# Machine Learning-Based Intelligent Image Color Processing for Color Teaching Analysis of Chinese Painting

Authors: Haiming Qiao; Yanshun Feng
Journal: Mobile Information Systems
 (2022)

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## Abstract

Color itself is a beautiful and wonderful existence. Chinese traditional colors contain Chinese aesthetic taste and ancient cultural precipitation. It is said that color is a way to understand the world, while traditional Chinese color is integrated into life and caring for the soul [15]. With the continuous development of science and technology, computers are widely used in various fields, and intelligent image color processing technology is an independent theoretical and technical field, but it is also an extremely important technical support for machine perspective graphics processing. In this paper, combined with intelligent image color processing technology, the color teaching of Chinese painting is studied, and based on the wavelet variant, the best blur system parameters are used to obtain high-quality images using BFPSO, PSO, and BFO learning mechanisms to form suitable coding. Through the experiment, the color of Chinese painting is tested and verified by intelligent image color processing technology, and through the experimental results, it can be seen that the accuracy rate and recall rate after intelligent image processing technology are close to 1, indicating that, after optimizing with BFPSO algorithm, the optimal solution is given priority to a certain extent. Therefore, the use of intelligent image color processing technology has further improved the color teaching of Chinese painting.


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## Body

## 1. Introduction

Based on the analysis of color teaching of Chinese painting by intelligent image color processing technology, this paper analyzes the color teaching of Chinese painting by using intelligent image color processing technology. Intelligent processing is a computerized processing and analysis technique that can be adapted to different applications [1]. It is a separate theoretical and technical field, but at the same time, it is an extremely important technical support for graphical processing from a machine perspective. Intelligent image color processing technology is an image processing system that calculates and analyzes the image processing system of the scene according to the requirements of the application and controls the impact of the field device on the processing work according to the processing technology requirements [2]. It is used through image acquisition, image preprocessing, image segmentation, identification and classification of objects, positioning and measurement of objects, and detection and tracking of objects [3]. Image acquisition is the capture of landscapes, people, buildings, etch [4] on-site, which is the first step in mechanical management. While the camera collects a single image with a CCD or CMOS camera, the camera collects a real image of each cycle, and a photograph is a color reflection of a point (luminance and chromaticity) on a projection photo of a stereoscopic image. This is the fundamental reason why we were able to replace the real scene with this whole photo [5]. If the camera is an analog signal outlet, the analog signal must be digitized and transmitted to the computer (including embedded systems). Most cameras can now output digital image information directly, thus avoiding the step of converting analog information. [6] In addition, the interface of digital cameras is now standardized, and it is also like camera VGA 1394, HDMI, WiFi, USB, etc., which are directly processed and sent to the computer. Image processing is then carried out usually in software form, via a computer or embedded system. [7] The preprocessing of the image is due to the influence of machine and environmental factors, and the images collected in the electronic field are often disturbed to varying degrees, including static electricity, geometry, and color imbalance, which hinder the joint. So, we must deal with these interferences first [8]. General processing includes noise removal, geometric correction, and histogram correction. Time or frequency filtering is usually used to remove noise from an image; geometric transformation methods are used to correct the geometric proportions of an image; hue retention and homochromatic filtering are used to reduce color deviations from an image. In short, this family of image processing technologies “processes” the collected images to provide “better” and “more useful” image applications [9]. The segmentation of the image is based on the needs of the application to segment the image into areas with different features and extract its target of interest [10]. Common features of images are grayscale, color, texture, edges and corners, and so on. For example, the image on the assembly line of a car is divided into a background area and an assembly area, and the next machining unit is provided to the frame assembly part. Images have been a difficult problem in the field of image processing for many years, and there are many subdivision algorithms, but this role is often not perfect. Recently, people have used neural network-based deep learning methods to explain that images divide their performance better than traditional algorithms. The recognition and classification of image objects is inseparable from the recognition and classification of actual image objects, and the subsequent evaluation and action are completed. Identify many similarities. Target classifications are often applied to targets only after they have been achieved [11]. In recent years, image recognition technology has gone beyond traditional methods and used traditional neural networks for sensitive image recognition methods, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other methods that perform well. Target positioning and measurement are in smart manufacturing, and the most common work is to install the target module, but they usually need to locate the target before installation and measure the target after installation. Both installation and measurement must be maintained with extremely high accuracy and speeds, such as millimeters (or even less) and millisecond speeds [12]. If you use ordinary mechanical or manual operation, it is certainly difficult to make very accurate and fast positioning and measurement. Image processing is the use of image processing technology to process images according to the complex outline relationship between the target and the image to quickly and accurately complete positioning and measurement tasks. Its object detection and tracking are to identify and track dynamic targets in image processing and can be used to identify whether the image itself has a moving target and predict its next action and trend (known as tracking) [13]. And timely send the motion data to the future analysis and control strategy to generate the corresponding control action. You typically use a separate camera to edit pictures. If desired, two cameras can also be used to replicate binocular visual images of humans to obtain three-dimensional information in the scene [14]. Color itself is a beautiful and wonderful existence, and traditional Chinese color contains Chinese aesthetic taste and ancient cultural precipitation; it is said that color is a way to understand the world, while traditional Chinese color is integrated into life and caring for the soul [15]. In different countries, colors have different identities and names, peach dreams, autumn fragrances, Tianmiao, moon white. Without too much explanation, one can imagine the colors that words symbolize. Pink peach blossoms sway in the wind, earth-yellow rice ripens in autumn, and the sky is clear and blue and extremely pure. In traditional Chinese colors, the names are introverted and obscure; they represent an image, melting scenery into one color. Before the Wei, Jin, and Northern and Southern dynasties, there was a profound influence on the use of color in Chinese painting, because the reflection on yin and yang and the five elements had an impact on the subjective color consciousness of the painter. China introduced Buddhism, brought new colors to China, and greatly improved the color of Chinese painting. The use of color in Chinese painting in the Tang Dynasty varied a lot. After the tang dynasty, the colors used in Chinese painting traveled to this extreme. Datang was the richest era in China. Painters like to express paintings with bright and powerful colors. Whether it is landscape painting or pattern, they are extremely concerned about the use of color, reflecting the richness of Darang [16]. With the demise of the Song Dynasty, people also used colors less and less, because in the society of the time, colors became less and less, and politics became more and more corrupt. In the country’s collapsed society, some scholars and writers wanted to express their patriotism, and the only way to do that was through calligraphy, painting, and poetry. During the Ming and Qing dynasties, the state was closed, and people’s thoughts and thoughts were trapped inside. During this time, ink was the main color in Chinese painting, and after it was modified, it was removed as a monochromatic color. In modern times, China has opened its doors, reformed and opened up, and accepted new things. Breaking the shackles of tradition in painting, introducing Western painting concepts, and combining with Chinese painting, Chinese painting has a certain breakthrough and development. Modern China has opened its doors, implemented reforms, and accepted new things. Breaking through the shackles of painting tradition, introducing Western techniques, and joining Chinese painting, this has enabled Chinese painting to have a certain breakthrough and development. Analyzing and studying color, we can say that impressionism reached a certain height, and many ink traditional pen and ink and color will dominate the restrictions on the use of color in Chinese painting, so that our use of color in Chinese painting should be strengthened. The combination of East and West can still be used as a typical example of integrating China and the West. It added the color use of Western silver and combined the traditional Chinese color use with Western technology and innovated in the traditional technology. While we learn Western painting techniques, we should return to the traditional concept of Chinese painting. Western painting pays more attention to the image of things, while Chinese painting pays more attention to images and does not make specific depictions of things. Therefore, we should not abandon the traditional Chinese painting technology and carry out reform and innovation on this basis. Modern society is developing relatively rapidly. The expressive power of Chinese painting depends on color [17]. Many painters studied painting and also thought about color in painting. Based on the application of traditional painting, the color application of modern Chinese painting has been greatly expanded. Modern painters are inspired by both traditional and Western painting, so they add a subjective element to the use of color. Although the subjectivity of modern Chinese painting is still very strong, painting reveals the characteristics of the times. In the long course of history, the works of art of each era can highlight its attributes and taste Chinese painting is a treasure in history. With the development of society, changes in human perspectives, and the influence of foreign cultures, the colors of Chinese painting should pursue innovation and show the world a unique national culture based on traditional concepts. Therefore, the use of intelligent image color processing technology to teach and analyze the color of Chinese painting is a new direction of great significance to itself.


