Brightness gradient error determination method and system of LED display screen

Abstract

The invention discloses a brightness gradient error determination method and system for an LED display screen, and relates to the technical field of LED display, and the method comprises the steps: constructing a self-adaptive brightness prediction model based on the preprocessed ambient light intensity and real-time image data, and predicting the optimal brightness value of the LED display screen under different ambient light intensities; evaluating the predicted optimal brightness value, and preliminarily adjusting the brightness of the LED display screen according to an evaluation result; deploying a brightness error detection and correction algorithm on the edge computing device, and collecting adjusted LED display screen image data in real time; and detecting the brightness gradient error of the adjusted LED display screen through brightness error detection on the edge computing equipment, and evaluating and correcting a detection result. The brightness gradient error is predicted and corrected in real time, so that the display quality, the visual comfort and the energy efficiency ratio of the LED display screen are remarkably improved, visual discomfort caused by local over-brightness or over-darkness is effectively avoided, and the sense of reality and the ornamental value of display content are enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of LED display, and in particular to a method and system for determining a brightness gradient error of an LED display screen. Background Art

[0002] With the advancement of technology, LED displays have not only made breakthroughs in resolution and clarity, but also made significant progress in intelligent management. However, in order to achieve a more realistic visual experience, the automatic brightness adjustment function of the display under different ambient light conditions has become particularly important. Early LED displays usually used fixed brightness settings or simple ambient light sensing to adjust the screen brightness, which was difficult to meet the needs of maintaining a good viewing experience in complex and changing lighting environments. In recent years, with the development of artificial intelligence technology, especially the application of deep learning algorithms, a new trend is emerging, that is, to achieve intelligent response to changes in ambient light by building complex models, and dynamically adjust the brightness on this basis. This type of method can more finely control the brightness output of LED displays to achieve the purpose of both energy saving and protecting the audience's eyesight.

[0003] Although the above-mentioned technological advances have significantly improved the user experience of LED displays, there are still some problems that need to be solved in practical applications. First, the existing brightness adjustment algorithms often only consider the changes in ambient light intensity and ignore the influence of the image content of the display itself. As a result, in some cases, although the ambient light intensity is appropriate, due to the characteristics of the image content on the display, the human eye does not see well. Secondly, traditional brightness adjustment methods are usually unable to accurately evaluate the brightness gradient error of the display in different environments, which means that even if the brightness is adjusted, there may be problems of local overbrightness or overdarkness, affecting the overall display effect. In addition, due to the lack of effective brightness gradient error detection methods, it is difficult for the current system to achieve real-time error correction, which limits the expressiveness of LED displays in high-end applications. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for determining brightness gradient errors of an LED display screen to solve the problems of inaccurate brightness adjustment and insufficient brightness gradient error detection and correction in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a method for determining the brightness gradient error of an LED display screen, comprising collecting ambient light intensity and a real-time image of an LED display screen and performing preprocessing; constructing an adaptive brightness prediction model based on the preprocessed ambient light intensity and real-time image data, and predicting the optimal brightness value of the LED display screen under different ambient light intensities; evaluating the predicted optimal brightness value, and preliminarily adjusting the brightness of the LED display screen according to the evaluation result; deploying a brightness error detection and correction algorithm on an edge computing device, and collecting adjusted LED display screen image data in real time; detecting the brightness gradient error of the adjusted LED display screen through brightness error detection on the edge computing device, evaluating the detection result and performing correction.

[0007] As a preferred solution of the method for determining the brightness gradient error of the LED display screen of the present invention, the specific steps of collecting the ambient light intensity and the real-time image of the LED display screen and preprocessing are as follows: Use ambient light sensor to detect the surrounding light intensity; Use high-precision cameras to capture real-time images of LED displays; Standardize the ambient light intensity data and perform smoothing filtering on the ambient light intensity data; Perform denoising, color correction, image enhancement, and size normalization on real-time image data.

