A method for displaying smooth transformation of visual training stimulus vector graphics

Abstract

The application relates to the technical field of image processing, in particular to a smooth transformation display method of a visual training stimulation vector graph, which comprises the following steps: acquiring a vector image of a user visual area, calculating line distribution disorder degrees of local areas in the vector image according to the distribution state of lines in the local areas; analyzing the difference between the local areas at the same positions before and after the movement of the user visual area, and calculating background change degrees; calculating stripe complexity increase degrees of the movement of the user visual area according to the distribution state of the background change degrees of the local areas; comparing the stripe complexity increase degrees of the movement of the user visual area before and after the current time, and acquiring a refresh rate of the current movement of the user visual area. The application aims to improve the rendering effect of the vector graph by adjusting the refresh rate, and ensure that the rendering effect meets the visual perception habit of the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, in particular to a smooth transformation display method of a visual training stimulation vector pattern. BACKGROUND

[0002] The visual stimulation training technology stimulates the user's eyes through the VR virtual space to perform a visual stimulation training and a visual learning task, stimulates the user's visual signal pathway, and improves and strengthens the user's brain information processing ability. The visual stimulation training and the visual learning task of the user are performed by displaying a specific vector pattern to the user. The vector pattern needs to be rendered during the display to the user to ensure the high precision and high smoothness of the vector stripes, thereby improving the training effect.

[0003] In the rendering process of the vector pattern, a pixel-based rendering method is usually used. However, when facing a high complexity vector pattern, the rendering speed of this method is slow. In order to ensure the real-time performance of the user in the visual stimulation training, the traditional method is to reduce the video frame rate. However, since the complexity of the vector pattern at different time points is different, the rendering method with a fixed frame rate will cause the rendering time of each frame to be inconsistent, thereby affecting the stability of the rendering effect. SUMMARY

[0004] In view of the above, it is necessary to provide a smooth transformation display method of a visual training stimulation vector pattern to solve the above problems.

[0005] An embodiment of the present application provides a smooth transformation display method of a visual training stimulation vector pattern, the method comprising:

[0006] When the user performs the visual training stimulation, the vector images before and after each movement of the user's visual area are acquired, and the acquired vector images are segmented to obtain local areas;

[0007] The line shape features of each connected domain in the local area are analyzed to obtain a line distribution sequence of each connected domain. The dispersion degree of elements in all line distribution sequences in the local area and the overall distribution features are comprehensively considered to determine the line distribution disorder degree of each local area;

[0008] The binary images of the vector images before and after each movement of the user's visual area are acquired, the change features of the pixel values at the same positions in each local area are analyzed, and the background variation degree of each local area is determined in combination with the difference in the line distribution disorder degree;

[0009] screen all local regions based on the background variation degree, obtain the distribution discrete degree of the user visual area after each movement based on the distance distribution characteristics between the center coordinates of the screened local regions and the center coordinates of the vector image, and obtain the stripe complexity increase degree of the user visual area after each movement in combination with the background variation degree;

[0010] adjust the refresh rate corresponding to the last movement of the user visual area based on the change characteristics of the stripe complexity increase degree, and render the vector graphics based on the adjusted refresh rate.

[0011] Preferably, the line distribution sequence of each connected domain is specifically a sequence composed of the number of all column pixel points in each connected domain.

[0012] Preferably, the line distribution disorder degree of each local region is specifically:

[0013] obtain the mean and variance of all elements in each line distribution sequence in each local region, and denote them as a first mean and a first variance, respectively;

[0014] denote the variance of the first mean of all line distribution sequences as a second variance, denote the mean of the first variance of all line distribution sequences as a second mean, and take the result of forward fusion of the second mean and the second variance of each connected domain as the line distribution disorder degree of each local region.

[0015] Preferably, the background variation degree of each local region is specifically:

[0016] obtain a difference value image based on the absolute value of the difference between the pixel values of the same positions of the vector image and the binary image of the moving vector image, obtain the region variation degree according to the distribution of the pixel values of all pixel points in each local region of the difference value image;

[0017] obtain the difference between the line distribution disorder degrees of each local region of the vector image and the moving vector image, forward fuse the region variation degree of each local region, and obtain the background variation degree of each local region.

[0018] Preferably, the region variation degree is specifically the cumulative sum of the pixel values of all pixel points in each local region of the difference value image.

