# Visual Planarization in Oil Painting Techniques in Digital Information Age

Authors: Yiping Zhang
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

The arrival of the digital information age has realized the transformation of people’s production and lifestyle, which has also promoted the vigorous development of the art field. At present, the application of informatization in oil painting techniques is increasing. The purpose of this paper is to realize the research on the visual planarization of oil painting techniques in the digital information environment to promote the ecological development of the computer and art field. By using the advanced methods and technologies of its information display, this paper proposed the use of image processing algorithms in the digital information age to guide the integration of digital information technology and oil painting techniques. This helped to analyze and solve objective problems such as the profound meaning of the objective existence in the art field and the inability to understand the emotion in it. It can be seen from the analysis of the oil painting color model that, in the RGB model, when the wavelength is within a certain range, it can be used. But when the wavelength is between 444 nanometers and 526 nanometers, the red intensity is too low. That is to say, it can not effectively express its corresponding color in this wavelength range. This model is very challenging. In the XYZ color model, when the wavelength is in a certain region, the output of red light becomes a value less than 0. Any color in this space can be well represented by a linear combination ofX, Y, and Z. Therefore, the study of visual flattening of oil painting is very important in the field of art.


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## Body

## 1. Introduction

In modern society, with the rapid development of science and technology, the high-speed transmission of Internet information, and the proliferation of image resources, the themes and expressions of oil painting creation are also constantly enriched. In the long history of oil painting, pigments suitable for outdoor use appeared. Technologies of modern visual media, such as camera technology, computer technology, image processing, and other technologies, are now ubiquitous. This has become an important part of artistic creation. Visual media has influenced artistic creation to a certain extent, which has brought new problems to oil painting, for example, the problem of material collection in oil painting, the spiritual pursuit and emotional expression of painters in oil painting, and how to correctly understand the role of visual media technology in oil painting. Regarding these issues, some scholars believe that due to the application of visual media technology, the painter’s basic skills in collecting materials in traditional oil paintings gradually receded, which affected the painter’s personal experience of the subject matter. The emotional relevance and expressiveness of the painting are reduced. The dependence of oil painting creators on visual media technology is increased. This limits the creativity of oil painting creators to a certain extent. Some scholars believed that the application of visual media technology brought a lot of convenience to oil painting creation in terms of material selection, expression, and shaping details, which made up for the shortcomings and defects of traditional oil painting and changes in new forms.From the perspective of art history research, visual information in images is one of the core parameters of the investigation. Most oil painting works analyze image data based on entry points such as color and brushstroke. The analysis results largely depend on the researcher’s own knowledge reserve and the ability to understand the image data. However, with the development of the era of big data, digital humanities research has gradually been used by researchers in the field of art history research. On the one hand, the sample research of big data can improve the representativeness, and on the other hand, it can explore its implicit relationship from a macro and multidimensional perspective. This paper mainly involved cross-exploration of design semiotics, cognitive psychology, information design, interaction design, art history, and other disciplines. The overall design process from theory to creative practice was comprehensively adopted, such as interdisciplinary research methods, comparative research methods, and literature research methods. This paper reinterpreted the works of representative figures in oil paintings. Through visual design, the nonvisual information in oil painting works could be read from a multidimensional perspective and from a distance, which allowed viewers to freely and interactively explore the relationship with the oil painting works. Although it is impossible to express the analysis of abstract concepts in artworks or the theoretical results of subverting traditional research with the expression of data, it can still assist its related research and provide research methods for this kind of art research.The study of oil painting in the field of art has always been a hot topic at present. Among them, Uti presented an empirical study showing how the plagiarism of illustrations affected students and impaired their ability to illustrate. Only federal and state colleges of fine and applied arts with graphics majors were selected [1]. Szabo presented a report on the computer graphics exhibition “Revisiting Computer Graphics”. This was a virtual reconstruction of one of the earliest computer-generated graphics exhibitions [2]. Anguera observed that “The graphic designer is the poet and linguist of the visual images of the world.” His work consistently achieved natural balance and subtly used poetic emotion. At the same time, the clarity of language information was not disturbed [3]. Bai proposed a visual art design method based on virtual reality. The wavelet transform method was used to remove the noise signal in the visual image; the fuzzy spatial visual fusion binary model was established. The space for oil painting was planned. The spatial distribution information of oil paintings was obtained [4]. Wang provided a practical and quantitative approach to investigate the impact of the increasing popularity of digital art on the national market for today’s oil painting artists [5]. However, due to the inability to follow up with equipment and insufficient data collection, the above research is currently only in the theoretical stage and cannot be practiced.The use of digital informatization to study the visual planarization of oil painting is a very novel topic. Among them, Karimnia A aimed to critically evaluate the current quality of visual arts ESP textbooks by conducting a case study of the visual art appreciation (painting, graphics, and sculpture) textbook developed and published by SAMT [6]. Dutta attempted to use ambient light and color perception to automatically transfer the convergence of user emotional problems in DOP. Therefore, an emotion-based oil painting technique was proposed, which could be implemented and used in real-time in current smart devices without using any explicit hardware. The proposed technique was significantly optimized by reducing processing time and power consumption [7]. Annum aimed to demonstrate basic editing procedures and skills to assist digital photographers in Ghana with the ability and technical knowledge to colorize grayscale images [8]. Zhang proposed an algorithm to generate digital painting lighting effects from a single image. This algorithm was based on a key observation: The artist used many overlapping strokes to paint lighting effects [9]. Zeng proposed a novel controllable image restoration framework that could combine expert advice to help artists imagine what restored ancient paintings might look like [10]. However, the above research on digital informatization and oil painting still cannot get rid of the traditional definition and thinking of oil painting appreciation. This makes it impossible for the two to fully integrate and play their advantages. However, the current analysis of network information security supervision in the big data environment still does not get rid of the definition and thinking based on the traditional network information security model. In-depth analysis and discussion of the functionality of big data are lacking. The highly integrated and advantageous play of big data technology and network information security supervision mode is hindered.The innovative part of this paper is as follows. Through this design research, the oil painting works were analyzed from two perspectives: microscopic and macroscopic. This paper analyzed the concept of painting and the impressionism it represented from multidimensional information data such as time, color system, frame size, work influence, and semantic annotation. Nonvisual information such as stylistic turns and aesthetic features of oil paintings was deeply dissected. The core idea of oil painting works was generally grasped. Through the multidimensional perspective analysis, the partial judgment caused by linear interpretation can be properly avoided, which is helpful for scholars to carry out related research. Visual analysis is an important method of data analysis. This paper organically integrated the computer's data processing ability and human self-cognition ability. With the help of interactive expression techniques, viewers could intuitively and efficiently gain insight into the data collection of nonvisual information in oil paintings.


