Display screen calibration method, system, product and medium

Abstract

A display screen calibration method, system, product and medium, relating to the technical field of display screen calibration, the method comprising: collecting position feature data of a target edge user, calculating the partition parameters of the target display screen based on the position feature data; dividing the target display screen into multiple dynamic display areas based on the partition parameters, the dynamic display area comprising multiple gradient overlapping boundaries, the gradient overlapping boundary width being dynamically adjusted according to the visual parameter difference of adjacent display areas, calculating the display compensation value of each of the dynamic display areas based on the visual parameters; generating calibration image data based on the display compensation value, and outputting the calibration image data to each of the dynamic display areas. Implementing this method can improve the display screen viewing experience of edge users.

Description

Technical Field

[0001] The present application relates to the technical field of display screen calibration, and in particular to a display screen calibration method, system, product and medium. Background Art

[0002] With the development of display technology, large-size display screens are increasingly used in conference rooms, exhibition halls and other scenes. Due to the large size of the display screen, viewers often need to watch the display content at different positions and angles, which puts higher requirements on the display effect of the display screen.

[0003] In the related art, fixed compensation parameters are usually used to uniformly adjust the entire display screen, including adjusting display parameters such as brightness, contrast and color of the display screen to improve the display effect.

[0004] However, when the user is viewing from the edge of the display, due to the large viewing angle, the displayed content will experience severe brightness attenuation and color distortion, resulting in a decrease in the display viewing experience for edge users. Summary of the invention

[0005] The present application provides a display screen calibration method, system, product and medium for improving the display screen viewing experience of edge users.

[0006] In a first aspect, the present application provides a display screen calibration method, which is applied to a display screen calibration system, the method comprising: collecting position feature data of a target edge user, the position feature data including the spatial position coordinates of the head, the angle between the line of sight and the plane of the display screen, and the coordinates of the eye gaze point; based on the position feature data, calculating the partition parameters of the target display screen; based on the partition parameters, dividing the target display screen into multiple dynamic display areas, the dynamic display area comprising multiple gradient overlapping boundaries, the gradient overlapping boundary width is dynamically adjusted according to the visual parameter difference between adjacent display areas, the visual parameters including a brightness attenuation coefficient, a color offset value, and a contrast reduction ratio; based on the visual parameters, calculating a display compensation value for each of the dynamic display areas; generating calibration image data based on the display compensation value, and outputting the calibration image data to each of the dynamic display areas.

[0007] By adopting the above technical solution, the location feature data of the target edge users are collected to provide a basis for subsequent calculations. Based on these data, the partition parameters are calculated to divide the display screen according to the actual viewing situation of the user. The setting of dynamic display areas and gradual overlapping boundaries can make the transition between different areas natural. The compensation value is calculated according to the visual parameters to improve the display effect in a targeted manner. The calibration image data is generated and output, so that each area has a suitable display effect and improves the viewing experience of edge users.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of calculating the partition parameters of the target display screen based on the position feature data specifically includes: establishing a user viewing heat distribution map based on the head space position coordinates; dividing the main viewing area and the secondary viewing area according to the heat distribution map, and calculating the visual comfort index of each viewing area according to the angle between the line of sight and the plane of the display screen; prioritizing each viewing area according to the visual comfort index, and determining the partition parameters of the target display screen in the preset matching parameter data based on the priority sorting result.

[0009] By adopting the above technical solution, a heat distribution map is established based on the spatial position coordinates of the head, which can intuitively present the user's viewing distribution. By dividing the primary and secondary viewing areas and calculating the visual comfort index, the difference in visual perception in different areas can be clearly identified. The partition parameters are determined according to the index priority ranking to make the display screen partition more scientific and reasonable. This partitioning method can better adapt to the viewing habits of different users, effectively solve the brightness attenuation and color distortion problems caused by the large viewing angle of edge users, ensure a good visual experience in different viewing areas, and improve the overall viewing effect.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of calculating the display compensation value of each of the dynamic display areas based on the visual parameters specifically includes: calculating a brightness compensation coefficient based on the brightness attenuation coefficient of each of the dynamic display areas, calculating a color correction parameter based on the color offset value of each of the dynamic display areas, and calculating a contrast compensation factor based on the contrast reduction ratio of each of the dynamic display areas; and performing a weighted combination of the brightness compensation coefficient, the color correction parameter, and the contrast compensation factor to generate a display compensation value for each of the dynamic display areas.

[0011] By adopting the above technical solution, compensation coefficients, correction parameters and compensation factors are calculated for the brightness attenuation coefficient, color offset value and contrast reduction ratio of each dynamic display area, taking into account the key factors affecting the display effect. These parameters are weighted and combined to generate display compensation values, achieving accurate optimization of the display effect. The visual parameters of different areas are effectively compensated, so that the display screen can achieve a more ideal state in terms of brightness, color and contrast.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the step of generating calibration image data based on the display compensation value and outputting the calibration image data to each of the dynamic display areas specifically includes: obtaining the display content type of the dynamic display area, the display content type including image content and text content; when the dynamic display area is the image content, extracting key feature parameters of the image content, the key feature parameters including brightness distribution, color saturation and edge clarity of the image; adjusting the display compensation value based on the key feature parameters to obtain image feature compensation parameters; processing the original image data according to the image feature compensation parameters to generate calibration image data; when the dynamic display area is the text content, identifying the font type and font size of the text content; calculating the stroke feature weight according to the font type, and determining the edge sharpening degree according to the font size; optimizing the display compensation value according to the stroke feature weight and the edge sharpening degree to obtain text clarity compensation parameters; applying the text clarity compensation parameters to the original text data to generate calibration text data; outputting the calibration image data and the calibration text data as the calibration image data to each of the dynamic display areas.

[0013] By adopting the above technical solution, after obtaining the display content type of the dynamic display area, the key feature parameters are extracted for the image content, and the display compensation value can be accurately adjusted according to the image's own characteristics such as brightness distribution, color saturation and edge clarity, and the image feature compensation parameters are generated to process the original image data, which can optimize the image display effect. For text content, the font type and font size are identified to calculate the relevant weights and sharpening levels, and then the display compensation value is optimized to obtain the text clarity compensation parameters to process the text data, which can improve the text clarity. Finally, the image and text data output are calibrated to ensure that different types of content have the best display effect.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of generating calibration image data based on the display compensation value and outputting the calibration image data to each of the dynamic display areas, the method also includes: obtaining real-time eye movement data of the target edge user; identifying the gaze duration and gaze frequency of the target edge user on the dynamic display area based on the real-time eye movement data; calculating the user attention index of each of the dynamic display areas based on the gaze duration and gaze frequency; when it is detected that the user attention index of the target dynamic display area exceeds a preset threshold, adjusting the gradient overlapping border width of the target dynamic display area to a preset width; recalculating the target display compensation value based on the preset width, and outputting the target display compensation value to the dynamic display area.