[0019] Preferably, the specific process of screening all local regions based on the background variation degree is:

[0020] perform threshold segmentation on the background variation degrees of all local regions to obtain a segmentation threshold, and screen all local regions with a background variation degree greater than the segmentation threshold.

[0021] Preferably, the distribution dispersion of the user visual area after each movement is obtained, specifically as follows:

[0022] The distance between the center coordinates of each selected local area and the center coordinates of the vector image is obtained, and a first-order difference sequence of a sequence arranged from small to large of the distances obtained by all local areas is obtained. The sum of all elements of the first-order difference sequence is taken as the distribution dispersion of the vector image.

[0023] Preferably, the stripe complexity increase degree of the user visual area after each movement is obtained, specifically as follows:

[0024] The cumulative sum of the background variation degrees of all local areas is positively fused with the distribution dispersion to obtain the stripe complexity increase degree of the vector image before and after the movement of the user visual area.

[0025] Preferably, the formula for adjusting the refresh rate corresponding to the last movement of the user visual area is as follows:

[0026]

[0027] In the formula, f represents the refresh rate corresponding to the current movement of the user visual area; f represents the refresh rate corresponding to the last movement of the user visual area; represents the stripe complexity increase degree of the last movement of the user visual area; represents the stripe complexity increase degree of the current movement of the user visual area; represents the preset minimum refresh rate; represents the preset maximum refresh rate; represents the rounding function; represents the maximum function; and represents the minimum function. represents the refresh rate corresponding to the current movement of the user visual area; represents the refresh rate corresponding to the last movement of the user visual area; represents the stripe complexity increase degree of the last movement of the user visual area; represents the stripe complexity increase degree of the current movement of the user visual area; , respectively represent the preset minimum refresh rate and the preset maximum refresh rate; represents the rounding function; represents the maximum function; represents the minimum function.

[0028] Preferably, the refresh rate of the initial user visual area is the preset minimum refresh rate.

[0029] The application has at least the following beneficial effects:

[0030] The application first obtains the vector images before and after each movement of the user's visual area, and segments the obtained vector images to obtain local areas. The line shape features of each connected domain in the local area are analyzed, and the line distribution sequence of each connected domain is obtained. By analyzing the line shape features of the connected domain, the structure and layout of the lines in the visual area can be deeply understood, which helps to detect and analyze the complexity of the image and helps to determine the difficulty of visual training. The dispersion degree of the elements in all line distribution sequences in the local area and the overall distribution characteristics are integrated to determine the line distribution disorder degree of each local area, which can quantify the complexity of the image, thereby providing a basis for the next visual stimulation adjustment, effectively adjusting the challenge of visual training, and preventing it from being too simple or too difficult. The binary images of the vector images before and after each movement of the user's visual area are obtained, and the change characteristics of the pixel values at the same position in each local area are analyzed. Combined with the difference in line distribution disorder degree, the background variation degree of each local area is determined. By analyzing the pixel changes in the binary image, the dynamic changes in the image can be effectively captured, which helps to monitor the activity of the visual area. Combined with the change of the background, the reaction of the user's visual system to different stimuli can be more accurately understood, and the training intensity can be further adjusted. Based on the background variation degree, all local areas are screened, and based on the distance distribution characteristics between the center coordinates of the screened local areas and the center coordinates of the vector image, the distribution dispersion degree of the user's visual area after each movement is obtained. Screening local areas with large variation degrees and calculating the distribution dispersion degree can help identify the most active area of the user's visual system. This helps to accurately adjust the area and content of visual training and improves the user's visual training effect. By combining the changes in background variation degree and stripe complexity, the complexity increase degree of the visual area can be quantified, which helps to monitor the user's visual adaptation ability in real time and prevents overstimulation or insufficient stimulation. Based on the change characteristics of the stripe complexity increase degree, the refresh rate corresponding to the last movement of the user's visual area is adjusted. Adjusting the refresh rate can be optimized according to the user's visual feedback to ensure that the stimulation in the visual training is not too frequent or sparse, thereby better adapting to the user's visual response and training needs. Based on the adjusted refresh rate, the vector graphics are rendered. By adjusting the refresh rate, the rendering effect of the vector graphics is improved, the influence of short-term complex changes on the stability of visual training is eliminated, the rendering effect is ensured to meet the user's visual perception habits, visual fatigue or information overload is avoided, the visual training process is more efficient, and the user's visual response speed and accuracy are improved. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 A flowchart of a visual training stimulation vector graphics smooth transformation display method provided by the application;