## 2. Perception of the Content of the Study

### 2.1. Cognition of Intelligent Image Color Processing Technology

First, if employees use hundreds or thousands of cameras at the same time, when they are at risk, they cannot really prevent or deal with those risks, and most of them believe in the associated images. Next, non-security applications around us such as commercial river statistics will be transferred to the computer in terms of risk analysis and tracking or chip computer if there is a problem, the alarm staff will respond, and once the target violates the predefined analysis rules in a scene, the system will activate the prenamed link rules to achieve the function of active reminder. Implemented by an Intelligent Algorithm (DSP), it is typically present in intelligent analysis cameras and ad hoc analysis image servers, the latter of which are installed in “front-facing” mode [18]. There are many systems that are currently using this approach: moving software and hardware with intelligent analysis capabilities to the front of the image measurement device. In traditional image monitoring systems, images are damaged by extremely large memory and communication bandwidth. Solving these problems is the most fundamental problem. A large amount of useless video information is stored and transmitted, which not only wastes memory, but also generates bandwidth. The purpose of intelligent analysis is to reduce the space required for image storage and image density, or to compress and transmit some unimportant low-bitstream images. This can help increase the application value of the monitoring system. This information allows the algorithm editing process to work “precisely.” The other is the working mode for terminal computers and intelligent analysis software. Therefore, the processing power of the end computer in the terminal computer is better than the smart processing power of the front-end computer. However, because the algorithm is obtained from many hardware, some analyses cannot use the capacity of the hardware at the same time. Because the end computer (as opposed to the front-end computer software) has strong analysis and processing capabilities, it is generally possible to use the processing method of the personal computer to carry out important intelligent analysis opportunities. So, this intelligent image color processing technology is shown in Figure 1.Figure 1
Intelligent image color processing technology working diagram.


### 2.2. Teaching Color in Chinese Painting

#### 2.2.1. Chinese Painting Color

In China, “color” actually did not refer to color in the earliest ancient times but represented the meaning of “facial color,” and in the Tang Dynasty, the word “color” begun to be used as a general term for colors in nature. The Tang Dynasty was open, women could ride horses, and casual pants were the biggest national force in the Tang Dynasty, with an open and inclusive culture, and a country that was rich and strong. The use of color in many fields of application technology is very skilled; that is, the so-called “Tang Sancai” three colors appear at the same time, such as plant color and Tang makeup, and thus developed the concept of the earliest color system in China.Color is the most important emotional factor in the plastic arts [19]. The effect is to create visual attention and emotional response in an instant. There are different color arrangements in the cultural backgrounds of different countries. In the West, their color culture will be more inclined to physical properties and scientific theories, and plants, animals, and natural phenomena will be widely used and extended to color. The most classic is that Newton decomposed white into red, orange, yellow, green, green, blue, and purple. Since then, physicists have begun to study the scientific nature of color, obtaining the three properties of brightness, solid color, and hue, and the number of each color. The traditional Chinese color comes not only from the heavens and the Earth, but also from the imagination of our ancient civilization, with “concept” as the main purpose, paying attention to the color of the picture and having the concept of color. Different from the West, China’s color matching is distinguished by positive color and intermediate (miscellaneous) color.


#### 2.2.2. Color Teaching Analysis of Traditional Chinese Painting

Traditional Chinese painting color teaching first sets the goal of the course, grasps the key points, improves the students’ aesthetic ability, and then consolidates and improves the students’ professional skills, and its teaching design is shown in Table1.Table 1
Color teaching design of Chinese painting.
Teaching purposeKnowledge skills(1) use the tools and materials of Chinese painting(2) understand the characteristics of Chinese pigments and different colors.(3) the use of different materials, as well as the properties of different colors and pigments, all represent different pens and colors.(4) Be able to predict the use and expression of colors and related techniquesEmotional goalsFeeling the colors of Chinese painting will have a deeper understanding of Chinese painting and will also develop a love for Chinese painting.Process goalsLearn the relationship between the materials of the analytical toolbox and the properties of pigments in Chinese painting.Difficulty analysisFocusMethods and techniques used in pens, inks and inks in Chinese painting;Suitable tools, materials and pigments, etc. (the law can be found through the actual actions of the students)NodusFlexibly use the tools, materials, and pigments of Chinese painting to show the visual and psychological sensations of different colors (these results can be achieved through student self-governing learning, exploration and related practices)Knowledge and skill key point analysisKnowledge focusCharacteristics of paper, ink, ink, and pigments;Interest and interest in all aspects;Classification of Chinese paintings;The type of pen, ink, and color usedSkill pointsTechniques for using pens, inks and colors of types of pens, ink (ticks, dyes, wipes, dots, etc.), colors (dyeing, tracing, erasing, brushing, wiping, etc.);Sketching skills (starting, line, and closing).Teaching methodsTeachingDoctrine, demonstration, methodStudy the faIndependent inquiry, comparison, group workEnrich and integrate teaching materialsThis course combines the use of tools and materials to learn how to learn using pen and ink and pigments. By guiding teachers and students to independently learn and explore problems in relation to their different tools and materials and ink pens, and to train students to explore and solve problems, students work in groups to cultivate teamwork skills to help each other and use each other. By learning line patterns, palm painting, and careful painting, students can compare themselves with each other, find out their own personalities, and better grasp the learning techniques of Chinese painting.Teaching process(1) review students’ tools and materials.(2) show that they prepared the tools and materials for Chinese painting, allowing students to touch them with their hands, look at the brushes and pigments of this material, see all the colors that affect the hair and eyes and understand whether each color attribute is green paint or non-green paint.(3) class activity 1: use thick ink paper and cooked paper, and use fountain pens in different locations. What are the characteristics of these pens? How much luminosity is the ink? The class is divided into groups, and several students participate in exercises in the same group. When they present their research, the teacher summarizes it so that the students can understand the results.(4) class activity 2: add different amounts of water under different lengths of paper and then compare the ink blocks of different depths. The teacher instructs the students to analyze the relationship between ink content and ink depth. Students will report the results of the study to their classmates before having a class discussion. The teacher made a final summary and introduced the knowledge and ability of Chinese painting.(5) class activity 3: use pigments in the teacher’s suggestion to make colored blocks according to the teacher’s suggestions, and use these pigments to express the artistic designs in the teacher’s suggestions, such as spring, summer, autumn, winter, enjoyment, anger, sorrow, happiness. Students present the results of the research based on their own practice, and then the students and teachers comment together so that the students can understand the color of the application and the emotions it brings. Finally, the teachers will make a summary to introduce the knowledge and ability of color in Chinese painting.(6) class activity 4: students try to complete simple flowers and grasses independently, rather than following the methods they have learned to use pens, inks, and colors.AppraiseThe assessment is divided into three types:The first method: students present their work on the podium and then evaluate their own work.The second method: divide students into small groups, conduct self-evaluation and mutual evaluation, and then select several excellent works for people to view and evaluate.The third method is that the teacher selects and evaluates the representative works of different students in the class.


## 2.1. Cognition of Intelligent Image Color Processing Technology

First, if employees use hundreds or thousands of cameras at the same time, when they are at risk, they cannot really prevent or deal with those risks, and most of them believe in the associated images. Next, non-security applications around us such as commercial river statistics will be transferred to the computer in terms of risk analysis and tracking or chip computer if there is a problem, the alarm staff will respond, and once the target violates the predefined analysis rules in a scene, the system will activate the prenamed link rules to achieve the function of active reminder. Implemented by an Intelligent Algorithm (DSP), it is typically present in intelligent analysis cameras and ad hoc analysis image servers, the latter of which are installed in “front-facing” mode [18]. There are many systems that are currently using this approach: moving software and hardware with intelligent analysis capabilities to the front of the image measurement device. In traditional image monitoring systems, images are damaged by extremely large memory and communication bandwidth. Solving these problems is the most fundamental problem. A large amount of useless video information is stored and transmitted, which not only wastes memory, but also generates bandwidth. The purpose of intelligent analysis is to reduce the space required for image storage and image density, or to compress and transmit some unimportant low-bitstream images. This can help increase the application value of the monitoring system. This information allows the algorithm editing process to work “precisely.” The other is the working mode for terminal computers and intelligent analysis software. Therefore, the processing power of the end computer in the terminal computer is better than the smart processing power of the front-end computer. However, because the algorithm is obtained from many hardware, some analyses cannot use the capacity of the hardware at the same time. Because the end computer (as opposed to the front-end computer software) has strong analysis and processing capabilities, it is generally possible to use the processing method of the personal computer to carry out important intelligent analysis opportunities. So, this intelligent image color processing technology is shown in Figure 1.Figure 1
Intelligent image color processing technology working diagram.


