# Application of Neural Network and Virtual Reality Technology in Digital Video Effects

Authors: Liao Jiang
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

With the continuous innovation of technology, digital technology is more and more widely used in the production of TV shows and movies and plays an important role. The effective application of digital video effects technology can enhance the visual effects of the overall TV and film works and improve the quality of the works. After the use of digital special effects in film and television production, people see more exquisite film and television pictures, but today’s technology is not enough to meet people’s growing demand for film and television pictures. This paper applied neural network and virtual reality technology to the production process of digital video effects and conducted a comparative experiment with the digital video effects produced by using the current technology. The special effects were evaluated in four aspects: image fidelity, sound distortion, picture fluency, and production efficiency. The experimental results show that after applying neural network and virtual reality technologies, the image fidelity of digital video effects is increased by 6.74%, the sound distortion is reduced by 7.69%, the picture fluency is increased by 6.89%, and the production efficiency is increased by 7.91%. Applying the neural network and virtual reality technology can improve the quality and efficiency of digital video special effects production.


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## Body

## 1. Introduction

With the continuous development of science and technology, people’s quality requirements for TV dramas and movies are also increasing. In order to produce high-quality finished products, other technologies must be used. Digital video effects are a very important part of TV dramas and movie production. Its appearance simplifies the shooting of TV dramas and movies. For example, some actions that cannot be completed by humans can be completed by adding special effects in the latter stage. Digital video effects also have powerful simulation capabilities, which can create visual experiences that do not exist or cannot be realized in real life so as to better express the creator’s creative intention. Digital special effects are widely used in TV dramas and movies. Most directors will add special effects to TV dramas and movies to enrich the visual and auditory effects. Therefore, research on digital video effects is very necessary.In the production process of films and television works, digital technology is usually introduced to make special effects to enrich the audio-visual effects of film and television works. Many scholars also conduct research on digital film and television special effects. In order to drive the sales of movie tickets, Andrew compared two films by Chinese contemporary artists to explore the integration of the use of digital technology to generate film special effects and how they can be transformed into film art [1]. Karasavvvidis selected 70 undergraduates to participate in the introductory course on digital video special effects, produced in order to study the effect of digital technology on film and television special effects. At the end of the course, students were asked to submit a short digital video and a video of the traditional method. The teacher graded the video. It has been analyzed to show that high-scoring videos basically use digital technology [2]. With audiences generally ignoring the material conditions of digital filmmaking, Lyczba reconnected digitally constructed and synthesized images to reality, highlighting this labor with Foucault’s concept of hetero space to highlight the importance of digital technology in synthetic filmmaking [3]. In order to improve the effectiveness of students’ learning, Hsu applied digital technology to the course of digital audio and video after production and set up a questionnaire after class to analyze the students’ learning effect. Statistical questionnaires show students’ satisfaction with digital teaching methods is relatively high [4]. Leung applied digital technology to data collected by digital devices to explore the role of digital video in early education. The study found that children who participated in the study were able to explore digital installations through video [5]. Lu took the teaching of film and television special effects postproduction as an example to solve the problem of the low quality of education related to special effects production and finally put forward reform suggestions for special effects production [6]. The above research shows that there is still room for improvement in digital video effects.Neural networks process information by adjusting the interconnected relationships between internal nodes and can improve the accuracy of machine learning predictions. Seng proposed a comprehensive prediction model based on the neural network, input the air quality sequence of different representative stations into the model, got the predicted concentration value of the air quality index, and compared it with another model to verify the performance of the model. The research found that the model in this paper outperformed other baseline models [7]. Mannem and Ghosh proposed a correction scheme for improved air tissue boundary segmentation based on deep neural networks by using a normal grid-based method to generate the input and output targets required for training. The results show that this method is more accurate in regularizing distances [8]. Sagheer and Kotb envisioned a neural network-based model for oil production forecasting. The model consisted of multiple hidden layers, each of which had multiple nodes, and different methods were used to benchmark the established model. The study found that the model proposed based on the neural network method had higher prediction accuracy than existing methods [9]. Virtual reality technology realizes human-computer interaction through a simulation system established by using a computer and has been widely used in many fields. Lv and Gong proposed a method for optimizing students’ online interactive learning efficiency based on virtual reality technology. This technology is used to quickly track students’ corresponding learning content and update and provide feedback on students’ online interactive learning. The study found that using this technology could improve students’ learning fluency, sex, and interactivity [10]. Dymora et al. discussed some problems of virtual reality technology in supporting the educational process and used virtual reality technology and traditional methods for environmental training applications. The results show that the correct rate of training by using virtual reality technology increases by 21% compared with traditional methods [11]. Akdere et al. studied the effectiveness of virtual reality technology in cultivating cross-cultural competence. 101 undergraduates from a university in the United States collected test data before and after the intervention in the form of an online questionnaire and conducted a linear regression test. The test results found that the learning environment based on technology improved the effectiveness of cultivating learners’ intercultural competence [12]. The application of neural network and virtual reality technology is very extensive but not used in digital video effects at the same time.The neural network has associative memory function and good fault tolerance, and virtual reality technology can use 3D modeling technology and human-computer interaction function. The combination of neural networks and virtual reality technology can make the production of digital video effects better. In this paper, the neural network and virtual reality technology were jointly applied to digital video effects, and a comparative experiment was carried out with the current digital video effects.