[0032] Figure 2 A refresh rate adjustment flowchart provided by the application. DETAILED DESCRIPTION

[0033] In the description of the embodiments of the present application, the words "exemplary", "or", "for example" are used to mean serving as an example, instance, or illustration, and not to imply any preference or superiority. In the embodiments of the present application, any embodiment or design scheme described as "exemplary" or "for example" should not be interpreted as being more preferred or having superiority over other embodiments or design schemes. Rather, the use of "exemplary", "or", "for example" is intended to present the relevant concept in a specific manner.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0035] In addition, it should be pointed out that the terms "first", "second" in the present application and its drawings are used to distinguish similar objects, and are not used to describe a specific order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flowchart includes one or more steps for implementing the method, and the execution order of the steps can be interchanged with each other without departing from the scope of the present application, and some steps can also be deleted.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0037] The present application provides a smooth transformation display method of visual training stimulation vector graphics, applied to the field of image processing technology, with reference to the accompanying Figure 1 , the method comprises the following steps:

[0038] S1: When the user is stimulated for visual training, the vector images before and after each movement of the user's visual area are acquired, and the acquired vector images are segmented to obtain local areas.

[0039] Before the user is stimulated for visual training, the user is stimulated for visual training vector graphics into the VR virtual device. Further, since the vector graphics are stored, not only the function of the vector image is stored, but also the distribution position of the vector function is stored, thereby obtaining the center of all vector function distribution positions, which is recorded as the visual center at the initial moment. In the process of the user's visual training, the area that the user can directly view is recorded as the visual area. Then, taking the visual center at the initial moment as the reference, when the visual center and the center of the visual area completely coincide, the coordinates of all points in the visual area are acquired, and the coordinates of the visual area are substituted into the vector function of the vector graphics to acquire the vector image. Note: the vector graphics are stored in the form of vector functions of images in storage.

[0040] In the process of visual stimulation training of the user, a target ball is randomly generated in the vector image, the user moves the VR device rotating the head to move the visual center to the center of the target ball, and the vector image also changes with the visual center. Thus, the vector image is divided into N x N local regions to represent the local change state of the background of the vector image. In this embodiment, the value of N is 10, and the implementer can adjust the value of N according to the actual situation, and the present application does not limit this.

[0041] Further, the visual area is moved so that the center of the visual area coincides with the center of the target ball, and all coordinates in the visual area are obtained. The calculation method is as follows: the horizontal and vertical coordinates of the center of the visual area before moving are subtracted from the horizontal and vertical coordinates of the center of the target ball to obtain the offset of the horizontal and vertical coordinates. And the horizontal and vertical coordinates of each coordinate of the visual area before moving are subtracted from the corresponding dimensional offset to obtain the coordinates of the visual area after moving. The coordinates of the visual area after moving are substituted into the vector function of the vector image to obtain the moving vector image. And the moving vector image is divided into N x N local regions to represent the local change state of the background after the user's visual movement.

[0042] S2: analyze the line shape features of each connected domain in the local region to obtain a line distribution sequence of each connected domain; and determine the line distribution disorder degree of each local region by comprehensively considering the discrete degree and overall distribution characteristics of the elements in all line distribution sequences in the local region.

[0043] In the process of visual training stimulation of the user, the center of the user's visual area needs to move with the position of the target ball. In the moving process, the vector graphics in each local region in the user's visual area is different, so that the complexity of the graphics in the image also changes. For the visual training stimulation of the user, adaptability needs to be maintained, so timely rendering of the user's visual area is required to enable the training stimulation vector image to change smoothly and quickly to meet the needs of visual training of different users and improve the training effect.

[0044] In the training process, the background image usually adopts non-overlapping black lines, the width of each part of the same line is different, and the degree of line bending is also different. In this application, each local region in the vector image is denoted as local region. For each local region, the following operations are performed: first, the local region is taken as the input of the Otsu method, and the output is a binary image of the local region. Further, the binary image of the local region is taken as the input of the Two-Pass Algorithm, and the output is all connected domains in the binary image of the local region, which represents the distribution position of each background line in the stimulation training. The Otsu method and the Two-Pass Algorithm are both known technologies, and the specific calculation steps are not described here.

