A curved screen brightness uniformity intelligent testing method and system

Abstract

This application relates to the field of curved screen brightness detection technology, specifically to an intelligent testing method and system for the brightness uniformity of curved screens. The method includes: obtaining a fully bright screen image and a dark field image of the curved screen based on darkroom operation specifications, and performing brightness conversion on the fully bright screen image; establishing an approximate perspective projection parabolic model; acquiring observation angles in different directions on the surface corresponding to the approximate perspective projection parabolic model, and establishing an observation angle matrix; determining the brightness-angle response function based on the functional relationship between the observation angle and brightness value in the observation angle matrix, and obtaining the intrinsic brightness value; determining the screen brightness uniformity at each pixel location based on the difference in the intrinsic brightness value distribution between each pixel location and all pixel locations; and obtaining the test result of the curved screen brightness uniformity based on the screen brightness uniformity. This application aims to improve the accuracy of curved screen brightness test results.

Description

Technical Field

[0001] This application relates to the field of curved screen brightness detection technology, specifically to an intelligent testing method and system for the brightness uniformity of curved screens. Background Technology

[0002] Curved display structures, due to their conformity to the physiological characteristics of the human eye and their ability to provide an immersive visual experience, have been widely used in high-end displays, automotive cockpits, and mobile terminals. Currently, relevant standards such as GB / T 38001.52-2024 have been released in China, clearly defining brightness uniformity as a key indicator for evaluating display quality, which directly affects the application effects in professional fields such as image display and color design.

[0003] Currently, brightness uniformity testing technologies are mainly divided into two categories: point scanning and area array imaging. In point scanning testing, the probe axis must be strictly perpendicular to the local tangent plane of the screen at the measurement point. This single-point measurement method results in drawbacks such as point-by-point movement and long reading times, which cannot meet the high-speed full inspection requirements of modern production lines. On the other hand, imaging luminance meter measurement technology uses an area array CCD to capture the brightness distribution image of the entire screen, which has the characteristics of a large field of view, high speed, and high resolution. However, in practical applications, even if the screen itself emits light uniformly, the geometric curvature of the curved screen increases the viewing angle between the screen edge and the camera, making it difficult for existing testing systems to accurately distinguish between them, leading to misjudgments. Summary of the Invention

[0004] In view of the above, it is necessary to provide an intelligent testing method and system for the brightness uniformity of curved screens to solve the above problems.

[0005] The first aspect of this application provides an intelligent testing method for the brightness uniformity of a curved screen, the method comprising:

[0006] Based on the darkroom operation specifications, exposure and photography were performed on the curved screen under the full white field signal to obtain a fully bright screen image. The brightness of the screen center obtained by different luminance meters was compared, and the brightness of the fully bright screen image was converted by combining it with the dark field image of the curved screen when there was no light.

[0007] Based on the edge detection results of the full-brightness screen image, an approximate perspective projection parabolic model is established; on the surface corresponding to the approximate perspective projection parabolic model, observation angles in different directions are obtained, and an observation angle matrix is ​​established.

[0008] The functional relationship between the viewing angle and the brightness value in the observation viewing angle matrix is ​​analyzed to determine the brightness-angle response function, and the brightness value obtained at each pixel position is compensated to obtain the intrinsic brightness value.

[0009] Based on the difference in intrinsic brightness value distribution between each pixel location and all pixel locations, the screen brightness uniformity at each pixel location is determined; based on the screen brightness uniformity, the test results of the curved screen brightness uniformity are obtained.

[0010] Preferably, the brightness conversion of the full-brightness screen image specifically includes:

[0011] The grayscale difference between the full-brightness screen image and the dark field image at each pixel position is obtained, and the results of the comparison between the screen center brightness obtained by different luminance meters at each pixel position are positively fused to obtain the first positive fusion result; the result of positively fusing the pre-obtained flat field correction coefficient with the exposure time of the full-brightness screen image is used as the second positive fusion result.

[0012] The brightness value at each pixel location is determined by the ratio of the first forward fusion result to the second forward fusion result.

