Curved screen display method and device, computer equipment and storage medium

Abstract

The embodiment of the invention discloses a curved screen display method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring relative position data of a user relative to a curved screen at a current frame moment, wherein the relative position data comprises user view angle data and user distance data; determining a distortion correction parameter of the current frame moment based on the user view angle data, the user distance data and a distortion parameter of a previous frame moment of the current frame moment; according to the distortion correction parameter and the geometric parameter of the curved screen, performing pixel value distortion compensation processing on the current frame image of the edge area in the curved screen to obtain compensated pixel data; and driving the curved screen to display based on the compensated pixel data. According to the embodiment of the invention, the display precision of the curved screen can be improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to display methods, apparatus, computer equipment and storage media for curved screens. Background Technology

[0002] As display technology evolves towards higher screen-to-body ratios and immersive experiences, curved screens have become the mainstream choice for high-end electronic devices due to their advantages such as narrow bezels and borderless visuals. However, the physical characteristics of curved screens (such as edge curvature and differences in cover glass refractive index) cause traditional flat-panel display compensation algorithms to fail, leading to core issues such as edge stretching distortion and dynamic distortion.

[0003] Existing curved screen correction technologies have several limitations: 1. Static Demura compensation corrects Mura defects using a preset compensation matrix, but it cannot handle the complex nonlinear distortions at the edges of curved screens; 2. Symmetrical mapping algorithms perform symmetrical compensation on pixels on both sides of the curved surface, but do not consider changes in user viewing angle, resulting in insufficient compensation accuracy. Therefore, the display accuracy of existing curved screens needs further improvement. Summary of the Invention

[0004] This application provides a display method, apparatus, computer equipment, and storage medium for curved screens, which can improve the display accuracy of curved screens.

[0005] In a first aspect, embodiments of this application provide a display method for a curved screen, comprising: Obtain the user's relative position data with respect to the curved screen at the current frame time. The relative position data includes user viewpoint data and user distance data. The distortion correction parameters for the current frame are determined based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time. Based on the distortion correction parameters and the geometric parameters of the curved screen, pixel value distortion compensation processing is performed on the current frame image of the edge region in the curved screen to obtain the compensated pixel data; The curved screen display is driven based on the compensated pixel data.

[0006] In some embodiments, the method is applied to a target terminal, the target terminal being equipped with the curved screen, and the target terminal embedding a miniature ToF camera, an infrared structured light module, and a gyroscope array; acquiring the user's relative position data relative to the curved screen at the current frame time includes: At the current frame time, the depth value between the user's eye and the curved screen is determined by measuring the round-trip time difference of the light signal from the miniature ToF camera; A first distance value is determined based on the depth value, and a second distance value and a horizontal distance value are determined based on the degree of distortion of the dot matrix projected by the infrared structured light module on the user's face at the current frame time. A weighted fusion algorithm is used to determine the user distance data based on the first distance value and the second distance value; The head rotation angle and head rotation angular velocity at the current frame time are obtained through the gyroscope array; The user's perspective data is determined based on the depth value, the horizontal distance value, the head rotation angle, and the head rotation angular velocity.

[0007] In some embodiments, determining the user's viewpoint data based on the depth value, the horizontal distance value, the head rotation angle, and the head rotation angular velocity includes: The user perspective data is determined based on a preset user perspective calculation formula, which is as follows: ; Where θ is the user's perspective data, ∆x is the horizontal distance value, and ∆z is the depth value. The head rotation angle is... The angular velocity of the head rotation is denoted as t, and the time corresponding to the current frame moment is t.

[0008] In some embodiments, determining the distortion correction parameters for the current frame based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time includes: Based on the user's perspective data, the user's distance data, and the distortion parameters from the previous frame at the current frame time, the distortion correction parameters for the current frame time are dynamically calculated using a preset spatiotemporal joint calibration algorithm. The spatiotemporal joint calibration algorithm is as follows: ; Among them, P k+1 P is the distortion correction parameter. k Let z be the distortion parameter of the previous frame. k For the user distance data, θ k Let f be the user's perspective data, and Q be a nonlinear function. k The noise covariance matrix is ​​a multi-sensor noise matrix, where the multi-sensor refers to multiple sensors that acquire the user's viewpoint data and the user's distance data.

[0009] In some embodiments, the step of performing pixel value distortion compensation processing on the current frame image of the edge region in the curved screen according to the distortion correction parameters and the geometric parameters of the curved screen to obtain compensated pixel data includes: Based on the distortion correction parameters, perform geometric position correction processing on each pixel in the current frame image; Based on the geometric parameters, pixel value distortion compensation processing is performed on the pixels in the edge region that have undergone geometric position correction processing to obtain the compensated pixel data.

[0010] In some embodiments, the step of performing pixel value distortion compensation processing on the pixels in the edge region that have undergone geometric position correction processing according to the geometric parameters to obtain the compensated pixel data includes: Based on the geometric parameters, pixel value distortion compensation processing is performed on the pixels in the edge region that have undergone geometric position correction processing using a nonlinear mapping algorithm to obtain the compensated pixel data. The nonlinear mapping algorithm is as follows: ; in, Let I(u,v) be the pixel coordinates in the edge region. The original pixel data of the pixel, I′(u,v) is the coordinates of the pixel. The compensated pixel data of the pixels, wherein the geometric parameters include the center of curvature and the radius of curvature (x). c ,y c ), It is the edge attenuation coefficient determined based on the corresponding pixel coordinates, the curvature center, and the curvature radius.