## 2. Visual Planarization in the Context of Digital Information

### 2.1. Visual Design

Visual design in information design is the extraction of data from unstructured information. After the data is authored and encoded, the data visualization takes shape. Viewers use memory and experience to identify what is displayed. Through the perception and understanding of data information, the viewer's inherent knowledge and personal cognitive range are enhanced, as shown in Figure1.Figure 1
Visual flat design process.As can be seen from Figure1, the visual design in this paper is to encode the nonvisual information in the oil painting, and then, the artistic and visual processing is carried out. Interactivity is enhanced so that information is more logically and intuitively disseminated to the viewer. Through visualization, the information and semantic network that the viewer originally needed to dig in depth are presented. This allows viewers to quickly complete a series of experiences such as identification and cognition of a large number of sample information [11].


### 2.2. Word Frequency Investigation

After completing the statistics of 7 items of data of 1788 oil paintings, this paper conducts statistics on the semantically labeled data [12, 13]. The titles of image data are all in English, so it is more accurate to use English annotations and image data, and English word frequency statistics tools are more popular and convenient than Chinese phrase statistics. Therefore, this paper unifies the language of artificial semantic annotation to English. This paper tried a variety of word frequency statistics tools, such as textmechanic and word frequency. In the process of automatic computer recognition, there will be prompts for unrecognized content, such as hyphens and phrases. This paper will manually correct them one by one according to the prompts. The analysis of textmechanic is more comprehensive and convenient, so the final decision is to use the statistics of textmechanic as a result. After automatic identification and manual correction, the database statistics of semantic annotation are completed, as shown in Table 1.Table 1
Partial table of semantic annotation frequency of oil painting works.
RankingWordsFrequencyThe proportion (%)RankingWordsFrequencyThe proportion (%)1Tree41112.45%65Dock10.03%2Sea3069.27%66Dog20.03%3River3049.21%67Egg20.03%4Boat1775.36%68Food10.03%5House1364.12%69Geese10.03%6Cliff1223.70%70Leaf20.03%7Waterlily1063.21%71Magpie20.03%8Seine1033.12%72Port10.03%9Flower952.88%73Rain20.03%10Bridge902.73%74Studio10.03%————75Wind10.03%From Table1, it can be found that the top ten high-frequency words are all related to outdoor landscapes. 84% of the top 75 high-frequency words contain outdoor landscapes. The subject matter of oil painting works is mostly outdoor works, which also corresponds to impressionist art’s “emphasis on external light” and the love of painting outdoors [14]. Traditional art values the content of the work, while impressionists enjoy depicting moments in the natural environment. Since the appearance of optics and color science, impressionist painters began to be keen on painting outdoors. Therefore, impressionism is also called “Pleinairism.” In terms of painting techniques, impressionists also developed the use of external light to describe objects. Colors are applied depending on the viewing position and the degree of exposure to light.


### 2.3. Visual Interaction Architecture of Nonvisual Information in Oil Painting

Visual design is not images or photography. If the source data is an image and it is used as an image in the result, it cannot be called a visual design. Images of oil paintings in the visualization are used as source data. Through program processing, viewer-readable, recognizable, and understandable data images are generated. Visualization also has properties such as interaction and visual efficiency, but achieving data communication goals is always the first priority. Therefore, in the design, this paper not only needs to control the artistry of the work but also takes into account the legibility of the information [15, 16]. Combined with the preanalysis after the data obtained in this paper, the whole work is mainly divided into four parts. The structure of the work is shown in Figure 2. The four sections are relatively independent and have no cross relationship with each other. The main distribution shows the results of the cross analysis after extracting information in this paper.Figure 2
Structure of oil painting appreciation.


#### 2.3.1. Interactive Frame Diagram Design

Static visualization works are suitable for showing a macro perspective, which can bring a visually impactful experience. But the need for microscopic observation cannot be met. The presentation method of interactive image visualization allows viewers to freely switch between macro and micro perspectives. Viewers can not only understand the macro contextual relationship but also understand the detailed data microscopically. Therefore, this paper chooses the interactive visual design presentation. The interactive flow chart is shown in Figure3, and the following processes are all cyclic.Figure 3
Information interaction design flow chart.As can be seen from Figure3, the interactivity in information interaction design is different from the reading mode of traditional design. The reading mode in traditional design is too content-centric, and the viewer is contained by the content. However, through visual processing and interactive design, the viewer can use the existing knowledge structure in the brain to have a certain compensatory understanding of the object being viewed [17]. The Interaction Framework defines the workflow, behavior, and organization of the product. This is not necessarily a linear process but is often an iterative loop.


#### 2.3.2. Framework and Construction of Interactive Technology

Based on the previous design concept, the main program is mainly divided into 3 parts: data reading, image color picking, and page design. It is divided into 4 interfaces. The information structure is shown in Figure4.Figure 4
Information architecture diagram.As can be seen from Figure4, the main difficulty of the program is the hueProcess color wheel part. The first step is to automatically load the image and take the color pixel by pixel. Each image generates an array; the second step is to sort the array tone values of each picture to form a database; the third step is that the database finds relevant data and corresponding pictures according to the request; the fourth step is based on the hue value × saturation coefficient, which is mapped to the ring one by one and distinguishes the colors; the fifth step is mapped to the visual display behavior according to the mouse command; the above steps can be repeated.


