
uate the question. The scoring interval is 010 points. The
higher the score, the higher the satisfaction with the ques-
tion. The nal questionnaire results are shown in Table 2.
It can be seen from the results in Table 2 that the overall
satisfaction of the 50 volunteers in the study is good with the
imaging information acquisition and display system pro-
posed in this paper. The average satisfaction score of the 5
questions is above 8.7 points, of which the highest average
score is 9 points and the lowest is 8.76 points. And none of
the 50 volunteers scored below 6 on each of the 5 questions.
This shows that the overall performance of the system is
excellent in all angles.
In addition, this paper compares and analyzes the per-
formance of the original designed imaging information
acquisition and display system and the currently widely used
traditional system. The advantages and disadvantages of the
system in the simulation environment compared to the tra-
ditional system are explored. The simulation test is carried
out for the important imaging image stitching eect in the
whole imaging process. Imaging image stitching is the inter-
mediate stage of information acquisition and imaging dis-
play. The implementation eect of this stage can determine
the degree of absorption of the results of the previous stage
and the degree of completion of the nal presentation
results. In this paper, a group of data that has gone through
the imaging information acquisition stage is selected as the
experimental sample. The above two methods are used to
test the time-consuming condition of this sample 8 times,
respectively. The test results are shown in Table 3.
It can be seen from Table 3 that the two methods are not
ideal in the processing speed of the imaging image stitching
stage, and the time required to complete the entire stage is
more than 5800 ms. However, the information acquisition
and display system proposed in this paper is signicantly
better than the traditional system in a total of 8 test time
consumption. The time consumption of each test is reduced
by more than 28% in comparison, with an average reduction
rate of 30.8%.
In view of the relatively slow processing speed in the
image stage, this paper appropriately reduces the testing
range of the entire imaging image. Whether the time
required for stitching of imaged images can be reduced
under such optimized conditions is explored. Table 4 shows
the specic data enumeration.
From the data in Table 4, it can be seen that after the
range of the image to be tested is appropriately reduced,
the time consumption of the two systems in the imaging
image stitching stage is greatly shortened. The average con-
sumption time of the traditional system is reduced from
8534.45 ms to 1926.88 ms. The average consumption time
of the system proposed in this paper also has an optimiza-
tion dierence of 4606.42 ms, and in terms of the average
reduction rate, the time consumption of the system designed
in this paper is reduced by 32.1% compared with the tradi-
tional system after using the above improved method. This
also proves the time-consuming advantage of the imaging
data acquisition and display system designed in this paper.
In order to detect the nal imaging eect of the imaging
information acquisition and display system, this paper selects
the human body structure as the experimental object. The
image rendering level of the human body under the system
environment of this paper is explored, as shown in Figure 5.
It can be seen from Figure 5 that in the nal display
eect, the display completion of the entire human body is
close to perfect. At the same time, the imaging image is clear.
The light and shadow handling is also done just right. The
presented portrait is very close to the actual collection scene,
and the degree of restoration is very high.
4.2. Application of Virtual Reality Technology. In order to
verify the overall performance optimization eect of the
system using virtual reality technology, this paper compares
the relative error performance of the system with the real
operating environment without virtual reality technology.
In this paper, a total of 1000 imaging image information
samples are selected as experimental objects, and the sam-
ples are applied in these two environments, respectively.
At the same time, in order to improve the accuracy of the
data, this paper will conduct two comparative experiments
under the same experimental conditions, and the nal
results are shown in Figure 6.
It can be seen from Figure 6 that in the two comparisons
of relative errors under the same conditions, the relative
error control performance in the environment using virtual
Table 1: Comprehensive evaluation of the system by professionals.
Evaluation angle Evaluator Mean
P1 P2 P3 P4
Design eect of information collection part 5 4 5 5 4.75
Information display part of the design eect 4 5 4 4 4.25
System algorithm design 5 5 5 4 4.75
System overall structure design 5 5 4 4 4.5
8 Wireless Communications and Mobile Computing
reality technology is signicantly better. The highest relative
error is only 1.44, and the lowest is an excellent error control
level of 0.18.