## 2. Neural Network and Virtual Reality Technology Algorithm

### 2.1. Neural Network

Neural networks are composed of a large number of neurons connected together to perform operations. The biggest feature of the neural network is that it can learn. The core of learning is to learn appropriate weight parameters to perform the nonlinear transformation on data to extract key features or decisions. Neural networks have highly nonlinear local effects, good self-adaptive and self-learning capabilities, and distributed storage of knowledge. They are very powerful and have many application scenarios. The neural networks used in this paper are mainly convolutional neural networks, recurrent neural networks, and long-short-term memory neural networks.


#### 2.1.1. Convolutional Neural Network

Convolutional neural networks are mainly good at object recognition in image data and are mostly used for visual tasks such as object detection [13]. The technique is scalable based on data and model size and can be trained via backpropagation. The convolutional layer is what distinguishes it from other neural networks. This layer performs component multiplication and addition operations. There is no need to manually select features for high-dimensional data processing, train weights, and finally, get good feature classification results. An operation of the convolutional layer is shown in Figure 1.Figure 1
A simple operation process of the convolutional layer.The convolutional computing layer is the most important layer in the convolutional neural network. If the stride is set to 1, the unfilled formula is as follows:(1)Padding=K−12.K represents the convolution kernel size. When zero-padding, the spatial dimensions of the input and output content can be kept the same.The formula for the output of any given convolutional layer is as follows:(2)O=W−K+2PS+1.O is the output size, P is the padding, and S is the stride.


#### 2.1.2. Recurrent Neural Network

The recurrent neural networks is a neural network specialized in processing sequences [14]. This network is commonly used in connected sequence applications, and because of its hidden state neural network, it is very efficient at processing text, and it is often used in natural language processing and speech processing.For any sequence timet, the hidden state ht is obtained by xt and ht−1:(3)ht=σzt=σUxt+Wht+b,σ is the activation function of the recurrent neural network and b is the bias term.The outputOt expression of the model is as follows:(4)Ot=Vht+c.Finally, at the sequence timet, the predicted output is as follows:(5)y^t=σOt.Recurrent neural networks get mostly predicted results and are also widely used in music generation, image captioning and forecasting stock market fluctuations.


#### 2.1.3. Long-Short-Term Memory Neural Network

Recurrent neural networks are mainly used in the field of prediction, but they cannot solve the problems of long-term dependencies and vanishing gradients. Long-short-term memory neural networks can effectively solve this problem. Long-short-term memory Neural Networks have the same chain structure as recurrent Neural Networks, but the repeating module structure is different [15]. This structure allows the network to retain a large number of previous stage values, which are mainly useful in language translation systems but can also be applied to sequence modeling tasks such as anomaly detection and speech recognition. Three gates are introduced in this neural network, namely the input gate, forget gate, and output gate, and memory cells with the same shape as the hidden state. Figure 2 shows the model diagram of the neural network structure.Figure 2
Structural model of long-short-term memory neural networks.The forget gate determines what information to discard from the cell state and outputs a number between 0 and 1, given each number in the cell stateCt−1. 1 means “reserved”, 0 means “discarded”.The forget gate calculation formula is as follows:(6)ft=σWf⋅ht−1,xt+bf.The formulas for the input gate and output gate are as follows:(7)it=σWi⋅ht−1,xt+bi,(8)ot=σWo⋅ht−1,xt+bo.Memory cells are(9)Ct=ft⋅Ct−1+it⋅C˜t,where C˜t represents candidate memory cells, and the formula is as follows:(10)C˜t=tanhWC⋅ht−1,xt+bC.Finally, the output at the current moment is computed as follows:(11)ht=ot⋅tanhCt.The sensitivity of the output layer in this architecture can be controlled by opening and closing the output gate without affecting the cell state.