## 2.2. Teaching Color in Chinese Painting

### 2.2.1. Chinese Painting Color

In China, “color” actually did not refer to color in the earliest ancient times but represented the meaning of “facial color,” and in the Tang Dynasty, the word “color” begun to be used as a general term for colors in nature. The Tang Dynasty was open, women could ride horses, and casual pants were the biggest national force in the Tang Dynasty, with an open and inclusive culture, and a country that was rich and strong. The use of color in many fields of application technology is very skilled; that is, the so-called “Tang Sancai” three colors appear at the same time, such as plant color and Tang makeup, and thus developed the concept of the earliest color system in China.Color is the most important emotional factor in the plastic arts [19]. The effect is to create visual attention and emotional response in an instant. There are different color arrangements in the cultural backgrounds of different countries. In the West, their color culture will be more inclined to physical properties and scientific theories, and plants, animals, and natural phenomena will be widely used and extended to color. The most classic is that Newton decomposed white into red, orange, yellow, green, green, blue, and purple. Since then, physicists have begun to study the scientific nature of color, obtaining the three properties of brightness, solid color, and hue, and the number of each color. The traditional Chinese color comes not only from the heavens and the Earth, but also from the imagination of our ancient civilization, with “concept” as the main purpose, paying attention to the color of the picture and having the concept of color. Different from the West, China’s color matching is distinguished by positive color and intermediate (miscellaneous) color.


### 2.2.2. Color Teaching Analysis of Traditional Chinese Painting

Traditional Chinese painting color teaching first sets the goal of the course, grasps the key points, improves the students’ aesthetic ability, and then consolidates and improves the students’ professional skills, and its teaching design is shown in Table1.Table 1
Color teaching design of Chinese painting.
Teaching purposeKnowledge skills(1) use the tools and materials of Chinese painting(2) understand the characteristics of Chinese pigments and different colors.(3) the use of different materials, as well as the properties of different colors and pigments, all represent different pens and colors.(4) Be able to predict the use and expression of colors and related techniquesEmotional goalsFeeling the colors of Chinese painting will have a deeper understanding of Chinese painting and will also develop a love for Chinese painting.Process goalsLearn the relationship between the materials of the analytical toolbox and the properties of pigments in Chinese painting.Difficulty analysisFocusMethods and techniques used in pens, inks and inks in Chinese painting;Suitable tools, materials and pigments, etc. (the law can be found through the actual actions of the students)NodusFlexibly use the tools, materials, and pigments of Chinese painting to show the visual and psychological sensations of different colors (these results can be achieved through student self-governing learning, exploration and related practices)Knowledge and skill key point analysisKnowledge focusCharacteristics of paper, ink, ink, and pigments;Interest and interest in all aspects;Classification of Chinese paintings;The type of pen, ink, and color usedSkill pointsTechniques for using pens, inks and colors of types of pens, ink (ticks, dyes, wipes, dots, etc.), colors (dyeing, tracing, erasing, brushing, wiping, etc.);Sketching skills (starting, line, and closing).Teaching methodsTeachingDoctrine, demonstration, methodStudy the faIndependent inquiry, comparison, group workEnrich and integrate teaching materialsThis course combines the use of tools and materials to learn how to learn using pen and ink and pigments. By guiding teachers and students to independently learn and explore problems in relation to their different tools and materials and ink pens, and to train students to explore and solve problems, students work in groups to cultivate teamwork skills to help each other and use each other. By learning line patterns, palm painting, and careful painting, students can compare themselves with each other, find out their own personalities, and better grasp the learning techniques of Chinese painting.Teaching process(1) review students’ tools and materials.(2) show that they prepared the tools and materials for Chinese painting, allowing students to touch them with their hands, look at the brushes and pigments of this material, see all the colors that affect the hair and eyes and understand whether each color attribute is green paint or non-green paint.(3) class activity 1: use thick ink paper and cooked paper, and use fountain pens in different locations. What are the characteristics of these pens? How much luminosity is the ink? The class is divided into groups, and several students participate in exercises in the same group. When they present their research, the teacher summarizes it so that the students can understand the results.(4) class activity 2: add different amounts of water under different lengths of paper and then compare the ink blocks of different depths. The teacher instructs the students to analyze the relationship between ink content and ink depth. Students will report the results of the study to their classmates before having a class discussion. The teacher made a final summary and introduced the knowledge and ability of Chinese painting.(5) class activity 3: use pigments in the teacher’s suggestion to make colored blocks according to the teacher’s suggestions, and use these pigments to express the artistic designs in the teacher’s suggestions, such as spring, summer, autumn, winter, enjoyment, anger, sorrow, happiness. Students present the results of the research based on their own practice, and then the students and teachers comment together so that the students can understand the color of the application and the emotions it brings. Finally, the teachers will make a summary to introduce the knowledge and ability of color in Chinese painting.(6) class activity 4: students try to complete simple flowers and grasses independently, rather than following the methods they have learned to use pens, inks, and colors.AppraiseThe assessment is divided into three types:The first method: students present their work on the podium and then evaluate their own work.The second method: divide students into small groups, conduct self-evaluation and mutual evaluation, and then select several excellent works for people to view and evaluate.The third method is that the teacher selects and evaluates the representative works of different students in the class.


## 2.2.1. Chinese Painting Color

In China, “color” actually did not refer to color in the earliest ancient times but represented the meaning of “facial color,” and in the Tang Dynasty, the word “color” begun to be used as a general term for colors in nature. The Tang Dynasty was open, women could ride horses, and casual pants were the biggest national force in the Tang Dynasty, with an open and inclusive culture, and a country that was rich and strong. The use of color in many fields of application technology is very skilled; that is, the so-called “Tang Sancai” three colors appear at the same time, such as plant color and Tang makeup, and thus developed the concept of the earliest color system in China.Color is the most important emotional factor in the plastic arts [19]. The effect is to create visual attention and emotional response in an instant. There are different color arrangements in the cultural backgrounds of different countries. In the West, their color culture will be more inclined to physical properties and scientific theories, and plants, animals, and natural phenomena will be widely used and extended to color. The most classic is that Newton decomposed white into red, orange, yellow, green, green, blue, and purple. Since then, physicists have begun to study the scientific nature of color, obtaining the three properties of brightness, solid color, and hue, and the number of each color. The traditional Chinese color comes not only from the heavens and the Earth, but also from the imagination of our ancient civilization, with “concept” as the main purpose, paying attention to the color of the picture and having the concept of color. Different from the West, China’s color matching is distinguished by positive color and intermediate (miscellaneous) color.


## 2.2.2. Color Teaching Analysis of Traditional Chinese Painting

Traditional Chinese painting color teaching first sets the goal of the course, grasps the key points, improves the students’ aesthetic ability, and then consolidates and improves the students’ professional skills, and its teaching design is shown in Table1.Table 1
Color teaching design of Chinese painting.
Teaching purposeKnowledge skills(1) use the tools and materials of Chinese painting(2) understand the characteristics of Chinese pigments and different colors.(3) the use of different materials, as well as the properties of different colors and pigments, all represent different pens and colors.(4) Be able to predict the use and expression of colors and related techniquesEmotional goalsFeeling the colors of Chinese painting will have a deeper understanding of Chinese painting and will also develop a love for Chinese painting.Process goalsLearn the relationship between the materials of the analytical toolbox and the properties of pigments in Chinese painting.Difficulty analysisFocusMethods and techniques used in pens, inks and inks in Chinese painting;Suitable tools, materials and pigments, etc. (the law can be found through the actual actions of the students)NodusFlexibly use the tools, materials, and pigments of Chinese painting to show the visual and psychological sensations of different colors (these results can be achieved through student self-governing learning, exploration and related practices)Knowledge and skill key point analysisKnowledge focusCharacteristics of paper, ink, ink, and pigments;Interest and interest in all aspects;Classification of Chinese paintings;The type of pen, ink, and color usedSkill pointsTechniques for using pens, inks and colors of types of pens, ink (ticks, dyes, wipes, dots, etc.), colors (dyeing, tracing, erasing, brushing, wiping, etc.);Sketching skills (starting, line, and closing).Teaching methodsTeachingDoctrine, demonstration, methodStudy the faIndependent inquiry, comparison, group workEnrich and integrate teaching materialsThis course combines the use of tools and materials to learn how to learn using pen and ink and pigments. By guiding teachers and students to independently learn and explore problems in relation to their different tools and materials and ink pens, and to train students to explore and solve problems, students work in groups to cultivate teamwork skills to help each other and use each other. By learning line patterns, palm painting, and careful painting, students can compare themselves with each other, find out their own personalities, and better grasp the learning techniques of Chinese painting.Teaching process(1) review students’ tools and materials.(2) show that they prepared the tools and materials for Chinese painting, allowing students to touch them with their hands, look at the brushes and pigments of this material, see all the colors that affect the hair and eyes and understand whether each color attribute is green paint or non-green paint.(3) class activity 1: use thick ink paper and cooked paper, and use fountain pens in different locations. What are the characteristics of these pens? How much luminosity is the ink? The class is divided into groups, and several students participate in exercises in the same group. When they present their research, the teacher summarizes it so that the students can understand the results.(4) class activity 2: add different amounts of water under different lengths of paper and then compare the ink blocks of different depths. The teacher instructs the students to analyze the relationship between ink content and ink depth. Students will report the results of the study to their classmates before having a class discussion. The teacher made a final summary and introduced the knowledge and ability of Chinese painting.(5) class activity 3: use pigments in the teacher’s suggestion to make colored blocks according to the teacher’s suggestions, and use these pigments to express the artistic designs in the teacher’s suggestions, such as spring, summer, autumn, winter, enjoyment, anger, sorrow, happiness. Students present the results of the research based on their own practice, and then the students and teachers comment together so that the students can understand the color of the application and the emotions it brings. Finally, the teachers will make a summary to introduce the knowledge and ability of color in Chinese painting.(6) class activity 4: students try to complete simple flowers and grasses independently, rather than following the methods they have learned to use pens, inks, and colors.AppraiseThe assessment is divided into three types:The first method: students present their work on the podium and then evaluate their own work.The second method: divide students into small groups, conduct self-evaluation and mutual evaluation, and then select several excellent works for people to view and evaluate.The third method is that the teacher selects and evaluates the representative works of different students in the class.


