# Symbolic Model of New Media Art Expression Based on Artificial Intelligence Big Data

Authors: Duksoon Lee
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the rapid development of China’s economy and the continuous optimization and improvement of Internet information technology, a new thing, new media, has emerged. On this basis, a kind of artistic behavior of creating, carrying, spreading, and appreciating is also called new media art. New media art has developed for a long time in foreign countries, but its development and popularization are relatively late in China, and it is still in a stage of improvement. As far as visual communication design is concerned, the emergence and development of new media have strongly promoted the development of its work and the dissemination of various works. Developing visual communication design based on new media art has become a key issue for all visual communication designers. Nowadays, deep learning is widely used in different fields in different ways, and image art representation is a hot research field in computer vision. Therefore, this paper mainly studies and analyzes the symbolism of new media art expression forms and proposes a new media art symbolism model based on artificial intelligence big data. In order to provide a method of real-time representation of art images, the deep learning method is used to model the artistic expression of images, and spatial reorganization of content features and style features is carried out. This paper designs simulation experiments to verify the effectiveness of the model, which can provide some help for the development and improvement of new media art in China. We compared the results of our model with a number of other models to test its effectiveness. We achieved a matching accuracy of 98.4% which is higher than or equal to the results of the existing models.


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## Body

## 1. Introduction

With the rapid development and improvement of computer and Internet information technology, network media has begun to be popularized in people’s life and work [1]. People’s experiences and information sources are also quietly changing, and the relevant behavior habits and cognition are also changing [2]. Many artists have begun to use nontraditional materials and new models to express their artistic views in their actual creations, which is what we call new media art [3]. Different from the traditional art using media, as a new art form, it is not a thing that appears out of thin air; that is to say, it does not completely separate from the traditional art and exists independently. Novel technology art is an effective combination of art and novel technology. It pays more attention to process and is a spiritual activity [4]. As a relatively broad concept, a symbol takes people or things as the symbolic body and presents abstract concepts, thoughts, and emotions in a concrete form according to the relevance between different things.At the level of communication, the so-called novel technology is no longer limited to natural reality but also reflected in virtual reality. In other words, using various media to express the author’s artistic views have gradually become a new direction. The symbol has crossmedia characteristics. It can be found that the cross and sculpture can represent some meanings in daily life. In fact, from a certain point of view, this is what we call a symbol [5]. The symbol in the development of art can be divided into three levels according to its own definition and development concept, namely, its own concept, abstract spirit, imagination, and association. For example, in the development and promotion of novel technology art, its meaning is not only the graphics it expresses but also accurate symbolism [6]. Through the integration of concepts, works can present a new meaning effect. In the actual development process of novel technology art, it is not only necessary to effectively interpret the media itself but also to express some auxiliary information. For example, a specific image will have different emotions and meanings. In this process, in order to complete the expression of aesthetic and creative ideas, we need to expand the meaning of the image. Different forms, colors, rhythms, and sounds will convey different feelings to people. We can even understand that the symbolization of objects is the beginning of the realistic simulacrum, and the simulacrum world and the real world are a kind of unequal relationship [7]. Compared with the symbolism of traditional media, novel technology is not only a continuation but also an expansion and improvement. The development and improvement of Internet information technology have broken the traditional concept of time and space, making the dissemination of information more rapid. People can also obtain the information they need through the network. The symbolism of the novel technology art expression is shown in Figure 1.Figure 1
The symbolism of novel technology art expression.With the development and improvement of modern technology and Internet IT technology, people can use novel technology to create more works, especially in some visual expression form [8]. In order to better realize the relevant expression, the relevant technical personnel must efficiently do investigation and analysis in advance. In addition, in the process of relevant novel technology art creation, we should also learn about the required computer information technology and relevant rules and regulations and also explore the relationship between art and human nature. Only in this way the creative content and related forms of expression of novel technology art can be optimized and improved, thus providing people with a new enjoyment of beauty. As one of the most important measures in the development of cultural communication, the communication mode of novel technology will play a great role in the development and integration of traditional culture and modern culture [9]. The image value of novel technology art in the era of consumption and the characteristics of cultural commodities can make it combined with cultural and creative industries. In the process of the development of China’s cultural and creative industries, the relevant management departments must strengthen the research investment in novel technology art and find a suitable development path as soon as possible. Only in this way can we effectively realize the integration of commercial value and artistic value of novel Technology art [10]. This paper mainly studies and analyzes the symbolism of novel technology art expression forms and proposes a novel technology art symbolism model based on artificial intelligence big data. In order to provide a method of real-time representation of art images, the deep learning method is used to model the artistic expression of images, and spatial reorganization of content features and style features is carried out. The main contributions of this study include the following.We study and discuss the symbolism of new media art expression forms and introduce a new media art symbolism model based on artificial intelligence big data. We use convolutional neural network to extract specific features of the image and generate different versions of its filtering results. We propose a style migration algorithm for preserving salient regions which generates a style migration model with the style after many iterations. We generated a dataset by collecting art works of a number of painters to train the model. At the end, we present a detailed comparison of our model with the existing models.