4.3. Optimized Application of Virtual Reality Technology.
This paper tests the main performance of virtual reality tech-
nology optimized by convolutional neural network-related
technologies. This paper compares the performance of this
algorithm with three commonly used algorithms applied to
the imaging image processing systems under the same
experimental conditions. High precision has always been
the key standard of imaging image processing, so this paper
rst selects relevant samples to test the accuracy of imaging
image processing. In order to ensure the feasibility of the
experiment, this paper divides the samples into test samples
and training samples on average. Among them, the require-
ments of various indicators in the experimental environment
of the training samples are more stringent. The nal accu-
racy data of the two samples are shown in Figure 7.
It can be clearly seen from Figure 7 that the optimized
virtual reality technology proposed in this paper has absolute
advantages in both sample accuracies. Among them, the
training accuracy is reached an extremely high-precision
level of 99.3%, and the test sample accuracy is also reached
the level of 97.9%; in contrast, the other three algorithms
do not achieve a very good level of accuracy on the imaged
images. The highest accuracy values of the random forest
algorithm, KNN, and NNGA algorithms are 84%, 87.5%,
and 77.3%, respectively.
After testing the precision comparison, this paper also
selects another important analysis indicator for testing, that
is, the recall rate. The processed imaging image samples are
also divided into test and training samples. Other factors in
the experimental environment are also unchanged. The specic
recall performance of the four algorithms is shown as follows.
Figure 8 shows the dierent recall performance of two
experimental samples of the four algorithms. The recall rates
of the optimized virtual reality technology training samples
and test samples proposed in this paper are 0.85% and
0.83%, respectively. Compared with the average recall rate
of the KNN algorithm of two samples of 1.93% and the aver-
age recall rate of NNGA of 2.33%, the algorithm in this
paper is in a backward position in terms of recall rate.
Although this is a normal phenomenon in the pursuit of
higher accuracy, it also reects the improvement space of
the algorithm in this paper in terms of recall optimization.
In addition, in order to make an intuitive comparison
between the virtual reality technology optimized by the con-
volutional neural network algorithm and the unoptimized
technology, this paper selects 2000 imaging image samples
and also conducts two comparison experiments under the
same conditions. The actual performance of the two VR
technologies is tested, and the performance of the two tech-
nologies is compared according to the tested error data. The
experimental data is shown in Figure 9.
Table 2: System satisfaction survey results.
Question Score Mean
10 9 8 7 6 <6
Q1 18 13 12 5 2 0 8.8
Q2 21 11 14 2 2 0 8.94
Q3 17 16 10 6 1 0 8.84
Q4 18 14 8 8 2 0 8.76
Q5 23 12 7 8 0 0 9
Table 3: Time consumption comparison of the two systems.
Number of
tests
Time consuming Reduction
rate
Legacy system
(ms)
Optimized system
(ms)
1 8699.5 5820.6 33.1%
2 8455.3 5962 29.5%
3 8416.9 5643.1 33.0%
4 8523.6 5933.6 30.4%
5 8551.7 6121.4 28.4%
6 8449.3 6002.9 30.0%
7 8632.9 5952.4 31.0%
8 8546.4 5883.4 31.2%
Mean 8534.45 5914.93 30.8%
Table 4: Time consumption comparison of the two systems after
the improved strategy.
Number of
tests
Time consuming Reduction
rate
Legacy system
(ms)
Optimized system
(ms)
1 1865.9 1362.5 27.0%
2 1882.4 1259.6 33.1%
3 1933.6 1296.3 33.0%
4 1946.5 1299.3 33.2%
5 1889.1 1316.5 30.3%
6 1886.3 1278.2 32.2%
7 2013.6 1377.3 31.6%
8 1997.6 1278.4 36.0%
Mean 1926.88 1308.51 32.1%
Figure 5: Human body imaging display eect.