[0045] Since the thickness of each part of the line represents the complexity of the line, and the distribution position of the connected domain determines the distribution state of the line. Therefore, the number of each column of pixels in each connected domain is obtained, and each column number is arranged in the order of the first column, the second column, and the last column. It should be noted that the element value of 0 is not arranged because it cannot represent the thickness state of the line; the line distribution sequence of each connected domain is obtained, which is used to represent the thickness distribution state of the line.

[0046] Therefore, the line distribution disorder degree of the local area is calculated. Specifically, the mean and variance of all elements in each line distribution sequence in each local area are obtained, which are denoted as the first mean and the first variance, respectively. The variance of the first mean of all line distribution sequences is denoted as the second variance. The mean of the first variance of all line distribution sequences is denoted as the second mean. The result of the forward fusion of the second mean and the second variance of each connected domain is used as the line distribution disorder degree of each local area.

[0047] In this embodiment, the second mean is denoted as A, and the second variance is denoted as B. The formula is The line distribution disorder degree of each local area is obtained; wherein a represents a zero adjustment factor, which prevents the factor from being zero, resulting in too small line distribution disorder degree. The default value range is [0.1, 1], and the value is 1 in this example.

[0048] It should be understood that when the line thickness degree of each connected domain in the local area is more uneven, it means that the complexity of the line is higher, so that the first mean is larger. At the same time, the difference between different lines is larger, so that the difference between lines is larger, i.e. the second variance is larger, so that the value of the line distribution disorder degree of the local area is larger. The higher the complexity of the background line in the local area, the lower the refresh frequency needs to be during the frame rate refresh process, so that the VR system can reduce the total amount of rendering per unit time, improve the smoothing degree of the vector image during smoothing, and maintain the stability of the user using the VR device.

[0049] S3: Obtain the binary image of the vector image before and after each movement of the user's visual area, analyze the change characteristics of the pixel value at the same position in each local area, and determine the background variation degree of each local area in combination with the difference in the line distribution disorder degree.

[0050] The greater the difference between the local areas of the same position after the user's vision moves with the target ball, the greater the change difference of the local area, and a smaller refresh frequency should be used. Therefore, the greater the difference between the line distribution of the two local areas, the greater the change of the complexity of the background in the local area. Since the background image is composed of multiple black lines, during the movement, there are certain pixel points with unchanged pixel values. The greater the number of pixel points with unchanged pixel values in the local area, the simpler the change of the local area.

[0051] Based on the absolute value of the difference between the pixel values of the same position of the binary image of the vector image and the moving vector image, a difference image is obtained, and each local area of the difference image is analyzed. It should be noted that the difference image is also divided into uniform N×N local areas, denoted as a region difference image, which is used to represent the change difference of each local area when the vector image is changed to the moving vector image. The calculation of the difference image is a known technology, and the specific calculation steps are not repeated here.

[0052] In the region difference image, the pixel value of the non-zero point represents the background change position. The sum of the pixel values of all pixel points in the region difference image is denoted as the region change degree, which is used to represent the background change of the local area. The greater the value of the region change degree, the greater the change of the local area, and the refresh frequency needs to be reduced during the refresh process.

[0053] The difference between the line distribution disorder degree of each local area of the vector image and the moving vector image is obtained, and the region change degree of each local area is obtained by forward fusion. In this embodiment, the difference between the variables is calculated by the absolute value of the difference; the variables are forward fused by the multiplication calculation method.

[0054] It should be understood that during the visual training stimulation process of the user, the greater the difference between the background changes of the visual area before and after the user moves, the greater the difference between the number of line distributions and the thickness of each position in the same local area before and after the movement, which increases the difference between the line distribution disorder degrees of the vector image and the moving vector image in the same local area, and increases the number of pixel value changes of the pixel points in the same local area, i.e. increases the region change degree of the local area, thereby increasing the background change degree of the local area. In this case, the refresh frequency of the vector image needs to be reduced to make the display of the vector image during the movement more smooth.

[0055] S4: screening all local regions based on the background variation degree, obtaining the distribution discrete degree of the user visual area after each movement based on the distance distribution characteristics between the center coordinates of the screened local regions and the center coordinates of the vector image, and combining the background variation degree to obtain the stripe complexity increase degree of the user visual area after each movement.

[0056] For the background variation degree of different regions, the greater the numerical value, the more chaotic the distribution, which means that the vector graphics changes more unevenly in the user visual area, and the complexity of the vector graphics changes in the user visual area increases. Such uneven changes may affect the user's perception of the target ball, and therefore, it is necessary to keep the vector graphics changes in the user visual area smooth to improve the user's perception of the target ball.