[0013] Preferably, the process for obtaining the flat field correction coefficient is as follows:

[0014] N consecutive images of a standard uniform light source without any objects are acquired, and the pixel values ​​of all images are averaged to obtain an average image. The pixel value at each pixel position in the average image is used as the corresponding flat field correction coefficient.

[0015] Preferably, the specific formula for establishing the approximate perspective projection parabolic model is as follows:

[0016] ;In the formula, For curvature parameters, C is the x-coordinate of the image center, and C is the intercept. Let u be a parabolic function for a surface, where u represents the x-coordinate of the pixel position.

[0017] Preferably, the curvature parameter and the intercept are obtained by minimizing the error between the surface parabolic function and the edge detection result of the full-brightness screen image.

[0018] Preferably, the observation angle is specifically the angle between the direction vector of the imaging brightness meter's optical axis pointing to the center of the screen and the normal vector of the surface corresponding to the approximate perspective projection parabolic model at each pixel position.

[0019] Preferably, the intrinsic brightness value is specifically the ratio of the brightness value at each pixel location to the brightness-angle response function value corresponding to the observation angle at that location.

[0020] Preferably, determining the screen brightness uniformity at each pixel location specifically involves:

[0021] The average intrinsic brightness value at all pixel locations in the fully bright screen image, excluding the maximum and minimum intrinsic brightness values, is used as the global reference brightness value.

[0022] Calculate the difference between the intrinsic luminance value and the global reference luminance value at each pixel location; calculate the product of the global reference luminance value and the preset Weber fraction, and use the ratio of the difference to the product as the screen luminance uniformity at each pixel location.

[0023] Preferably, the process of obtaining the test results of the brightness uniformity of the curved screen is as follows:

[0024] The percentage of pixels with screen brightness uniformity greater than a first preset value at all pixel locations on the entire curved screen is counted. If the percentage is less than a preset percentage and the maximum value of screen brightness uniformity is less than a second preset value, the curved screen is judged to have uniform brightness; otherwise, the curved screen has non-uniform brightness. The second preset value is greater than the first preset value.

[0025] Secondly, embodiments of this application also provide an intelligent testing system for the brightness uniformity of curved screens, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0026] This application has at least the following beneficial effects:

[0027] This application avoids the reliance of traditional point measurement methods on six-axis robotic arms and universal adjustment frames. By using single-imaging superimposed virtual geometric reconstruction technology, it completes full-screen data acquisition, reducing the detection cycle to the second level. This perfectly adapts to the high-speed full inspection requirements of modern production lines and significantly reduces equipment hardware investment and maintenance difficulty.

[0028] In terms of improving detection accuracy and yield, this application overcomes the unique "viewing angle effect" problem of curved screens. By utilizing virtual cylinder fitting based on image contours and a self-reference photometric compensation model, it automatically removes optical attenuation caused by geometric curvature and accurately restores the intrinsic brightness of the screen. It effectively distinguishes between "false dark areas" caused by viewing angle and non-uniform defects caused by material stress or backlight module defects, significantly reducing the misjudgment rate in edge areas;

[0029] Furthermore, this application introduces a visual perception evaluation index based on JND to replace the traditional rigid physical numerical ratio, making the judgment results highly consistent with the subjective visual perception of the human eye. It can sensitively capture weak stress marks and membrane wrinkles, ensuring the guarantee of high product quality. Attached Figure Description

[0030] Figure 1This is a flowchart illustrating the steps of an intelligent testing method for the brightness uniformity of a curved screen, provided in one embodiment of this application. Detailed Implementation

[0031] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0033] It should also be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged, and some steps can also be deleted.

[0034] In all the formulas involved in this application, in order to prevent the formula from being meaningless due to the denominator being 0, unless it is necessary to specify, when the denominator is 0, a preset value can be added to the denominator.

[0035] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent testing method and system for the brightness uniformity of curved screens provided in this application.

[0036] Please see Figure 1 The diagram illustrates a flowchart of a method for intelligent testing of brightness uniformity of a curved screen according to an embodiment of this application. The method includes the following steps:

[0037] The first step: Based on the darkroom operation specifications, exposure and photography are performed on the curved screen under a full white field signal to obtain a fully bright screen image. The brightness of the screen center obtained from different luminance meters is compared, and combined with the dark field image of the curved screen when there is no light, the brightness of the fully bright screen image is converted.