### 2.2. Virtual Reality Technology

Virtual reality technology is a combination of various technologies, mainly including immersion, interactivity, and multisensitivity, and is widely used in entertainment, education, aerospace, and other fields.Immersion refers to the real degree of the user’s existence in the virtual world, and interactivity reflects the user’s operability of objects in the virtual world. Multisensibility refers to the perception of various organs such as vision, hearing, and touch that users obtain in the virtual world [16]. The combined application of the three technologies of immersion, interactivity, and multisensitivity closely links the virtual world with the real world, which can improve the breadth and depth of human cognition and ultimately reflect the essence of the objective world. Figure 3 shows the fusion of these three technologies in virtual reality.Figure 3
The main technology of virtual reality.The virtual reality technology can process the image by using the modeling technology to set the image as anM × N matrix, M,N represents the pixel point in a certain position of the current image, and the value is the rendered height information.(12)L=R+G+B3.R, G, and B are the red, green, and blue color components of the pixel, respectively.The average height value for the entireM × N matrix is calculated(13)L′=1MN∑x=1M∑y=1NLx,y.Finally, the relative height valueHx,y of the actual position x,y is obtained(14)Hx,y=k⋅Lx,yL′,k is a constant coefficient.The sine height model is used to model the image subconvex geometric state:(15)L″=Hsinω+H−π2<ω<3π2.Since the relative change in surface height is the key point for texture generation, the convex geometric model is derived from transforming it into a gradient model(16)Δ=dL″dω=Hcosω−π2<ω<3π2.Modeling technology has the characteristics of strong interactivity and high accuracy, which can promote the benign development of virtual reality production technology. Although modeling technology is only a small branch of virtual reality technology, it plays an important role in the production of digital video effects.


## 2.1. Neural Network

Neural networks are composed of a large number of neurons connected together to perform operations. The biggest feature of the neural network is that it can learn. The core of learning is to learn appropriate weight parameters to perform the nonlinear transformation on data to extract key features or decisions. Neural networks have highly nonlinear local effects, good self-adaptive and self-learning capabilities, and distributed storage of knowledge. They are very powerful and have many application scenarios. The neural networks used in this paper are mainly convolutional neural networks, recurrent neural networks, and long-short-term memory neural networks.


### 2.1.1. Convolutional Neural Network

Convolutional neural networks are mainly good at object recognition in image data and are mostly used for visual tasks such as object detection [13]. The technique is scalable based on data and model size and can be trained via backpropagation. The convolutional layer is what distinguishes it from other neural networks. This layer performs component multiplication and addition operations. There is no need to manually select features for high-dimensional data processing, train weights, and finally, get good feature classification results. An operation of the convolutional layer is shown in Figure 1.Figure 1
A simple operation process of the convolutional layer.The convolutional computing layer is the most important layer in the convolutional neural network. If the stride is set to 1, the unfilled formula is as follows:(1)Padding=K−12.K represents the convolution kernel size. When zero-padding, the spatial dimensions of the input and output content can be kept the same.The formula for the output of any given convolutional layer is as follows:(2)O=W−K+2PS+1.O is the output size, P is the padding, and S is the stride.


### 2.1.2. Recurrent Neural Network

The recurrent neural networks is a neural network specialized in processing sequences [14]. This network is commonly used in connected sequence applications, and because of its hidden state neural network, it is very efficient at processing text, and it is often used in natural language processing and speech processing.For any sequence timet, the hidden state ht is obtained by xt and ht−1:(3)ht=σzt=σUxt+Wht+b,σ is the activation function of the recurrent neural network and b is the bias term.The outputOt expression of the model is as follows:(4)Ot=Vht+c.Finally, at the sequence timet, the predicted output is as follows:(5)y^t=σOt.Recurrent neural networks get mostly predicted results and are also widely used in music generation, image captioning and forecasting stock market fluctuations.


### 2.1.3. Long-Short-Term Memory Neural Network

Recurrent neural networks are mainly used in the field of prediction, but they cannot solve the problems of long-term dependencies and vanishing gradients. Long-short-term memory neural networks can effectively solve this problem. Long-short-term memory Neural Networks have the same chain structure as recurrent Neural Networks, but the repeating module structure is different [15]. This structure allows the network to retain a large number of previous stage values, which are mainly useful in language translation systems but can also be applied to sequence modeling tasks such as anomaly detection and speech recognition. Three gates are introduced in this neural network, namely the input gate, forget gate, and output gate, and memory cells with the same shape as the hidden state. Figure 2 shows the model diagram of the neural network structure.Figure 2
Structural model of long-short-term memory neural networks.The forget gate determines what information to discard from the cell state and outputs a number between 0 and 1, given each number in the cell stateCt−1. 1 means “reserved”, 0 means “discarded”.The forget gate calculation formula is as follows:(6)ft=σWf⋅ht−1,xt+bf.The formulas for the input gate and output gate are as follows:(7)it=σWi⋅ht−1,xt+bi,(8)ot=σWo⋅ht−1,xt+bo.Memory cells are(9)Ct=ft⋅Ct−1+it⋅C˜t,where C˜t represents candidate memory cells, and the formula is as follows:(10)C˜t=tanhWC⋅ht−1,xt+bC.Finally, the output at the current moment is computed as follows:(11)ht=ot⋅tanhCt.The sensitivity of the output layer in this architecture can be controlled by opening and closing the output gate without affecting the cell state.