[0015] By adopting the above technical solution, the real-time eye movement data of the target edge user is obtained, and the user's attention index is calculated by identifying the gaze duration and the number of times the gaze is fixed, so that the user's visual attention distribution can be understood in real time. When the attention index exceeds the threshold, the width of the gradient overlap boundary is adjusted to the preset width, which can highlight the user's attention area and make the display transition more natural. The compensation value is recalculated and output based on the preset width to further optimize the display effect of the area.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of generating calibration image data based on the display compensation value and outputting the calibration image data to each of the dynamic display areas, the method also includes: when target human activity is detected within a preset distance range, local occlusion of the target human body is detected to obtain the occlusion area ratio of the occlusion area; when the continuous occlusion time exceeds a preset time threshold and the occlusion area ratio exceeds a preset occlusion threshold, text content in the occlusion area is detected; if the text content is detected, the position layout display of the text content is adjusted to an unobstructed area.

[0017] By adopting the above technical solution, human activities and occlusion within a preset distance are detected. When the continuous occlusion meets the conditions, the text content in the occluded area is detected, and the situation that may affect viewing can be discovered in time. If text content is detected, its display priority and length are obtained, and the minimum display area is calculated based on priority grouping. In the unoccluded area, the candidate position is selected using a greedy strategy and filled with text according to priority, which can ensure that important text is displayed first and the layout is reasonable, solve the problem of human body occlusion of text display, and ensure the complete presentation of text information.

[0018] In combination with some embodiments of the first aspect, in some embodiments, if the existence of the text content is detected, the step of adjusting the position layout display of the text content to an unobstructed area specifically includes: obtaining the display priority and text length of the text content; grouping the text content based on the display priority, and calculating the minimum display area required for each group of text content; in the unobstructed area, selecting a candidate display position that meets the minimum display area requirement based on a greedy strategy; and filling the text content into the corresponding candidate display positions in order of display priority from high to low.

[0019] By adopting the above technical solution, when the text content is detected, its display priority and text length are obtained. Display priority helps to distinguish the importance of text and provide a basis for subsequent reasonable layout. Calculating the minimum display area required for each group of text can ensure that the text is clear and readable. In the unobstructed area, candidate display positions are selected based on a greedy strategy, and areas close to the original position and with appropriate area are given priority to ensure that the text movement distance is reasonable and can be fully displayed. The text is filled according to the display priority, so that important text is properly placed first, and the orderly rearrangement of the text is achieved as a whole.

[0020] In a second aspect, an embodiment of the present application provides a display screen calibration system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the display screen calibration system to perform the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, and when the above-mentioned computer program product runs on a display screen calibration system, the above-mentioned display screen calibration system executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the instructions are executed on a display screen calibration system, the display screen calibration system executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] It can be understood that the display screen calibration system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. This application provides a basis for subsequent calculations by collecting location feature data of target edge users. Based on these data, the partition parameters are calculated to divide the display screen according to the actual viewing situation of the user. The setting of dynamic display areas and gradual overlapping boundaries can make the transition between different areas natural. The compensation value is calculated according to the visual parameters to improve the display effect in a targeted manner. Generating and outputting calibrated image data can make each area have a suitable display effect and enhance the viewing experience of edge users.

[0026] 2. This application can intuitively present the user's viewing distribution by establishing a heat distribution map based on the spatial position coordinates of the head. By dividing the primary and secondary viewing areas and calculating the visual comfort index, the difference in visual perception in different areas can be clarified. The partition parameters are determined according to the index priority ranking to make the display screen partition more scientific and reasonable. This partitioning method can better adapt to the viewing habits of different users, effectively solve the brightness attenuation and color distortion problems caused by the large viewing angle of edge users, ensure a good visual experience in different viewing areas, and improve the overall viewing effect.

[0027] 3. This application obtains the display priority and text length after detecting the text content. Display priority helps to distinguish the importance of text and provides a basis for subsequent reasonable layout. Calculating the minimum display area required for each group of text can ensure that the text is clear and readable. In the unobstructed area, candidate display positions are selected based on a greedy strategy, and areas close to the original position and with appropriate area are given priority to ensure that the text movement distance is reasonable and can be fully displayed. The text is filled according to the display priority, so that important text is properly placed first, and the orderly rearrangement of the text is achieved as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of a display screen calibration method in an embodiment of the present application;

[0029] Figure 2 is another flowchart of the display screen calibration method in an embodiment of the present application;

[0030] Figure 3 is another flow chart of the display screen calibration method in the embodiment of the present application;

[0031] Figure 4 It is a schematic diagram of a physical device structure of a display screen calibration system in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0033] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0034] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , is a flow chart of a display screen calibration method in an embodiment of the present application.

[0035] S101. Collecting position feature data of a target edge user, where the position feature data includes the spatial position coordinates of the head, the angle between the line of sight and the plane of the display screen, and the coordinates of the eye gaze point.

[0036] Among them, the target edge user refers to the viewer within the viewing angle range of 45 degrees outside the front of the display screen. The head space position coordinates refer to the position information of the center point of the user's head in three-dimensional space, and are expressed using the Cartesian coordinate system (x, y, z). The angle between the line of sight and the plane of the display screen represents the angle formed by the user's line of sight and the plane of the display screen, expressed as an angle value, and the value range is 0-180 degrees. The eye gaze point coordinates refer to the intersection position of the user's line of sight and the plane of the display screen, and are expressed using two-dimensional coordinates (x, y). Position feature data refers to a set of spatial information that describes the user's viewing posture, which is used for subsequent display optimization calculations.

[0037] This step is performed when the display screen is turned on and the presence of an edge user is detected. Specifically, a depth camera and an eye tracking device are used to monitor the user in real time, with a sampling frequency of no less than 60Hz. The depth camera collects the three-dimensional spatial information of the user's head and determines the center position of the head by detecting facial feature points. The eye tracking device collects the user's eye movement data, including the eye rotation angle and gaze direction. The system performs coordinate system conversion and noise filtering on the collected data to generate a standardized position feature data stream.

[0038] In some embodiments, the user position feature data can be collected in a variety of ways: Optionally, a binocular vision system is used for data collection: first, the internal and external parameter matrix of the binocular camera is calibrated; then the binocular image sequence is collected and stereo matching is performed; finally, the head spatial position is calculated by triangulation, and the line of sight direction is calculated by the pupil detection algorithm; Optionally, a structured light depth camera is used for data collection: first, a structured light pattern is projected onto the user surface; then a three-dimensional point cloud is reconstructed by deforming the pattern; finally, the key point positions of the head and the eye feature points are extracted to calculate the spatial posture information. It is understandable that other sensors or sensor combinations can also be used to collect position feature data, which is not limited here.

[0039] S102: Calculate partition parameters of the target display screen based on the position feature data.

[0040] Among them, the position feature data refers to the user viewing data set containing the head position, sight direction and gaze point information. The target display screen refers to the display device that needs to be optimized for display. The partition parameter refers to the configuration data used for display area division, including information such as the number of areas, boundary positions and display weights. Display area division refers to dividing the display screen into multiple independently controlled sub-areas based on the user's viewing characteristics.

[0041] This step is executed immediately after completing the location feature data collection. Specifically, the system calculates the optimal display partitioning scheme based on the user's spatial position and line of sight information. First, the user's viewing behavior pattern is analyzed, including the head movement trajectory and the distribution of line of sight. Then, the area that needs to be optimized is determined based on the spatial distribution characteristics of the displayed content. Finally, the partition parameter configuration is generated based on the visual comfort evaluation results.