## 2. Related Work

### 2.1. Novel Technology Art Design

The rapid development of information technology has brought great influence to art design, which has rapidly produced a variety of art forms. The rapid development of novel technology art design has had a great impact on all walks of life [11]. It is the development direction and goal of visual communication design at present and in the future. With the continuous evolution of the novel technology environment, visual communication has made significant innovation and development on the basis of traditional design, whether from the design process, design methods, or design principles. It not only retains the similar needs of the audience but also achieves the innovation in visual display and communication means so that its communication gets twice the result with half the effort [12]. With the digital development of art technology, it has gradually replaced some traditional art design technologies. Adhering to the principle of getting the best from the rough, on the basis of the original technology, the method of digital visual communication is becoming more and more mature and widely used. It enhances the initiative and exchangeability of art design so that art design can really spread to the audience. Therefore, visual communication in the novel technology environment can be truly all-around and wide-ranging, which fully reflects the media form [13].Compared with traditional art, the design of novel technology art products is more prominent in the level of dynamic art, which is reflected in the transition from two-dimensional to three-dimensional by adding the concept of time and making full use of the dynamic perspective. Today, with the rapid development of science and information technology, novel technology art as a new art form is presented to people [14]. The so-called novel technology art refers to a kind of artwork that is recreated or processed through the adoption of corresponding design techniques. In daily production and life, people do not have a specific and clear perception of the concept of novel technology art, and it is difficult to make accurate positioning of novel technology art [15]. For some people, when judging whether artworks belong to the category of novel technology art, the standard they follow is to see whether the broadcasting platform of art films is a new carrier. The novel technology requires the works to add the thoughts of the creators, take the advanced network technology and digital information technology as the support, and integrate the innovative ideas into the artworks. Then, it will be spread through novel technology. Generally, novel technology works of art can highlight the innovative value and reflect the thinking of creators on art [16]. The visual communication of novel technology art’s impact is shown in Figure 2.Figure 2
Visual communication of novel technology art’s impact.Whether in form or content, novel technology art emphasizes novelty and diversity. Supported by the advantages of novel technology communication, novel technology art has achieved a relatively ideal development. Under the background of the expanding radiation range, people hold a higher and higher sense of identity for novel technology art. Influenced by this actual situation, visual communication design is placed in a corresponding development dilemma to a certain extent [17]. For the traditional visual communication design, it is difficult for them to respond well to a series of requirements that put forward by the development of novel technology art design under the background of the novel technology era. In the context of the new development situation, visual communication design cannot ignore the impact of novel technology and novel technology art [18]. It is necessary to make full use of the differences between itself and novel technology art in design form and positioning, so as to explore the new vitality of visual communication design and actively explore a new development path.


### 2.2. Development and Dissemination of Novel Technology Art

As a digital art, this art form can be realized by relying on computer and the related software technology. Artists can use digital equipment to achieve artistic creation and show their own artistic ideas [19]. This art form can be created and realized with the help of multimedia equipment and digital technology and has realized the effective integration with the commercial field. This has led to the birth of the digital art industry, in which animation art has developed rapidly. This art form attaches great importance to the audience’s sense of on-site participation. Recently, this art form of expression has been widely used in art galleries.The development of Internet technology has enabled network art to rise and occupy a place in the art field. It can be predicted that in the process of the continuous improvement of scientific and technological levels, the extension of network art as an art form will continue to expand [20]. In the communication process of this form of artistic expression, it can rely on television media, network video media, and other forms to achieve efficient communication, can also rely on interpersonal communication forms to carry out point-to-point communication, and can also rely on art exhibitions and other means to spread. In the process of the rapid development of digital technology, it has had an all-around impact on the production, dissemination, acceptance, and other nodes of novel technology art, especially the improvement of the iteration rate at the scientific and technological levels [21]. The multiangle integration of novel technology art and traditional art focuses on the combination of novel technology art and emerging technology. In view of this, the creators of novel technology art should deeply analyze the humanistic elements applied in traditional art. Through the combination of humanistic elements, the audience can have a sense of dislocation in the process of appreciating novel technology works of art, so that the works can show the humanistic charm [22]. From the perspective of the development process of novel technology art, despite the blessing of high-tech means and the integration of Western art elements, creators should still pay attention to the integration and use of traditional elements so as to enhance the aesthetic connotation of novel technology artworks and enhance the audience’s sense of identity with the traditional culture [23]. The development and dissemination of novel technology art are shown in Figure 3.Figure 3
The development and dissemination of novel technology art.In the process of practicing the creation of novel technology artworks, the creators of novel technology art should pay attention to the creative concept of multi-integration. In other words, the creator should integrate more artistic and humanistic elements, more content, and more creative forms into his works as far as possible on the premise of ensuring that the theme of his works is highlighted [24]. In this way, it will create a multidimensional artistic aesthetic space. Create composite novel technology artworks to ensure that the audience can enjoy the field of multiple art worlds. In this process, novel technology art creators should ensure the validity of integration, that is, the effective connection and synergy between technical language and artistic language [25]. Novel technology art is a combination of art creation and emerging technology. It can be said that with the support of science and technology, novel technology art creators can better use their works to realize the interpretation of personal aesthetic value, artistic expression, and humanistic spirit. The development of novel technology art with Internet media as the medium and digital technology as the driving force is very important for the current art theory [26]. The open artistic theory system and framework based on novel technology art can greatly enhance the vitality and vitality of art.