## 2.1.1. Convolutional Neural Network

Convolutional neural networks are mainly good at object recognition in image data and are mostly used for visual tasks such as object detection [13]. The technique is scalable based on data and model size and can be trained via backpropagation. The convolutional layer is what distinguishes it from other neural networks. This layer performs component multiplication and addition operations. There is no need to manually select features for high-dimensional data processing, train weights, and finally, get good feature classification results. An operation of the convolutional layer is shown in Figure 1.Figure 1
A simple operation process of the convolutional layer.The convolutional computing layer is the most important layer in the convolutional neural network. If the stride is set to 1, the unfilled formula is as follows:(1)Padding=K−12.K represents the convolution kernel size. When zero-padding, the spatial dimensions of the input and output content can be kept the same.The formula for the output of any given convolutional layer is as follows:(2)O=W−K+2PS+1.O is the output size, P is the padding, and S is the stride.


## 2.1.2. Recurrent Neural Network

The recurrent neural networks is a neural network specialized in processing sequences [14]. This network is commonly used in connected sequence applications, and because of its hidden state neural network, it is very efficient at processing text, and it is often used in natural language processing and speech processing.For any sequence timet, the hidden state ht is obtained by xt and ht−1:(3)ht=σzt=σUxt+Wht+b,σ is the activation function of the recurrent neural network and b is the bias term.The outputOt expression of the model is as follows:(4)Ot=Vht+c.Finally, at the sequence timet, the predicted output is as follows:(5)y^t=σOt.Recurrent neural networks get mostly predicted results and are also widely used in music generation, image captioning and forecasting stock market fluctuations.


## 2.1.3. Long-Short-Term Memory Neural Network

Recurrent neural networks are mainly used in the field of prediction, but they cannot solve the problems of long-term dependencies and vanishing gradients. Long-short-term memory neural networks can effectively solve this problem. Long-short-term memory Neural Networks have the same chain structure as recurrent Neural Networks, but the repeating module structure is different [15]. This structure allows the network to retain a large number of previous stage values, which are mainly useful in language translation systems but can also be applied to sequence modeling tasks such as anomaly detection and speech recognition. Three gates are introduced in this neural network, namely the input gate, forget gate, and output gate, and memory cells with the same shape as the hidden state. Figure 2 shows the model diagram of the neural network structure.Figure 2
Structural model of long-short-term memory neural networks.The forget gate determines what information to discard from the cell state and outputs a number between 0 and 1, given each number in the cell stateCt−1. 1 means “reserved”, 0 means “discarded”.The forget gate calculation formula is as follows:(6)ft=σWf⋅ht−1,xt+bf.The formulas for the input gate and output gate are as follows:(7)it=σWi⋅ht−1,xt+bi,(8)ot=σWo⋅ht−1,xt+bo.Memory cells are(9)Ct=ft⋅Ct−1+it⋅C˜t,where C˜t represents candidate memory cells, and the formula is as follows:(10)C˜t=tanhWC⋅ht−1,xt+bC.Finally, the output at the current moment is computed as follows:(11)ht=ot⋅tanhCt.The sensitivity of the output layer in this architecture can be controlled by opening and closing the output gate without affecting the cell state.