[0038] During high-precision optical testing, stray light from the environment can cause unexpected specular reflections on the curved screen surface, which are then superimposed on the screen's self-emissive brightness. Furthermore, in the "cold start" state immediately after power-on, the photoelectric conversion efficiency and luminous characteristics of OLED display devices are not yet stable, leading to data fluctuations.

[0039] This embodiment of the application follows darkroom operating procedures to ensure that the ambient illuminance of the test environment is below 1 lux. Simultaneously, the curved screen under test and the imaging luminance meter are powered on and run continuously for more than 30 minutes until they reach a stable operating temperature and optical state. A full white field signal with RGB values ​​of (255, 255, 255) is input to the screen as a standard excitation source. The imaging luminance meter is then positioned at the center of curvature of the curved screen, ensuring that the optical axis is strictly aligned with the geometric center of the screen. A single exposure is performed to acquire a high-resolution RAW format grayscale image with complete pixel information of the entire screen, which serves as the full-brightness screen image.

[0040] Since the RAW image output by the sensor only contains the grayscale values ​​of pixels, and these values ​​have a non-linear relationship with the absolute brightness perceived by the human eye, they cannot be directly used for subsequent brightness uniformity analysis. This embodiment provides a method for calculating the conversion between grayscale and brightness, specifically:

[0041]

[0042] In the above formula: for The brightness value at the location. The value corresponding to the exposure time is K, which is the absolute brightness calibration coefficient, and its physical meaning is the physical brightness corresponding to a unit gray value. Represents the coordinates of the original RAW image The grayscale value at the location, Represents dark field image The grayscale value at the location, This is the flat field correction factor; This represents a preset value, which is 0.001 in this embodiment. Because optical lenses exhibit a phenomenon where the center is bright and the edges are dark, the flat field correction coefficient can effectively eliminate the viewing angle darkness effect at the screen edges, i.e., the optical attenuation of the imaging system itself (such as lens vignetting). This is denoted as grayscale difference; This is recorded as the first positive fusion result; This is recorded as the second positive fusion result.

[0043] It should be noted that, All coordinates are corrected coordinates after distortion correction using the Zhang Zhengyou calibration method; all subsequent pixel processing (edge ​​detection, fitting, compensation) is performed in this linearized coordinate system.

[0044] In a completely dark environment, i.e. with the lens cap on, images taken with the same exposure time are recorded as dark field images; the method for obtaining the absolute brightness calibration coefficient is as follows: obtain the brightness value of the center of the screen using a dot luminance meter and an imaging luminance meter respectively, and calculate the ratio of the two, which is the absolute brightness calibration coefficient.

[0045] Among them, the flat field correction coefficient The specific method for obtaining the data is as follows: continuously acquire N standard uniform light source images without any objects, and average the pixel values ​​of all images to obtain the average pixel value in the average image. Pixel value; in this embodiment, N is set to 16, but the implementer can adjust it according to the actual situation. Since the flat field correction coefficient reflects the attenuation trend of illumination light under large-scale conditions, it is necessary to perform Gaussian blurring on the obtained standard uniform light source image to obtain a Gaussian blurred image. In this embodiment, the image size is set to... The value of the Gaussian kernel The value is 101, approximately 1 / 10 of the image width. To obtain the maximum pixel value of a Gaussian blurred image, for any point coordinate... The position is determined by dividing the pixel value at that position by the maximum pixel value, and the result is recorded as the flat field correction coefficient for that position.

[0046] The above formula converts the grayscale pixel values ​​at different pixel locations in an image into their corresponding brightness values. In this embodiment, a dark-field image is used to filter out background noise present during the shooting process. Simultaneously, a grayscale-to-brightness conversion function is constructed to achieve the mapping and conversion of brightness values ​​to the actual physical screen.

[0047] At this point, the brightness values ​​at different pixel locations have been obtained.

[0048] The second step is to establish an approximate perspective projection parabolic model based on the edge detection results of the fully bright screen image; and to obtain the observation viewpoints in different directions on the surface corresponding to the approximate perspective projection parabolic model, and establish an observation viewpoint matrix.