### 2.4. Oil Painting Based on Digital Image Processing Technology

#### 2.4.1. Image Preprocessing

There are many specific methods of color image processing, but they all belong to the following two main methods. The first type of method is that if a color image contains a component image, it needs to be decomposed first. These component images are processed separately. These processed component images are then used to synthesize a color image. Then, the processed color image can be obtained. The color image also contains pixels, and these color pixels are directly processed. Then, the processed image can be obtained. This is the second type of method. Since there are at least three component images in a color image, in fact, a color pixel in a color image is a vector [18, 19].When doing color image processing, all colors in nature are not processed one by one. However, color images are processed by building a color model. A color model is a model that is described using quantitative methods. Based on the established color model, it is convenient for the computer to carry out subsequent processing and representation.c is assumed to be any vector in the RGB color model space; then, there is(1)c=cRcGcB=RGB.In this formula, it is pointed out thatc contains three components and the components only represent a color image. It has three components at a pixel, namely, RGB components. Color components can be represented as functions containing coordinates (x, y):(2)cx,y=cRx,ycGx,ycBx,y=Rx,yGx,yBx,y.For an image of size M × N, among them,x = 01, 2 .. M−1, y- = 0, 2, 2, ..., N−1, there are MN such vectors c(x, y). In the color model, red, green, and blue are called the three primary colors. Other colors can be formed by mixing these three basic colors in different proportions, that is,(3)C=rR+gG+bB.


#### 2.4.2. Sharpening Technology in Images

Sometimes, it is also necessary to sharpen the image; the purpose is also to improve the quality of the image. The most commonly used is the Laplace method in the spatial domain. The method is that a discrete formulation of the second-order differential is first defined. Then a formula is constructed based on this formula [20]. Laplacian is an isotropic differential operator. That is, the image is filtered first and then rotated. The result is the same as the result of rotating the original image and then filtering it. For a first-order function fx, its first-order differential is defined as follows:(4)∂f∂x=fx+1−fx.The definition of the second-order differential is(5)∂f∂x2=fx+1+fx−1−2fx.Therefore, the Laplace operator of a two-dimensional image function f(x, y) can be defined as(6)∇2f=∂2f∂x2+∂2f∂y2.Any order differentiation is a linear operation, so the Laplace transform is a linear operator. In the horizontalx direction, for a two-dimensional function, the second derivative is defined as(7)∂2f∂x2=fx+1,y+fx−1,y−2fx,y.Likewise, a two-dimensional function in the verticaly direction is similarly defined as(8)∂2f∂y2=fx,y+1+fx,y−1−2fx,y.Therefore, the Laplace operator derived from these three formulas is(9)∇2f=fx+1,y+fx−1,y+fx,y+1+fx,y−1−4fx,y.Laplace is a differential operator. A basic method of image enhancement using Laplace can be expressed as(10)gx,y=fx,y+c∇2fx,y.Therefore, there are many ways to sharpen color images. The Laplace method is used in this paper [21]. In the RGB color model, the three components of R, G, and B can be sharpened separately; that is, the Laplacian of the color image is calculated. It can be obtained by computing the Laplacian of each component image separately. In the RGB color model, the Laplace transform of the vector c in formula 3 is(11)∇2cx,y=∇2Rx,y∇2Gx,y∇2Bx,y.


#### 2.4.3. Calculation of Gradient Image

Since the boundary of the image is where the image information changes drastically, the response is the discontinuity of local features. This has a large gradient value, which can be used for edge detection of the image. Before performing edge detection, gradient images and related knowledge are first learned. For a given image f(x,y), in order to find the edge strength and direction at the coordinate (x,y) position, the tool used is the gradient. The gradient is denoted by ∇f and defined by a vector:(12)∇f=gradf=gxgy.This vector has many properties. One of the important properties is that the vector points in the direction of the maximum rate of change off at the (x,y) position. The magnitude value of this vector ∇f is represented by M(x, y), that is,(13)Mx,y=mag∇f=g2x+g2y.gx, gy, and M(x,y) are all images of the same size as the original image, and they are generated when x and y vary overall pixel positions in f. In practical applications, this image is often referred to as a gradient image. In some implementations, in order to fit and simplify computation, magnitude values are often approximated by absolute values to square and square root operations, that is,(14)Mx,y≈gx+gy.The direction angle a(x, y) of this gradient vector is(15)ax,y=arctangygx.Among them, a(x, y) is a measure relative to the x-axis. If the gradient of an image is to be obtained, then the partial derivatives ∂f/∂x and ∂f/∂y are required to be calculated at each pixel location in the image. Digital images are discontinuous and discrete, so the difference is often used to replace the differential operation when calculating the partial derivative. For a one-dimensional function f(x), the basic definition of the first-order differential is the difference value:(16)∂f∂x=fx+1−fx.Therefore, the two-dimensional function f(x,y) is similarly defined as follows:(17)gx=∂fx,y∂x=fx+1,y−fx,y,(18)gy=∂fx,y∂y=fx,y+1−fx,y.The partial derivatives of these formulas are calculated for each pixel in the image. When there is an edge in the image, then there must be a relatively large gradient value. However, in the part of the image that is not an edge, the gray value changes less and generally has a smaller gradient value.


## 2.1. Visual Design

Visual design in information design is the extraction of data from unstructured information. After the data is authored and encoded, the data visualization takes shape. Viewers use memory and experience to identify what is displayed. Through the perception and understanding of data information, the viewer's inherent knowledge and personal cognitive range are enhanced, as shown in Figure1.Figure 1
Visual flat design process.As can be seen from Figure1, the visual design in this paper is to encode the nonvisual information in the oil painting, and then, the artistic and visual processing is carried out. Interactivity is enhanced so that information is more logically and intuitively disseminated to the viewer. Through visualization, the information and semantic network that the viewer originally needed to dig in depth are presented. This allows viewers to quickly complete a series of experiences such as identification and cognition of a large number of sample information [11].