[0057] The background variation degrees of all local regions are taken as the input of the maximum inter-class variance method, and the segmentation threshold is output. The local region corresponding to the background variation degree greater than the threshold is marked as 1, and the local region corresponding to the background variation degree less than or equal to the threshold is marked as 0.

[0058] Then, each local region is scanned to obtain the center coordinates of all local regions with a marked value of 1. Further, the Euclidean distance of the center coordinates of each local region with a marked value of 1 from the image center coordinates is obtained. All the obtained Euclidean distances are arranged in order from small to large. If the sizes of two Euclidean distances are the same, they are arranged continuously. The arranged data is taken as the input of the first-order difference algorithm, and the sum of all elements in the output first-order difference sequence is taken as the distribution discrete degree of the vector graphics, which is used to represent the distribution discrete degree of the region corresponding to the higher background variation degree. The calculation of the maximum inter-class variance method, the first-order difference method and the Euclidean distance is a known technology, and the specific calculation steps are not repeated here.

[0059] Based on this, the stripe complexity increase degree of the vector graphics before and after the movement of the user visual area is calculated. Specifically, the cumulative sum of the background variation degrees of all local regions is positively fused with the distribution discrete degree to obtain the stripe complexity increase degree of the vector graphics before and after the movement of the user visual area. In this embodiment, the multiplication calculation method is used for positively fusing multiple variables.

[0060] It should be understood that when the vector graphics changes before and after the user visual area moves, when the change is large, the line changes in the background image are large, so that the number and position of the lines change, resulting in an increase in the difference in the background variation degree of the same area before and after the change, that is, an increase in the distribution dispersion of the vector graphics before and after the user visual area moves, thereby increasing the stripe complexity increase degree of the vector graphics before and after the user visual area moves. At this time, the refresh rate for the user to watch the vector graphics needs to be reduced to ensure that the vector graphics can be rendered in time, improve the smoothness of the vector graphics in the user visual area, and improve the user experience effect.

[0061] S5: adjusting the refresh rate corresponding to the last movement of the user visual area based on the change feature of the stripe complexity increase degree; and rendering the vector graphics based on the adjusted refresh rate.

[0062] When the user is visually trained and stimulated, a target ball will randomly appear in the user's visual area. The user will move the VR device to move the center of the visual area to the target ball. Then, a target ball will reappear in the current visual area, and the user will move the visual area again. The above operations are repeated, so that the center of the user's visual area continuously tracks the target ball, thereby exercising the user's eyeballs and brain. Therefore, the refresh rate of the current user moving the visual area is calculated, and the specific formula is as follows:

[0063]

[0064] In the formula, represents the refresh rate corresponding to the current movement of the user visual area; represents the refresh rate corresponding to the last movement of the user visual area; represents the stripe complexity increase degree of the last movement of the user visual area; represents the stripe complexity increase degree of the current movement of the user visual area; , respectively represent a preset minimum refresh rate and a preset maximum refresh rate. Since the minimum frame that can be recognized by the human eye is 30, in the present example, 30, and the initial refresh rate is also 30; since after exceeding 120 frames / second, the user will usually feel symptoms of motion sickness, in the present embodiment, the value is 120; represents a rounding function; represents a maximum function; represents a minimum function.

[0065] The adjustment flowchart of the refresh rate is shown in Figure 2 .

[0066] In the user visual stimulation training, if the current moving stripe complexity increases compared with the value of the previous moving stripe complexity, the refresh rate of this moving should be lower than the previous vector graphics refresh rate, but in order to ensure the continuity of the human eye to the vector graphics and reduce the lag, the minimum refresh rate needs to be maintained, so the refresh rate of the current user moving visual area cannot be lower than 30.

[0067] The refresh rate of the current user moving visual area is input to the vector image rendering module, which will render the vector graphics by using the CPU to realize the smooth transformation of the vector graphics.