### 2.3. Design Strategy under the Impact of Novel Technology Art

To give full play to the artistic value of visual communication and promote the development and progress of visual communication, we must fully integrate the concept of visual communication into the novel technology art design. With the rapid development of novel technology, people not only pursue realistic design works but also pursue the aesthetic value of virtual art [27]. Therefore, through innovative visual design to improve the public’s aesthetic ability and aesthetic taste, the traditional visual communication design regards the public as its whole audience correspondingly, the audience of visual communication design, which is constantly developing in the novel technology environment, and has changed to those who use the Internet more, especially young people [28]. In fact, for visual communication design, the emergence and continuous development of novel technology art in the novel technology environment not only brings certain development opportunities but also severe challenges.If visual communication design wants to occupy a place in the field of novel technology and achieve practical and effective development, it must carry out deep thinking through the comprehensive collection of effective data from various parties, statistics, and induction of various characteristics of novel technology [29]. In consideration of this actual situation, visual communication designers can publish some of their own works on the network, so that the audience can appreciate their works on the network platform. The works released by designers should cover a variety of styles and types, so as to better understand the interests of the audience. In general, based on the impact of novel technology art, the development of visual communication design must be closely combined with the connotation of novel technology art and its various characteristics [30]. Through continuous improvement and innovation, it keeps a high degree of consistency with novel technology art, so as to promote the success of visual communication design. The traditional sense of visual experience has been difficult to affect the new audience with young people as the main body. In this regard, visual communication designers should focus on the improvement of the visual communication design experience. Taking this as the primary goal, when designing a specific work, we should seriously consider the theme of the work design and carry out design activities on the basis of a clear theme, so as to ensure that the designed works are consistent with the original intention of the design, and give full play to the due theme communication effect [31]. Because the media of visual communication design works based on the novel technology environment and its technical support has changed greatly, the traditional paper communication has changed to today’s network communication. Therefore, in order to better optimize the audience’s visual perception of the design work, visual communication designers should properly innovate the design form to better play a role in attracting the attention of the audience.Under the background of the novel technology era and under the impact of novel technology art, visual communication design must pay more attention to the material and content in order to achieve better development. In this regard, designers need to actively and actively look for and integrate modern factors, find social hot spots with the support of Internet technology, and take some meaningful content as the contents and materials of visual communication design [32]. In addition, visual communication designers can also combine with the design theme to boost the innovation of works. Through the attempt of new content and new materials, we can explore the rules of visual communication design and finally realize the emancipation of the mind and the improvement of comprehensive quality.


## 2.1. Novel Technology Art Design

The rapid development of information technology has brought great influence to art design, which has rapidly produced a variety of art forms. The rapid development of novel technology art design has had a great impact on all walks of life [11]. It is the development direction and goal of visual communication design at present and in the future. With the continuous evolution of the novel technology environment, visual communication has made significant innovation and development on the basis of traditional design, whether from the design process, design methods, or design principles. It not only retains the similar needs of the audience but also achieves the innovation in visual display and communication means so that its communication gets twice the result with half the effort [12]. With the digital development of art technology, it has gradually replaced some traditional art design technologies. Adhering to the principle of getting the best from the rough, on the basis of the original technology, the method of digital visual communication is becoming more and more mature and widely used. It enhances the initiative and exchangeability of art design so that art design can really spread to the audience. Therefore, visual communication in the novel technology environment can be truly all-around and wide-ranging, which fully reflects the media form [13].Compared with traditional art, the design of novel technology art products is more prominent in the level of dynamic art, which is reflected in the transition from two-dimensional to three-dimensional by adding the concept of time and making full use of the dynamic perspective. Today, with the rapid development of science and information technology, novel technology art as a new art form is presented to people [14]. The so-called novel technology art refers to a kind of artwork that is recreated or processed through the adoption of corresponding design techniques. In daily production and life, people do not have a specific and clear perception of the concept of novel technology art, and it is difficult to make accurate positioning of novel technology art [15]. For some people, when judging whether artworks belong to the category of novel technology art, the standard they follow is to see whether the broadcasting platform of art films is a new carrier. The novel technology requires the works to add the thoughts of the creators, take the advanced network technology and digital information technology as the support, and integrate the innovative ideas into the artworks. Then, it will be spread through novel technology. Generally, novel technology works of art can highlight the innovative value and reflect the thinking of creators on art [16]. The visual communication of novel technology art’s impact is shown in Figure 2.Figure 2
Visual communication of novel technology art’s impact.Whether in form or content, novel technology art emphasizes novelty and diversity. Supported by the advantages of novel technology communication, novel technology art has achieved a relatively ideal development. Under the background of the expanding radiation range, people hold a higher and higher sense of identity for novel technology art. Influenced by this actual situation, visual communication design is placed in a corresponding development dilemma to a certain extent [17]. For the traditional visual communication design, it is difficult for them to respond well to a series of requirements that put forward by the development of novel technology art design under the background of the novel technology era. In the context of the new development situation, visual communication design cannot ignore the impact of novel technology and novel technology art [18]. It is necessary to make full use of the differences between itself and novel technology art in design form and positioning, so as to explore the new vitality of visual communication design and actively explore a new development path.