[0042] In some embodiments, the calculation of partition parameters can be implemented in a variety of ways: Optionally, a partition method based on user behavior is used: first, a user viewing heat map is established; then, the main viewing area is identified through a clustering algorithm; finally, partition boundaries and weights are set according to the importance of the area; Optionally, a partition method based on content features is used: first, the spatial features of the displayed content are extracted; then, the correlation between the content area and the user's line of sight is calculated; finally, a partition scheme is determined according to the distribution of the correlation. It is understandable that other algorithms or optimization strategies can also be used to implement the calculation of partition parameters, which are not limited here.

[0043] S103, dividing the target display screen into multiple dynamic display areas based on the partition parameters, the dynamic display area includes multiple gradient overlapping boundaries, the width of the gradient overlapping boundaries is dynamically adjusted according to the visual parameter difference between adjacent display areas, the visual parameters include brightness attenuation coefficient, color offset value and contrast reduction ratio.

[0044] S104: Calculate a display compensation value of each of the dynamic display areas according to the visual parameter.

[0045] Among them, visual parameters refer to a set of quantitative indicators that describe the visual quality of the display area, including brightness attenuation coefficient, color shift value and contrast reduction ratio. The brightness attenuation coefficient indicates the degree of attenuation of the display brightness, and the value range is 0-1. The color shift value refers to the color deviation of the three RGB channels, represented by a three-dimensional vector. The contrast reduction ratio indicates the degree of loss of image contrast, and the value range is 0-1. The dynamic display area refers to the sub-area of ​​the display screen that is dynamically divided according to the user's visual characteristics. The display compensation value refers to a set of parameters used to correct the display quality, including the brightness compensation coefficient, the color correction matrix and the contrast enhancement factor.

[0046] This step is performed after the display area division is completed, and is used to calculate the display quality compensation parameters of each area. Specifically, the system first performs a visual quality assessment on each dynamic display area to obtain the current visual parameter value. Then, based on the difference between the visual parameters and the standard display effect, the required compensation amount is calculated. The brightness compensation coefficient is calculated by the inverse relationship of the brightness attenuation coefficient, the color correction matrix is ​​constructed based on the color offset value, and the contrast enhancement factor is determined based on the contrast reduction ratio. Finally, these compensation parameters are combined to form a complete display compensation value.

[0047] In some embodiments, the calculation of the display compensation value can be implemented in a variety of ways: optionally, a linear compensation method is used: first, a linear mapping relationship between visual parameters and compensation values ​​is established; then, the compensation coefficients of each parameter are calculated respectively; finally, the compensation coefficients are weighted and combined to obtain the final compensation value; optionally, a nonlinear optimization method is used: first, a display quality evaluation function is constructed; then, an optimization target is set based on the visual parameters; finally, the optimal compensation value is solved by an iterative optimization algorithm. It is understandable that other algorithms or compensation strategies can also be used to implement the calculation of the display compensation value, which is not limited here.

[0048] S105 , generating calibration image data based on the display compensation value, and outputting the calibration image data to each of the dynamic display areas.

[0049] Among them, the display compensation value refers to a set of parameters used to optimize the display effect, including adjustment parameters for brightness, color, and contrast. The calibrated image data refers to the display data after compensation processing, keeping the original resolution unchanged. The dynamic display area refers to the independent control area divided on the display screen. Image data refers to the display content stored in the form of a pixel matrix, including RGB color information and Alpha transparency information. The output operation refers to the process of transferring the processed image data to the display hardware.

[0050] This step is performed after the display compensation value is calculated to generate the final display image. Specifically, the system first reads the original image data and converts it to a standard processing color space. Then the image is processed in different regions according to the display compensation value: brightness compensation is applied to adjust the pixel brightness value, color correction matrix is ​​used to correct the color reproduction, and contrast enhancement is used to improve the image detail performance. The processed image data is output to the corresponding display area through the display driver to ensure the continuity and smoothness of the display effect.

[0051] In some embodiments, the generation and output of the calibration image can be achieved in a variety of ways: optionally, using a lookup table method: first generate a color mapping table based on the display compensation value; then perform pixel-level mapping on the original image; and finally write the processed image data to the display buffer; optionally, using a shader processing method: first convert the display compensation value into a shader parameter; then perform image processing operations in the GPU; and finally render the processing results directly to the display device. It is understandable that other image processing or display output methods can also be used to achieve the generation and display of the calibration image, which is not limited here.

[0052] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the display screen calibration method in an embodiment of the present application.

[0053] S201. Collecting position feature data of a target edge user, where the position feature data includes the spatial position coordinates of the head, the angle between the line of sight and the plane of the display screen, and the coordinates of the eye gaze point.

[0054] The target edge user refers to the viewer at the edge of the display screen, usually within a viewing angle of more than 45 degrees in front of the display screen. The head space position coordinates refer to the position of the user's head in three-dimensional space, expressed using the Cartesian coordinate system (x, y, z). The angle between the line of sight and the display screen plane refers to the angle between the user's line of sight and the display screen plane, expressed as an angle value. The eyeball fixation point coordinates refer to the position of the projection point of the user's line of sight on the display screen plane, expressed using two-dimensional coordinates (x, y).

[0055] The data collection process uses a multi-sensor fusion solution: a 3D depth camera is used to collect the user's head position information with a sampling frequency of 60Hz; the eye movement is tracked through a binocular vision system with an accuracy of 0.5 degrees. The specific implementation steps are: the depth camera obtains the user's head depth image, the face feature point detection algorithm is used to extract the coordinates of the key points of the head, the binocular camera synchronously collects the user's eye image, the pupil detection algorithm is used to calculate the eye direction vector, the line of sight and the display plane are calculated according to the head position and eye direction, and the line of sight vector is intersected with the display plane to obtain the gaze point coordinates. The final output data format is: {timestamp, the head coordinates (x, y, z), the line of sight angle θ, the gaze point coordinates (x, y)}.

[0056] S202: Establish a user viewing heat distribution map based on the head spatial position coordinates.

[0057] The head space position coordinates are the real-time position data stream of the user's head in three-dimensional space. The heat map is a two-dimensional visualization of the user's viewing density, using color depth to represent the viewing frequency of different areas. The viewing density value range is 0-1, where 0 means never watched and 1 means continuous viewing.

[0058] The heat map creation process uses the kernel density estimation method to divide the display plane into 50×50 grid units. For each head position at each sampling moment, the system calculates its contribution value to each grid unit, and the contribution value shows Gaussian decay with increasing distance. The system calculates the Euclidean distance d between each grid unit (i, j) and the head position, and uses the Gaussian kernel function K(d)=exp(-d² / 2σ²) to calculate the influence factor, where σ is the bandwidth parameter, which is 10% of the diagonal length of the display. By accumulating the influence factors at each moment, the viewing density value of the grid unit is obtained, and then the density value is normalized and mapped to the 0-1 range. The final generated heat map is represented by a color gradient, with red representing high-density areas and blue representing low-density areas.

[0059] S203, dividing the primary viewing area and the secondary viewing area according to the thermal distribution map, and calculating the visual comfort index of each viewing area according to the angle between the line of sight and the plane of the display screen.