## 3. Technology Utilization

### 3.1. Wavelet Variant

#### 3.1.1. Wavelet Transform

The use of wavelet transforms has significant advantages when processing image information or nonlinear signals. In the wavelet transform, the function is set, and the spectrum satisfies the conditions of equation (1) as follows:φt∈L′R∩L2Rφtψω(1)Cψ=∫−∞+∞ψω2ωdω<+∞.Or it satisfied the wavelet mother function [∫−∞+∞φtdt=0φt [20], which undergoes a series of translational and telescopic transformations to obtain (2), which represents the wavelet function family: φtφa,bt(2)φa,bt=a−1/2φt−ba,(2) is a continuous wavelet, where a-scale parameters; b- translation parameters.Moreover,b∈R,a∈R−0.The continuous wavelet transform is defined as(3)Wfa,b=ft,φa,bt=a−1/2∫−∞+∞φ¯t−baftdt,where ft- the original signal; Wfa,b- Weights.The formula for the reorganization of continuous raw information isft(4)ft=Cψ−1∫−∞+∞∫−∞+∞Wfa,bψa,btdadba2.Continuous wavelet transforms generally employ power series discretization (usually); at that time, the order (usually), then,a=a0kk=0,1,2,…a0=2a=a0b=b0b0=1a=a0kb=ma0kb0m∈Z(5)φk,mt=2−k/2φ2−kt−m.The above equation is a discrete wavelet, whereink,m∈Zk decides on scaling parameters; m- translation parameters; 2k/2- wavelet function height.Then, the two-inlet discrete wavelet is(6)Wfk,m=2k/2∫−∞+∞φ¯t−m2kftdt.Ifft = , combine equation (6) to get the following equation: fnn∈Z(7)Wfk,m=fn,φk,mn=2−k/2∑n=Zφn−m2kfn.In the formula,Wfk,mindicates the degree of similarity; if it is orthogonal, it consists of similar components: fnφk,mnφk,mnfnφk,mn. ft. The refactoring formula is(8)ft=∑k∈R∑m∈RWfk,mφk,mt.Since the wavelet decomposition is the sample value obtained by a series of filters with discontinuous bandwidths at different scales, a binary mesh can be obtained, as shown in Figure2: ft.Figure 2
Discrete wavelet transform.LetH (n) be the low-pass filter, and let G (n) be the high-pass filter, and the decomposed low-frequency part and the high-frequency part are as follows, and then, the interrelationship between the low-pass filter and the high-pass filter can be represented by (9), Figure 3: AjDj.(9)Gn=−1nH2N−1−n.Figure 3
Discrete wavelet transform decomposition.Continue to decompose, and you can get (its original signal)AjAj+1,Aj+2A0=f.


#### 3.1.2. Implementation of Wavelet Transform Images

The two-dimensional signal space of the wavelet master function is that the continuous wavelets are obtained after the translational expansion and contraction change as shown in the following equation:φx,y(10)φa,bx,byx,y=a−1φx−bxa,y−bya.When the original two-dimensional signal of the continuous wavelet is f(x,y), its transformation is as follows:fx,y(11)Wfa,bx,by=∫−∞+∞∫−∞+∞fx,yφa,bx,byx.ydxdy.Its inverter is as follows:(12)fx,y=Cψ−1∫0∞∫−∞+∞∫−∞+∞Wfa,bx,byφa,bx,byx,ydbxdbydaa2.The expression of the two-dimensional wavelet function is as follows:(13)ψ1x1,x2=ψx1φx2,ψ2x1,x2=φx1ψx2,ψ3x1,x2=ψx1ψx2.If a set of canonical orthogonal radicals constitutes space, the two-dimensional discrete wavelet transform is as follows:ψ=ψ1,ψ2,ψ3ψWj2fx,y,(14)f=∑j∈Z2,k∈KCj,kiψj,k+∑j∈Z2dj,kφj,ki=1,2,3,whereφj,k-scale function; Cj,ki=f,φi-detail coefficient; dj,k=f,φj,k-approximate coefficient.


#### 3.1.3. Transverse Wavelet Transform and Longitudinal Wavelet Transform

The entire 2D wavelet variant can be carried out in two steps: horizontal transformation and vertical transformation. Figure4 is a schematic diagram of the lateral wavelets, and its decomposition action is synthesized from left to right, and vice versa [21]. The wavelet transforms each horizontal level on the image into a separate original sequence and performs an independent wavelet transform for each independent original sequence. In general, due to the lateral wavelet decomposition, the image data will form a low-frequency region on the left and a high-frequency data region on the right. We can use the letter H to represent the low-frequency data area and the letter K to represent the high-frequency data region.Figure 4
Lateral wavelet transform.From the transverse wavelet transformation, it can be seen that the change of longitudinal wavelets is similar to it. Figure5 shows the longitudinal wavelet transform, and its decomposition action is decomposed from left to right, and vice versa.Figure 5
Longitudinal wavelet conversion.As shown in Figure5, combined with the transverse wavelets that have been transformed, the overall data can be divided into four partitions: horizontal and vertical low-frequency (HH), horizontal and low vertical high (HK), horizontal high and vertical low (K H), and horizontal high and vertical high (KK).


### 3.2. Image Encoding

As we know, EZW encoding is an embedded image encoding. This method is done on the direction tree of the wavelet transformation. After the image of the wavelet transform is decomposed, there is a zero tree in the wavelet field. If encoded, the zero-tree root is marked as a zero-tree whole, thereby reducing code flow [22]. Its parameters include positive coefficient (POS), negative coefficient (NEG), zero-tree root (ZTR), and isolated zero point (LZ); the image coefficient of the wavelet transform is passed by the EZW threshold T0,T1,T2,…,Ti−1. The numerical value is given, where the initial threshold is as follows, so there is a wavelet coefficient. Ti=Ti−1/21≤Ti≤T0T0xi,jxi,j<T0,(15)T0=2∧log2Maxxi,j.The EZW encoder must establish a master table and a list of special encodings [23]. The former stores the undefined importance coordinates of the wavelet transform image coefficient, while the latter stores the remaining value of the wavelet transform image coefficient. The former’s mapping table is plotted by the path of Figure 6, while the latter corresponding wavelet transition coefficient needs to be evaluated. The steps are as follows:(1)
Ifxi,j>T, and it is satisfied, use P xi,j>0 OS for encoding, and assign the remaining value of the wavelet transform image coefficient in the list as xi,j−T−T/2.(2)
Ifxi,j>T, and it is satisfied, encode using xi,j<0 NEG, and assign the remaining values of the wavelet transform image coefficients in the checklist as xi,j−T+T/2.(3)
If the isolated zero isxi,j, it is encoded with L Z, and the remaining values are unchanged.(4)
If the zero-tree root is encoded usingZxi,j TR, the values in the list remain unchanged, and all values from the node are not scanned.(5)
Keep updating and initializing the main table, repeating the above 4 steps, and ending at timeT=T/2T=1.Figure 6
Wavelet transition coefficient table scan sequence.