## 2.2. Development and Dissemination of Novel Technology Art

As a digital art, this art form can be realized by relying on computer and the related software technology. Artists can use digital equipment to achieve artistic creation and show their own artistic ideas [19]. This art form can be created and realized with the help of multimedia equipment and digital technology and has realized the effective integration with the commercial field. This has led to the birth of the digital art industry, in which animation art has developed rapidly. This art form attaches great importance to the audience’s sense of on-site participation. Recently, this art form of expression has been widely used in art galleries.The development of Internet technology has enabled network art to rise and occupy a place in the art field. It can be predicted that in the process of the continuous improvement of scientific and technological levels, the extension of network art as an art form will continue to expand [20]. In the communication process of this form of artistic expression, it can rely on television media, network video media, and other forms to achieve efficient communication, can also rely on interpersonal communication forms to carry out point-to-point communication, and can also rely on art exhibitions and other means to spread. In the process of the rapid development of digital technology, it has had an all-around impact on the production, dissemination, acceptance, and other nodes of novel technology art, especially the improvement of the iteration rate at the scientific and technological levels [21]. The multiangle integration of novel technology art and traditional art focuses on the combination of novel technology art and emerging technology. In view of this, the creators of novel technology art should deeply analyze the humanistic elements applied in traditional art. Through the combination of humanistic elements, the audience can have a sense of dislocation in the process of appreciating novel technology works of art, so that the works can show the humanistic charm [22]. From the perspective of the development process of novel technology art, despite the blessing of high-tech means and the integration of Western art elements, creators should still pay attention to the integration and use of traditional elements so as to enhance the aesthetic connotation of novel technology artworks and enhance the audience’s sense of identity with the traditional culture [23]. The development and dissemination of novel technology art are shown in Figure 3.Figure 3
The development and dissemination of novel technology art.In the process of practicing the creation of novel technology artworks, the creators of novel technology art should pay attention to the creative concept of multi-integration. In other words, the creator should integrate more artistic and humanistic elements, more content, and more creative forms into his works as far as possible on the premise of ensuring that the theme of his works is highlighted [24]. In this way, it will create a multidimensional artistic aesthetic space. Create composite novel technology artworks to ensure that the audience can enjoy the field of multiple art worlds. In this process, novel technology art creators should ensure the validity of integration, that is, the effective connection and synergy between technical language and artistic language [25]. Novel technology art is a combination of art creation and emerging technology. It can be said that with the support of science and technology, novel technology art creators can better use their works to realize the interpretation of personal aesthetic value, artistic expression, and humanistic spirit. The development of novel technology art with Internet media as the medium and digital technology as the driving force is very important for the current art theory [26]. The open artistic theory system and framework based on novel technology art can greatly enhance the vitality and vitality of art.


## 2.3. Design Strategy under the Impact of Novel Technology Art

To give full play to the artistic value of visual communication and promote the development and progress of visual communication, we must fully integrate the concept of visual communication into the novel technology art design. With the rapid development of novel technology, people not only pursue realistic design works but also pursue the aesthetic value of virtual art [27]. Therefore, through innovative visual design to improve the public’s aesthetic ability and aesthetic taste, the traditional visual communication design regards the public as its whole audience correspondingly, the audience of visual communication design, which is constantly developing in the novel technology environment, and has changed to those who use the Internet more, especially young people [28]. In fact, for visual communication design, the emergence and continuous development of novel technology art in the novel technology environment not only brings certain development opportunities but also severe challenges.If visual communication design wants to occupy a place in the field of novel technology and achieve practical and effective development, it must carry out deep thinking through the comprehensive collection of effective data from various parties, statistics, and induction of various characteristics of novel technology [29]. In consideration of this actual situation, visual communication designers can publish some of their own works on the network, so that the audience can appreciate their works on the network platform. The works released by designers should cover a variety of styles and types, so as to better understand the interests of the audience. In general, based on the impact of novel technology art, the development of visual communication design must be closely combined with the connotation of novel technology art and its various characteristics [30]. Through continuous improvement and innovation, it keeps a high degree of consistency with novel technology art, so as to promote the success of visual communication design. The traditional sense of visual experience has been difficult to affect the new audience with young people as the main body. In this regard, visual communication designers should focus on the improvement of the visual communication design experience. Taking this as the primary goal, when designing a specific work, we should seriously consider the theme of the work design and carry out design activities on the basis of a clear theme, so as to ensure that the designed works are consistent with the original intention of the design, and give full play to the due theme communication effect [31]. Because the media of visual communication design works based on the novel technology environment and its technical support has changed greatly, the traditional paper communication has changed to today’s network communication. Therefore, in order to better optimize the audience’s visual perception of the design work, visual communication designers should properly innovate the design form to better play a role in attracting the attention of the audience.Under the background of the novel technology era and under the impact of novel technology art, visual communication design must pay more attention to the material and content in order to achieve better development. In this regard, designers need to actively and actively look for and integrate modern factors, find social hot spots with the support of Internet technology, and take some meaningful content as the contents and materials of visual communication design [32]. In addition, visual communication designers can also combine with the design theme to boost the innovation of works. Through the attempt of new content and new materials, we can explore the rules of visual communication design and finally realize the emancipation of the mind and the improvement of comprehensive quality.