## 2.2. Virtual Reality Technology

Virtual reality technology is a combination of various technologies, mainly including immersion, interactivity, and multisensitivity, and is widely used in entertainment, education, aerospace, and other fields.Immersion refers to the real degree of the user’s existence in the virtual world, and interactivity reflects the user’s operability of objects in the virtual world. Multisensibility refers to the perception of various organs such as vision, hearing, and touch that users obtain in the virtual world [16]. The combined application of the three technologies of immersion, interactivity, and multisensitivity closely links the virtual world with the real world, which can improve the breadth and depth of human cognition and ultimately reflect the essence of the objective world. Figure 3 shows the fusion of these three technologies in virtual reality.Figure 3
The main technology of virtual reality.The virtual reality technology can process the image by using the modeling technology to set the image as anM × N matrix, M,N represents the pixel point in a certain position of the current image, and the value is the rendered height information.(12)L=R+G+B3.R, G, and B are the red, green, and blue color components of the pixel, respectively.The average height value for the entireM × N matrix is calculated(13)L′=1MN∑x=1M∑y=1NLx,y.Finally, the relative height valueHx,y of the actual position x,y is obtained(14)Hx,y=k⋅Lx,yL′,k is a constant coefficient.The sine height model is used to model the image subconvex geometric state:(15)L″=Hsinω+H−π2<ω<3π2.Since the relative change in surface height is the key point for texture generation, the convex geometric model is derived from transforming it into a gradient model(16)Δ=dL″dω=Hcosω−π2<ω<3π2.Modeling technology has the characteristics of strong interactivity and high accuracy, which can promote the benign development of virtual reality production technology. Although modeling technology is only a small branch of virtual reality technology, it plays an important role in the production of digital video effects.


## 3. Digital Video Special Effects Production Technology

Digital special effects refer to special effects realized by using computer graphics technology, which is widely infiltrated into all aspects of TV series and movie production. The production process of special effects for film and television is very complex, and the routine operations include steps such as original painting, 3D modeling, binding, element special effects, scene synthesis, audio and video synthesis, and rendering. The film and television pictures that people see are composed of paintings. The paintings are generally static, but what people can see is dynamic. This is because the pictures of TV dramas and movies that I usually see are a dozen or more pictures in one second. The transient is stronger. Special effects production is usually carried out frame by frame, and a one-second movie is composed of at least a dozen pictures. The more frames, the smoother the picture. In the production of special effects, digital technology is mostly used now, and this paper mainly explores the effect of applying neural networks and virtual reality technology to the production of digital video effects.


### 3.1. Digital Technology

The special effects produced by applying CG technology are more commonly used now. CG special effects refer to the use of digital technology on a computer to create special effects [17]. CG special effects can greatly shorten the production time, and the processing effect is better. The emergence of this technology has quickly become an indispensable technical means for the TV series and movie production industry. It can use its own technical advantages to perfectly present the content that the author wants to express and also meet the needs of the audience, so it can be quickly and widely used in the film and television industry.Optical special effects in digital special effects are a very common technical means by which can change the complicated process of making pictures step by step in the traditional production process. Scene design can confirm the external outline and internal appearance of things, such as by using a series of orientation adjustments to confirm the external contour and internal appearance of things. Even producers can rely on a lot of technology to write expressions, and only through the screen can make the audience have a complex visual experience. This not only brings a qualitative leap in the quality of the film and television works but also brings a satisfying viewing experience. Figure4 shows the production process of digital video special effects.Figure 4
Application of digital technology in special effects for film and television.


### 3.2. Neural Network and Virtual Reality Technology

Neural networks mirror the behavior of the human brain, allow computer programs to recognize patterns, and can solve common problems in artificial intelligence. Due to the self-learning function of the neural network, the use of the agent system can create a cluster animation effect of tens of thousands of people and improve the realistic action of a single task [18]. Neural networks are applied to motion capture technology to improve the acquisition efficiency and production effects of human muscle animation materials. For example, a part of the data to be processed is first selected, cleaned manually, and then a deep neural network is trained to replace manual labor. The rest of the data is processed automatically. The application of neural networks in film and television special effects production is shown in Figure 5.Figure 5
The application of neural networks in image making.Virtual reality technology refers to the technology that is generated by using computers and allows users to observe and operate in the virtual world interactively [19]. Virtual reality technology uses 3D modeling technology and integrates with other technologies to gradually integrate into an integrated technology. By using virtual imaging technology to shoot, the method of shooting first and then production has been changed in the past, which has become a new direction for the development of the TV and movie production industry. The application of virtual technology will present the scene of the TV and movie, and it can greatly improve the shooting efficiency of TV and movie works, reduce shooting costs, save shooting resources, and enhance the ability of directors and photographers to control the entire scene [20]. The actor’s performance is integrated into the virtual scene, which improves the effect of watching movies. The application of virtual reality technology in TV and movie special effects is shown in Figure 6.Figure 6
The application of virtual reality technology in digital video special effects.The self-learning and storage functions of neural networks and the modeling interaction function of virtual reality technology make the tracking of tiny things more accurate in the production of digital video effects, and the production of special effects is also more accurate and efficient, which is in line with the trend of the times. Movies and TV series have a new level of advancement and can even be extended to fields such as animation production, games, and mobile applications. After the combination of the two, the application of digital video effects is shown in Figure7.Figure 7
Comprehensive application of neural network and virtual reality technology.