## 2.2. Word Frequency Investigation

After completing the statistics of 7 items of data of 1788 oil paintings, this paper conducts statistics on the semantically labeled data [12, 13]. The titles of image data are all in English, so it is more accurate to use English annotations and image data, and English word frequency statistics tools are more popular and convenient than Chinese phrase statistics. Therefore, this paper unifies the language of artificial semantic annotation to English. This paper tried a variety of word frequency statistics tools, such as textmechanic and word frequency. In the process of automatic computer recognition, there will be prompts for unrecognized content, such as hyphens and phrases. This paper will manually correct them one by one according to the prompts. The analysis of textmechanic is more comprehensive and convenient, so the final decision is to use the statistics of textmechanic as a result. After automatic identification and manual correction, the database statistics of semantic annotation are completed, as shown in Table 1.Table 1
Partial table of semantic annotation frequency of oil painting works.
RankingWordsFrequencyThe proportion (%)RankingWordsFrequencyThe proportion (%)1Tree41112.45%65Dock10.03%2Sea3069.27%66Dog20.03%3River3049.21%67Egg20.03%4Boat1775.36%68Food10.03%5House1364.12%69Geese10.03%6Cliff1223.70%70Leaf20.03%7Waterlily1063.21%71Magpie20.03%8Seine1033.12%72Port10.03%9Flower952.88%73Rain20.03%10Bridge902.73%74Studio10.03%————75Wind10.03%From Table1, it can be found that the top ten high-frequency words are all related to outdoor landscapes. 84% of the top 75 high-frequency words contain outdoor landscapes. The subject matter of oil painting works is mostly outdoor works, which also corresponds to impressionist art’s “emphasis on external light” and the love of painting outdoors [14]. Traditional art values the content of the work, while impressionists enjoy depicting moments in the natural environment. Since the appearance of optics and color science, impressionist painters began to be keen on painting outdoors. Therefore, impressionism is also called “Pleinairism.” In terms of painting techniques, impressionists also developed the use of external light to describe objects. Colors are applied depending on the viewing position and the degree of exposure to light.


## 2.3. Visual Interaction Architecture of Nonvisual Information in Oil Painting

Visual design is not images or photography. If the source data is an image and it is used as an image in the result, it cannot be called a visual design. Images of oil paintings in the visualization are used as source data. Through program processing, viewer-readable, recognizable, and understandable data images are generated. Visualization also has properties such as interaction and visual efficiency, but achieving data communication goals is always the first priority. Therefore, in the design, this paper not only needs to control the artistry of the work but also takes into account the legibility of the information [15, 16]. Combined with the preanalysis after the data obtained in this paper, the whole work is mainly divided into four parts. The structure of the work is shown in Figure 2. The four sections are relatively independent and have no cross relationship with each other. The main distribution shows the results of the cross analysis after extracting information in this paper.Figure 2
Structure of oil painting appreciation.


### 2.3.1. Interactive Frame Diagram Design

Static visualization works are suitable for showing a macro perspective, which can bring a visually impactful experience. But the need for microscopic observation cannot be met. The presentation method of interactive image visualization allows viewers to freely switch between macro and micro perspectives. Viewers can not only understand the macro contextual relationship but also understand the detailed data microscopically. Therefore, this paper chooses the interactive visual design presentation. The interactive flow chart is shown in Figure3, and the following processes are all cyclic.Figure 3
Information interaction design flow chart.As can be seen from Figure3, the interactivity in information interaction design is different from the reading mode of traditional design. The reading mode in traditional design is too content-centric, and the viewer is contained by the content. However, through visual processing and interactive design, the viewer can use the existing knowledge structure in the brain to have a certain compensatory understanding of the object being viewed [17]. The Interaction Framework defines the workflow, behavior, and organization of the product. This is not necessarily a linear process but is often an iterative loop.


### 2.3.2. Framework and Construction of Interactive Technology

Based on the previous design concept, the main program is mainly divided into 3 parts: data reading, image color picking, and page design. It is divided into 4 interfaces. The information structure is shown in Figure4.Figure 4
Information architecture diagram.As can be seen from Figure4, the main difficulty of the program is the hueProcess color wheel part. The first step is to automatically load the image and take the color pixel by pixel. Each image generates an array; the second step is to sort the array tone values of each picture to form a database; the third step is that the database finds relevant data and corresponding pictures according to the request; the fourth step is based on the hue value × saturation coefficient, which is mapped to the ring one by one and distinguishes the colors; the fifth step is mapped to the visual display behavior according to the mouse command; the above steps can be repeated.


## 2.3.1. Interactive Frame Diagram Design

Static visualization works are suitable for showing a macro perspective, which can bring a visually impactful experience. But the need for microscopic observation cannot be met. The presentation method of interactive image visualization allows viewers to freely switch between macro and micro perspectives. Viewers can not only understand the macro contextual relationship but also understand the detailed data microscopically. Therefore, this paper chooses the interactive visual design presentation. The interactive flow chart is shown in Figure3, and the following processes are all cyclic.Figure 3
Information interaction design flow chart.As can be seen from Figure3, the interactivity in information interaction design is different from the reading mode of traditional design. The reading mode in traditional design is too content-centric, and the viewer is contained by the content. However, through visual processing and interactive design, the viewer can use the existing knowledge structure in the brain to have a certain compensatory understanding of the object being viewed [17]. The Interaction Framework defines the workflow, behavior, and organization of the product. This is not necessarily a linear process but is often an iterative loop.


## 2.3.2. Framework and Construction of Interactive Technology

Based on the previous design concept, the main program is mainly divided into 3 parts: data reading, image color picking, and page design. It is divided into 4 interfaces. The information structure is shown in Figure4.Figure 4
Information architecture diagram.As can be seen from Figure4, the main difficulty of the program is the hueProcess color wheel part. The first step is to automatically load the image and take the color pixel by pixel. Each image generates an array; the second step is to sort the array tone values of each picture to form a database; the third step is that the database finds relevant data and corresponding pictures according to the request; the fourth step is based on the hue value × saturation coefficient, which is mapped to the ring one by one and distinguishes the colors; the fifth step is mapped to the visual display behavior according to the mouse command; the above steps can be repeated.