## 3. Design of Application Model

We introduce a new media art symbolism model based on artificial intelligence big data. We use convolutional neural network to extract specific features of the image: a style migration algorithm for preserving salient regions which generates a style migration model. In this section, we will discuss the design of our model in detail.


### 3.1. Style Transfer Based on Art Image Optimization

The convolution layer in the convolution neural network can be understood as a filter to extract specific features of the image; that is, convolution in different layers generates different versions of the filtering results of the input image. In order to visualize the feature information extracted from the input image in different layers of the VGG network, the method of image reconstruction is used to iteratively optimize a white noise image, so as to generate a new image matching the feature information of each layer. The content image reconstruction results of the low-level network are relatively clear and complete and will become fuzzy and abstract with the deepening of the network level. In the process of content image reconstruction, the square error loss of content image reconstruction is(1)Lcontentp⟶,x⟶,l=12∑i,jFijl−Pijl2.The derivative of the content loss function is(2)∂Lcontent∂Fijl=Fl−PlijifFijl>0,0ifFijl<0.Style features are more abstract and complex. It depends not only on the feature information extracted at a certain layer but also on the correlation of features extracted at multiple convolution layers. Gram matrix can well express this correlation. This matrix can not only measure the characteristics of its own dimensions but also measures the relationship between them. Among them, the diagonal elements represent the information of different feature maps, and the other elements represent the relationship between these feature maps. Therefore, the matrix is widely used in image style transfer. The inner product of the characteristic graph is as follows:(3)GijL=∑kFiklFjkl.Through Gram matrix, we can get different sizes of style image information, but this part of information only contains texture information, and there is no global information about the image. Therefore, in order to generate a new image that matches the given style image, it is necessary to minimize the mean square error of the Gram matrix of the white noise image and the given style image and continue iterative optimization to obtain the final texture. The mathematical expression of style loss is as follows.(4)El=14Nl2Ml2∑i,jGijl−Aijl2.The style of image information extracted from different convolution layers in the network can be visualized by image reconstruction. The low-level features correspond to the low-level pixel features in the image, reflecting the local content in the image. The output of the high-level network corresponds to the high-level features of the input image, reflecting the overall style of the input image. The total loss during iteration is(5)Ltotalp⟶,a⟶,x⟶=αLcontentp⟶,x⟶+βLstylea⟶,x⟶.The image with the above two characteristics is constructed through image reconstruction, and the final stylized image expresses a specific style in the visual effect. The above style migration algorithm based on image optimization will consume a lot of time and computing resources in each iteration process, especially to convert several content images into the same style. Therefore, inputting the content images to be converted into the trained style transfer model can quickly generate stylized images. A schematic diagram of the style migration algorithm based on model optimization is shown in Figure4.Figure 4
Schematic diagram of style migration algorithm based on optimization.The style migration algorithm based on model optimization greatly improves the running efficiency and reduces the computational overhead. Therefore, there are many models with excellent performance. These models are mainly divided into one model and one style learning algorithm according to the number of learnable styles. Training different style conversion networks for different styles to improve conversion efficiency and reduce the consumption of computing resources.


### 3.2. Image Style Migration with Significant Area Preservation

In this chapter, we propose a style migration algorithm for preserving salient regions, which can preserve the salient regions of content images in stylized images. According to the feedback, the style migration network adjusts the weight and generates a style migration model with this style after many iterations. Generally speaking, increasing the width and depth of the network can improve the performance of the network, and the deep-seated network is generally better than the shallow network. This kind of connection can make it easier for the network to learn recognition ability and optimize. This characteristic of the residual network can ensure that the input image and the output image share the same structure. It is beneficial for the image conversion task to maintain a consistent image structure. Figure5 shows a residual structure.Figure 5
Residual network diagram.The main part of the style migration network consists of five residual blocks, and each residual block contains two convolution layers. The residual connection of the residual network can make the output image and the input image share the structure and improve the learning ability and recognition function of the network. The content image and composite image calculate the content loss at the high level of the perceptual network, and the style image and composite image calculate the content loss at each level of the perceptual network. The two-loss measures the similarity of content and style, rather than accurately calculating the difference of each pixel of the image. The characteristics can be expressed as a matrix:(6)Tl∈ℝHl×Wl×Cl.The content loss function is to calculate the mean square error between the characteristic images of the content image and the composite image after passing through the VGG network. The content loss is(7)lcontenty^,x∗=1Hl×Wl×ClTly^−Tlx∗22.The style loss function calculates the square of the f norm of the Gram matrix difference between the style image and the composite image after they pass through the VGG network. The style loss is as follows:(8)lstyley^,ys=Gly^−GlysF2.The Gram matrix is obtained for each layer of the two images in the VGG-16 network, and the square of theF norm of the matrix difference between the corresponding layers is calculated. The Gram matrix is calculated as follows:
(9)Gc,c′lx=1Hl×Wl×Cl∑h=1Hl∑w=1WlTh,w,clxTh,w,c′lx.Deep output can highlight the key area of the image, connect its output end to the last pooled layer of the network, and add two convolution layers with different sizes and channel numbers in each side output. Based on this, properly combine the side output of different layers; that is, establish a short connection from the deep side output layer by layer to the shallow side output for information exchange. The deep information can describe the prominent area, the shallow information can describe the image details, and the two types of information can be properly combined to form a dense and accurate saliency map. The conceptual diagram of the saliency detection algorithm is shown in Figure6.Figure 6
The conceptual diagram of the saliency detection algorithm.The significant loss function is used to calculate the loss of both. Significant loss is defined as(10)lsalienty^,x∗=ωy^−ωx∗22.The total loss function is defined as(11)ltotaly^,ys,x∗=λ1lcontenty^,x∗+λ2lstyley^,ys+λ3lsalienty^,x∗+λ4lTV.According to the total loss function, the style migration network is iteratively optimized many times, and finally, a style migration model of this style is generated. The high-level features extracted by the network are used to represent the loss, and the mathematical expression is as follows.(12)Lcontentp⟶,x⟶,l=12∑i,jFijl−pijl2.The difference between the generated image and the style image is expressed as(13)El=14N2M2∑ijGijl−Aijl2.According to the mathematical formula, the loss of image stylization is composed of content loss and style loss. However, the number of additional parameters increases linearly with the number of styles. Therefore, this method has great limitations, and it is difficult to apply it to large-scale style transfer.