[0011] In some embodiments, the user includes multiple sub-users, the relative position data further includes gaze point heatmap data corresponding to each sub-user, the user viewpoint data includes user viewpoint data corresponding to each sub-user, and the user distance data includes user distance data corresponding to each sub-user; before determining the distortion correction parameter for the current frame based on the user viewpoint data, the user distance data, and the distortion parameter of the previous frame at the current frame time, the method further includes: Based on the gaze point hotspot data and user distance data of each sub-user, calculate the priority weight corresponding to each sub-user; Based on the priority weights, weighted least squares method is used to allocate independent correction resources to each of the sub-users. The correction resources include computational resources for determining the distortion correction parameters and / or computational resources for performing pixel value distortion compensation processing.

[0012] Secondly, embodiments of this application also provide a curved screen display device, which includes: The transceiver unit is used to acquire the user's relative position data with respect to the curved screen at the current frame time. The relative position data includes user view data and user distance data. The processing unit is configured to determine the distortion correction parameters for the current frame based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time; perform pixel value distortion compensation processing on the current frame image of the edge region in the curved screen according to the distortion correction parameters and the geometric parameters of the curved screen to obtain compensated pixel data; and drive the curved screen display based on the compensated pixel data.

[0013] Thirdly, embodiments of this application also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, can implement the above-described method.

[0015] This application provides a display method, apparatus, computer device, and storage medium for a curved screen. The method includes: acquiring relative position data of a user relative to the curved screen at the current frame moment, the relative position data including user viewpoint data and user distance data; determining distortion correction parameters for the current frame moment based on the user viewpoint data, the user distance data, and distortion parameters from the previous frame moment; performing pixel value distortion compensation processing on the current frame image in the edge region of the curved screen according to the distortion correction parameters and the geometric parameters of the curved screen, obtaining compensated pixel data; and driving the curved screen display based on the compensated pixel data. This embodiment can dynamically determine the current distortion correction parameters by real-time acquiring user viewpoint data and user distance data at the current frame moment, and perform pixel value distortion compensation processing on the image in the edge region of the curved screen based on the distortion correction parameters and the current geometric parameters of the curved screen. Therefore, this embodiment considers the geometric parameters of the curved screen, as well as the user's current viewpoint and distance, when correcting the image in the curved screen, thereby improving the display accuracy of the curved screen. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic flowchart illustrating the curved screen display method provided in this application embodiment; Figure 2 A schematic diagram of a sub-process of the curved screen display method provided in an embodiment of this application; Figure 3 This is another schematic diagram of a sub-process of the curved screen display method provided in an embodiment of this application; Figure 4 A schematic block diagram of a curved screen display device provided in an embodiment of this application; Figure 5 A schematic block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0020] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] This application provides a method, apparatus, computer device, and storage medium for displaying curved screens.

[0023] The entity executing the curved screen display method can be the curved screen display device provided in the embodiments of this application, or a target terminal that integrates the curved screen display device, and the target terminal is equipped with a curved screen.

[0024] Figure 1This is a schematic flowchart of the curved screen display method provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps S110-S140.

[0025] S110. Obtain the relative position data of the user relative to the curved screen at the current frame time. The relative position data includes user viewpoint data and user distance data.

[0026] In this embodiment, the target terminal is embedded with multiple sensors, including a miniature ToF camera, an infrared structured light module, and a gyroscope array. Specifically, these sensors are embedded in the screen bezel / curved transition area.

[0027] Among them, the miniature ToF camera emits near-infrared light and receives reflected light, and calculates the distance d (the z-axis in three-dimensional coordinates) between the user and the screen through time of flight. Infrared structured light module: Projects an invisible structured light pattern, captures the deformed pattern through a camera, and calculates the user's viewing angle θ (the angle between the face orientation and the screen normal). Gyroscope array: Embedded in the screen bezel, it captures the user's head movements (such as turning left and right, tilting up and down) to help correct the viewing angle θ; In addition, in some embodiments, multiple sensors also include an eye-tracking unit, which tracks the position and movement trajectory of the user's pupils through an infrared light source and a camera, and generates a gaze point heatmap (reflecting the area the user is focused on, such as the left side or center of the screen).

[0028] In addition, after obtaining the raw sensor data corresponding to each sensor in this embodiment, the raw sensor data also needs to be denoised and aligned. Specifically: Noise filtering: Gaussian filtering is used for the depth data of the miniature ToF camera (which is susceptible to speckle noise), and outliers are removed from the feature points of infrared structured light (which are susceptible to ambient light interference) using the 3σ principle. Time alignment: The sampling frequencies of different sensors are unified by linear interpolation (e.g., 30Hz for miniature ToF cameras and 60Hz for eye tracking) to ensure data synchronization; Spatial alignment: Transform all data to the screen coordinate system (with the screen center as the origin, the x-axis horizontal, the y-axis vertical, and the z-axis perpendicular to the screen and outward).

[0029] Specifically, in some embodiments, please refer to Figure 2 Step S110 includes: S1101. At the current frame time, the depth value between the user's eye and the curved screen is determined by measuring the round-trip time difference of the light signal from the miniature ToF camera.

[0030] Specifically, the depth value is determined by emitting modulated infrared light and measuring the round-trip time difference of the light signal.

[0031] S1102. Determine a first distance value based on the depth value, and determine a second distance value and a horizontal distance value based on the degree of distortion of the dot matrix projected by the infrared structured light module on the user's face at the current frame time.

[0032] Specifically, the depth value is determined as the first distance value, and at the current frame time, the second distance value and the horizontal distance value are determined by the degree of distortion of the dot matrix projected by the infrared structured light module on the user's face.