## 2.4. Oil Painting Based on Digital Image Processing Technology

### 2.4.1. Image Preprocessing

There are many specific methods of color image processing, but they all belong to the following two main methods. The first type of method is that if a color image contains a component image, it needs to be decomposed first. These component images are processed separately. These processed component images are then used to synthesize a color image. Then, the processed color image can be obtained. The color image also contains pixels, and these color pixels are directly processed. Then, the processed image can be obtained. This is the second type of method. Since there are at least three component images in a color image, in fact, a color pixel in a color image is a vector [18, 19].When doing color image processing, all colors in nature are not processed one by one. However, color images are processed by building a color model. A color model is a model that is described using quantitative methods. Based on the established color model, it is convenient for the computer to carry out subsequent processing and representation.c is assumed to be any vector in the RGB color model space; then, there is(1)c=cRcGcB=RGB.In this formula, it is pointed out thatc contains three components and the components only represent a color image. It has three components at a pixel, namely, RGB components. Color components can be represented as functions containing coordinates (x, y):(2)cx,y=cRx,ycGx,ycBx,y=Rx,yGx,yBx,y.For an image of size M × N, among them,x = 01, 2 .. M−1, y- = 0, 2, 2, ..., N−1, there are MN such vectors c(x, y). In the color model, red, green, and blue are called the three primary colors. Other colors can be formed by mixing these three basic colors in different proportions, that is,(3)C=rR+gG+bB.


### 2.4.2. Sharpening Technology in Images

Sometimes, it is also necessary to sharpen the image; the purpose is also to improve the quality of the image. The most commonly used is the Laplace method in the spatial domain. The method is that a discrete formulation of the second-order differential is first defined. Then a formula is constructed based on this formula [20]. Laplacian is an isotropic differential operator. That is, the image is filtered first and then rotated. The result is the same as the result of rotating the original image and then filtering it. For a first-order function fx, its first-order differential is defined as follows:(4)∂f∂x=fx+1−fx.The definition of the second-order differential is(5)∂f∂x2=fx+1+fx−1−2fx.Therefore, the Laplace operator of a two-dimensional image function f(x, y) can be defined as(6)∇2f=∂2f∂x2+∂2f∂y2.Any order differentiation is a linear operation, so the Laplace transform is a linear operator. In the horizontalx direction, for a two-dimensional function, the second derivative is defined as(7)∂2f∂x2=fx+1,y+fx−1,y−2fx,y.Likewise, a two-dimensional function in the verticaly direction is similarly defined as(8)∂2f∂y2=fx,y+1+fx,y−1−2fx,y.Therefore, the Laplace operator derived from these three formulas is(9)∇2f=fx+1,y+fx−1,y+fx,y+1+fx,y−1−4fx,y.Laplace is a differential operator. A basic method of image enhancement using Laplace can be expressed as(10)gx,y=fx,y+c∇2fx,y.Therefore, there are many ways to sharpen color images. The Laplace method is used in this paper [21]. In the RGB color model, the three components of R, G, and B can be sharpened separately; that is, the Laplacian of the color image is calculated. It can be obtained by computing the Laplacian of each component image separately. In the RGB color model, the Laplace transform of the vector c in formula 3 is(11)∇2cx,y=∇2Rx,y∇2Gx,y∇2Bx,y.


### 2.4.3. Calculation of Gradient Image

Since the boundary of the image is where the image information changes drastically, the response is the discontinuity of local features. This has a large gradient value, which can be used for edge detection of the image. Before performing edge detection, gradient images and related knowledge are first learned. For a given image f(x,y), in order to find the edge strength and direction at the coordinate (x,y) position, the tool used is the gradient. The gradient is denoted by ∇f and defined by a vector:(12)∇f=gradf=gxgy.This vector has many properties. One of the important properties is that the vector points in the direction of the maximum rate of change off at the (x,y) position. The magnitude value of this vector ∇f is represented by M(x, y), that is,(13)Mx,y=mag∇f=g2x+g2y.gx, gy, and M(x,y) are all images of the same size as the original image, and they are generated when x and y vary overall pixel positions in f. In practical applications, this image is often referred to as a gradient image. In some implementations, in order to fit and simplify computation, magnitude values are often approximated by absolute values to square and square root operations, that is,(14)Mx,y≈gx+gy.The direction angle a(x, y) of this gradient vector is(15)ax,y=arctangygx.Among them, a(x, y) is a measure relative to the x-axis. If the gradient of an image is to be obtained, then the partial derivatives ∂f/∂x and ∂f/∂y are required to be calculated at each pixel location in the image. Digital images are discontinuous and discrete, so the difference is often used to replace the differential operation when calculating the partial derivative. For a one-dimensional function f(x), the basic definition of the first-order differential is the difference value:(16)∂f∂x=fx+1−fx.Therefore, the two-dimensional function f(x,y) is similarly defined as follows:(17)gx=∂fx,y∂x=fx+1,y−fx,y,(18)gy=∂fx,y∂y=fx,y+1−fx,y.The partial derivatives of these formulas are calculated for each pixel in the image. When there is an edge in the image, then there must be a relatively large gradient value. However, in the part of the image that is not an edge, the gray value changes less and generally has a smaller gradient value.


## 2.4.1. Image Preprocessing

There are many specific methods of color image processing, but they all belong to the following two main methods. The first type of method is that if a color image contains a component image, it needs to be decomposed first. These component images are processed separately. These processed component images are then used to synthesize a color image. Then, the processed color image can be obtained. The color image also contains pixels, and these color pixels are directly processed. Then, the processed image can be obtained. This is the second type of method. Since there are at least three component images in a color image, in fact, a color pixel in a color image is a vector [18, 19].When doing color image processing, all colors in nature are not processed one by one. However, color images are processed by building a color model. A color model is a model that is described using quantitative methods. Based on the established color model, it is convenient for the computer to carry out subsequent processing and representation.c is assumed to be any vector in the RGB color model space; then, there is(1)c=cRcGcB=RGB.In this formula, it is pointed out thatc contains three components and the components only represent a color image. It has three components at a pixel, namely, RGB components. Color components can be represented as functions containing coordinates (x, y):(2)cx,y=cRx,ycGx,ycBx,y=Rx,yGx,yBx,y.For an image of size M × N, among them,x = 01, 2 .. M−1, y- = 0, 2, 2, ..., N−1, there are MN such vectors c(x, y). In the color model, red, green, and blue are called the three primary colors. Other colors can be formed by mixing these three basic colors in different proportions, that is,(3)C=rR+gG+bB.