## 3.1. Style Transfer Based on Art Image Optimization

The convolution layer in the convolution neural network can be understood as a filter to extract specific features of the image; that is, convolution in different layers generates different versions of the filtering results of the input image. In order to visualize the feature information extracted from the input image in different layers of the VGG network, the method of image reconstruction is used to iteratively optimize a white noise image, so as to generate a new image matching the feature information of each layer. The content image reconstruction results of the low-level network are relatively clear and complete and will become fuzzy and abstract with the deepening of the network level. In the process of content image reconstruction, the square error loss of content image reconstruction is(1)Lcontentp⟶,x⟶,l=12∑i,jFijl−Pijl2.The derivative of the content loss function is(2)∂Lcontent∂Fijl=Fl−PlijifFijl>0,0ifFijl<0.Style features are more abstract and complex. It depends not only on the feature information extracted at a certain layer but also on the correlation of features extracted at multiple convolution layers. Gram matrix can well express this correlation. This matrix can not only measure the characteristics of its own dimensions but also measures the relationship between them. Among them, the diagonal elements represent the information of different feature maps, and the other elements represent the relationship between these feature maps. Therefore, the matrix is widely used in image style transfer. The inner product of the characteristic graph is as follows:(3)GijL=∑kFiklFjkl.Through Gram matrix, we can get different sizes of style image information, but this part of information only contains texture information, and there is no global information about the image. Therefore, in order to generate a new image that matches the given style image, it is necessary to minimize the mean square error of the Gram matrix of the white noise image and the given style image and continue iterative optimization to obtain the final texture. The mathematical expression of style loss is as follows.(4)El=14Nl2Ml2∑i,jGijl−Aijl2.The style of image information extracted from different convolution layers in the network can be visualized by image reconstruction. The low-level features correspond to the low-level pixel features in the image, reflecting the local content in the image. The output of the high-level network corresponds to the high-level features of the input image, reflecting the overall style of the input image. The total loss during iteration is(5)Ltotalp⟶,a⟶,x⟶=αLcontentp⟶,x⟶+βLstylea⟶,x⟶.The image with the above two characteristics is constructed through image reconstruction, and the final stylized image expresses a specific style in the visual effect. The above style migration algorithm based on image optimization will consume a lot of time and computing resources in each iteration process, especially to convert several content images into the same style. Therefore, inputting the content images to be converted into the trained style transfer model can quickly generate stylized images. A schematic diagram of the style migration algorithm based on model optimization is shown in Figure4.Figure 4
Schematic diagram of style migration algorithm based on optimization.The style migration algorithm based on model optimization greatly improves the running efficiency and reduces the computational overhead. Therefore, there are many models with excellent performance. These models are mainly divided into one model and one style learning algorithm according to the number of learnable styles. Training different style conversion networks for different styles to improve conversion efficiency and reduce the consumption of computing resources.