[0033] The degree of distortion of the dot matrix projected by infrared structured light on the user's face is positively correlated with the distance (the closer the distance, the greater the dot matrix distortion). The second distance value can be calculated by using the dot matrix distortion coefficient.

[0034] S1103. Using a weighted fusion algorithm, determine the user distance data based on the first distance value and the second distance value.

[0035] In this embodiment, a weighted fusion algorithm is used, which combines the high precision of the miniature ToF camera with the high resolution of infrared structured light to output the final distance, resulting in high accuracy of the user distance data.

[0036] Specifically, corresponding weights can be pre-set for the first distance value and the second distance value, and then the first distance value and the second distance value can be weighted and fused based on the corresponding weights to obtain user distance data.

[0037] S1104. Obtain the head rotation angle and head rotation angular velocity at the current frame time through the gyroscope array.

[0038] Specifically, in this embodiment, self-illuminating calibration points (wavelength λ=940nm) are embedded at the four corners of the curved screen. The measurement of the head rotation angle is easily affected by noise interference (such as slight jitter). The four corner calibration points provide multiple sets of head rotation angle values. Noise can be suppressed and the calculation accuracy of the head rotation angle can be improved by fusing redundant data (such as Kalman filtering).

[0039] S1105. Determine the user's perspective data based on the depth value, the horizontal distance value, the head rotation angle, and the head rotation angular velocity.

[0040] Specifically, the user perspective data is determined based on a preset user perspective calculation formula, which is as follows: ; Where θ is the user's perspective data, ∆x is the horizontal distance value, and ∆z is the depth value. The head rotation angle is... The angular velocity of the head rotation is denoted as t, and the time corresponding to the current frame moment is t.

[0041] Furthermore, this embodiment also provides an anti-occlusion enhancement algorithm. If the user's face cannot be identified at present (e.g., the head is turned to the side or the head is occluded), this embodiment can identify hand data, construct a three-dimensional space of the hand, and infer the user's perspective data and user distance data based on the three-dimensional space of the hand.

[0042] Specifically, by utilizing the self-emissive properties of Micro-LEDs, compensation pixels are activated in the user-occluded area, and the occluded content is reconstructed using a spatiotemporal interpolation algorithm: ; Among them, I comp (x, y) represents the "interpolation compensation result" of the pixels in the occluded region, r is the pixel spacing, and w k is the weighting coefficient (based on the distance decay function), and n is the number of compensated pixels (usually taken as 1.5 times the perimeter of the occluded area). The dynamic compensation coefficient (adjusted according to the occlusion speed, formula as follows): ; in, For baseline compensation intensity, As a speed-sensitive factor, To obscure the edge movement rate.

[0043] The specific implementation of the anti-occlusion enhancement algorithm is as follows: 1. Triggering conditions: If any of the following conditions are met for more than N frames (e.g., N=5), the system determines that face tracking has failed and triggers the anti-occlusion enhancement algorithm: Facial feature point loss: The infrared structured light module cannot detect effective facial feature points; Depth data anomaly: The depth values ​​returned by the ToF sensor continuously exceed the reasonable range (e.g., <0.2m or >5m); Low confidence: The confidence score output by the facial recognition algorithm is lower than the threshold (e.g., <0.6).

[0044] 2. Hand key point recognition and 3D space construction: Recognition method: The system switches to using a dot matrix pattern projected by an infrared structured light module, and in conjunction with a camera, identifies 21 key skeletal points of the hand through a pre-trained lightweight convolutional neural network (using the MediaPipe Hands model architecture). 3D Construction: Combining the 2D pixel coordinates (u, v) of keypoints with the corresponding region depth value z provided by Time-of-Flight (ToF), each keypoint is converted into 3D spatial coordinates (X, v) using the camera intrinsic parameter matrix. h Y h Z h This forms a "hand-shaped dot cloud".

[0045] 3. User location mapping model: The system assumes the user is in a typical posture of "sitting at work" or "watching with hands," and uses a statistical mapping model from hand space to user head space: Angle of view θest estimation: Taking the wrist key point as the origin, the projection of the direction vector pointing to the index finger root key point onto the horizontal plane can be approximated as the user's horizontal line of sight deflection angle. Through fitting with a large amount of calibration data, the estimation formula is obtained: θest = α × arctan(V x / V z ) +β, where (V x V z ) represents the horizontal component of the direction vector, and α and β are correction coefficients.

[0046] Zest distance estimation: There is a strong correlation between the distance from the user's head to the screen and the distance from their hand to the screen. The median Z value of the depth values ​​at all keypoints on the hand is used. hand median z is estimated through a linear mapping. est = k×Z hand median + b, where k and b are obtained through user pose calibration.

[0047] S120. Determine the distortion correction parameters for the current frame based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time.

[0048] In this embodiment, specifically, based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time, the distortion correction parameters for the current frame time are dynamically calculated using a preset spatiotemporal joint calibration algorithm. The spatiotemporal joint calibration algorithm is as follows: ; Among them, P k+1 P is the distortion correction parameter. k Let z be the distortion parameter of the previous frame. k For the user distance data, θ k Let f be the user's perspective data, and Q be a nonlinear function. kThe noise covariance matrix is ​​a multi-sensor noise matrix, where the multi-sensor refers to multiple sensors that acquire the user's viewpoint data and the user's distance data.

[0049] The distortion correction parameters include the distortion coefficient matrix K and the projection matrix H. The distortion coefficient matrix K and the projection matrix H are the core parameters of the spatiotemporal joint calibration algorithm. They have a clear division of labor and work together to solve the problems of "inherent geometric distortion of the screen" and "projection deviation caused by the user's perspective" respectively, and together achieve accurate mapping of the image from "ideal plane" to "curved screen adapted display".