### 3.3. Fuzzy Vector Compressor Design

POS (a biomimetic optimization method, particle swarm optimization method) is a combination of the evolutionary advantages of social and cognitive models [24] to optimize the system design. The process is as follows:Social Model:(16)vj,dt=vj,dt−1+ϕj,d2txj,dt#−xj,dt−1.Cognitive patterns:(17)vj,dt=vj,dt−1+ϕj,d1txj,dt∗−xj,dt−1.Particle Swarm Optimization Combinatorial Model:(18)vj,dt=vj,dt−1+ϕj,d1txj,dt∗−xj,dt−1+ϕj,d2txj,dt#−xj,dt−1,xj,dt=xj,dt−1+vj,dt,where v- the speed at which particles move in a single dimension; x- the parameter position of the particle in a single dimension; ϕ- random variables in a certain interval; xt∗- the optimal parameters of the particle; xt#- group optimal parameter solution.Thus, the position of each parameter is determined by the optimal parameter of the particle and the solution of the best parameter of the population.The distortion between the image data and the thin code is tested by the moderation function in PSOs, which is defined as follows:(19)Fitness=k0∑i=1Nt∑j=1MSij⋅xi¯−cj2,where Nt- the number of trainees; k0- initial constant; Sij- defined as follows:(20)Sij=1,if CFxi¯,cj=Arg maxCF1,CF2,…,CFM,0,otherwise,where xi¯- the sample value of the first image; icj- low code vector value. jAs can be seen from (19) and (20), the smaller the actual error, the smaller the distortion relative to the smaller. That is, this coding design is equivalent to searching for problems.(21)MIN∑i=1Nc∑j=1MSij⋅xi−cj2≡MAXk0∑i=1Nt∑j=1MSij⋅xi¯−cj2.As can be seen from the above equation, the larger the value of the adaptation function, the better the fuzzy inference system describes the code thinner.Due to the advantages of P SO’s few parameters and ease of implementation, its convergence rate is very fast, so it is easy to make particles fall into the region of the best solution, and in order to solve this disadvantage, the use of bacterial optimization search method (BFO) to optimize PSOS, the formula is as follows:(1)
Formula of bacterial tendency:(22)Pnf+1,g,u=Pnf,g,u+Cn∗Vf,whereP- the location of the fungus; S indicates the total number of bacteria; Nc indicates the total number of executions; Nre indicates the total number of reproductions; Nedindicates the total number of environmental changes; C represents the distance each time the bacteria move in a specific time; Vindicates the direction in which the bacteria move randomly; n=1,2,3…,S represents the nth bacterium; f=1,2,…,Nc indicates the first few learning steps; g=1,2,…,Nre indicates the first breeding; u=1,2,…,Nedindicates the number of environmental changes.(2)
Cluster dispersion formula:(23)Jan=P,Pnf,g,u=−dattractexp−ω¯attract∑x=1qpopi−pxn2+drepellentexp−ω¯repellent∑x=1qpopi−pxn2,whereJan-the interaction mode of transmitting messages to each other for the bacteria; q optimizes the problem dimension; ω¯attract, - learning the attraction constant; dattractdrepellent- learning to exclude constants; ω¯repellentpopi- the known optimal solution of the setting.The advantage of this design is that the combination of PSO and BFO to develop a series of evolutionary learning of bacterial optimization particle algorithm (BFPSO), and after optimization, the optimization parameters are found, the code thin can be better described, the image extraction technology has been improved with high quality, and the steps of the bacterial optimization particle swarm algorithm can be obtained as in Figure7.Figure 7
Flowchart of bacteria optimization particle swarm algorithm.The bacterial optimized particle algorithm (BFPSO) is used in the design of fuzzy vectors and combined with the image compression extraction system process as shown in Figure8.Figure 8
Optimization of the fuzzy vector compressor design flow.


## 3.1. Wavelet Variant

### 3.1.1. Wavelet Transform

The use of wavelet transforms has significant advantages when processing image information or nonlinear signals. In the wavelet transform, the function is set, and the spectrum satisfies the conditions of equation (1) as follows:φt∈L′R∩L2Rφtψω(1)Cψ=∫−∞+∞ψω2ωdω<+∞.Or it satisfied the wavelet mother function [∫−∞+∞φtdt=0φt [20], which undergoes a series of translational and telescopic transformations to obtain (2), which represents the wavelet function family: φtφa,bt(2)φa,bt=a−1/2φt−ba,(2) is a continuous wavelet, where a-scale parameters; b- translation parameters.Moreover,b∈R,a∈R−0.The continuous wavelet transform is defined as(3)Wfa,b=ft,φa,bt=a−1/2∫−∞+∞φ¯t−baftdt,where ft- the original signal; Wfa,b- Weights.The formula for the reorganization of continuous raw information isft(4)ft=Cψ−1∫−∞+∞∫−∞+∞Wfa,bψa,btdadba2.Continuous wavelet transforms generally employ power series discretization (usually); at that time, the order (usually), then,a=a0kk=0,1,2,…a0=2a=a0b=b0b0=1a=a0kb=ma0kb0m∈Z(5)φk,mt=2−k/2φ2−kt−m.The above equation is a discrete wavelet, whereink,m∈Zk decides on scaling parameters; m- translation parameters; 2k/2- wavelet function height.Then, the two-inlet discrete wavelet is(6)Wfk,m=2k/2∫−∞+∞φ¯t−m2kftdt.Ifft = , combine equation (6) to get the following equation: fnn∈Z(7)Wfk,m=fn,φk,mn=2−k/2∑n=Zφn−m2kfn.In the formula,Wfk,mindicates the degree of similarity; if it is orthogonal, it consists of similar components: fnφk,mnφk,mnfnφk,mn. ft. The refactoring formula is(8)ft=∑k∈R∑m∈RWfk,mφk,mt.Since the wavelet decomposition is the sample value obtained by a series of filters with discontinuous bandwidths at different scales, a binary mesh can be obtained, as shown in Figure2: ft.Figure 2
Discrete wavelet transform.LetH (n) be the low-pass filter, and let G (n) be the high-pass filter, and the decomposed low-frequency part and the high-frequency part are as follows, and then, the interrelationship between the low-pass filter and the high-pass filter can be represented by (9), Figure 3: AjDj.(9)Gn=−1nH2N−1−n.Figure 3
Discrete wavelet transform decomposition.Continue to decompose, and you can get (its original signal)AjAj+1,Aj+2A0=f.


### 3.1.2. Implementation of Wavelet Transform Images

The two-dimensional signal space of the wavelet master function is that the continuous wavelets are obtained after the translational expansion and contraction change as shown in the following equation:φx,y(10)φa,bx,byx,y=a−1φx−bxa,y−bya.When the original two-dimensional signal of the continuous wavelet is f(x,y), its transformation is as follows:fx,y(11)Wfa,bx,by=∫−∞+∞∫−∞+∞fx,yφa,bx,byx.ydxdy.Its inverter is as follows:(12)fx,y=Cψ−1∫0∞∫−∞+∞∫−∞+∞Wfa,bx,byφa,bx,byx,ydbxdbydaa2.The expression of the two-dimensional wavelet function is as follows:(13)ψ1x1,x2=ψx1φx2,ψ2x1,x2=φx1ψx2,ψ3x1,x2=ψx1ψx2.If a set of canonical orthogonal radicals constitutes space, the two-dimensional discrete wavelet transform is as follows:ψ=ψ1,ψ2,ψ3ψWj2fx,y,(14)f=∑j∈Z2,k∈KCj,kiψj,k+∑j∈Z2dj,kφj,ki=1,2,3,whereφj,k-scale function; Cj,ki=f,φi-detail coefficient; dj,k=f,φj,k-approximate coefficient.


### 3.1.3. Transverse Wavelet Transform and Longitudinal Wavelet Transform

The entire 2D wavelet variant can be carried out in two steps: horizontal transformation and vertical transformation. Figure4 is a schematic diagram of the lateral wavelets, and its decomposition action is synthesized from left to right, and vice versa [21]. The wavelet transforms each horizontal level on the image into a separate original sequence and performs an independent wavelet transform for each independent original sequence. In general, due to the lateral wavelet decomposition, the image data will form a low-frequency region on the left and a high-frequency data region on the right. We can use the letter H to represent the low-frequency data area and the letter K to represent the high-frequency data region.Figure 4
Lateral wavelet transform.From the transverse wavelet transformation, it can be seen that the change of longitudinal wavelets is similar to it. Figure5 shows the longitudinal wavelet transform, and its decomposition action is decomposed from left to right, and vice versa.Figure 5
Longitudinal wavelet conversion.As shown in Figure5, combined with the transverse wavelets that have been transformed, the overall data can be divided into four partitions: horizontal and vertical low-frequency (HH), horizontal and low vertical high (HK), horizontal high and vertical low (K H), and horizontal high and vertical high (KK).