## 3.2. Image Style Migration with Significant Area Preservation

In this chapter, we propose a style migration algorithm for preserving salient regions, which can preserve the salient regions of content images in stylized images. According to the feedback, the style migration network adjusts the weight and generates a style migration model with this style after many iterations. Generally speaking, increasing the width and depth of the network can improve the performance of the network, and the deep-seated network is generally better than the shallow network. This kind of connection can make it easier for the network to learn recognition ability and optimize. This characteristic of the residual network can ensure that the input image and the output image share the same structure. It is beneficial for the image conversion task to maintain a consistent image structure. Figure5 shows a residual structure.Figure 5
Residual network diagram.The main part of the style migration network consists of five residual blocks, and each residual block contains two convolution layers. The residual connection of the residual network can make the output image and the input image share the structure and improve the learning ability and recognition function of the network. The content image and composite image calculate the content loss at the high level of the perceptual network, and the style image and composite image calculate the content loss at each level of the perceptual network. The two-loss measures the similarity of content and style, rather than accurately calculating the difference of each pixel of the image. The characteristics can be expressed as a matrix:(6)Tl∈ℝHl×Wl×Cl.The content loss function is to calculate the mean square error between the characteristic images of the content image and the composite image after passing through the VGG network. The content loss is(7)lcontenty^,x∗=1Hl×Wl×ClTly^−Tlx∗22.The style loss function calculates the square of the f norm of the Gram matrix difference between the style image and the composite image after they pass through the VGG network. The style loss is as follows:(8)lstyley^,ys=Gly^−GlysF2.The Gram matrix is obtained for each layer of the two images in the VGG-16 network, and the square of theF norm of the matrix difference between the corresponding layers is calculated. The Gram matrix is calculated as follows:
(9)Gc,c′lx=1Hl×Wl×Cl∑h=1Hl∑w=1WlTh,w,clxTh,w,c′lx.Deep output can highlight the key area of the image, connect its output end to the last pooled layer of the network, and add two convolution layers with different sizes and channel numbers in each side output. Based on this, properly combine the side output of different layers; that is, establish a short connection from the deep side output layer by layer to the shallow side output for information exchange. The deep information can describe the prominent area, the shallow information can describe the image details, and the two types of information can be properly combined to form a dense and accurate saliency map. The conceptual diagram of the saliency detection algorithm is shown in Figure6.Figure 6
The conceptual diagram of the saliency detection algorithm.The significant loss function is used to calculate the loss of both. Significant loss is defined as(10)lsalienty^,x∗=ωy^−ωx∗22.The total loss function is defined as(11)ltotaly^,ys,x∗=λ1lcontenty^,x∗+λ2lstyley^,ys+λ3lsalienty^,x∗+λ4lTV.According to the total loss function, the style migration network is iteratively optimized many times, and finally, a style migration model of this style is generated. The high-level features extracted by the network are used to represent the loss, and the mathematical expression is as follows.(12)Lcontentp⟶,x⟶,l=12∑i,jFijl−pijl2.The difference between the generated image and the style image is expressed as(13)El=14N2M2∑ijGijl−Aijl2.According to the mathematical formula, the loss of image stylization is composed of content loss and style loss. However, the number of additional parameters increases linearly with the number of styles. Therefore, this method has great limitations, and it is difficult to apply it to large-scale style transfer.


## 4. Experimental Results and Evaluation

We collected the excellent works of nearly 50 European painters as the style images of this paper to participate in the training. In this section, we are going to discuss the experimental results and compare them with the results of some existing models.


### 4.1. Experimental Results

The style map dataset contains many paintings of different styles. The design idea of an image conversion network is widely used. It mainly uses step convolution and microstep convolution to complete downsampling and upsampling operations, respectively, and uses normalization and activation functions after the two operations. And the remaining connections are used between the first and second modules and between the fourth and fifth modules. The specific network structure is shown in Table1.Table 1
The specific network structure.
Input3×256×256Convkernelsize=9, stride=132×252×252Convkernelsize=3, stride=264×128×128Convkernelsize=3, stride=2128×64×64FireRes block256×64×64Fire block384×64×64ConvTranskernelsize=3, stride=2384×64×64Convkernelsize=9, stride=13×256×256The downsampling operation can conveniently use a larger convolution layer for feature extraction without increasing the amount of network computation. Secondly, it can effectively improve the size of the receptive field, because the style migration network with excellent performance needs to constantly change the area in the original image. After testing the model, it is found that the picture can be iterated about 500 times to produce a better effect. The following are the different effect diagrams displayed by different iteration times, as shown in Figure7.Figure 7
Artistic image processing rendering.As can be seen from the figure, the image quality generated by the model in this chapter is excellent on the whole. After the fusion of the content map and style map, the features of the content map, i.e., structure information, will not be covered up, and the fusion of the two is better.


### 4.2. Experimental Evaluation

Image style transfer is an art editing work, and the evaluation of its stylization effect is a highly subjective task. Therefore, this section uses the method of user research and questionnaire to evaluate the stylization effect. For the evaluation of stylization effect, this section designs two links: the first link requires that the generated stylized images be matched with several given style images, and a total of 20 questionnaires are used. Each algorithm in each questionnaire generates 4 stylized images, and each stylized image gives 3 stylized images, one of which is the style of the stylized image. Let the user choose which style the stylized image comes from and calculate the correct matching rate of each algorithm and take the average value. In the second step, different algorithms for the same content image and style image are used to generate stylized images, so that the user can select the algorithm with the best visual effect and then make statistics to calculate the percentage of each algorithm selected as the user’s preference score for the algorithm. The results of the two surveys are shown in Table2 and Figure 8.Table 2
User questionnaire.
MethodMatching accuracyPreference scoreGatys [33]92.0%15.81%Johnson [34]98.5%27.52%AdaIN [35]98.8%29.64%Our model98.4%27.03%Figure 8
User questionnaire.Through user investigation, it can be found that the stylized image generated by the lightweight model of this algorithm is basically consistent with the stylized image generated by the Johnson algorithm in terms of visual perception. In addition to the subjective evaluation of the quality of the stylized image generated by this algorithm, the quantitative analysis of the model is also carried out. The model is evaluated from the aspects of model parameters, memory occupied by the model at one time, floating-point arithmetic, multiplication and addition index, model size, and model running time. From the above quantitative analysis table, it can be seen that the lightweight network used in this algorithm is effective and can greatly reduce the parameters of the model. Compared with the Johnson model, the network model of this algorithm is more than 50% smaller than the residual network in terms of model parameters, memory, and model size. The model quantitative analysis table is shown in Table3.Table 3
The model quantitative analysis table.
MethodModel parameterMemoryFLOPsModel sizeRunning timeJohnson1679235202.08 MB20.16 G6.43 MB0.55 sOur model728546139.83 MB8.74 G2.80 MB0.48 s