[0050] In the spatiotemporal joint calibration algorithm, Q k The (multi-sensor noise covariance matrix) is not a fixed value; it needs to be dynamically adjusted according to environmental changes and sensor characteristics. k Its role in dynamic calibration is as follows: Process noise modeling: Q k The time-varying characteristics of sensor noise and model uncertainty were quantified. For example: When a miniature ToF camera detects a sudden change in surface reflectivity, increasing Q... k Reduce the variance of the mid-depth component and decrease its weight in the distortion parameter update; When infrared structured light detects motion blur, adjust Q. k The covariance of the visual component is used to suppress noise interference.

[0051] Multi-sensor collaborative optimization: Cross-modal error compensation is achieved by jointly estimating the noise covariance between the miniature ToF camera and the vision sensor. For example, if the depth data from the miniature ToF camera exhibits systematic bias due to multipath effects, it can be compensated through Q... k The projection matrix H is corrected to guide the visual feature points.

[0052] S130. Based on the distortion correction parameters and the geometric parameters of the curved screen, perform pixel value distortion compensation processing on the current frame image of the edge region in the curved screen to obtain the compensated pixel data.

[0053] In some embodiments, specifically, please refer to Figure 3 Step S130 includes: S1301. Perform geometric position correction processing on each pixel in the current frame image based on the distortion correction parameters.

[0054] This involves mapping each pixel in the current frame image to its corresponding position on the curved screen based on the user's current viewpoint and distance. This step does not change the pixel value of the pixel, but rather its displayed coordinate value.

[0055] S1302. Perform pixel value distortion compensation processing on the pixels in the edge region that have undergone geometric position correction processing according to the geometric parameters to obtain the compensated pixel data.

[0056] In this embodiment, the geometric parameters include the radius of curvature R and the center of curvature (xc, yc). The radius of curvature R and the center of curvature (xc, yc) are not fixed values, but are parameters that are dynamically updated with screen deformation and user viewing angle. Their function is to ensure the "dynamic adaptability" of the correction.

[0057] For example, when the IMU detects that the edge of the screen is pressed (such as when a user touches a curved area), the radius of curvature R will become smaller and the center of curvature (xc, yc) will shift. The spatiotemporal joint calibration will update these two parameters in real time, and the edge compensation algorithm will adjust the edge area range and compensation intensity simultaneously to avoid "the compensation parameters becoming disconnected from the actual shape after the screen is deformed". When the user's perspective changes (such as from the front to the side), the relative position of the curvature center (xc, yc) in the user's vision will change. By updating the parameters, it can be ensured that edge compensation is always aimed at the "edge area in the user's current perspective", rather than the physical edge area of ​​the screen.

[0058] Specifically, in some embodiments, step S1302 includes: Based on the geometric parameters, pixel value distortion compensation processing is performed on the pixels in the edge region that have undergone geometric position correction processing using a nonlinear mapping algorithm to obtain the compensated pixel data. The nonlinear mapping algorithm is as follows: ; in, Let I(u,v) be the pixel coordinates in the edge region. The original pixel data of the pixel, I′(u,v) is the coordinates of the pixel. The compensated pixel data of the pixels, wherein the geometric parameters include the center of curvature and the radius of curvature (x). c ,y c ), It is the edge attenuation coefficient determined based on the corresponding pixel coordinates, the curvature center, and the curvature radius.

[0059] The logic for determining the edge attenuation coefficient is a three-layer superposition of "baseline value + scene correction term + pixel position correction". The core principle is that "the more severe the distortion, the higher the error rate." The larger the value.

[0060] The baseline value is determined by the curvature characteristics of the curved screen. The smaller the curvature radius R (the more "curved" the surface, such as a waterfall screen), the faster the distance r from the edge pixel to the curvature center increases, and the degree of distortion increases exponentially, so the baseline value is larger; conversely, the larger R is (micro-curved screen), the smaller the baseline value is.

[0061] The scene correction term is a dynamic fine-tuning of the baseline value, determined by the user's perspective data and distance data, to adapt to different user viewing scenarios. The closer the user is to the screen, the more sensitive they are to edge distortion, and the larger the value of the scene correction term. The greater the user's perspective shift, the larger the value of the scene correction term.

[0062] Pixel position correction means that the farther the corresponding pixel coordinates are from the center of curvature, the stronger the compensation and the larger the value.

[0063] S140. Drive the curved screen display based on the compensated pixel data.

[0064] In this embodiment, after obtaining the compensated pixel data, the curved screen display is driven based on the compensated pixel data. The displayed image is adapted to the user's current angle and distance, which improves the user experience and the display accuracy relative to the current user.

[0065] In addition, this embodiment provides a hardware co-design, through which the screen and sensor are integrated into a single package. Specifically, it adopts the heterogeneous integration technology of Micro-LED display module and optical sensor, and realizes 3D stacking of sensor and driving circuit through TSV (through silicon via), reducing signal transmission delay to <1ms.

[0066] Furthermore, this embodiment also provides dynamic power management to save device power consumption, specifically: The sensor operating frequency is dynamically adjusted based on a user location prediction model (LSTM network), using the following formula: ; Among them, f sensor It is the actual operating frequency at the current moment, d is the user distance data, and f is the actual operating frequency at the current moment. base It is the basic operating frequency (unit: Hz), the default operating frequency of the sensor determined by the hardware design, f mod To compensate for instantaneous changes in the user's movement, periodic fluctuations are introduced at the modulation frequency (fixed at 10Hz). -0.3d It is the distance attenuation factor, which characterizes the negative correlation between signal strength and power consumption (the frequency decreases by about 30% for every 1 meter increase in distance). 0.2 is the modulation coefficient, used to control the weight of the sine wave amplitude, balancing the response speed and stability of dynamic adjustment. t is the time variable (unit: seconds), driving the temporal changes of the sine wave.