[0060] The primary viewing area refers to the display area with a thermal value higher than the threshold, and the viewing density threshold is usually set to 0.7. The secondary viewing area refers to the display area with a thermal value between 0.3-0.7. The visual comfort index is a quantitative indicator to measure the viewing experience, with a value range of 0-100, taking into account factors such as sight angle and viewing distance.

[0061] The system uses the heat map threshold segmentation method to extract the boundaries of the main viewing area and the secondary viewing area. The system calculates the visual comfort index of each area, and the calculation formula is: comfort index = 100 × (1 - θ / 90) × (1 - d / d_max), where θ is the line of sight angle (degrees), d is the viewing distance (meters), and d_max is the maximum comfortable viewing distance (usually 2 meters). The system extracts the coordinates of the center point of each viewing area, calculates the angle formed by the user's line of sight and the line connecting the point, measures the distance from the user to the point, and substitutes it into the formula to calculate the comfort index. For example, when the line of sight angle of a certain area is 30 degrees and the viewing distance is 1.5 meters, its comfort index is calculated as: 100 × (1 - 30 / 90) × (1 - 1.5 / 2) = 25.

[0062] S204: Prioritize each viewing area according to the visual comfort index, and determine the partition parameters of the target display screen in the preset matching parameter data based on the priority ranking result.

[0063] The visual comfort index is a value between 0 and 100, indicating the degree of visual experience of the viewing area. Priority sorting is the process of arranging the viewing areas from high to low according to the comfort index. The preset matching parameter data is a database containing the display partition strategy, which stores the partition parameters corresponding to different priority combinations. The partition parameters include configuration information such as the number of areas, area boundary coordinates, and area display weight.

[0064] The sorting process uses an improved quick sorting algorithm to sort all viewing areas in descending order of comfort index. The system divides the sorted areas into four priority levels: Class A (comfort index 75-100), Class B (50-75), Class C (25-50), and Class D (0-25). In the preset matching parameter database, each priority combination corresponds to a set of optimal partition parameters. The partition parameters in the database are obtained through statistics of a large amount of user test data, including the number of regional divisions (usually 2-6), the boundary coordinate ratio of each area, the display weight coefficient and other information. The system searches for the parameter combination that best matches the current priority distribution as the partition parameter of the target display. For example, when one Class A area and two Class C areas are detected, the system selects a three-partition parameter configuration, in which the Class A area occupies 50% of the display area and the two Class C areas each occupy 25%.

[0065] S205. Divide the target display screen into multiple dynamic display areas based on the partition parameters, wherein the dynamic display area includes multiple gradient overlapping boundaries, and the width of the gradient overlapping boundaries is dynamically adjusted according to the difference in visual parameters of adjacent display areas, wherein the visual parameters include a brightness attenuation coefficient, a color offset value, and a contrast reduction ratio.

[0066] The dynamic display area is a sub-area of ​​the display screen divided according to the partition parameters. The gradient overlap boundary is a transition zone between adjacent display areas. The brightness attenuation coefficient indicates the degree of attenuation of the display brightness, and the value range is 0-1. The color offset value indicates the degree of deviation of color restoration, and is expressed using RGB color difference values. The contrast reduction ratio indicates the percentage of contrast loss, and the value range is 0-1.

[0067] The system divides the display area according to the boundary coordinate data of the partition parameters, and establishes a gradient overlapping boundary between adjacent areas. The initial width of the gradient boundary is set to 5% of the width of the display screen. The system detects the difference in visual parameters of adjacent areas in real time: calculates the difference in brightness attenuation coefficient ΔL, the difference in color offset value ΔC, and the difference in contrast reduction ratio ΔK. The gradient boundary width is adjusted according to the parameter difference, and the adjustment formula is: boundary width = base width × (1 + α × ΔL + β × ΔC + γ × ΔK), where α, β, and γ are weight coefficients, taking values ​​of 0.4, 0.3, and 0.3 respectively. For example, when the brightness attenuation coefficient difference of adjacent areas is 0.2, the color offset value difference is 0.1, and the contrast reduction ratio difference is 0.1, the gradient boundary width is adjusted to 1.14 times the base width.

[0068] S206, calculating a brightness compensation coefficient based on the brightness attenuation coefficient of each of the dynamic display areas, calculating a color correction parameter based on the color offset value of each of the dynamic display areas, and calculating a contrast compensation factor according to a contrast reduction ratio of each of the dynamic display areas.

[0069] The brightness compensation coefficient is a correction parameter used to compensate for the display brightness attenuation, which is inversely proportional to the brightness attenuation coefficient. The color correction parameter is the RGB channel gain value that corrects the color shift. The contrast compensation factor is a coefficient used to enhance the image contrast, which is inversely proportional to the contrast reduction ratio.

[0070] The system calculates display compensation parameters for each dynamic display area. The brightness compensation coefficient is calculated as follows: KL = 1 / (1-L), where L is the brightness attenuation coefficient. The color correction parameters are calculated using the color mapping matrix: [R', G', B'] = M × [R, G, B], where M is a 3 × 3 color correction matrix built based on color offset values. The contrast compensation factor is calculated as follows: KC = 1 + K × (1-K), where K is the contrast reduction ratio. For example, the brightness attenuation coefficient of a display area is 0.2, and the calculated brightness compensation coefficient is 1.25; the color offset value is [0.1, 0.05, 0.08], generating the corresponding color correction matrix; the contrast reduction ratio is 0.3, and the calculated contrast compensation factor is 1.21.

[0071] S207: Perform weighted combination on the brightness compensation coefficient, the color correction parameter and the contrast compensation factor to generate a display compensation value for each of the dynamic display areas.

[0072] The brightness compensation coefficient is a scalar value used to adjust the display brightness, usually in the range of 0.8-1.5. The color correction parameter is a 3×3 matrix used to adjust the gain values ​​of the three RGB channels. The contrast compensation factor is a scalar value used to enhance the image contrast, ranging from 1.0-2.0. The display compensation value is a comprehensive parameter that contains adjustment information for three dimensions: brightness, color, and contrast.

[0073] The display compensation value is generated by a hierarchical weighted fusion method. The system first normalizes each compensation parameter and uniformly maps the value range to the 0-1 interval. Then the final display compensation value is generated by weighted matrix operation. The calculation formula is: compensation value = [KL×WL, M×WC, KC×WK], where WL, WC, and WK are the weight coefficients of brightness, color, and contrast, respectively, and the values ​​are [0.4, 0.35, and 0.25]. For example, the brightness compensation coefficient of a display area is 1.25, the color correction matrix is ​​[[1.1, 0.05, 0.02], [0.03, 1.08, 0.04], [0.02, 0.03, 1.12]], and the contrast compensation factor is 1.21. The calculated display compensation value is [0.5, [0.385, 0.378, 0.392], 0.3025].

[0074] S208: Obtain the display content type of the dynamic display area, where the display content type includes image content and text content.

[0075] Image content refers to the bitmap data in the display area, including visual elements such as photos, charts, and illustrations. Text content refers to the character information in the display area, including text elements such as titles, paragraphs, and annotations. The display content type is a classification identifier for the nature of the content in the display area, which is used for subsequent differentiation processing.