9Wireless Communications and Mobile Computing
In the error performance of the two experiments in
Figure 9, the optimized VR technology leads the whole stage
in terms of mean square error. Among them, after testing
400 samples in the two experiments, the error performance
is controlled below 0.32, and the best error control perfor-
mance is 0.07; the unoptimized virtual reality technology
error control is not ideal. The minimum error failed to break
below 0.2.
1.44
0.73
0.54
0.22 0.23 0.19
3.68
3.22 3.11 3.09 2.96 2.87
0
0.5
1
1.5
2
2.5
3
3.5
4
0 200 400 600 800 1000
Relative error
Training samples
1.41
0.77
0.5
0.21 0.23 0.18
3.72
3.12 3.08 3.14
2.86 2.81
0
0.5
1
1.5
2
2.5
3
3.5
4
0 200 400 600 800 1000
Relative error
Training samples
VR environment
Real operating environment
VR environment
Real operating environment
Figure 6: Comparison of relative errors in two operating environments.
99.3
80.2 82.8
77.3
97.9
84 87.5
71.2
0
20
40
60
80
100
120
CNN with VR Random forest KNN NNGA
Accuracy (%)
Algorithm
Training accuracy
Testing accuracy
Figure 7: Accuracy comparison of the four algorithms.
10 Wireless Communications and Mobile Computing
5. Conclusions
The advantages of digital image processing are high process-
ing accuracy, rich processing content, complex nonlinear
processing, and exible adaptability. Generally speaking,
the content can be processed only by changing the software;
so, it is widely used. How to design an imaging image pro-
cessing system to show stunning visual communication
eects on the three-dimensional appearance of actual objects
and at the same time expand the extension of contemporary
visual communication design, this has always been a prob-
lem that relevant designers are eagerly concerned about
and researched. This paper provided a critical review of
traditional imaging image acquisition devices and tech-
niques. The use of 3D imaging technology to construct a
very ecient acquisition process was proposed. Computer
3D reconstruction methods were used to perform key recon-
struction operations on the acquired imaging images.
0.85 0.81
1.97
2.32
0.83 0.77
1.89
2.34
0
0.5
1
1.5
2
2.5
CNN with VR Random forest KNN NNGA
Recall (%)
Algorithm
Training recall
Testing recall
Figure 8: Comparison of recall rates of the four algorithms.
1.22
0.28 0.24
0.15 0.12 0.07
1.66
0.98
0.54 0.47
0.25 0.21
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 400 800 1200 1600 2000
Mean square error
Training samples
CNN with VR
VR technology
CNN with VR
VR technology
1.27
0.31
0.22
0.13 0.09 0.08
1.69
0.92
0.66
0.44
0.29 0.23
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 400 800 1200 1600 2000
Mean square error
Training samples
Figure 9: Two comparisons of the error performance of the two algorithms.
11Wireless Communications and Mobile Computing
Professional image display technology was used to achieve
realistic visual communication eects. In addition, this paper
optimized the virtual vision technology by introducing the
convolutional neural network algorithm, and the recall rates
of the optimized VR training samples and test samples are
0.85% and 0.83%, respectively. The optimized algorithm
model could provide strong technical support for the entire
system. While the accuracy was greatly improved, the exper-
imental error was reduced. This undoubtedly promoted the
rapid development of imaging image information acquisi-
tion and display systems. And in the course of digital media
technology, the abovementioned technologies can be used to
construct three-dimensional images, outline vivid virtual
information images, and greatly enhance the dynamic and
comprehensive eects of visual communication design.
Data Availability
Data sharing is not applicable to this article as no datasets
were generated or analyzed during the current study.
Conflicts of Interest
The authors declare that there is no conict of interest with
any nancial organizations regarding the material reported
in this manuscript.
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