[0067] Specifically, f in this embodiment sensor It is the actual operating frequency of the sensor (such as a miniature ToF camera, an infrared structured light module, or a gyroscope array) at the current moment, rather than a fixed fundamental frequency.

[0068] Traditional sensors typically operate at a fixed frequency (e.g., 30Hz by default for miniature ToF cameras), but this leads to data redundancy at high frequencies when the user is far away, and insufficient accuracy at low frequencies when the user is close. This embodiment, however, utilizes f... sensor Dynamic adjustment allows the sensor frequency to adapt to changes in user distance and movement, achieving on-demand adaptation of "high frequency (high precision) near and low frequency (low power consumption) far".

[0069] Furthermore, the curved screen provided in this application supports multiple independent viewports for users simultaneously. Further, the user includes multiple sub-users, the relative position data includes gaze point heatmap data corresponding to each sub-user, the user viewpoint data includes user viewpoint data corresponding to each sub-user, and the user distance data includes user distance data corresponding to each sub-user. The method further includes: Based on the gaze point hotspot data and user distance data of each sub-user, the priority weight corresponding to each sub-user is calculated; based on the priority weight, the weighted least squares method is used to allocate independent correction resources to each sub-user, the correction resources including the computational resources for determining the distortion correction parameters and / or the computational resources for performing pixel value distortion compensation processing.

[0070] Specifically, this embodiment prioritizes users by using gaze point heatmap analysis and weighted least squares method to classify users into primary and secondary priorities, providing a weighting basis for resource allocation. The technical implementation is as follows: Fixation point heatmap analysis: Combining the heatmap from eye tracking, the percentage of the user's visible area S is calculated. i (For example, user A looks at the center of the screen, accounting for 60%; user B looks at the edge, accounting for 40%). Weight Calculation: The Sigmoid function is used to calculate the user priority weight wi, with users closer to the screen having higher weights. ; in, The distance between user i and the screen (in meters); For example, when =1.5 meters, =0.5; When <1.5 meters, >0.5 (Users who are closer to the user have a higher weight); When >1.5 meters, <0.5 (users at a distance have a lower weight).

[0071] In some embodiments, priority weights are determined by combining gaze point heatmap data and user distance data. The larger the screen proportion of gaze point heatmap data and the smaller the user distance data, the greater the corresponding priority weight.

[0072] Weighted least squares allocation: With the goal of minimizing correction error and ensuring parameter continuity, correction resources (such as distortion parameters) are allocated. The objective function is: ; The first item ( ): Minimize the correction error of user i ( Let X be the projection matrix of the current frame, and X be the original image. (for the corrected image) The second item ( ): Guarantee the projection matrix of adjacent frames and The differences are small (to avoid screen flickering caused by sudden parameter changes); λ: Regularization coefficient (weighting to balance error and continuity).

[0073] Furthermore, in some embodiments, the method also includes Shapley value resource allocation, which, based on the game theory-based Shapley value model, achieves fair allocation of resources among multiple users, maximizing the overall user experience (QoE). The technical implementation is as follows: Utility function definition: The effectiveness of each user's experience It consists of three parts: ; Wherein, α, β, γ: weighting coefficients (calibrated through user surveys, such as α=0.4, β=0.3, γ=0.3); Percentage of visible area (from gaze point hotspot); The distortion degree (derived from the result after edge distortion compensation); This is the delay time (feedback from the rendering engine).

[0074] Shapley value calculation: The Shapley value is a metric in game theory that measures the marginal contribution of participants. The formula is: ; N: The set of all users (e.g., N={user A, user B, user C}); S: A subset of N that does not contain user i; v(S): Total utility of subset S (excluding user i); v(S∪{i}): The total utility of subset S after adding user i; Shapley value of user i (representing their contribution to total utility).

[0075] By allocating screen partition resources using Shapley values ​​(e.g., user A is allocated the left 1 / 3 of the screen, user B is allocated the right 1 / 3 of the screen, and user C is allocated the center 1 / 3 of the screen), the total QoE is maximized.

[0076] Resource constraint checks: Verify that resource allocation meets the physical limitations of screen partitioning (such as maximum resolution and maximum brightness for each partition). If it does not meet these limitations (e.g., ... , For user i's partition resources, If the resource limit for partition j is 0, then return to the previous level using the weighted least squares method to redistribute the weights.

[0077] Through a distributed rendering engine, independent viewports are generated based on the game decision results, and multi-viewport overlay display is achieved through Micro-LED pixel-level driving.

[0078] To facilitate understanding, the following is an example of Shapley value resource allocation calculation: Assume a dual-user (A, B) scenario where the screen is divided into two equally sized partitions. Preset parameters: α=0.4, β=0.3, γ=0.3.

[0079] Step 1: Calculate the independent utility value U for each user. i： Assuming the measurement is obtained through a sensor: User A: S A = 0.6 (focusing on the central area, accounting for 60%), C A = 0.1 (small distortion), D A = 10ms. U A = 0.4×0.6 - 0.3×0.1 + 0.3×(1-0.01) = 0.24 - 0.03 + 0.297 = 0.507; User B: S B = 0.4 (focusing on the edge), C B = 0.3 (significant distortion), D B = 15ms.