[0076] Content type recognition uses a deep learning classification method. The system uses a pre-trained convolutional neural network model to extract features and classify display areas. The network model uses the VGG16 architecture, which contains 13 convolutional layers and 3 fully connected layers. The input is the pixel data of the display area, and the output is the probability distribution of the content type. The classification threshold is set to 0.85. When the probability of a certain type exceeds the threshold, the area is marked as the corresponding content type. For mixed content areas, the system further uses an image segmentation algorithm to divide the sub-areas to obtain the content type label for each sub-area. For example, a display area contains a product photo in the upper half and a product description text in the lower half. The system divides it into an image sub-area and a text sub-area for subsequent processing.

[0077] S209: When the dynamic display area is the image content, extract key feature parameters of the image content, where the key feature parameters include brightness distribution, color saturation, and edge clarity of the image.

[0078] Brightness distribution describes the statistical characteristics of the brightness values ​​of image pixels, including mean, variance, histogram and other information. Color saturation indicates the purity and vividness of image colors, and is represented by the S channel value in the HSV color space. Edge clarity quantifies the clarity of image edge details, and is calculated through edge detection and gradient statistics.

[0079] The feature parameter extraction process adopts a multi-scale analysis method. The system first converts the image into multiple color spaces: RGB is used for basic color analysis, HSV is used for saturation calculation, and YUV is used for brightness analysis. The brightness distribution feature is calculated by calculating the statistical moment of the Y channel: the mean reflects the overall brightness level, the variance represents the brightness contrast, and the skewness describes the asymmetry of the brightness distribution. The color saturation feature is calculated through the S channel of the HSV space: the mean and standard deviation of the S channel are extracted to reflect the color richness of the image. The edge clarity feature is extracted by the Sobel operator: the gradient of the image is calculated in the horizontal and vertical directions, and the mean and variance of the gradient amplitude are statistically calculated. The larger the value, the clearer the edge. For example, for a landscape photo, the extracted feature parameters are: brightness mean 0.6, brightness variance 0.15, saturation mean 0.7, edge clarity index 0.8.

[0080] S210: Adjust the display compensation value based on the key feature parameter to obtain an image feature compensation parameter.

[0081] Key feature parameters are a set of values ​​that describe the visual characteristics of an image, including brightness distribution statistics, color saturation indicators, and edge definition quantization values. Display compensation values ​​are basic compensation parameters for the display area, including adjustment information in three dimensions: brightness, color, and contrast. Image feature compensation parameters are refined compensation parameters that are combined with image content features, including a corrected brightness mapping curve, color gain matrix, and contrast enhancement coefficient.

[0082] The compensation parameters are adjusted using an adaptive mapping method. The system adjusts the brightness compensation curve according to the brightness distribution characteristics: by matching the image brightness histogram with the target brightness distribution, a nonlinear mapping function f(x) is generated, which is = x^γ, where the γ value is calculated by the brightness mean and variance. The color compensation matrix is ​​corrected according to the saturation characteristics: when the saturation mean is low, the color gain coefficient is increased; when the saturation mean is high, the gain is reduced to avoid oversaturation. The contrast compensation factor is adjusted according to the edge clarity characteristics: when the edge clarity is low, the contrast enhancement intensity is increased; when the edge clarity is high, the enhancement intensity is reduced to prevent ringing artifacts. For example, for image areas with low saturation (0.3) and high edge clarity (0.9), the coefficient of the color gain matrix is ​​increased by 20%, while the contrast compensation factor is reduced by 15%.

[0083] S211 . Process the original image data according to the image feature compensation parameter to generate calibrated image data.

[0084] The raw image data is the unprocessed pixel data in the display area, stored in RGB format. The image feature compensation parameters are a set of processing parameters used for image enhancement. The calibrated image data is the optimized image data after compensation processing, keeping the original resolution unchanged.

[0085] The image processing process adopts a pipeline architecture, and is processed in the order of brightness adjustment, color correction, and contrast enhancement. Brightness adjustment is achieved through a lookup table (LUT): a 256-level grayscale mapping table is generated according to the brightness mapping curve, and the image is mapped at the pixel level. Color correction uses a 3×3 matrix transformation: the RGB pixel value is multiplied by the color gain matrix to obtain the corrected color value. Contrast enhancement uses an adaptive histogram equalization method: the image is divided into 8×8 sub-blocks, each sub-block is histogram-equalized, and a smooth transition is achieved between sub-blocks through bilinear interpolation. The final output calibrated image data maintains the same bit depth and color format as the input. For example, for an image with a resolution of 1920×1080, the processing time is controlled within 16.7 milliseconds to ensure real-time display performance.

[0086] S212: When the dynamic display area is the text content, identify the font type and font size of the text content.

[0087] Font type is the font style used for text display, including Chinese fonts such as Songti, Heiti, Kaiti, and English fonts such as Arial, Times, New, and Roman. Font size is the display size of the font, measured in pixels or points. Text content is the text information in the display area, including single-line text, multi-line paragraphs, etc.

[0088] Text feature recognition uses a deep learning method. The system uses a convolutional neural network to extract visual features of text areas. The network structure includes 5 convolutional layers and 2 fully connected layers. The font recognition model training data includes a common font sample library, covering 100 mainstream fonts. Font size recognition is achieved by estimating the height of text lines: first, the text line is extracted using connected region analysis to calculate the text baseline position; then the distance from the upper and lower boundaries of the character to the baseline is measured, and the actual font size value is obtained by combining the display resolution. For mixed Chinese and English text, the font features of characters in different languages ​​are identified separately. For example, the recognition result shows that a certain text uses the "Microsoft Yahei" font, the body text size is 16 pixels, and the title font size is 24 pixels.

[0089] S213: Calculate the stroke feature weight according to the font type, and determine the edge sharpening degree according to the font size.

[0090] The stroke feature weight is a numerical indicator that describes the thickness and straightness of the font strokes, ranging from 0 to 1. The stroke features of Chinese fonts include the thickness ratio, turning angle, and head and tail features of basic strokes such as horizontal, vertical, left, and right strokes; the stroke features of English fonts include the thickness of the main stem, serif features, and curvature changes. The edge sharpening degree is the edge enhancement strength when rendering text, ranging from 0 to 1, where 0 means no sharpening and 1 means the maximum sharpening strength.

[0091] The feature calculation process uses the font structure analysis method. The calculation of stroke feature weights is based on the vector contour data of the font: for Song-type fonts, due to the obvious contrast in stroke thickness, the weight value is set at 0.7-0.9; for bold-type fonts, the stroke thickness is uniform, and the weight value is set at 0.4-0.6. The degree of edge sharpening is inversely proportional to the font size: when the font size is less than 16 pixels, the degree of sharpening increases linearly, and the calculation formula is: degree of sharpening = (16 - font size) × 0.1; when the font size is greater than 16 pixels, the degree of sharpening remains at a low level, and the calculation formula is: degree of sharpening = 0.2 × (24 / font size). For example, for 12-pixel Song-type text, the stroke feature weight is 0.85 and the edge sharpening is 0.4.

[0092] S214: Optimize the display compensation value according to the stroke feature weight and the edge sharpening degree to obtain a text clarity compensation parameter.

[0093] The text clarity compensation parameters are a set of processing parameters used to improve the text display quality, including sub-pixel rendering weight, edge enhancement coefficient, and contrast adjustment value. The display compensation value is the basic compensation parameter of the display area, which contains adjustment information in three dimensions: brightness, color, and contrast. The stroke feature weight and edge sharpening degree are the optimization basis obtained from the text feature analysis.