## 2.4.2. Sharpening Technology in Images

Sometimes, it is also necessary to sharpen the image; the purpose is also to improve the quality of the image. The most commonly used is the Laplace method in the spatial domain. The method is that a discrete formulation of the second-order differential is first defined. Then a formula is constructed based on this formula [20]. Laplacian is an isotropic differential operator. That is, the image is filtered first and then rotated. The result is the same as the result of rotating the original image and then filtering it. For a first-order function fx, its first-order differential is defined as follows:(4)∂f∂x=fx+1−fx.The definition of the second-order differential is(5)∂f∂x2=fx+1+fx−1−2fx.Therefore, the Laplace operator of a two-dimensional image function f(x, y) can be defined as(6)∇2f=∂2f∂x2+∂2f∂y2.Any order differentiation is a linear operation, so the Laplace transform is a linear operator. In the horizontalx direction, for a two-dimensional function, the second derivative is defined as(7)∂2f∂x2=fx+1,y+fx−1,y−2fx,y.Likewise, a two-dimensional function in the verticaly direction is similarly defined as(8)∂2f∂y2=fx,y+1+fx,y−1−2fx,y.Therefore, the Laplace operator derived from these three formulas is(9)∇2f=fx+1,y+fx−1,y+fx,y+1+fx,y−1−4fx,y.Laplace is a differential operator. A basic method of image enhancement using Laplace can be expressed as(10)gx,y=fx,y+c∇2fx,y.Therefore, there are many ways to sharpen color images. The Laplace method is used in this paper [21]. In the RGB color model, the three components of R, G, and B can be sharpened separately; that is, the Laplacian of the color image is calculated. It can be obtained by computing the Laplacian of each component image separately. In the RGB color model, the Laplace transform of the vector c in formula 3 is(11)∇2cx,y=∇2Rx,y∇2Gx,y∇2Bx,y.


## 2.4.3. Calculation of Gradient Image

Since the boundary of the image is where the image information changes drastically, the response is the discontinuity of local features. This has a large gradient value, which can be used for edge detection of the image. Before performing edge detection, gradient images and related knowledge are first learned. For a given image f(x,y), in order to find the edge strength and direction at the coordinate (x,y) position, the tool used is the gradient. The gradient is denoted by ∇f and defined by a vector:(12)∇f=gradf=gxgy.This vector has many properties. One of the important properties is that the vector points in the direction of the maximum rate of change off at the (x,y) position. The magnitude value of this vector ∇f is represented by M(x, y), that is,(13)Mx,y=mag∇f=g2x+g2y.gx, gy, and M(x,y) are all images of the same size as the original image, and they are generated when x and y vary overall pixel positions in f. In practical applications, this image is often referred to as a gradient image. In some implementations, in order to fit and simplify computation, magnitude values are often approximated by absolute values to square and square root operations, that is,(14)Mx,y≈gx+gy.The direction angle a(x, y) of this gradient vector is(15)ax,y=arctangygx.Among them, a(x, y) is a measure relative to the x-axis. If the gradient of an image is to be obtained, then the partial derivatives ∂f/∂x and ∂f/∂y are required to be calculated at each pixel location in the image. Digital images are discontinuous and discrete, so the difference is often used to replace the differential operation when calculating the partial derivative. For a one-dimensional function f(x), the basic definition of the first-order differential is the difference value:(16)∂f∂x=fx+1−fx.Therefore, the two-dimensional function f(x,y) is similarly defined as follows:(17)gx=∂fx,y∂x=fx+1,y−fx,y,(18)gy=∂fx,y∂y=fx,y+1−fx,y.The partial derivatives of these formulas are calculated for each pixel in the image. When there is an edge in the image, then there must be a relatively large gradient value. However, in the part of the image that is not an edge, the gray value changes less and generally has a smaller gradient value.