[0080] U B= 0.4×0.4 - 0.3×0.3 + 0.3×(1-0.015) = 0.16 - 0.09 + 0.2955 = 0.3655; Step 2: Calculate the total utility of the coalition v(S) and the Shapley value φ. i： Consider all possible user alliances: Alliance {}: v({}) = 0; Alliance {A}: v({A}) = U A = 0.507; Alliance {B}: v({B}) = U B = 0.3655; Union {A, B}: v({A,B}) = U A + U B = 0.8725 (assuming the utilities are summable under conflict-free conditions); According to the Shapley value formula: For user A (i = A): S = {}: Weight = (0! × 1!) / 2! = 1 / 2, Marginal contribution = v({A}) - v({}) = 0.507; S = {B}: Weight = (1! × 0!) / 2! = 1 / 2, Marginal contribution = v({A,B}) - v({B}) = 0.8725 - 0.3655 = 0.507; φ A = (1 / 2)×0.507 + (1 / 2)×0.507 = 0.507; For user B (i = B): S = {}: Weight = 1 / 2, Marginal contribution = v({B}) - v({}) = 0.3655; S = {A}: Weight = 1 / 2, Marginal contribution = v({A,B}) - v({A}) = 0.8725 - 0.507 = 0.3655; φ_B = (1 / 2)×0.3655 + (1 / 2)×0.3655 = 0.3655; Step 3: Allocate resources proportionally to Shapley value: Total Shapley value φ total = φ A + φ B = 0.8725.

[0081] User A's resource allocation ratio: φ A / φ total = 0.507 / 0.8725 ≈ 58.1%; User B's resource allocation ratio: φ B / φ total = 0.3655 / 0.8725 ≈ 41.9%; Therefore, in the allocation of computing resources (such as GPU time slices) for screen partition rendering, user A will receive approximately 58% of the resource priority, while user B will receive approximately 42%, achieving a fair allocation while ensuring the maximization of the overall experience (total utility).

[0082] As can be seen, this embodiment provides a priority allocation mechanism through multi-user game theory resource allocation: it divides users into primary and secondary users by analyzing user gaze point heatmaps (eye tracking + head posture), and uses weighted least squares method to allocate correction resources to solve the "attention to one thing and neglect of another" effect among multiple users (such as viewport conflict when multiple people are watching in a public space); it provides Shapley value resource allocation: it constructs a user utility function based on game theory, and allocates GPU rendering resources through Shapley values ​​(such as ensuring that the sum of QoE of multiple users is maximized).

[0083] Furthermore, in some embodiments, the method further includes: determining the number of viewport overlaps in each preset sub-region of the curved screen based on the gaze point hotspot data of each sub-user; determining the regional weight of each preset sub-region based on the number of viewport overlaps; and allocating regional computing resources based on the regional weight, with the larger the weight, the more computing resources are allocated, so that the system prioritizes optimizing the region with the most common attention.

[0084] In some embodiments, if a region corresponds to the viewports of multiple users, the compensated pixel data of the multiple corresponding users is calculated in the region, and then the average of the compensated pixel data of the multiple corresponding users is taken as the final compensated pixel data for display.

[0085] In summary, this embodiment acquires the user's relative position data with respect to the curved screen at the current frame moment. This relative position data includes user viewpoint data and user distance data. Based on the user viewpoint data, the user distance data, and the distortion parameters of the previous frame moment, a distortion correction parameter for the current frame moment is determined. According to the distortion correction parameter and the geometric parameters of the curved screen, pixel value distortion compensation processing is performed on the current frame image in the edge region of the curved screen to obtain compensated pixel data. The curved screen display is driven based on the compensated pixel data. This embodiment can dynamically determine the current distortion correction parameter by real-time acquisition of user viewpoint data and user distance data at the current frame moment, and perform pixel value distortion compensation processing on the image in the edge region of the curved screen based on the distortion correction parameter and the current geometric parameters of the curved screen. Therefore, this embodiment considers the geometric parameters of the curved screen, as well as the user's current viewpoint and distance, when correcting the image in the curved screen, thereby improving the display accuracy of the curved screen.

[0086] Figure 4 This is a schematic block diagram of a curved screen display device provided in an embodiment of this application. Figure 4 As shown, corresponding to the above-described curved screen display method, this application also provides a curved screen display device 400. The curved screen display device 400 includes a unit for performing the above-described curved screen display method, and can be configured in curved terminals such as mobile phones, desktop computers, tablet computers, and laptops. For details, please refer to... Figure 4 The curved screen display device 400 includes a transceiver unit 401 and a processing unit 402, wherein: The transceiver unit 401 is used to acquire the relative position data of the user relative to the curved screen at the current frame time. The relative position data includes user view data and user distance data. The processing unit 402 is configured to determine the distortion correction parameters for the current frame based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time; perform pixel value distortion compensation processing on the current frame image of the edge region of the curved screen according to the distortion correction parameters and the geometric parameters of the curved screen to obtain compensated pixel data; and drive the curved screen to display based on the compensated pixel data.