[0094] The compensation parameter optimization adopts a multi-level adjustment strategy. The sub-pixel rendering weight is calculated based on the stroke feature weight: when the weight value is high, the sub-pixel rendering intensity in the horizontal direction is increased to improve the clarity of the horizontal strokes; when the weight value is low, a balanced sub-pixel rendering scheme is adopted. The edge enhancement coefficient is obtained by combining the sharpening degree and the display compensation value, and the calculation formula is: enhancement coefficient = basic compensation value × (1 + sharpening degree × stroke weight). The contrast adjustment value is adaptively calculated based on the difference in text and background color to ensure that the appropriate contrast is maintained between the text and the background. For example, when the stroke feature weight is 0.8 and the edge sharpening degree is 0.4, the sub-pixel rendering weight is set to [0.3, 0.4, 0.3], and the edge enhancement coefficient is 1.32.

[0095] S215 , applying the text clarity compensation parameter to the original text data to generate calibrated text data.

[0096] The original text data includes the character encoding, font information and layout attributes of the text. The text clarity compensation parameters are a set of processing parameters for optimizing the text display effect. The calibrated text data is display data that has been processed for clarity optimization, including the optimized glyph bitmap and rendering parameters.

[0097] The calibration process uses a high-quality text rendering pipeline. First, glyph rasterization is performed: using an improved FreeType engine, a high-precision glyph bitmap is generated based on the text clarity compensation parameters, and the bitmap resolution is 4 times the display resolution. Then sub-pixel rendering is performed: the high-precision bitmap is filtered according to the sub-pixel rendering weight to generate sub-pixel data for the three RGB channels. Finally, edge enhancement is applied: an adaptive sharpening filter is used to enhance the edges of the sub-pixel data while controlling ringing artifacts. For mixed Chinese and English content, texts in different languages ​​are processed separately to ensure consistency in display effects. For example, for a 16-pixel mixed Chinese and English text, after calibration, the clarity of Chinese strokes is improved by 30%, the sharpness of English characters is improved by 25%, and the overall readability is significantly improved.

[0098] S216: Output the calibration image data and the calibration text data as the calibration image data to each of the dynamic display areas.

[0099] Calibrated image data is image pixel data that has been optimized for brightness, color, and contrast, stored in RGBA format, and has the same resolution as the display area. Calibrated text data is text rendering data that has been processed for clarity enhancement, and contains optimized glyph bitmaps and transparency information. The dynamic display area is a sub-area on the display screen that is divided according to the user's visual characteristics, and has independent display parameters and refresh mechanisms. Calibrated image data is the final rendering data for the display device, containing complete display information for all visual elements.

[0100] The data output process uses layered synthesis technology. The system first establishes a display buffer whose size matches the size of the dynamic display area and whose pixel format is 32-bit RGBA. The calibrated image data is used as the bottom layer and is directly written into the RGB channel of the buffer. The calibrated text data is used as the top layer and is synthesized with the bottom image through Alpha blending: for each pixel position, the blending weight is calculated according to the Alpha value of the text to achieve a smooth transition between the text and the background. The synthesis calculation formula is: output pixel = background pixel × (1 - Alpha) + text pixel × Alpha. After the synthesis is completed, the system transfers the buffer data to the corresponding dynamic display area through the display driver. The transmission adopts a double buffering mechanism to avoid screen tearing. For example, a display area contains product pictures and description text. The pictures are color enhanced and the text is optimized for clarity. The two are seamlessly merged through the Alpha channel and finally output to the display device at a refresh rate of 60Hz. The color depth of the output data is maintained at 10 bits / channel to ensure the smoothness of the display transition. The processed text edge and background achieve a natural transition without obvious jagged edges and luminous artifacts. Image details remain clear while ensuring text readability without background distraction.

[0101] The following is a more detailed description of the process of the method provided by this implementation. Figure 3 , is another flow chart of the display screen calibration method in an embodiment of the present application.

[0102] It should be noted that S301 to S311 may be executed after step S105 or after step S216, which is not limited here.

[0103] S301: Acquire real-time eye movement data of the target edge user.

[0104] Target edge users refer to users at the edge of the display screen, usually within a 45-degree viewing angle directly opposite the display screen. Real-time eye movement data refers to the continuous tracking data of the user's eye movement, including parameters such as eye rotation angle, eye movement speed, and pupil size change. Eye movement data is collected through an eye tracking device, which usually contains an infrared light source and a high-speed camera. It calculates the eye direction by detecting the position of the reflection point of infrared light on the cornea.

[0105] During the specific acquisition process, the eye tracking device tracks the user's eye movement at a sampling frequency of no less than 60Hz. First, the user's eyeballs are illuminated by an infrared light source, and the high-speed camera captures the eyeball image. Then, the image processing algorithm is used to locate the pupil center and the corneal reflection point, and the eyeball direction vector is calculated based on the relative position relationship of these feature points. Finally, the eyeball direction data is associated with the timestamp to form a continuous eye movement trajectory data stream. The eye movement data is stored in the data format of {timestamp, the x-direction angle, the y-direction angle, the pupil diameter}.

[0106] S302: Identify the duration and number of gazes of the target edge user on the dynamic display area according to the real-time eye movement data.

[0107] Gaze duration refers to the cumulative time that the user's gaze stays on a certain display area. Gaze times refers to the number of times the user's gaze repeatedly scans a certain display area. Dynamic display area refers to the screen sub-area dynamically divided according to the user's visual characteristics, and each area has independent display parameters.

[0108] The recognition process first preprocesses the eye movement data, including removing blink noise and smoothing. Then, using the gaze point detection algorithm, when the eye movement speed is lower than the preset threshold (usually 15 degrees / second) and the duration exceeds the preset duration (usually 100 milliseconds), it is determined as a fixation. The detected fixation point is spatially mapped to the display area, and the cumulative fixation duration of each area is counted. At the same time, the sequence of line of sight jumping between display areas is recorded, and the frequency of fixation is calculated. Finally, the statistical values ​​of {total fixation duration, number of fixations} for each display area are obtained.

[0109] S303: Calculate the user attention index of each dynamic display area based on the gaze duration and the gaze times.

[0110] The user attention index is a numerical indicator that quantifies the user's attention to the displayed content. The index comprehensively considers the two dimensions of gaze duration and gaze frequency, reflecting the user's visual attention distribution. The attention index of the dynamic display area is updated in real time with the user's visual behavior.

[0111] The calculation process adopts the weighted fusion method. First, the fixation duration and the frequency of fixation are normalized and the values ​​are mapped to the interval [0, 1]. Then, the two indicators are linearly combined according to the preset weight coefficient (the duration weight is usually 0.7 and the frequency weight is 0.3). The specific calculation formula is: attention index = normalized fixation duration × duration weight + normalized frequency of fixation × frequency weight.

[0112] The final attention index ranges from 0 to 1, where 0 means that the user pays no attention to the area at all and 1 means the highest level of attention.

[0113] S304: When it is detected that the user attention index of the target dynamic display area exceeds a preset threshold, adjust the width of the gradient overlap border of the target dynamic display area to a preset width.