## 3. Visual Planarization of Oil Painting Technology Based on Digital Information

### 3.1. Color Model of Oil Painting

On the basis of summarizing and analyzing the drawing process and mode of traditional oil painting, it is not difficult to find that the colors and pigments that can be used to draw pictures are very important factors. Painters often try to create different atmospheres through the use of different kinds of colors. The emotion of the painter is expressed and conveyed in the form of painting and color. The painter’s values, ideas, and ideal pursuits are conveyed to the audience [22]. Based on the calculation of the relevant knowledge of oil painting color, this paper compares and analyzes the use of color in traditional art and natural images. At the same time, the colors of natural images and artistic images should be transformed to achieve the visual effect of simulating painting. In the drawing of oil paintings, a special model of the color model is often used. The color model mainly shows people the colors needed in a variety of painting processes through the shaping of different colors. When a beam of light contains many light sources of different frequencies, people only need to change the brightness and intensity of these light sources to easily obtain several kinds of light with colors different from the main light source. Among the different lights obtained by this method, the color of the main light source that can generate other lights of different colors is called the primary color. Among the commonly used color models, the models formed by primary colors are more commonly used, such as the RGB model and the XYZ color model, as shown in Figure 5.Figure 5
Statistics of color distribution under different models. (a) RGB color model. (b) XYZ color model.
(a)(b)It can be seen from Figure5(a) that the color model of the RGB model can not only be applied as a three-primary color model but also can be combined into the most visible colors to meet the needs of most oil painting creators. However, the RGB model also has its own inherent defects. It can be applied when the wavelength is in a certain region, but when the wavelength is between 444 nanometers and 526 nanometers, the intensity of red is too low. That is to say, when it is in this wavelength range, it is difficult for the model to effectively express its corresponding color. It can be seen from Figure 5(b) that, in fact, it is difficult to have a color model capable of expressing all visible colors, or a model capable of expressing all visible colors does not exist at all. Thus, in 1931, the International Commission on Illumination defined the XYZ color model in the field of mathematics. This model is able to output all colors as positive values. Unlike the RGB color mode, when the wavelength is in a certain region, the output of red light becomes a negative value. In the XYZ color model, any color in this space can be well expressed by the linear combination of X, Y, and Z.Color statistics: by comparing the color of natural images with oil paintings, the distribution and statistics of color and many other aspects are compared and analyzed. It can be found that the color of the image and the oil painting do have different rules from each other in terms of color presentation. Figure6 is a comparison of the tone distribution of the two.Figure 6
Color distribution map. (a) Oil painting. (b) Natural images.
(a)(b)As can be seen from Figure6, through the comparative analysis of the two oil paintings, a regular feature can be found. Usually, painters of oil paintings tend to choose warm colors such as red, yellow, and orange when painting; painters of natural images tend to give the image a larger color scale while painting peaks in places like sky blue and orange. The color distribution in natural images is uneven, while the color distribution in oil painting is more harmonious. Among them, as the saturation of the color changes, the color temperature also changes. This paper also studies the distribution of natural images and oil paintings on the psychological color temperature, as shown in Figure 7, red is an oil painting, and blue is a natural image.Figure 7
Color temperature gradient map in different directions. (a) Color temperature distribution in thex direction. (b) Color temperature distribution in the y direction.
(a)(b)As can be seen from Figure7, in the color temperature distribution diagram, it is not difficult to see that in terms of psychological color temperature, oil paintings show a warmer trend; in terms of saturation, the distribution of oil paintings is relatively uniform. This paper also explores and studies the correlation quantities of statistical gradients such as image brightness and psychological color temperature. The gradient map of image brightness is shown in Figure 8, where red is an oil painting and blue is a natural image.Figure 8
Brightness comparison statistics in different directions. (a) Brightness comparison chart in theX direction. (b) Brightness comparison chart in the Y direction.
(a)(b)By comparing the images in Figure8, it is not difficult to deduce that the local contrast (including brightness and psychological color temperature) of natural images is lower than that of oil paintings. The main watershed between oil paintings and natural images also often lies in the difference in color temperature and color temperature gradient.


### 3.2. Surface of Oil Painting in Visual Planarization

In this paper, BRDF (Bidirectional Reflection Distribution Function) measurement system is used to collect BRDF data. Combined with HSI color space theory, the texture and color information of the homemade oil paint color card are measured and analyzed. Measured values at varying illumination angles are fitted. By fitting the actual measured data, a fitting function is obtained. The texture, material, and lighting information of the oil painting are also obtained. At the same time, the PCA method is combined to reconstruct the spectral reflectance of multispectral images. The true color information of the oil painting is obtained. The two are combined into one to realize the analysis and research of the texture, material, lighting, and color information of cross-media oil painting artworks. The change curves of the brightness (I) of the oil painting color patches of different colors in Figures7 and8 as a function of the illumination angle are analyzed. The Asym2Sig fitting function is used to fit the luminance value 1 component of the HSI color space of the color patch. The Asym2Sig function can be expressed as(19)fx=A+B∗1+e−x−a+b1/2/b2∗1−1+e−x−a+b1/2/b3.From formula19, the functional relationship of the change rule of the brightness (I) information of each oil paint color block can be determined. According to the real-shot image of any standard reference color block, the brightness information of any illumination angle in the full-plane space can be generalized and calculated. Full-plane texture information can be obtained. The Asym2Sig function is used to fit and analyze the 6 oil paint color patches. The results are shown in Tables 2 and 3.Table 2
The  function fitting coefficient table of the brightness value of each oil paint color block changing with the illumination angle.
Asym2sigABaCoefficient−41.179542.257687.143711−85.160786.112390.224112−102.8352103.609889.227214−1.3737.040585.430410.06041.893387.2262440.03061.050580.7547Table 3
Root mean square and variance of function fitting for each oil paint patch.
R-Square (R-square)Found difference (RMSE)110.99330.0346120.99340.0265140.96520.0274440.98620.0377460.96350.0308480.94760.0674It can be seen from Tables2 and 3 that the RMSE statistical parameters of the brightness of the six oil paint color patches analyzed using the Asym2Sig function are close to 0. Therefore, the error between the experimental data and the fitted data is small. The R-square coefficient of determination of the bright value is close to 1, which indicates that the variables of the fitted functional formula have a strong ability to explain the dependent variable. This works well for the experimental data, so the fit of the data is good. Therefore, the technology of oil painting is of great significance to visual planarization in the stage of digital informatization.