[0087] In some embodiments, the curved screen display device 400 is deployed on a target terminal, the target terminal is equipped with the curved screen, and the target terminal embeds a miniature ToF camera, an infrared structured light module, and a gyroscope array; when the transceiver unit 401 performs the step of acquiring the user's relative position data with respect to the curved screen at the current frame time, it is specifically used for: At the current frame time, the depth value between the user's eyes and the curved screen is determined by measuring the round-trip time difference of the light signal from the miniature ToF camera; the processing unit 402 determines a first distance value based on the depth value, and determines a second distance value and a horizontal distance value based on the degree of distortion of the dot matrix projected by the infrared structured light module on the user's face at the current frame time; a weighted fusion algorithm is used to determine the user's distance data based on the first distance value and the second distance value; the head rotation angle and head rotation angular velocity at the current frame time are obtained by the gyroscope array; the processing unit 402 determines the user's viewing angle data based on the depth value, the horizontal distance value, the head rotation angle, and the head rotation angular velocity.

[0088] In some embodiments, when the processing unit 402 performs the step of determining the user's viewpoint data based on the depth value, the horizontal distance value, the head rotation angle, and the head rotation angular velocity, it is specifically used for: The user perspective data is determined based on a preset user perspective calculation formula, which is as follows: ; Where θ is the user's perspective data, ∆x is the horizontal distance value, and ∆z is the depth value. The head rotation angle is... The angular velocity of the head rotation is denoted as t, and the time corresponding to the current frame moment is t.

[0089] In some embodiments, when the processing unit 402 performs the step of determining the distortion correction parameters for the current frame based on the user view data, the user distance data, and the distortion parameters of the previous frame at the current frame time, it is specifically used for: Based on the user's perspective data, the user's distance data, and the distortion parameters from the previous frame at the current frame time, the distortion correction parameters for the current frame time are dynamically calculated using a preset spatiotemporal joint calibration algorithm. The spatiotemporal joint calibration algorithm is as follows: ; Among them, P k+1 P is the distortion correction parameter. k Let z be the distortion parameter of the previous frame. k For the user distance data, θ k Let f be the user's perspective data, and Q be a nonlinear function. k The noise covariance matrix is ​​a multi-sensor noise matrix, where the multi-sensor refers to multiple sensors that acquire the user's viewpoint data and the user's distance data.

[0090] In some embodiments, when the processing unit 402 performs the step of performing pixel value distortion compensation processing on the current frame image of the edge region of the curved screen according to the distortion correction parameters and the geometric parameters of the curved screen to obtain compensated pixel data, it is specifically used for: Based on the distortion correction parameters, perform geometric position correction processing on each pixel in the current frame image; Based on the geometric parameters, pixel value distortion compensation processing is performed on the pixels in the edge region that have undergone geometric position correction processing to obtain the compensated pixel data.

[0091] In some embodiments, when the processing unit 402 performs the step of performing pixel value distortion compensation processing on the pixels in the edge region that have undergone geometric position correction processing according to the geometric parameters to obtain the compensated pixel data, it is specifically used for: Based on the geometric parameters, pixel value distortion compensation processing is performed on the pixels in the edge region that have undergone geometric position correction processing using a nonlinear mapping algorithm to obtain the compensated pixel data. The nonlinear mapping algorithm is as follows: ; in, Let I(u,v) be the pixel coordinates in the edge region. The original pixel data of the pixel, I′(u,v) is the coordinates of the pixel. The compensated pixel data of the pixels, wherein the geometric parameters include the center of curvature and the radius of curvature (x). c ,y c ), It is the edge attenuation coefficient determined based on the corresponding pixel coordinates, the curvature center, and the curvature radius.

[0092] In some embodiments, the user includes multiple sub-users, the relative position data further includes gaze point heatmap data corresponding to each of the sub-users, the user viewpoint data includes user viewpoint data corresponding to each of the sub-users, and the user distance data includes user distance data corresponding to each of the sub-users. The processing unit 402 is further configured to calculate the priority weight corresponding to each of the sub-users based on the gaze point hotspot data and the user distance data of each sub-user; and to allocate independent correction resources to each of the sub-users using the weighted least squares method based on the priority weights, wherein the correction resources include computational resources for determining the distortion correction parameters and / or computational resources for performing pixel value distortion compensation processing.

[0093] In summary, this embodiment can dynamically determine the current distortion correction parameters by collecting user viewpoint data and user distance data in real time at the current frame moment, and perform pixel value distortion compensation processing on the image in the edge area of ​​the curved screen based on the distortion correction parameters and the current geometric parameters of the curved screen. It can be seen that this embodiment considers the geometric parameters of the curved screen and the user's current viewpoint and distance when correcting the image in the curved screen, thereby improving the display accuracy of the curved screen.

[0094] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the curved screen display device 400 and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0095] The aforementioned curved screen display device can be implemented as a computer program, which can be used in, for example... Figure 5 It runs on the computer device shown.

[0096] Please see Figure 5 , Figure 5 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 is a curved screen terminal.

[0097] See Figure 5 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0098] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a display method for a curved screen.

[0099] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0100] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a curved screen display method.

[0101] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0102] The processor 502 is used to run a computer program 5032 stored in the memory to perform the following steps: Obtain the user's relative position data with respect to the curved screen at the current frame time. The relative position data includes user viewpoint data and user distance data. The distortion correction parameters for the current frame are determined based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time. Based on the distortion correction parameters and the geometric parameters of the curved screen, pixel value distortion compensation processing is performed on the current frame image of the edge region in the curved screen to obtain the compensated pixel data; The curved screen display is driven based on the compensated pixel data.

[0103] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0104] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0105] Therefore, this application also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When executed by a processor, the program instructions cause the processor to perform the following steps: Obtain the user's relative position data with respect to the curved screen at the current frame time. The relative position data includes user viewpoint data and user distance data. The distortion correction parameters for the current frame are determined based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time. Based on the distortion correction parameters and the geometric parameters of the curved screen, pixel value distortion compensation processing is performed on the current frame image of the edge region in the curved screen to obtain the compensated pixel data; The curved screen display is driven based on the compensated pixel data.