[0114] The target dynamic display area refers to the display area that the user is currently focusing on. The user attention index is a value between 0 and 1, which is used to quantify the user's attention to a specific area. The preset threshold is usually set to 0.8, indicating a high attention criterion. The gradient overlap boundary refers to the transition area between adjacent display areas, and its width determines the smoothness of the display effect. The preset width is the optimal transition band width pre-calculated based on the physical size of the display, usually 5% of the diagonal length of the display.

[0115] During the actual implementation, the system continuously monitors the user attention index of each dynamic display area. When the attention index of a certain area exceeds 0.8 for more than 3 seconds, the boundary adjustment mechanism is triggered. When adjusting, first determine the boundary position between the target area and the adjacent area, and then adjust the original gradient overlap boundary width to the preset width by linear interpolation. In specific implementation, the quadratic Bezier curve is used as the transition function to ensure that the display parameters change smoothly at the boundary. If the diagonal length of the display screen is 500 mm, the preset width of the gradient boundary is set to 25 mm.

[0116] S305: recalculate the target display compensation value based on the preset width, and output the target display compensation value to the dynamic display area.

[0117] The target display compensation value is a set of parameters used to optimize the display effect, including brightness compensation coefficient, contrast adjustment factor and color correction parameter. The preset width is a determined gradient overlap border width value, which serves as a reference parameter for recalculating the compensation value. The dynamic display area refers to the screen area that needs to be optimized for display, including the main display area and the gradient transition area.

[0118] The recalculation process of the compensation value adopts an adaptive algorithm: first, the spatial range of the gradient area is determined according to the preset width, and a coordinate mapping relationship is established. Then, for each pixel in the gradient area, the weight coefficient is calculated according to its relative position. On this basis, the original image data is convolved with the weight coefficient to obtain a new display compensation value. The calculation formula of the compensation value is: compensation value = basic compensation value × (1 - relative position) + adjacent area compensation value × relative position. Finally, the calculated compensation value is applied to the corresponding display area through the display driver.

[0119] S306: When it is detected that there is a target human activity within a preset distance range, a partial occlusion situation of the target human is detected to obtain an occlusion area ratio of the occlusion area.

[0120] The preset distance range refers to a specific spatial area in front of the display screen, usually set to a range of 0.5-2 meters in front of the display screen. Target human activities include human movement, arm swinging and other behaviors that may cause the display content to be blocked. Partial occlusion refers to the degree and range of human body blocking the display content. The occlusion area ratio is the ratio of the occlusion area to the total display area.

[0121] The detection process uses depth cameras and image processing technology: First, the depth camera collects depth images within a preset range in real time, with a sampling frequency of no less than 30Hz. The foreground target is extracted through the depth threshold segmentation algorithm to identify the human body contour. Then, the human body contour is projected onto the display plane, and the intersection of the projection area and the display area is calculated, which is the occlusion area. Finally, the number of pixels in the occlusion area is calculated and divided by the total number of pixels in the display area to obtain the occlusion area ratio. If the display resolution is 1920×1080 and the number of pixels in the occlusion area is 207360, the occlusion area ratio is 0.1, indicating that 10% of the display content is blocked.

[0122] S307: When the continuous occlusion time exceeds a preset time threshold and the occlusion area ratio exceeds a preset occlusion threshold, the text content in the occlusion area is detected.

[0123] The continuous occlusion time refers to the length of time that the displayed content is continuously blocked, measured in milliseconds. The preset time threshold is usually set to 2000 milliseconds, indicating the minimum occlusion duration required to trigger text detection. The occlusion area ratio is the percentage of the occluded area to the total area of ​​the display area. The preset occlusion threshold is usually set to 15%, indicating the minimum occlusion level that requires content adjustment. Text content refers to the text information in the display area, including text elements such as titles, paragraphs, and labels.

[0124] The text detection process uses optical character recognition (OCR) technology: First, the occluded area is segmented to extract the local features of the pixel grayscale value. Then a convolutional neural network is used to extract features from the image and identify the location and boundaries of the text area. The text area is located using the EAST (Efficient and Accurate Scene Text Detector) algorithm, which outputs the rectangular bounding box and direction angle of the text area. For each detected text area, a fine-grained text recognition model is used to extract the specific text content. The recognition result contains the location coordinates, direction information, and specific character sequence of the text.

[0125] S308: If the text content is detected to exist, the display priority and text length of the text content are obtained.

[0126] Display priority is the importance level of text content, usually divided into four levels from 0 to 3: Level 0 represents decorative text, Level 1 represents ordinary content, Level 2 represents important information, and Level 3 represents critical information. Text length includes two dimensions: the number of characters and the actual display size. The number of characters directly counts the number of characters in the text, while the actual display size takes into account typesetting parameters such as font size and line spacing.

[0127] The acquisition process is divided into two stages: First, the display priority is obtained through text analysis. The analysis methods include: priority determination based on predefined rules, such as the title text priority is 3, the body paragraph priority is 1, priority determination based on text position, such as the top of the page has a higher priority, and priority determination based on text style, such as bold fonts correspond to higher priorities. Then the text length is calculated. For Chinese text, each Chinese character is counted as 1 unit length, and punctuation marks are counted as 0.5 unit length; for English text, each word is counted as 1 unit length. At the same time, parameters such as font size and line height are considered to calculate the pixel area actually occupied by the text.

[0128] S309: Group the text contents based on the display priority, and calculate the minimum display area required for each group of text contents.

[0129] Grouping is the process of grouping text content with the same display priority. The minimum display area refers to the minimum pixel area required to ensure that the text is clear and readable, which is determined by the text length, font size and layout requirements. The calculation of the display area needs to take into account layout parameters such as text margins and line spacing.

[0130] The specific process of grouping and area calculation is as follows: First, divide the text content into four groups (0-3 levels) based on the display priority. For each group of text, calculate the minimum display area required: determine the basic font size, such as priority 3 text uses 16 pixels, priority 2 uses 14 pixels, and so on. Calculate the number of text lines, the maximum number of characters per line is the display area width divided by the character width, set the text margins, such as the top and bottom margins are 0.5 times the font size, the left and right margins are 1 times the font size, calculate the line spacing, set it to 1.2 times the font size, and the final display area is equal to (text width + left and right margins) × (text height + top and bottom margins + line spacing). For example, for a text of priority 2 containing 100 Chinese characters, when using a 14-pixel font, the calculated minimum display area is 280×168 pixels.

[0131] S310 . Selecting a candidate display position that meets the minimum display area requirement in the unblocked area based on a greedy strategy.

[0132] The unobstructed area refers to the visible area on the display screen that is not blocked by the human body or other objects, and is obtained through real-time detection by the depth camera. The greedy strategy is a local optimal selection algorithm that selects the current optimal solution at each step. The minimum display area requirement refers to the minimum pixel area size required for the text content to be clearly displayed. The candidate display position refers to the set of area coordinates in the unobstructed area that can be used to place text content.