[0008] As a preferred solution of the brightness gradient error determination method of the LED display screen of the present invention, wherein: based on the pre-processed ambient light intensity and real-time image data, an adaptive brightness prediction model is constructed to predict the optimal brightness value of the LED display screen under different ambient light intensities. The specific steps are as follows: Based on the preprocessed ambient light intensity and real-time image data, an adaptive brightness prediction model consisting of a convolutional neural network and a fully connected layer is constructed; The pre-processed ambient light intensity data and real-time image data are input into the adaptive brightness prediction model for training and optimization of model parameters; The mean square error is used during training to measure the difference between the predicted brightness and the actual brightness; Define a comprehensive brightness evaluation function based on the preprocessed ambient light intensity data and real-time image data , comprehensively evaluate the ambient light intensity and image data, and predict the optimal brightness value, the expression is: ; in, Represents the optimal brightness value predicted under given ambient light intensity and image data conditions, is the i-th data point in the ambient light intensity data, is real-time image data, is the ambient light intensity data The weight coefficient of Is the image data The weight coefficient of is the ambient light intensity The nonlinear transformation function is Is the image data The nonlinear transformation function is: n is the length of the ambient light intensity data, m is the length of the image data, i is the index of the ambient light intensity data, j is the index of the image data, It is the adjustment factor to prevent the predicted brightness value from being too low. is an adjustment factor to prevent the predicted brightness value from being too high, is the comprehensive brightness evaluation function.

[0009] As a preferred solution of the brightness gradient error determination method of the LED display screen of the present invention, wherein: the predicted optimal brightness value is evaluated, and the brightness of the LED display screen is preliminarily adjusted according to the evaluation result. The specific steps are as follows: Define a brightness adaptation threshold T, and evaluate the adaptability of the current optimal brightness value by comparing the predicted optimal brightness value B with the brightness adaptation threshold T; When B<T, it means that the current brightness is too low, and the brightness of the LED display should be reduced according to the predicted brightness value B; When B>T, it means that the current brightness is too high, and the brightness of the LED display should be increased according to the predicted brightness value B.

[0010] As a preferred solution of the brightness gradient error determination method of the LED display screen of the present invention, wherein: the brightness error detection and correction algorithm is deployed on the edge computing device, and the adjusted LED display screen image data is collected in real time. The specific steps are as follows: Select an embedded computer as the edge computing device and deploy a brightness error detection and correction algorithm on the embedded computer; A high-precision camera is used to collect the LED display image data in real time after preliminary adjustment, and the real-time image data is denoised, color corrected, enhanced and size standardized.

[0011] As a preferred solution of the method for determining the brightness gradient error of the LED display screen of the present invention, wherein: the brightness gradient error of the LED display screen after adjustment is calculated by the brightness error detection algorithm on the edge computing device, and the specific steps are as follows: Calculate the difference between the actual brightness value of each pixel in each LED display and the predicted optimal brightness value. The expression is: ; in, Represents the error between the brightness of the d-th pixel after adjustment and the predicted brightness value, Indicates the actual brightness of the d-th pixel, is the predicted optimal brightness value, The adjustment factor between brightness and color temperature difference, is the color saturation of the d-th pixel, is the predicted optimal color saturation value, and d is the index of the pixel; Through the brightness error detection algorithm on the edge computing device, the brightness error between each pixel and the adjacent pixel is calculated to determine the brightness gradient error of the LED display. The expression is: ; in, Represents the brightness gradient error between the dth pixel and the previous pixel, Indicates the brightness of the d-1th pixel.

[0012] As a preferred solution of the method for determining the brightness gradient error of the LED display screen of the present invention, the specific steps of evaluating the detection results and making corrections are as follows: Define a maximum allowed brightness gradient error , by comparing the brightness gradient error of each pixel The maximum allowed brightness gradient error The size of , evaluates the brightness gradient error; when ≤ When , it means that the brightness gradient error of the current pixel is within the normal range and no correction is required; when > When , it means that the brightness gradient error of the current pixel exceeds the normal range and needs to be corrected. The brightness value of the current pixel should be adjusted to reduce the brightness gradient error. The expression is: ; in, is the brightness value of the d-th pixel after correction, is the brightness error of the dth pixel, k is the brightness amplitude correction coefficient; According to the corrected brightness value ,Through the correction algorithm on the edge computing device, the LED driving signal corresponding to each pixel is adjusted to correct the brightness gradient error.