[0106] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0107] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0108] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0109] The steps in the methods of this application embodiment can be adjusted, merged, or deleted according to actual needs. The units in the apparatus of this application embodiment can be merged, divided, or deleted according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0110] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0111] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A display method for a curved screen, characterized in that, include: Obtain the user's relative position data with respect to the curved screen at the current frame time. The relative position data includes user viewpoint data and user distance data. The distortion correction parameters for the current frame are determined based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time. Based on the distortion correction parameters and the geometric parameters of the curved screen, pixel value distortion compensation processing is performed on the current frame image of the edge region in the curved screen to obtain the compensated pixel data; The curved screen display is driven based on the compensated pixel data.

2. The method according to claim 1, characterized in that, The method is applied to a target terminal, which is equipped with the curved screen and has embedded a miniature ToF camera, an infrared structured light module, and a gyroscope array; obtaining the user's relative position data with respect to the curved screen at the current frame time includes: At the current frame time, the depth value between the user's eye and the curved screen is determined by measuring the round-trip time difference of the light signal from the miniature ToF camera; A first distance value is determined based on the depth value, and a second distance value and a horizontal distance value are determined based on the degree of distortion of the dot matrix projected by the infrared structured light module on the user's face at the current frame time. A weighted fusion algorithm is used to determine the user distance data based on the first distance value and the second distance value; The head rotation angle and head rotation angular velocity at the current frame time are obtained through the gyroscope array; The user's perspective data is determined based on the depth value, the horizontal distance value, the head rotation angle, and the head rotation angular velocity.

3. The method according to claim 2, characterized in that, The process of determining the user's viewpoint data based on the depth value, the horizontal distance value, the head rotation angle, and the head rotation angular velocity includes: The user perspective data is determined based on a preset user perspective calculation formula, which is: ； Where θ is the user's perspective data, ∆x is the horizontal distance value, and ∆z is the depth value. The head rotation angle is... The angular velocity of the head rotation is denoted as t, and the time corresponding to the current frame moment is t.

4. The method according to claim 1, characterized in that, The step of determining the distortion correction parameters for the current frame based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time includes: Based on the user's perspective data, the user's distance data, and the distortion parameters from the previous frame at the current frame time, the distortion correction parameters for the current frame time are dynamically calculated using a preset spatiotemporal joint calibration algorithm. The spatiotemporal joint calibration algorithm is as follows: ； Among them, P k+1 P is the distortion correction parameter. k Let z be the distortion parameter of the previous frame. k For the user distance data, θ k Let f be the user's perspective data, and Q be a nonlinear function. k The noise covariance matrix is ​​a multi-sensor noise matrix, where the multi-sensor refers to multiple sensors that acquire the user's viewpoint data and the user's distance data.

5. The method according to claim 1, characterized in that, The step of performing pixel value distortion compensation processing on the current frame image of the edge region in the curved screen according to the distortion correction parameters and the geometric parameters of the curved screen to obtain compensated pixel data includes: Based on the distortion correction parameters, perform geometric position correction processing on each pixel in the current frame image; Based on the geometric parameters, pixel value distortion compensation processing is performed on the pixels in the edge region that have undergone geometric position correction processing to obtain the compensated pixel data.

6. The method according to claim 5, characterized in that, The step of performing pixel value distortion compensation processing on the pixels in the edge region that have undergone geometric position correction processing according to the geometric parameters to obtain the compensated pixel data includes: Based on the geometric parameters, pixel value distortion compensation processing is performed on the pixels in the edge region that have undergone geometric position correction processing using a nonlinear mapping algorithm to obtain the compensated pixel data. The nonlinear mapping algorithm is as follows: ； in, Let I(u,v) be the pixel coordinates in the edge region. The original pixel data of the pixel, I′(u,v) is the coordinates of the pixel. The compensated pixel data of the pixels, wherein the geometric parameters include the center of curvature and the radius of curvature (x). c ,y c ), It is the edge attenuation coefficient determined based on the corresponding pixel coordinates, the curvature center, and the curvature radius.

7. The method according to any one of claims 1 to 6, characterized in that, The user includes multiple sub-users, the relative position data further includes gaze point hotspot data corresponding to each sub-user, the user viewpoint data includes user viewpoint data corresponding to each sub-user, and the user distance data includes user distance data corresponding to each sub-user; before determining the distortion correction parameters for the current frame based on the user viewpoint data, the user distance data, and the distortion parameters of the previous frame at the current frame time, the method further includes: Based on the gaze point hotspot data and user distance data of each sub-user, calculate the priority weight corresponding to each sub-user; Based on the priority weights, weighted least squares method is used to allocate independent correction resources to each of the sub-users. The correction resources include computational resources for determining the distortion correction parameters and / or computational resources for performing pixel value distortion compensation processing.

8. A curved screen display device, characterized in that, include: The transceiver unit is used to acquire the user's relative position data with respect to the curved screen at the current frame time. The relative position data includes user view data and user distance data. The processing unit is configured to determine the distortion correction parameters for the current frame based on the user's perspective data, the user's distance data, and the distortion parameters of the previous frame at the current frame time; and to perform pixel value distortion compensation processing on the current frame image of the edge region in the curved screen according to the distortion correction parameters and the geometric parameters of the curved screen to obtain the compensated pixel data. The curved screen display is driven based on the compensated pixel data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the display method of the curved screen as described in any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions that, when executed by a processor, cause the processor to perform the display method for the curved screen as described in any one of claims 1-7.

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