[0049] Typically, when using a wide-angle lens to capture images of a curved screen, the inherent physical characteristics of the optical lens cause nonlinear geometric distortion in the imaging result. This leads to image edge shifts, and directly using the pixel coordinates of the original image for analysis results in significant positional deviations in subsequent calculations. In this embodiment, the Zhang Zhengyou calibration method is used to remove distortion from the original image coordinates, obtaining corrected two-dimensional coordinates; the Zhang Zhengyou calibration method is a well-known prior art and will not be described in detail here.

[0050] An edge detection algorithm is used to process the fully lit screen image to extract the two-dimensional coordinate set of the curves of the upper and lower edges of the screen's luminous area. This edge line is denoted as... In this embodiment, the Canny operator is used for processing.

[0051] To simulate the shape of a curved screen, this application embodiment constructs an approximate perspective projection parabola model using a quadratic polynomial model: , in the formula, The curvature parameter k indicates that the curvature of the curved screen is greater. C is the x-coordinate of the image center, and C is the intercept. Let be a parabolic function for the surface, and u represent the x-coordinate of the pixel position. It should be noted that the approximate perspective projection parabolic model uses a second-order expansion to approximate the edge curve of a cylindrical surface under perspective projection.

[0052] In this embodiment, the initial value k is set to 0.001, and C is set to 500, which is half the image height. To make the shape of the curved screen closer to the real shape, the curve shape is optimized and iteratively fitted. Calculation error. The parameters k and C are adjusted according to the gradient direction. When the error E between two iterations is less than 0.001 or reaches the preset number of iterations of 50, this embodiment determines that convergence has been completed.

[0053] By constructing an approximate perspective projection parabolic model, the problem of curvature calculation tending to infinity due to the existence of broken discrete points at the curve edge can be effectively avoided during the direct calculation of the edge detection curve. Constructing a parabolic plane can effectively improve the accuracy of curve edge simulation.

[0054] Extending the curve vertically yields a surface. Calculating the normal vector at each point on this surface allows us to obtain the view angle at each different coordinate position. (The coordinates are then set to...) The viewing angle at the location is denoted as Its formula is:

[0055]

[0056] In the formula, This represents the direction vector of the optical axis of the imaging luminance meter pointing towards the center of the screen. This represents the surface corresponding to the approximate perspective projection parabolic model in... The normal vector at the location, This represents the inverse trigonometric cosine function; where the normal vector and direction vector both point inwards from the screen and are both unit vectors. The corresponding viewing angle can be calculated for each pixel position, thus obtaining the observation viewing angle matrix. This is because the angle between the line of sight and the screen normal... The angle of view increases, which leads to a decrease in brightness. By observing the angle of view matrix, we can obtain the deflection of the shooting angle and provide data support for subsequent brightness compensation.

[0057] At this point, the observation view matrix is ​​obtained.

[0058] The third step is to analyze the functional relationship between the viewing angle and the brightness value in the observation viewing angle matrix, determine the brightness-angle response function, and compensate for the brightness value obtained at each pixel location to obtain the intrinsic brightness value.

[0059] Because curved screens cause significant variations in the angle between the observer's line of sight and the screen normal at different locations on the screen, resulting in a viewing angle effect, and assuming that the intrinsic luminous intensity of all pixels within the horizontal central axis band of the screen is uniform, the observed brightness fluctuations can be considered purely a viewing angle effect. Therefore, to avoid viewing angle errors caused during the shooting process, it is necessary to compensate for the acquired brightness values ​​using the observation viewing angle matrix.

[0060] Obtain the observation viewpoint matrix, select the strip-shaped region at the center of the screen, extract the viewing angles and normalized brightness values ​​corresponding to all pixels in this region, and denote them as the observation viewpoint set. and normalized brightness set In this embodiment, the strip region specifically refers to a rectangular area extending horizontally to the left and right by a distance of W / 2, with the horizontal center line of the screen as the axis of symmetry, and spanning the entire height of the screen horizontally. In this embodiment, W is taken as 20% of the screen width. The fourth-order brightness-angle response function is obtained, i.e. The coefficients of the fourth-order polynomial , , , , The least squares method is used to calculate the set of observation angles and the normalized brightness set. By establishing a brightness-angle response function, the brightness-angle co-response characteristics at each pixel location can be obtained.