[0013] In a second aspect, the present invention provides a brightness gradient error determination system for an LED display screen, including a data acquisition and preprocessing module, a brightness prediction and adjustment module, an edge computing device deployment module, and a brightness gradient error detection and correction module; The data acquisition and preprocessing module is used to collect the real-time image of the ambient light intensity and the LED display screen, and preprocess the collected ambient light data and image data; The brightness prediction and adjustment module is used to predict the optimal brightness value of the LED display under different ambient light intensities, and to make preliminary adjustments to the brightness of the LED display based on the predicted optimal brightness value; The edge computing device deployment module is used to deploy the brightness error detection and correction algorithm on the edge computing device; The brightness gradient error detection and correction module is used to detect and correct the brightness gradient error of the adjusted LED display using the brightness error detection algorithm deployed on the edge computing device.

[0014] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for determining the brightness gradient error of an LED display screen as described in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for determining the brightness gradient error of an LED display screen as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: by high-quality preprocessing of ambient light intensity and real-time image data of an LED display screen, the accuracy of input data of a subsequent brightness prediction model is ensured; an adaptive brightness prediction model including a convolutional neural network and a fully connected layer is constructed to provide the most suitable brightness setting for the LED display screen; subsequently, by evaluating the predicted optimal brightness value and preliminarily adjusting the brightness of the LED display screen, a dynamic brightness adjustment function based on the predicted brightness value is realized, thereby improving the visual comfort and energy efficiency of the display screen in different environments; a brightness error detection and correction algorithm is deployed on an edge computing device, thereby realizing real-time detection and correction of the brightness gradient error of the adjusted LED display screen, thereby significantly improving the overall display quality of the LED display screen, avoiding visual discomfort caused by local overbrightness or overdarkness, and enhancing the realism and viewing experience of the displayed content. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a flow chart of the method for determining the brightness gradient error of the LED display screen in Example 1; Figure 2 This is a flow chart of the brightness gradient error determination system of the LED display screen in Example 1. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for determining the brightness gradient error of an LED display screen, comprising the following steps: S1. Collect the ambient light intensity and the real-time image of the LED display screen and perform preprocessing.

[0023] Furthermore, an ambient light sensor is used to detect the ambient light intensity; Use high-precision cameras to capture real-time images of LED displays; Standardize the ambient light intensity data to eliminate the dimension effect so that different ambient light intensity data can be compared on the same scale, and perform smoothing and filtering on the ambient light intensity data to remove noise interference and improve data quality; It should be noted that the standardization process specifically requires first calculating the mean and standard deviation of the ambient light intensity data, and then, for each piece of ambient light intensity data, subtracting the mean and dividing it by the standard deviation to obtain the standardized data. This process can eliminate the dimensionality effect in the original data and convert the data into a relatively uniform range, which is convenient for subsequent model training and analysis. Specifically, when using moving average filtering, you can select a window size and then calculate the average value of all data in the window as the new data value of the center point. This process can effectively reduce the volatility of the data, reduce the impact of noise, and make the ambient light intensity data smoother and more stable; Perform denoising, color correction, image enhancement and size normalization on real-time image data to improve image quality and accuracy of subsequent processing; It should also be noted that image denoising: median filtering is used to remove random noise in the image.

[0024] Color Correction: Perform color correction on images to ensure the true color reproduction of the image.

[0025] Image enhancement: Improve image quality by performing contrast enhancement, brightness adjustment and other processing on the image.

[0026] Size Normalization: Resize all images to the same size to facilitate subsequent processing.

[0027] S2. Based on the preprocessed ambient light intensity and real-time image data, an adaptive brightness prediction model is constructed to predict the optimal brightness value of the LED display under different ambient light intensities.