## 3.1.1. Wavelet Transform

The use of wavelet transforms has significant advantages when processing image information or nonlinear signals. In the wavelet transform, the function is set, and the spectrum satisfies the conditions of equation (1) as follows:φt∈L′R∩L2Rφtψω(1)Cψ=∫−∞+∞ψω2ωdω<+∞.Or it satisfied the wavelet mother function [∫−∞+∞φtdt=0φt [20], which undergoes a series of translational and telescopic transformations to obtain (2), which represents the wavelet function family: φtφa,bt(2)φa,bt=a−1/2φt−ba,(2) is a continuous wavelet, where a-scale parameters; b- translation parameters.Moreover,b∈R,a∈R−0.The continuous wavelet transform is defined as(3)Wfa,b=ft,φa,bt=a−1/2∫−∞+∞φ¯t−baftdt,where ft- the original signal; Wfa,b- Weights.The formula for the reorganization of continuous raw information isft(4)ft=Cψ−1∫−∞+∞∫−∞+∞Wfa,bψa,btdadba2.Continuous wavelet transforms generally employ power series discretization (usually); at that time, the order (usually), then,a=a0kk=0,1,2,…a0=2a=a0b=b0b0=1a=a0kb=ma0kb0m∈Z(5)φk,mt=2−k/2φ2−kt−m.The above equation is a discrete wavelet, whereink,m∈Zk decides on scaling parameters; m- translation parameters; 2k/2- wavelet function height.Then, the two-inlet discrete wavelet is(6)Wfk,m=2k/2∫−∞+∞φ¯t−m2kftdt.Ifft = , combine equation (6) to get the following equation: fnn∈Z(7)Wfk,m=fn,φk,mn=2−k/2∑n=Zφn−m2kfn.In the formula,Wfk,mindicates the degree of similarity; if it is orthogonal, it consists of similar components: fnφk,mnφk,mnfnφk,mn. ft. The refactoring formula is(8)ft=∑k∈R∑m∈RWfk,mφk,mt.Since the wavelet decomposition is the sample value obtained by a series of filters with discontinuous bandwidths at different scales, a binary mesh can be obtained, as shown in Figure2: ft.Figure 2
Discrete wavelet transform.LetH (n) be the low-pass filter, and let G (n) be the high-pass filter, and the decomposed low-frequency part and the high-frequency part are as follows, and then, the interrelationship between the low-pass filter and the high-pass filter can be represented by (9), Figure 3: AjDj.(9)Gn=−1nH2N−1−n.Figure 3
Discrete wavelet transform decomposition.Continue to decompose, and you can get (its original signal)AjAj+1,Aj+2A0=f.


## 3.1.2. Implementation of Wavelet Transform Images

The two-dimensional signal space of the wavelet master function is that the continuous wavelets are obtained after the translational expansion and contraction change as shown in the following equation:φx,y(10)φa,bx,byx,y=a−1φx−bxa,y−bya.When the original two-dimensional signal of the continuous wavelet is f(x,y), its transformation is as follows:fx,y(11)Wfa,bx,by=∫−∞+∞∫−∞+∞fx,yφa,bx,byx.ydxdy.Its inverter is as follows:(12)fx,y=Cψ−1∫0∞∫−∞+∞∫−∞+∞Wfa,bx,byφa,bx,byx,ydbxdbydaa2.The expression of the two-dimensional wavelet function is as follows:(13)ψ1x1,x2=ψx1φx2,ψ2x1,x2=φx1ψx2,ψ3x1,x2=ψx1ψx2.If a set of canonical orthogonal radicals constitutes space, the two-dimensional discrete wavelet transform is as follows:ψ=ψ1,ψ2,ψ3ψWj2fx,y,(14)f=∑j∈Z2,k∈KCj,kiψj,k+∑j∈Z2dj,kφj,ki=1,2,3,whereφj,k-scale function; Cj,ki=f,φi-detail coefficient; dj,k=f,φj,k-approximate coefficient.


## 3.1.3. Transverse Wavelet Transform and Longitudinal Wavelet Transform

The entire 2D wavelet variant can be carried out in two steps: horizontal transformation and vertical transformation. Figure4 is a schematic diagram of the lateral wavelets, and its decomposition action is synthesized from left to right, and vice versa [21]. The wavelet transforms each horizontal level on the image into a separate original sequence and performs an independent wavelet transform for each independent original sequence. In general, due to the lateral wavelet decomposition, the image data will form a low-frequency region on the left and a high-frequency data region on the right. We can use the letter H to represent the low-frequency data area and the letter K to represent the high-frequency data region.Figure 4
Lateral wavelet transform.From the transverse wavelet transformation, it can be seen that the change of longitudinal wavelets is similar to it. Figure5 shows the longitudinal wavelet transform, and its decomposition action is decomposed from left to right, and vice versa.Figure 5
Longitudinal wavelet conversion.As shown in Figure5, combined with the transverse wavelets that have been transformed, the overall data can be divided into four partitions: horizontal and vertical low-frequency (HH), horizontal and low vertical high (HK), horizontal high and vertical low (K H), and horizontal high and vertical high (KK).


## 3.2. Image Encoding

As we know, EZW encoding is an embedded image encoding. This method is done on the direction tree of the wavelet transformation. After the image of the wavelet transform is decomposed, there is a zero tree in the wavelet field. If encoded, the zero-tree root is marked as a zero-tree whole, thereby reducing code flow [22]. Its parameters include positive coefficient (POS), negative coefficient (NEG), zero-tree root (ZTR), and isolated zero point (LZ); the image coefficient of the wavelet transform is passed by the EZW threshold T0,T1,T2,…,Ti−1. The numerical value is given, where the initial threshold is as follows, so there is a wavelet coefficient. Ti=Ti−1/21≤Ti≤T0T0xi,jxi,j<T0,(15)T0=2∧log2Maxxi,j.The EZW encoder must establish a master table and a list of special encodings [23]. The former stores the undefined importance coordinates of the wavelet transform image coefficient, while the latter stores the remaining value of the wavelet transform image coefficient. The former’s mapping table is plotted by the path of Figure 6, while the latter corresponding wavelet transition coefficient needs to be evaluated. The steps are as follows:(1)
Ifxi,j>T, and it is satisfied, use P xi,j>0 OS for encoding, and assign the remaining value of the wavelet transform image coefficient in the list as xi,j−T−T/2.(2)
Ifxi,j>T, and it is satisfied, encode using xi,j<0 NEG, and assign the remaining values of the wavelet transform image coefficients in the checklist as xi,j−T+T/2.(3)
If the isolated zero isxi,j, it is encoded with L Z, and the remaining values are unchanged.(4)
If the zero-tree root is encoded usingZxi,j TR, the values in the list remain unchanged, and all values from the node are not scanned.(5)
Keep updating and initializing the main table, repeating the above 4 steps, and ending at timeT=T/2T=1.Figure 6
Wavelet transition coefficient table scan sequence.