[0068] The flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the system, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, program segment or part of code containing one or more executable instructions for implementing a specified logic function. In some alternative implementations, the functions noted in the blocks can also occur in an order different from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. In the description corresponding to the flowcharts and block diagrams in the drawings, the operations or steps corresponding to different blocks can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0069] The above embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for smooth transition display of visual training stimulus vector graphics, characterized by, The method comprises the following steps: When the user performs a visual training stimulus, vector images before and after each movement of the user's visual area are acquired, and the acquired vector images are segmented to obtain local areas; The line shape features of each connected domain in the local area are analyzed to obtain a line distribution sequence of each connected domain; the dispersion degree of elements in all line distribution sequences in the local area and the overall distribution features are comprehensively considered to determine the line distribution disorder degree of each local area; Binary images of the vector images before and after each movement of the user's visual area are acquired, the change features of pixel values at the same position in each local area are analyzed, and the line distribution disorder degree is combined to determine the background variation degree of each local area; Based on the background variation degree, all local areas are screened, the distribution dispersion degree after each movement of the user's visual area is obtained based on the distance distribution features between the center coordinates of the screened local areas and the center coordinates of the vector images, and the background variation degree is combined to obtain the stripe complexity increase degree after each movement of the user's visual area; Based on the change features of the stripe complexity increase degree, the refresh rate corresponding to the last movement of the user's visual area is adjusted; and the vector graphics are rendered based on the adjusted refresh rate. The formula for adjusting the refresh rate corresponding to the last movement of the user's visual area is: In the formula, represents the refresh rate corresponding to the current movement of the user visual area; represents the refresh rate corresponding to the last movement of the user visual area; represents the stripe complexity increase degree of the last movement of the user visual area; represents the stripe complexity increase degree of the current movement of the user visual area; respectively represent the preset minimum refresh rate and the preset maximum refresh rate; represents a rounding function; represents a maximum function; represents a minimum function.​ 2. A method for smooth transition display of visual training stimulus vector graphics as claimed in claim 1, wherein, The line distribution sequence of each connected domain is obtained, and the sequence is composed of the number of all column pixel points in each connected domain.

3. A method for smoothly transforming and displaying a visual training stimulus vector graphic as claimed in claim 1, wherein, The line distribution disorder degree of each local area is determined, and the first mean value and the first variance of all elements in each line distribution sequence in each local area are obtained, and the first mean value and the first variance are denoted as the first mean value and the first variance respectively. The variance of the first mean value of all line distribution sequences is denoted as the second variance, the mean value of the first variance of all line distribution sequences is denoted as the second mean value, and the result of forward fusion of the second mean value and the second variance of each connected domain is used as the line distribution disorder degree of each local area. The background variation degree of each local area is determined, and the difference value image is obtained based on the absolute value of the difference between the pixel values at the same position in the vector image and the binary image of the moving vector image, and the region variation degree is obtained according to the distribution of pixel values of all pixel points in each local area of the difference value image.

4. A method for smoothly transforming and displaying a visual training stimulus vector graphic as claimed in claim 1, wherein, The difference between the line distribution disorder degrees of each local area in the vector image and the moving vector image is obtained, and the region variation degree of each local area is forward fused to obtain the background variation degree of each local area. The region variation degree is specifically the cumulative sum of the pixel values of all pixel points in each local area of the difference value image. The specific process of screening all local areas based on the background variation degree is as follows:

5. A method for smooth transition display of visual training stimulus vector graphics as claimed in claim 4, wherein, The background variation degrees of all local areas are threshold segmented to obtain a segmentation threshold, and all local areas with a background variation degree greater than the segmentation threshold are screened.

6. A method for smoothly transforming and displaying a visual training stimulus vector graphic as claimed in claim 1, wherein, The distribution dispersion degree after each movement of the user's visual area is obtained, and the specific process is as follows: ​ 7. A method for smoothly transforming and displaying a visual training stimulus vector graphic as claimed in claim 1, wherein, ​ The distance between the center coordinate of each local region and the center coordinate of the vector image is obtained, a first-order difference sequence of a sequence arranged from small to large of all distances obtained by all local regions is obtained, and a sum value of all elements of the first-order difference sequence is taken as the distribution dispersion degree of the vector image.

8. A method for smoothly transforming and displaying a visual training stimulus vector graphic as claimed in claim 1, wherein, The stripe complexity increase degree of the user visual area after each movement is obtained, and specifically comprises the following steps: The cumulative sum of the background variation degrees of all local regions and the distribution dispersion degree are positively fused to obtain the stripe complexity increase degree of the vector graphics before and after the movement of the user visual area.

9. A method for smoothly transforming and displaying a visual training stimulus vector graphic as claimed in claim 1, wherein, The refresh rate of the initial user visual area is the preset minimum refresh rate.

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