## 3.1. Digital Technology

The special effects produced by applying CG technology are more commonly used now. CG special effects refer to the use of digital technology on a computer to create special effects [17]. CG special effects can greatly shorten the production time, and the processing effect is better. The emergence of this technology has quickly become an indispensable technical means for the TV series and movie production industry. It can use its own technical advantages to perfectly present the content that the author wants to express and also meet the needs of the audience, so it can be quickly and widely used in the film and television industry.Optical special effects in digital special effects are a very common technical means by which can change the complicated process of making pictures step by step in the traditional production process. Scene design can confirm the external outline and internal appearance of things, such as by using a series of orientation adjustments to confirm the external contour and internal appearance of things. Even producers can rely on a lot of technology to write expressions, and only through the screen can make the audience have a complex visual experience. This not only brings a qualitative leap in the quality of the film and television works but also brings a satisfying viewing experience. Figure4 shows the production process of digital video special effects.Figure 4
Application of digital technology in special effects for film and television.


## 3.2. Neural Network and Virtual Reality Technology

Neural networks mirror the behavior of the human brain, allow computer programs to recognize patterns, and can solve common problems in artificial intelligence. Due to the self-learning function of the neural network, the use of the agent system can create a cluster animation effect of tens of thousands of people and improve the realistic action of a single task [18]. Neural networks are applied to motion capture technology to improve the acquisition efficiency and production effects of human muscle animation materials. For example, a part of the data to be processed is first selected, cleaned manually, and then a deep neural network is trained to replace manual labor. The rest of the data is processed automatically. The application of neural networks in film and television special effects production is shown in Figure 5.Figure 5
The application of neural networks in image making.Virtual reality technology refers to the technology that is generated by using computers and allows users to observe and operate in the virtual world interactively [19]. Virtual reality technology uses 3D modeling technology and integrates with other technologies to gradually integrate into an integrated technology. By using virtual imaging technology to shoot, the method of shooting first and then production has been changed in the past, which has become a new direction for the development of the TV and movie production industry. The application of virtual technology will present the scene of the TV and movie, and it can greatly improve the shooting efficiency of TV and movie works, reduce shooting costs, save shooting resources, and enhance the ability of directors and photographers to control the entire scene [20]. The actor’s performance is integrated into the virtual scene, which improves the effect of watching movies. The application of virtual reality technology in TV and movie special effects is shown in Figure 6.Figure 6
The application of virtual reality technology in digital video special effects.The self-learning and storage functions of neural networks and the modeling interaction function of virtual reality technology make the tracking of tiny things more accurate in the production of digital video effects, and the production of special effects is also more accurate and efficient, which is in line with the trend of the times. Movies and TV series have a new level of advancement and can even be extended to fields such as animation production, games, and mobile applications. After the combination of the two, the application of digital video effects is shown in Figure7.Figure 7
Comprehensive application of neural network and virtual reality technology.


## 4. Experiment Design of Digital Video Special Effects

### 4.1. Experimental Subjects

The production process of TV and movie special effects is very complicated. Special effects artists often do it frame by frame during the production process, and a 1-second picture includes at least 16 frames. In order to shorten the experimental period, this paper used a 1-minute video with characters and scenery without special effects processing as the experimental object.


### 4.2. Experimental Process

TV and movie special effects production by using neural networks and virtual digital technology is set as the experimental group, and the ordinary digital video effects are set as the control group, which is realized by using a computer. The specific software is after effects. After applying neural network and virtual reality technology to the production of digital video effects, it is compared with the current digital video effects to analyze the application effect of neural network and virtual reality technology in digital video effects. Digital film and television effects are mainly divided into visual effects and auditory effects, which can be analyzed in terms of image fidelity and sound distortion. The main purpose of applying new technology to special effects production is to improve the smoothness and production efficiency of the picture. Therefore, the four aspects of image fidelity, sound distortion, picture fluency, and production efficiency under the two methods are finally tested and analyzed [21].After the above two sets of experiments are produced, the video after special effects production takes 10 seconds as a cycle, and the data of the first 10 seconds, the first 20 seconds, the first 30 seconds, the first 40 seconds, the first 50 seconds, and the first 60 seconds are analyzed [22]. Taking 10 seconds as a cycle is equivalent to six comparative analyses, which improves the accuracy of the experiment and avoids the influence of accidental factors.


## 4.1. Experimental Subjects

The production process of TV and movie special effects is very complicated. Special effects artists often do it frame by frame during the production process, and a 1-second picture includes at least 16 frames. In order to shorten the experimental period, this paper used a 1-minute video with characters and scenery without special effects processing as the experimental object.