## 3.1. Color Model of Oil Painting

On the basis of summarizing and analyzing the drawing process and mode of traditional oil painting, it is not difficult to find that the colors and pigments that can be used to draw pictures are very important factors. Painters often try to create different atmospheres through the use of different kinds of colors. The emotion of the painter is expressed and conveyed in the form of painting and color. The painter’s values, ideas, and ideal pursuits are conveyed to the audience [22]. Based on the calculation of the relevant knowledge of oil painting color, this paper compares and analyzes the use of color in traditional art and natural images. At the same time, the colors of natural images and artistic images should be transformed to achieve the visual effect of simulating painting. In the drawing of oil paintings, a special model of the color model is often used. The color model mainly shows people the colors needed in a variety of painting processes through the shaping of different colors. When a beam of light contains many light sources of different frequencies, people only need to change the brightness and intensity of these light sources to easily obtain several kinds of light with colors different from the main light source. Among the different lights obtained by this method, the color of the main light source that can generate other lights of different colors is called the primary color. Among the commonly used color models, the models formed by primary colors are more commonly used, such as the RGB model and the XYZ color model, as shown in Figure 5.Figure 5
Statistics of color distribution under different models. (a) RGB color model. (b) XYZ color model.
(a)(b)It can be seen from Figure5(a) that the color model of the RGB model can not only be applied as a three-primary color model but also can be combined into the most visible colors to meet the needs of most oil painting creators. However, the RGB model also has its own inherent defects. It can be applied when the wavelength is in a certain region, but when the wavelength is between 444 nanometers and 526 nanometers, the intensity of red is too low. That is to say, when it is in this wavelength range, it is difficult for the model to effectively express its corresponding color. It can be seen from Figure 5(b) that, in fact, it is difficult to have a color model capable of expressing all visible colors, or a model capable of expressing all visible colors does not exist at all. Thus, in 1931, the International Commission on Illumination defined the XYZ color model in the field of mathematics. This model is able to output all colors as positive values. Unlike the RGB color mode, when the wavelength is in a certain region, the output of red light becomes a negative value. In the XYZ color model, any color in this space can be well expressed by the linear combination of X, Y, and Z.Color statistics: by comparing the color of natural images with oil paintings, the distribution and statistics of color and many other aspects are compared and analyzed. It can be found that the color of the image and the oil painting do have different rules from each other in terms of color presentation. Figure6 is a comparison of the tone distribution of the two.Figure 6
Color distribution map. (a) Oil painting. (b) Natural images.
(a)(b)As can be seen from Figure6, through the comparative analysis of the two oil paintings, a regular feature can be found. Usually, painters of oil paintings tend to choose warm colors such as red, yellow, and orange when painting; painters of natural images tend to give the image a larger color scale while painting peaks in places like sky blue and orange. The color distribution in natural images is uneven, while the color distribution in oil painting is more harmonious. Among them, as the saturation of the color changes, the color temperature also changes. This paper also studies the distribution of natural images and oil paintings on the psychological color temperature, as shown in Figure 7, red is an oil painting, and blue is a natural image.Figure 7
Color temperature gradient map in different directions. (a) Color temperature distribution in thex direction. (b) Color temperature distribution in the y direction.
(a)(b)As can be seen from Figure7, in the color temperature distribution diagram, it is not difficult to see that in terms of psychological color temperature, oil paintings show a warmer trend; in terms of saturation, the distribution of oil paintings is relatively uniform. This paper also explores and studies the correlation quantities of statistical gradients such as image brightness and psychological color temperature. The gradient map of image brightness is shown in Figure 8, where red is an oil painting and blue is a natural image.Figure 8
Brightness comparison statistics in different directions. (a) Brightness comparison chart in theX direction. (b) Brightness comparison chart in the Y direction.
(a)(b)By comparing the images in Figure8, it is not difficult to deduce that the local contrast (including brightness and psychological color temperature) of natural images is lower than that of oil paintings. The main watershed between oil paintings and natural images also often lies in the difference in color temperature and color temperature gradient.


## 3.2. Surface of Oil Painting in Visual Planarization

In this paper, BRDF (Bidirectional Reflection Distribution Function) measurement system is used to collect BRDF data. Combined with HSI color space theory, the texture and color information of the homemade oil paint color card are measured and analyzed. Measured values at varying illumination angles are fitted. By fitting the actual measured data, a fitting function is obtained. The texture, material, and lighting information of the oil painting are also obtained. At the same time, the PCA method is combined to reconstruct the spectral reflectance of multispectral images. The true color information of the oil painting is obtained. The two are combined into one to realize the analysis and research of the texture, material, lighting, and color information of cross-media oil painting artworks. The change curves of the brightness (I) of the oil painting color patches of different colors in Figures7 and8 as a function of the illumination angle are analyzed. The Asym2Sig fitting function is used to fit the luminance value 1 component of the HSI color space of the color patch. The Asym2Sig function can be expressed as(19)fx=A+B∗1+e−x−a+b1/2/b2∗1−1+e−x−a+b1/2/b3.From formula19, the functional relationship of the change rule of the brightness (I) information of each oil paint color block can be determined. According to the real-shot image of any standard reference color block, the brightness information of any illumination angle in the full-plane space can be generalized and calculated. Full-plane texture information can be obtained. The Asym2Sig function is used to fit and analyze the 6 oil paint color patches. The results are shown in Tables 2 and 3.Table 2
The  function fitting coefficient table of the brightness value of each oil paint color block changing with the illumination angle.
Asym2sigABaCoefficient−41.179542.257687.143711−85.160786.112390.224112−102.8352103.609889.227214−1.3737.040585.430410.06041.893387.2262440.03061.050580.7547Table 3
Root mean square and variance of function fitting for each oil paint patch.
R-Square (R-square)Found difference (RMSE)110.99330.0346120.99340.0265140.96520.0274440.98620.0377460.96350.0308480.94760.0674It can be seen from Tables2 and 3 that the RMSE statistical parameters of the brightness of the six oil paint color patches analyzed using the Asym2Sig function are close to 0. Therefore, the error between the experimental data and the fitted data is small. The R-square coefficient of determination of the bright value is close to 1, which indicates that the variables of the fitted functional formula have a strong ability to explain the dependent variable. This works well for the experimental data, so the fit of the data is good. Therefore, the technology of oil painting is of great significance to visual planarization in the stage of digital informatization.


## 4. Conclusions

This paper studied the visual planarization in oil painting based on digital information technology. The visual graphic design process and appreciation structure were analyzed and studied. Then, through the color temperature model and brightness comparison, the characteristics of the oil painting itself were analyzed. The visual plane creation method of oil painting art has been supporting the development of oil painting art since its birth. The exhibition of oil painting art, the unique way of expression, and the form of visual plane presentation affect human cognition of oil painting and play an important role in human understanding. With the help of oil painting, art can become more colorful visually. Space is weakened to the point of disappearing. But it is through the intervention of this selective method of creation that the so-called artistic creation with oil painting creates new expressiveness. However, this paper still has shortcomings. Digital art history is the application of computational methodologies and analytical art history research fields inspired by new technologies. It is difficult for computers to spy on the real emotions, intentions, and social status quo in the works. The history of digital art can only reduce the original complex theoretical principles of art to calculus in the statistical sense. In other words, these massive visual literature studies still require a lot of effort from researchers. The lack of technology and some data leads to limited conclusions in this paper. Some aspects may be a bit biased. In future research on it, it is necessary to focus on whether the computer can analyze and express the emotion in oil painting and make oil painting technology more appreciated by the public.



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*Source: 9939209-2022-09-22.xml*