## 3.3. Fuzzy Vector Compressor Design

POS (a biomimetic optimization method, particle swarm optimization method) is a combination of the evolutionary advantages of social and cognitive models [24] to optimize the system design. The process is as follows:Social Model:(16)vj,dt=vj,dt−1+ϕj,d2txj,dt#−xj,dt−1.Cognitive patterns:(17)vj,dt=vj,dt−1+ϕj,d1txj,dt∗−xj,dt−1.Particle Swarm Optimization Combinatorial Model:(18)vj,dt=vj,dt−1+ϕj,d1txj,dt∗−xj,dt−1+ϕj,d2txj,dt#−xj,dt−1,xj,dt=xj,dt−1+vj,dt,where v- the speed at which particles move in a single dimension; x- the parameter position of the particle in a single dimension; ϕ- random variables in a certain interval; xt∗- the optimal parameters of the particle; xt#- group optimal parameter solution.Thus, the position of each parameter is determined by the optimal parameter of the particle and the solution of the best parameter of the population.The distortion between the image data and the thin code is tested by the moderation function in PSOs, which is defined as follows:(19)Fitness=k0∑i=1Nt∑j=1MSij⋅xi¯−cj2,where Nt- the number of trainees; k0- initial constant; Sij- defined as follows:(20)Sij=1,if CFxi¯,cj=Arg maxCF1,CF2,…,CFM,0,otherwise,where xi¯- the sample value of the first image; icj- low code vector value. jAs can be seen from (19) and (20), the smaller the actual error, the smaller the distortion relative to the smaller. That is, this coding design is equivalent to searching for problems.(21)MIN∑i=1Nc∑j=1MSij⋅xi−cj2≡MAXk0∑i=1Nt∑j=1MSij⋅xi¯−cj2.As can be seen from the above equation, the larger the value of the adaptation function, the better the fuzzy inference system describes the code thinner.Due to the advantages of P SO’s few parameters and ease of implementation, its convergence rate is very fast, so it is easy to make particles fall into the region of the best solution, and in order to solve this disadvantage, the use of bacterial optimization search method (BFO) to optimize PSOS, the formula is as follows:(1)
Formula of bacterial tendency:(22)Pnf+1,g,u=Pnf,g,u+Cn∗Vf,whereP- the location of the fungus; S indicates the total number of bacteria; Nc indicates the total number of executions; Nre indicates the total number of reproductions; Nedindicates the total number of environmental changes; C represents the distance each time the bacteria move in a specific time; Vindicates the direction in which the bacteria move randomly; n=1,2,3…,S represents the nth bacterium; f=1,2,…,Nc indicates the first few learning steps; g=1,2,…,Nre indicates the first breeding; u=1,2,…,Nedindicates the number of environmental changes.(2)
Cluster dispersion formula:(23)Jan=P,Pnf,g,u=−dattractexp−ω¯attract∑x=1qpopi−pxn2+drepellentexp−ω¯repellent∑x=1qpopi−pxn2,whereJan-the interaction mode of transmitting messages to each other for the bacteria; q optimizes the problem dimension; ω¯attract, - learning the attraction constant; dattractdrepellent- learning to exclude constants; ω¯repellentpopi- the known optimal solution of the setting.The advantage of this design is that the combination of PSO and BFO to develop a series of evolutionary learning of bacterial optimization particle algorithm (BFPSO), and after optimization, the optimization parameters are found, the code thin can be better described, the image extraction technology has been improved with high quality, and the steps of the bacterial optimization particle swarm algorithm can be obtained as in Figure7.Figure 7
Flowchart of bacteria optimization particle swarm algorithm.The bacterial optimized particle algorithm (BFPSO) is used in the design of fuzzy vectors and combined with the image compression extraction system process as shown in Figure8.Figure 8
Optimization of the fuzzy vector compressor design flow.


## 4. Case Studies

### 4.1. Study Design

In this experiment, the Chinese paintings “L” and “P” were selected as the patterns, as shown in Figures9 and 10, their pixel size is ∗, the image can be decomposed into 16384 blocks, the parameters in B 28282×2 FPSO are defined as  =  c1=c2=1.5k0 50, and the reconstructed image is evaluated by P τ=0.75 SNR, and the formula is as follows:(24)PSNR=10log1025621/N×N∑i=0N−1∑j=0N−1fi,j−f∧i,j2,where N×N- initial image size; fi,j- primitive image prime values; f∧i,j- reconstructed pixel values.Figure 9
The Chinese painting “L.”Figure 10
The Chinese painting “P.”


### 4.2. Data Processing

We use the Chinese paintings “L” and “P” for their different code thinness sizes () usingM=22,23,24,25,26,27,28 the Bacterial Optimization Particle Algorithm (BFPSO), the Particle Swarm Optimization Method (PSO), and the Bacterial Optimization Search Method (BFO). After reconstructing the original Chinese paintings, the results of their PSNR are shown in Figures 11 and 12, respectively.Figure 11
Comparison of Chinese painting “L” images using B FPSO, PSO, and BFO methods in different code thins.Figure 12
Comparison of P SNR using BFPSO, P SO, and B FO methods in different size code books for Chinese painting “P” images.From Figures11 and 12, it can be seen that the results obtained by the one-time BFO and PSO learning methods are very susceptible to the initial situation and incomplete regional learning methods, which leads to unsatisfactory implementation results. Another scenario can be explained by the fact that as the number of cipher books increases, the complexity of dealing with the problem increases, and the traditional BFO and PSO methods will more easily fall into the regional optimal solution [25]. Table 2 compresses BFPSO, BFO, and PSO.Table 2
Comparison of B22−28 FPSO, B FO, and P SO performance in different encoding sizes ().
PSNR ()dBM=22M=23M=24M=25M=26M=27M=28LBFPS0125.127828.677229.966731.808232.817134.673636.7636BFPS0225.155828.711129.789031.796632.882734.758236.7999BFPS0325.185828.865229.929331.929832.868934.708136.8985BF0124.759127.231728.735830.902131.665233.789135.5891BF0225.012527.231728.653230.743131.589133.453135.7821BF0324.895627.321928.734130.758231.874133.679135.8912PS0124.574327.663228.503430.718631.756033.135635.3125PS0224.421926.283428.766630.407531.987633.256335.6289PS0324.691726.850827.891930.168731.096833.049535.4331PBFPS0124.310127.054827.956629.715231.516532.784636.1881BFPS0224.104026.715828.107829.926231.279632.692836.3401BFPS0324.211927.120827.979729.637331.376232.458136.5651BF0123.567135.341626.581228.789130.672131.891535.7812BF0223.617825.453126.175828.891230.672131.912335.6816BF0323.781225.718926.789128.891230.783131.875935.8901PS0123.453025.822826.173027.921729.992331.395334.8264PS0223.280325.711226.297028.126530.893431.453135.2823PS0322.918125.415226.748828.049530.217530.711435.5142From Table2, it can be seen that the improvement of the BFO and PSO methods by the BFPSO algorithm is more sufficient to obtain a very similar convergence result, so it can be concluded that the BFPSO method uses a complex coding design; that is, it is more in line with the color processing analysis of Chinese painting.


### 4.3. Teaching Effect Analysis

The data results obtained by comparing the intelligent image color processing technology of Chinese painting color teaching analysis with the Chinese painting teaching without using smart images are obtained, and the comparison chart of the accuracy rate and loss rate of the teacher’s teaching is shown in Figure13.Figure 13
Comparison of the loss rate curve between traditional teaching and intelligent image color teaching.From Figure14, it can be seen that the use of intelligent image processing technology is applied to the color teaching of Chinese painting to be more in line with the teaching of the times. The use of intelligent image color processing technology for Chinese painting is very prominent in improving the efficiency of teaching. The intelligent image color processing technology and traditional teaching were compared to the position loss, category loss, and confidence loss after 200 rounds.Figure 14
Position loss comparison chart.


#### 4.3.1. Loss of Location

From Figure14, it can be seen that the position loss before and after the use of intelligent image color processing technology is infinitely close to 0.02, but after using intelligent image color processing technology, the loss rate can be reduced in advance, indicating that the longer the intelligent image color processing technology is used, the more its status in the teaching of Chinese painting color will gradually increase, so that students are more motivated to learn.


#### 4.3.2. Categorical Losses

Shown in Figure15 is the use of intelligent image color processing technology before and after the analogy loss comparison chart, the teacher’s teaching methods are various, some methods students like, the teacher’s teaching content is better accepted, on the contrary, the effect is not ideal, and we need to guide students to the teaching content of interest. The loss rate wireless after using intelligent image color processing technology is close to zero.Figure 15
Comparison chart of analogy loss rates.


#### 4.3.3. Confidence Loss

Figure16 is the use of intelligent image color processing technology before and after the confidence loss rate comparison chart; for students, the teaching content of the teacher’s teaching is new and unknown; if the teacher’s teaching method is not vivid image, then the student will have doubts about the knowledge point, so that the student can not understand the teaching content and accept it; it is not conducive to the teacher to explain the knowledge. The use of intelligent image color processing technology will reduce the confidence loss rate in advance and improve the quality of teaching.Figure 16
Confidence loss rate comparison chart.In summary, it can be seen that the loss rate before the use of intelligent image color processing technology is faster than the loss rate after use, and the faster the loss rate is, the higher the teacher’s teaching quality is improved, so that the teacher’s teaching efficiency is improved.


### 4.4. Data Results

From the above teaching effect analysis, it can be obtained that the loss rate curve can converge faster after the use of intelligent image color processing technology, thereby improving the quality of students’ acceptance of knowledge and improving the teaching efficiency of teachers. The following figure is a comparison curve between the accuracy rate and the recall rate with the intelligent image color processing technology.As can be seen from Figures17 and 18, the accuracy rate and recall rate after using intelligent image processing technology are close to 1, indicating that, after optimizing with the BFPSO algorithm, it is preferential to reach the highest point to a certain extent, even if the use of intelligent image processing technology is more suitable for the teaching of Chinese painting.Figure 17
Intelligent image processing technology uses before and after accuracy ratios.Figure 18
Recall rate before and after the use of intelligent image processing technology.