## 4.2. Experimental Process

TV and movie special effects production by using neural networks and virtual digital technology is set as the experimental group, and the ordinary digital video effects are set as the control group, which is realized by using a computer. The specific software is after effects. After applying neural network and virtual reality technology to the production of digital video effects, it is compared with the current digital video effects to analyze the application effect of neural network and virtual reality technology in digital video effects. Digital film and television effects are mainly divided into visual effects and auditory effects, which can be analyzed in terms of image fidelity and sound distortion. The main purpose of applying new technology to special effects production is to improve the smoothness and production efficiency of the picture. Therefore, the four aspects of image fidelity, sound distortion, picture fluency, and production efficiency under the two methods are finally tested and analyzed [21].After the above two sets of experiments are produced, the video after special effects production takes 10 seconds as a cycle, and the data of the first 10 seconds, the first 20 seconds, the first 30 seconds, the first 40 seconds, the first 50 seconds, and the first 60 seconds are analyzed [22]. Taking 10 seconds as a cycle is equivalent to six comparative analyses, which improves the accuracy of the experiment and avoids the influence of accidental factors.


## 5. Results of Digital Video Special Effects

### 5.1. Image Fidelity

Image fidelity refers to the degree of similarity between an image and a reference image and reflects the quality of image transmission and processing performance. This paper uses the full reference method to calculate the fidelity. The image fidelity results of the two groups of film and television special effects are shown in Figure8.Figure 8
Image fidelity results from two sets of film and television effects.Figure8 shows higher image fidelity for special effects produced by using neural networks and virtual reality. The average image fidelity of the experimental group was 0.95, the average image fidelity of the control group was 0.89, and the experimental group was 6.74% higher than the control group. The main reason is that the neural network can recognize the image more accurately. After adding the virtual reality technology, 3D modeling is carried out, and the extraction position and features are more accurate, making the production effect more realistic and close to reality.


### 5.2. Sound Distortion

The degree of sound distortion is one of the factors used to measure the sound effects in the special effects production process. Usually, a distortion meter is used to measure the distortion degree of the output device. The sound distortion results of the two groups of film and television special effects are shown in Figure9.Figure 9
Comparison chart of the error degree of different algorithm system analysis.Figure9 shows that the sound distortion of the control group is higher than that of the experimental group. From the specific numerical point of view, the sound distortion of the control group is about 3.51%, and the sound distortion of the experimental group is about 3.24%, a decrease of 0.27%. Therefore, the sound distortion of the special effects production method by using neural networks and virtual reality technology is lower, which is 7.69% lower than that of traditional digital technology. The neurons of the neural network can recognize the human senses, and after the sound is processed by using the neural network and virtual reality technology, it is closer to the real feelings of people. The natural distortion is lower.


### 5.3. Screen Fluency

The smoothness of the picture can be measured by the frame rate. The frame rate refers to the number of frames of the transmitted picture in 1 second. The higher the number of frames, the higher the smoothness of the picture. The frame rate data of the experimental group and the control group are shown in Figure10.Figure 10
Frame rate data for experimental and control groups.It can be seen from Figure10 that, with the increase of time, the frame rate under the two methods increases at the same time, but the amount of increase is different. At different times, the frame rate of the experimental group is about 31 and the frame rate of the control group is about 29. In terms of frame rate, the experimental group is 6.89% higher than the control group. From this, it can be concluded that the film and television special effects produced by applying neural networks and virtual reality technology have higher picture fluency.


### 5.4. Production Efficiency

In the period of shooting to release, in addition to shooting time, a movie spends more time on special effects production. The importance of time is self-evident. Digital film and television special effects mainly include the steps of modeling, binding, special effects, rendering, audio and video homogeneity, and synthesis. Each step takes a lot of time. This article takes 10 seconds of special effects production time as a cycle, observes the time spent in each production cycle, compares the two methods, and finally integrates the data. The production time results of the two groups are shown in Figure11.Figure 11
Comparison of production time of two special effects techniques.As can be seen from the figure, in the same video time, the test group spent less time on special effects than the control group. After averaging the time data of the six time periods, it can be seen that by applying the neural network and virtual reality technology to special effects production, it takes an average of 373 hours to produce every 10 seconds of video. It takes an average of 405 hours to make every 10 seconds of video with the current method. The experimental group increased the production efficiency by 7.91%.In general, compared with the digital video special effects based on traditional algorithms, the neural network and virtual reality technology are better. Numerically, the image fidelity of digital video effects is increased by 6.74%, the sound distortion is reduced by 7.69%, the picture fluency is increased by 6.89%, and the production efficiency is increased by 7.91%. Neural networks can improve the accuracy and efficiency of image and sound processing. Virtual reality technology uses 3D modeling to locate the content that needs to be shot in advance, especially the use of human-computer interaction technology to improve the realism of special effects production. Therefore, the neural network and virtual reality technology have a good effect on the application of digital film and television special effects.