[0061] After the viewing angle is changed, the brightness of the curved screen will be reduced. For example, the brightness of the curved screen at a 45° viewing angle is only 80% of that at a normal viewing angle, that is, f(45)=0.8, which means that the obtained brightness value is 0.8 of the true brightness value. Therefore, it is necessary to compensate and correct the brightness according to the brightness angle-coordinated change characteristics to offset the brightness defect of the curved screen under the viewing angle change. , coordinate point The brightness value at the location. coordinate point Brightness angle co-response characteristics at location coordinate point The intrinsic brightness value after correction at the location.

[0062] Since the original brightness values ​​are caused by uneven self-illumination and irregular curved shape of the screen, they tend to be bright in the middle and dark at the edges. If not processed, they will cause the detection to fail during the brightness uniformity detection process, and it will be impossible to distinguish whether there are dark spots at the edges of the screen.

[0063] At this point, the intrinsic brightness value at each pixel location is obtained.

[0064] The fourth step: Based on the difference in intrinsic brightness value distribution between each pixel location and all pixel locations, determine the screen brightness uniformity at each pixel location; based on the screen brightness uniformity, obtain the test results of the curved screen brightness uniformity.

[0065] Traditional testing methods use the ratio of minimum brightness to maximum brightness as a key indicator for judgment. However, this method is extremely sensitive to dead pixel noise during the judgment calculation. Furthermore, this testing method lacks information about human visual perception. For example, a 10-nit difference on a 1000-nit screen may be imperceptible to the human eye, but a 5-nit difference on a 50-nit screen is very noticeable. Therefore, this application's embodiment uses a visual perception evaluation index based on JND (Joint Density Scale). The core idea is to quantify the maximum pixel intensity change or the minimum perceptible difference that is imperceptible to the human eye, and to calculate and test the uniformity of screen brightness.

[0066] After obtaining the intrinsic brightness values ​​at all pixel locations, sort all pixel intrinsic brightness values ​​in ascending order of value, remove the top 1% and bottom 1% of pixels in the value distribution, calculate the average of the remaining brightness values, and record this as the global baseline brightness value. The formula for screen brightness uniformity is as follows:

[0067]

[0068] In the above formula, Indicates the pixel point at The screen brightness uniformity at a given location is used to quantify the deviation as a multiple of the human eye's perception threshold. The value is the Weber fraction, representing the minimum rate of change in brightness that the human eye can perceive under a given background brightness. In most industrial scenarios, based on psychophysical statistics, the value ranges from 0.01 to 0.03. In this embodiment, the value is 0.02, which means that the maximum tolerable difference in brightness is 2%.

[0069] when When the pixel brightness at that location is below the human eye's perception threshold, the visual effect is very uniform and usually not easily perceived by the human eye; when When, determine that the pixel at that position is in a critical state; when At that time, the deviation is the threshold of the human eye. This indicates a significant unevenness in brightness at that location, where... .

[0070] In this embodiment, The area marked in green indicates a uniform region; Areas marked in yellow indicate areas of slight fluctuation; The area marked in red indicates a defective area, where 1.0 is the first preset value. This is the second preset value, which is set to 2 in this embodiment. The implementer can adjust it according to the actual situation.

[0071] Statistics of the entire curved screen The percentage of pixels with a value greater than 1.0; if the percentage is less than 1% and the maximum value of screen brightness uniformity is less than... If the brightness of the test screen is uniform, it is determined that the brightness is not uniform; otherwise, it is determined that the brightness is not uniform. The 1% is a preset percentage, which can be adjusted by the implementer according to the actual situation.

[0072] Based on the same inventive concept as the above method, this application embodiment also provides a curved screen brightness uniformity intelligent testing system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described curved screen brightness uniformity intelligent testing methods.

[0073] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0074] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some technical features, without causing the essence of the corresponding technical solutions to deviate from the scope of the technical solutions in the embodiments of this application, should all be included within the protection scope of this application.