## 4.1. Study Design

In this experiment, the Chinese paintings “L” and “P” were selected as the patterns, as shown in Figures9 and 10, their pixel size is ∗, the image can be decomposed into 16384 blocks, the parameters in B 28282×2 FPSO are defined as  =  c1=c2=1.5k0 50, and the reconstructed image is evaluated by P τ=0.75 SNR, and the formula is as follows:(24)PSNR=10log1025621/N×N∑i=0N−1∑j=0N−1fi,j−f∧i,j2,where N×N- initial image size; fi,j- primitive image prime values; f∧i,j- reconstructed pixel values.Figure 9
The Chinese painting “L.”Figure 10
The Chinese painting “P.”


## 4.2. Data Processing

We use the Chinese paintings “L” and “P” for their different code thinness sizes () usingM=22,23,24,25,26,27,28 the Bacterial Optimization Particle Algorithm (BFPSO), the Particle Swarm Optimization Method (PSO), and the Bacterial Optimization Search Method (BFO). After reconstructing the original Chinese paintings, the results of their PSNR are shown in Figures 11 and 12, respectively.Figure 11
Comparison of Chinese painting “L” images using B FPSO, PSO, and BFO methods in different code thins.Figure 12
Comparison of P SNR using BFPSO, P SO, and B FO methods in different size code books for Chinese painting “P” images.From Figures11 and 12, it can be seen that the results obtained by the one-time BFO and PSO learning methods are very susceptible to the initial situation and incomplete regional learning methods, which leads to unsatisfactory implementation results. Another scenario can be explained by the fact that as the number of cipher books increases, the complexity of dealing with the problem increases, and the traditional BFO and PSO methods will more easily fall into the regional optimal solution [25]. Table 2 compresses BFPSO, BFO, and PSO.Table 2
Comparison of B22−28 FPSO, B FO, and P SO performance in different encoding sizes ().
PSNR ()dBM=22M=23M=24M=25M=26M=27M=28LBFPS0125.127828.677229.966731.808232.817134.673636.7636BFPS0225.155828.711129.789031.796632.882734.758236.7999BFPS0325.185828.865229.929331.929832.868934.708136.8985BF0124.759127.231728.735830.902131.665233.789135.5891BF0225.012527.231728.653230.743131.589133.453135.7821BF0324.895627.321928.734130.758231.874133.679135.8912PS0124.574327.663228.503430.718631.756033.135635.3125PS0224.421926.283428.766630.407531.987633.256335.6289PS0324.691726.850827.891930.168731.096833.049535.4331PBFPS0124.310127.054827.956629.715231.516532.784636.1881BFPS0224.104026.715828.107829.926231.279632.692836.3401BFPS0324.211927.120827.979729.637331.376232.458136.5651BF0123.567135.341626.581228.789130.672131.891535.7812BF0223.617825.453126.175828.891230.672131.912335.6816BF0323.781225.718926.789128.891230.783131.875935.8901PS0123.453025.822826.173027.921729.992331.395334.8264PS0223.280325.711226.297028.126530.893431.453135.2823PS0322.918125.415226.748828.049530.217530.711435.5142From Table2, it can be seen that the improvement of the BFO and PSO methods by the BFPSO algorithm is more sufficient to obtain a very similar convergence result, so it can be concluded that the BFPSO method uses a complex coding design; that is, it is more in line with the color processing analysis of Chinese painting.


## 4.3. Teaching Effect Analysis

The data results obtained by comparing the intelligent image color processing technology of Chinese painting color teaching analysis with the Chinese painting teaching without using smart images are obtained, and the comparison chart of the accuracy rate and loss rate of the teacher’s teaching is shown in Figure13.Figure 13
Comparison of the loss rate curve between traditional teaching and intelligent image color teaching.From Figure14, it can be seen that the use of intelligent image processing technology is applied to the color teaching of Chinese painting to be more in line with the teaching of the times. The use of intelligent image color processing technology for Chinese painting is very prominent in improving the efficiency of teaching. The intelligent image color processing technology and traditional teaching were compared to the position loss, category loss, and confidence loss after 200 rounds.Figure 14
Position loss comparison chart.


### 4.3.1. Loss of Location

From Figure14, it can be seen that the position loss before and after the use of intelligent image color processing technology is infinitely close to 0.02, but after using intelligent image color processing technology, the loss rate can be reduced in advance, indicating that the longer the intelligent image color processing technology is used, the more its status in the teaching of Chinese painting color will gradually increase, so that students are more motivated to learn.


### 4.3.2. Categorical Losses

Shown in Figure15 is the use of intelligent image color processing technology before and after the analogy loss comparison chart, the teacher’s teaching methods are various, some methods students like, the teacher’s teaching content is better accepted, on the contrary, the effect is not ideal, and we need to guide students to the teaching content of interest. The loss rate wireless after using intelligent image color processing technology is close to zero.Figure 15
Comparison chart of analogy loss rates.


### 4.3.3. Confidence Loss

Figure16 is the use of intelligent image color processing technology before and after the confidence loss rate comparison chart; for students, the teaching content of the teacher’s teaching is new and unknown; if the teacher’s teaching method is not vivid image, then the student will have doubts about the knowledge point, so that the student can not understand the teaching content and accept it; it is not conducive to the teacher to explain the knowledge. The use of intelligent image color processing technology will reduce the confidence loss rate in advance and improve the quality of teaching.Figure 16
Confidence loss rate comparison chart.In summary, it can be seen that the loss rate before the use of intelligent image color processing technology is faster than the loss rate after use, and the faster the loss rate is, the higher the teacher’s teaching quality is improved, so that the teacher’s teaching efficiency is improved.


## 4.3.1. Loss of Location

From Figure14, it can be seen that the position loss before and after the use of intelligent image color processing technology is infinitely close to 0.02, but after using intelligent image color processing technology, the loss rate can be reduced in advance, indicating that the longer the intelligent image color processing technology is used, the more its status in the teaching of Chinese painting color will gradually increase, so that students are more motivated to learn.


## 4.3.2. Categorical Losses

Shown in Figure15 is the use of intelligent image color processing technology before and after the analogy loss comparison chart, the teacher’s teaching methods are various, some methods students like, the teacher’s teaching content is better accepted, on the contrary, the effect is not ideal, and we need to guide students to the teaching content of interest. The loss rate wireless after using intelligent image color processing technology is close to zero.Figure 15
Comparison chart of analogy loss rates.


## 4.3.3. Confidence Loss

Figure16 is the use of intelligent image color processing technology before and after the confidence loss rate comparison chart; for students, the teaching content of the teacher’s teaching is new and unknown; if the teacher’s teaching method is not vivid image, then the student will have doubts about the knowledge point, so that the student can not understand the teaching content and accept it; it is not conducive to the teacher to explain the knowledge. The use of intelligent image color processing technology will reduce the confidence loss rate in advance and improve the quality of teaching.Figure 16
Confidence loss rate comparison chart.In summary, it can be seen that the loss rate before the use of intelligent image color processing technology is faster than the loss rate after use, and the faster the loss rate is, the higher the teacher’s teaching quality is improved, so that the teacher’s teaching efficiency is improved.


## 4.4. Data Results

From the above teaching effect analysis, it can be obtained that the loss rate curve can converge faster after the use of intelligent image color processing technology, thereby improving the quality of students’ acceptance of knowledge and improving the teaching efficiency of teachers. The following figure is a comparison curve between the accuracy rate and the recall rate with the intelligent image color processing technology.As can be seen from Figures17 and 18, the accuracy rate and recall rate after using intelligent image processing technology are close to 1, indicating that, after optimizing with the BFPSO algorithm, it is preferential to reach the highest point to a certain extent, even if the use of intelligent image processing technology is more suitable for the teaching of Chinese painting.Figure 17
Intelligent image processing technology uses before and after accuracy ratios.Figure 18
Recall rate before and after the use of intelligent image processing technology.


## 5. Conclusion

This paper mainly applies intelligent image color technology to the teaching and research of Chinese painting color and uses image preprocessing (that is, image encoding) in data collection, uses wavelet variants to process images, and then uses BFPSO, PSO, and BFO. The learning mechanism finds the best blur system parameters to form a suitable encoding, to ensure that high-quality image can be collected, and through experiments, the use of intelligent image color processing technology in Chinese painting has been effectively verified, proving that intelligent image processing technology can improve the teaching of Chinese painting, making teaching ability stronger and more acceptable to students. The longer the intelligent image color processing technology is used in teaching, the more it can reflect its advantages. It shows that intelligent image color processing technology is suitable for teaching Chinese painting color. Although this paper has achieved some results here, and we can also combine the following aspects for deeper research application:(1)
Due to the huge amount of image data collected, the convolutional neural network of deep learning can be used to classify the acquired images in detail.(2)
The application of intelligent image color processing technology to teaching reflects its advantages, but it can also be further improved in its algorithm, which is also the direction of future research.



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*Source: 5335990-2022-09-15.xml*