[0133] The selection process uses an improved Maximum Empty Rectangle algorithm: First, the unobstructed area is represented as a two-dimensional grid with a grid unit size of 10×10 pixels. The grid is scanned to identify all continuous blank areas and generate a list of blank rectangles. Each blank rectangle records its upper left corner coordinates (x, y) and width and height values ​​(w, h). Then, for each set of text content requiring a minimum display area (w_min, h_min), a rectangle that meets the conditions (w≥w_min and h≥h_min) is searched in the blank rectangle list. The search adopts a greedy strategy, giving priority to rectangles close to the original text position. Among the rectangles that meet the position approximation, the rectangle with the area closest to the minimum display area requirement is selected. If there are multiple rectangles with similar areas, the rectangle closest to the center of the display screen is selected. Finally, a list of candidate display positions sorted by priority is generated.

[0134] S311 . Fill the text content into corresponding candidate display positions in order of display priority from high to low.

[0135] Display priority is a quantitative indicator of the importance of text content, ranging from level 3 (highest) to level 0 (lowest). Text content includes actual string data and corresponding display attributes (font, size, color, etc.). Candidate display positions are a set of rectangular regions selected by the greedy strategy, each of which is defined by coordinates and dimensions.

[0136] The text filling process uses an adaptive typesetting algorithm. First, the text content is sorted from high to low according to the display priority. For the text of priority 3, layout is performed in its corresponding candidate display position. The layout process includes calculating the line break point of the text in the new position, ensuring that the width of each line of text does not exceed the width of the display area, applying text alignment rules, such as justification or center alignment, and setting paragraph spacing, which is 1.5 times the font size. After completing the layout of the text of one priority, update the spatial information of the available display area and continue to process the text of the next priority. For the text of priority 2, repeat the above layout process in the remaining candidate display positions, and so on until all the text content is processed. For example, if a text of priority 2 is originally located at the upper left corner of the display area (100, 100), after occlusion is detected, the candidate position of the upper right corner (500, 100) is selected according to the greedy strategy, then the text and its display attributes are moved to the new position as a whole, and its internal typesetting format remains unchanged.

[0137] The following describes the display screen calibration system in the embodiment of the present invention from the perspective of hardware processing. Figure 4 , is a schematic diagram of a physical device structure of a display screen calibration system in an embodiment of the present application.

[0138] It should be noted that Figure 4 The structure of the display screen calibration system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0139] like Figure 4 As shown, the display screen calibration system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in RAM403. CPU401, ROM402 and RAM403 are connected to each other through bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0140] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, a button switch, etc.; an output section 407 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0141] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0142] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0143] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0144] Specifically, the display screen calibration system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the display screen calibration method provided in the above embodiment is implemented.

[0145] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the display screen calibration system described in the above embodiment; or may exist independently without being assembled into the display screen calibration system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the display screen calibration system, the display screen calibration system implements the display screen calibration method provided in the above embodiment.

[0146] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0147] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0148] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A display screen calibration method, characterized in that: Applied to a display screen calibration system, the method comprises: Collecting position feature data of the target edge user, wherein the position feature data includes the spatial position coordinates of the head, the angle between the line of sight and the plane of the display screen, and the coordinates of the eyeball gaze point; Based on the position feature data, calculating partition parameters of the target display screen; Dividing the target display screen into a plurality of dynamic display areas based on the partition parameters, the dynamic display areas comprising a plurality of gradient overlapping boundaries, the width of the gradient overlapping boundaries being dynamically adjusted according to a difference in visual parameters of adjacent display areas, the visual parameters comprising a brightness attenuation coefficient, a color offset value, and a contrast reduction ratio; Calculating a display compensation value of each of the dynamic display areas according to the visual parameters; generating calibration image data based on the display compensation value, and outputting the calibration image data to each of the dynamic display areas; Acquire real-time eye movement data of the target edge user; Identify the duration and number of times the target edge user gazes at the dynamic display area according to the real-time eye movement data; Calculate the user attention index of each of the dynamic display areas based on the gaze duration and the gaze times; When it is detected that the user attention index of the target dynamic display area exceeds a preset threshold, adjusting the width of the gradient overlapping border of the target dynamic display area to a preset width; A target display compensation value is recalculated based on the preset width, and the target display compensation value is output to the target dynamic display area.

2. The method according to claim 1, characterized in that The step of calculating the partition parameters of the target display screen based on the position feature data specifically includes: Establishing a user viewing heat distribution map based on the head space position coordinates; Dividing the primary viewing area and the secondary viewing area according to the thermal distribution map, and calculating the visual comfort index of each viewing area according to the angle between the sight line and the display screen plane; Prioritize each viewing area according to the visual comfort index, and determine the partition parameters of the target display screen in the preset matching parameter data based on the priority ranking result.

3. The method according to claim 1, characterized in that The step of calculating the display compensation value of each of the dynamic display areas according to the visual parameters specifically includes: Calculating a brightness compensation coefficient based on a brightness attenuation coefficient of each of the dynamic display areas, calculating a color correction parameter based on a color offset value of each of the dynamic display areas, and calculating a contrast compensation factor according to a contrast reduction ratio of each of the dynamic display areas; The brightness compensation coefficient, the color correction parameter and the contrast compensation factor are weightedly combined to generate a display compensation value for each of the dynamic display areas.

4. The method according to claim 1, characterized in that: The step of generating calibration image data based on the display compensation value and outputting the calibration image data to each of the dynamic display areas specifically includes: Acquire the display content type of the dynamic display area, where the display content type includes image content and text content; When the dynamic display area is the image content, extracting key feature parameters of the image content, the key feature parameters including brightness distribution, color saturation and edge definition of the image; Adjusting the display compensation value based on the key feature parameter to obtain an image feature compensation parameter; Processing the original image data according to the image feature compensation parameters to generate calibrated image data; When the dynamic display area is the text content, identifying the font type and font size of the text content; Calculating stroke feature weights according to the font type, and determining edge sharpening degree according to the font size; Optimizing the display compensation value according to the stroke feature weight and the edge sharpening degree to obtain a text clarity compensation parameter; Applying the text clarity compensation parameter to the original text data to generate calibrated text data; The calibration image data and the calibration text data are output as the calibration image data to each of the dynamic display areas.

5. The method according to claim 1, characterized in that After the step of generating calibration image data based on the display compensation value and outputting the calibration image data to each of the dynamic display areas, the method further includes: When a target human activity is detected within a preset distance range, a partial occlusion situation of the target human body is detected to obtain an occlusion area ratio of the occlusion area; When the continuous occlusion time exceeds a preset time threshold and the occlusion area ratio exceeds a preset occlusion threshold, detecting the text content in the occlusion area; If the existence of the text content is detected, the position arrangement and display of the text content is adjusted to an unobstructed area.

6. The method according to claim 5, characterized in that If the text content is detected, the step of adjusting the position arrangement and display of the text content to the unobstructed area specifically includes: If the text content is detected, obtaining the display priority and text length of the text content; Grouping the text contents based on the display priority and calculating the minimum display area required for each group of text contents; In the unblocked area, selecting a candidate display position that meets the minimum display area requirement based on a greedy strategy; The text contents are sequentially filled into corresponding candidate display positions in descending order of display priority.

7. A display screen calibration system, characterized in that: The display screen calibration system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the display screen calibration system to perform the method described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a display screen calibration system, the display screen calibration system is caused to execute the method according to any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program product runs on a display screen calibration system, the display screen calibration system is enabled to perform the method according to any one of claims 1 to 6.

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