## 5.1. Image Fidelity

Image fidelity refers to the degree of similarity between an image and a reference image and reflects the quality of image transmission and processing performance. This paper uses the full reference method to calculate the fidelity. The image fidelity results of the two groups of film and television special effects are shown in Figure8.Figure 8
Image fidelity results from two sets of film and television effects.Figure8 shows higher image fidelity for special effects produced by using neural networks and virtual reality. The average image fidelity of the experimental group was 0.95, the average image fidelity of the control group was 0.89, and the experimental group was 6.74% higher than the control group. The main reason is that the neural network can recognize the image more accurately. After adding the virtual reality technology, 3D modeling is carried out, and the extraction position and features are more accurate, making the production effect more realistic and close to reality.


## 5.2. Sound Distortion

The degree of sound distortion is one of the factors used to measure the sound effects in the special effects production process. Usually, a distortion meter is used to measure the distortion degree of the output device. The sound distortion results of the two groups of film and television special effects are shown in Figure9.Figure 9
Comparison chart of the error degree of different algorithm system analysis.Figure9 shows that the sound distortion of the control group is higher than that of the experimental group. From the specific numerical point of view, the sound distortion of the control group is about 3.51%, and the sound distortion of the experimental group is about 3.24%, a decrease of 0.27%. Therefore, the sound distortion of the special effects production method by using neural networks and virtual reality technology is lower, which is 7.69% lower than that of traditional digital technology. The neurons of the neural network can recognize the human senses, and after the sound is processed by using the neural network and virtual reality technology, it is closer to the real feelings of people. The natural distortion is lower.


## 5.3. Screen Fluency

The smoothness of the picture can be measured by the frame rate. The frame rate refers to the number of frames of the transmitted picture in 1 second. The higher the number of frames, the higher the smoothness of the picture. The frame rate data of the experimental group and the control group are shown in Figure10.Figure 10
Frame rate data for experimental and control groups.It can be seen from Figure10 that, with the increase of time, the frame rate under the two methods increases at the same time, but the amount of increase is different. At different times, the frame rate of the experimental group is about 31 and the frame rate of the control group is about 29. In terms of frame rate, the experimental group is 6.89% higher than the control group. From this, it can be concluded that the film and television special effects produced by applying neural networks and virtual reality technology have higher picture fluency.


## 5.4. Production Efficiency

In the period of shooting to release, in addition to shooting time, a movie spends more time on special effects production. The importance of time is self-evident. Digital film and television special effects mainly include the steps of modeling, binding, special effects, rendering, audio and video homogeneity, and synthesis. Each step takes a lot of time. This article takes 10 seconds of special effects production time as a cycle, observes the time spent in each production cycle, compares the two methods, and finally integrates the data. The production time results of the two groups are shown in Figure11.Figure 11
Comparison of production time of two special effects techniques.As can be seen from the figure, in the same video time, the test group spent less time on special effects than the control group. After averaging the time data of the six time periods, it can be seen that by applying the neural network and virtual reality technology to special effects production, it takes an average of 373 hours to produce every 10 seconds of video. It takes an average of 405 hours to make every 10 seconds of video with the current method. The experimental group increased the production efficiency by 7.91%.In general, compared with the digital video special effects based on traditional algorithms, the neural network and virtual reality technology are better. Numerically, the image fidelity of digital video effects is increased by 6.74%, the sound distortion is reduced by 7.69%, the picture fluency is increased by 6.89%, and the production efficiency is increased by 7.91%. Neural networks can improve the accuracy and efficiency of image and sound processing. Virtual reality technology uses 3D modeling to locate the content that needs to be shot in advance, especially the use of human-computer interaction technology to improve the realism of special effects production. Therefore, the neural network and virtual reality technology have a good effect on the application of digital film and television special effects.


## 6. Conclusion

The application of digital video effects in TV dramas and movies has improved the quality of film and television pictures, and the audience can be more immersive and have a better perception. This paper first introduced the algorithm of the neural network and virtual reality technology and then described the current situation of digital video special effects and the application effect of neural network and virtual reality technology in digital video special effects. The application of the neural network and virtual reality technology to the production of TV dramas and movies special effects and the current digital video effects production for special effects were then compared and tested separately. The main disadvantage of this experiment is that the video of the study is too short, the representativeness is not obvious enough, and efforts are still needed in subsequent research.



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*Source: 3362617-2022-09-22.xml*