## 4.1. Experimental Results

The style map dataset contains many paintings of different styles. The design idea of an image conversion network is widely used. It mainly uses step convolution and microstep convolution to complete downsampling and upsampling operations, respectively, and uses normalization and activation functions after the two operations. And the remaining connections are used between the first and second modules and between the fourth and fifth modules. The specific network structure is shown in Table1.Table 1
The specific network structure.
Input3×256×256Convkernelsize=9, stride=132×252×252Convkernelsize=3, stride=264×128×128Convkernelsize=3, stride=2128×64×64FireRes block256×64×64Fire block384×64×64ConvTranskernelsize=3, stride=2384×64×64Convkernelsize=9, stride=13×256×256The downsampling operation can conveniently use a larger convolution layer for feature extraction without increasing the amount of network computation. Secondly, it can effectively improve the size of the receptive field, because the style migration network with excellent performance needs to constantly change the area in the original image. After testing the model, it is found that the picture can be iterated about 500 times to produce a better effect. The following are the different effect diagrams displayed by different iteration times, as shown in Figure7.Figure 7
Artistic image processing rendering.As can be seen from the figure, the image quality generated by the model in this chapter is excellent on the whole. After the fusion of the content map and style map, the features of the content map, i.e., structure information, will not be covered up, and the fusion of the two is better.


## 4.2. Experimental Evaluation

Image style transfer is an art editing work, and the evaluation of its stylization effect is a highly subjective task. Therefore, this section uses the method of user research and questionnaire to evaluate the stylization effect. For the evaluation of stylization effect, this section designs two links: the first link requires that the generated stylized images be matched with several given style images, and a total of 20 questionnaires are used. Each algorithm in each questionnaire generates 4 stylized images, and each stylized image gives 3 stylized images, one of which is the style of the stylized image. Let the user choose which style the stylized image comes from and calculate the correct matching rate of each algorithm and take the average value. In the second step, different algorithms for the same content image and style image are used to generate stylized images, so that the user can select the algorithm with the best visual effect and then make statistics to calculate the percentage of each algorithm selected as the user’s preference score for the algorithm. The results of the two surveys are shown in Table2 and Figure 8.Table 2
User questionnaire.
MethodMatching accuracyPreference scoreGatys [33]92.0%15.81%Johnson [34]98.5%27.52%AdaIN [35]98.8%29.64%Our model98.4%27.03%Figure 8
User questionnaire.Through user investigation, it can be found that the stylized image generated by the lightweight model of this algorithm is basically consistent with the stylized image generated by the Johnson algorithm in terms of visual perception. In addition to the subjective evaluation of the quality of the stylized image generated by this algorithm, the quantitative analysis of the model is also carried out. The model is evaluated from the aspects of model parameters, memory occupied by the model at one time, floating-point arithmetic, multiplication and addition index, model size, and model running time. From the above quantitative analysis table, it can be seen that the lightweight network used in this algorithm is effective and can greatly reduce the parameters of the model. Compared with the Johnson model, the network model of this algorithm is more than 50% smaller than the residual network in terms of model parameters, memory, and model size. The model quantitative analysis table is shown in Table3.Table 3
The model quantitative analysis table.
MethodModel parameterMemoryFLOPsModel sizeRunning timeJohnson1679235202.08 MB20.16 G6.43 MB0.55 sOur model728546139.83 MB8.74 G2.80 MB0.48 s


## 5. Conclusion

In the era of rapid development of science and technology, the development space of digital media technology will be more and more extensive, and the application field will be gradually expanded. According to the current development trend, the development of digital media technology is still on the rise. With the progress of society, the corresponding novel technology art and technology are the symbolic products of modern communication. This paper proposes a novel technology art symbolism model based on artificial intelligence big data. In order to provide a method of real-time representation of art images, the deep learning method is used to model the artistic expression of images. We use convolutional neural network to extract specific features of the image and generate different versions of its filtering results. We propose a style migration algorithm for preserving salient regions which generates a style migration model. The effectiveness of the model is verified by simulation experiments. The proposed model can provide some help for the development and improvement of new media art in China.Promoting the innovation of novel technology art and technology teaching mode is not only the inevitable requirement of adapting to the development of the information age but also the only way to achieve the goal of talent training. Digital media technology is becoming more and more important in today’s development. In the follow-up development, it is necessary to continuously strengthen the construction of digital media in the deeper field of science, technology, and culture industry, so as to promote the faster and better development of the science, technology, and culture industry. More attention should be paid to the development of novel technology art and technology, so as to keep pace with the times and to promote the progress of modern society.



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*Source: 9630255-2022-09-07.xml*