Claims

1. A smart testing method for the brightness uniformity of a curved screen, characterized in that, The method includes the following steps: Based on the darkroom operation specifications, exposure and photography were performed on the curved screen under the full white field signal to obtain a fully bright screen image. The brightness of the screen center obtained by different luminance meters was compared, and the brightness of the fully bright screen image was converted by combining it with the dark field image of the curved screen when there was no light. Based on the edge detection results of the full-brightness screen image, an approximate perspective projection parabolic model is established; on the surface corresponding to the approximate perspective projection parabolic model, observation angles in different directions are obtained, and an observation angle matrix is ​​established. The functional relationship between the viewing angle and the brightness value in the observation viewing angle matrix is ​​analyzed to determine the brightness-angle response function, and the brightness value obtained at each pixel position is compensated to obtain the intrinsic brightness value. Based on the difference in intrinsic brightness value distribution between each pixel location and all pixel locations, the screen brightness uniformity at each pixel location is determined; based on the screen brightness uniformity, the test results of the curved screen brightness uniformity are obtained.

2. The intelligent testing method for brightness uniformity of curved screens as described in claim 1, characterized in that, The brightness conversion of the full-brightness screen image specifically involves: The grayscale difference between the full-brightness screen image and the dark field image at each pixel position is obtained, and the results of the comparison between the screen center brightness obtained by different luminance meters at each pixel position are positively fused to obtain the first positive fusion result; the result of positively fusing the pre-obtained flat field correction coefficient with the exposure time of the full-brightness screen image is used as the second positive fusion result. The brightness value at each pixel location is determined by the ratio of the first forward fusion result to the second forward fusion result.

3. The intelligent testing method for brightness uniformity of curved screens as described in claim 2, characterized in that, The process for obtaining the flat field correction coefficient is as follows: N consecutive images of a standard uniform light source without any objects are acquired, and the pixel values ​​at the same position in all images are averaged to obtain an average image. The pixel value at each pixel position in the average image is used as the corresponding flat field correction coefficient.

4. The intelligent testing method for brightness uniformity of curved screens as described in claim 1, characterized in that, The specific formula for establishing the approximate perspective projection parabolic model is as follows: ;In the formula, For curvature parameters, C is the x-coordinate of the image center, and C is the intercept. Let u be a parabolic function for a surface, where u represents the x-coordinate of the pixel position.

5. The intelligent testing method for brightness uniformity of curved screens as described in claim 4, characterized in that, The curvature parameter and the intercept are obtained by minimizing the error between the surface parabolic function and the edge detection results of the full-brightness screen image.

6. The intelligent testing method for brightness uniformity of curved screens as described in claim 1, characterized in that, The observation angle is specifically the angle between the direction vector of the imaging brightness meter's optical axis pointing to the center of the screen and the normal vector of the surface corresponding to the approximate perspective projection parabolic model at each pixel position.

7. The intelligent testing method for brightness uniformity of curved screens as described in claim 1, characterized in that, The intrinsic brightness value is specifically the ratio of the brightness value at each pixel location to the brightness-angle response function value corresponding to the observation angle at that location.

8. The intelligent testing method for brightness uniformity of curved screens as described in claim 1, characterized in that, The determination of screen brightness uniformity at each pixel location specifically involves: The average intrinsic brightness value at all pixel locations in the fully bright screen image, excluding the maximum and minimum intrinsic brightness values, is used as the global reference brightness value. Calculate the difference between the intrinsic luminance value at each pixel location and the global reference luminance value; Calculate the product of the global reference brightness value and the preset Weber score, and use the ratio of the difference to the product as the screen brightness uniformity at each pixel location.

9. The intelligent testing method for brightness uniformity of a curved screen as described in claim 1, characterized in that, The process of obtaining the test results for the brightness uniformity of the curved screen is as follows: The percentage of pixels with screen brightness uniformity greater than a first preset value at all pixel locations on the curved screen is counted. If the percentage is less than a preset percentage and the maximum value of screen brightness uniformity is less than a second preset value, the curved screen is judged to have uniform brightness; otherwise, the curved screen has uneven brightness.

10. A smart testing system for the brightness uniformity of a curved screen, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-9.

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