[0028] Furthermore, based on the preprocessed ambient light intensity and real-time image data, an adaptive brightness prediction model is constructed, including a convolutional neural network for processing image data and a fully connected layer for processing ambient light intensity data; The preprocessed ambient light intensity data and real-time image data are input into the adaptive brightness prediction model for training, the model parameters are optimized, and the prediction accuracy is improved. The specific process is as follows: the preprocessed ambient light intensity data and real-time image data are input into the pre-built adaptive brightness prediction model; next, the model parameters are optimized using the training data set to improve the accuracy of the model in predicting the optimal brightness value; finally, by continuously adjusting and optimizing the model parameters, it is ensured that the model can accurately predict the optimal brightness value of the LED display under different ambient light intensities.

[0029] The mean square error is used during training to measure the difference between the predicted brightness value and the actual brightness value. The expression is: ; in, is the brightness value predicted for the e-th sample, is the true brightness value of the e-th sample, r is the number of samples, and i is the sample index; Define a comprehensive brightness evaluation function based on the preprocessed ambient light intensity data and real-time image data , comprehensively evaluate the ambient light intensity and image data, and predict the optimal brightness value, the expression is: ; in, Represents the optimal brightness value predicted under given ambient light intensity and image data conditions, is the i-th data point in the ambient light intensity data, is real-time image data, is the ambient light intensity data The weight coefficient of Is the image data The weight coefficient of is the ambient light intensity The nonlinear transformation function is Is the image data The nonlinear transformation function is: n is the length of the ambient light intensity data, m is the length of the image data, i is the index of the ambient light intensity data, j is the index of the image data, It is the adjustment factor to prevent the predicted brightness value from being too low. is an adjustment factor to prevent the predicted brightness value from being too high, is the comprehensive brightness evaluation function; It should be noted that the sigmoid function is used as the nonlinear transformation function because the sigmoid function can map the input to the interval [0,1], which is suitable for brightness prediction. The expression is: ; ; in, is the slope coefficient of the ambient light intensity data, is the slope coefficient of the image data, is the mean value of the ambient light intensity data; S3. Evaluate the predicted optimal brightness value and preliminarily adjust the brightness of the LED display screen according to the evaluation result.

[0030] Furthermore, a brightness adaptation threshold T is defined, and the adaptation degree of the current optimal brightness value is evaluated by comparing the predicted optimal brightness value B with the brightness adaptation threshold T; When B<T, it means that the current brightness is too low. According to the predicted brightness value B, the brightness of the LED display should be appropriately reduced to improve the adaptability of the LED display brightness to the current ambient light intensity. When B>T, it means that the current brightness is too high. The brightness of the LED display should be appropriately increased according to the predicted brightness value B to improve the adaptability of the LED display brightness to the current ambient light intensity. It should also be noted that the selection of the threshold T can be adjusted according to actual conditions to adapt to different application scenarios. For example, if you want to maintain a lower brightness in a darker environment, you can set T to be lower; if you want to maintain a higher brightness in a brighter environment, you can set T to be higher. S4. Deploy the brightness error detection and correction algorithm on the edge computing device, and collect the adjusted LED display image data in real time.

[0031] Furthermore, an embedded computer is selected as the edge computing device, and a brightness error detection and correction algorithm is deployed on the embedded computer; A high-precision camera is used to collect the LED display image data in real time after preliminary adjustment, and the real-time image data is denoised, color corrected, enhanced and sized to improve the accuracy of subsequent processing.

[0032] It should be noted that the embedded computer was chosen as the edge computing device because of its real-time processing capability, low power consumption, compact size, high reliability and cost-effectiveness, making it very suitable for real-time monitoring and adjustment of the brightness of the LED display.

[0033] Deploying the brightness error detection and correction algorithm on the embedded computer specifically includes: installing the necessary software environment in the embedded computer, then porting the brightness error detection and correction algorithm to the embedded computer, and compiling and testing it to ensure that the algorithm can run stably on the platform; Error detection is used to calculate the brightness gradient error of the LED display screen based on the adjusted LED display screen image data collected in real time; The brightness correction algorithm is used to make corrections based on the detected brightness error; S5. Calculate the brightness gradient error of the adjusted LED display screen through the brightness error detection algorithm on the edge computing device, evaluate the detection results and make corrections.

[0034] Furthermore, the difference between the actual brightness value of each pixel in each LED display and the predicted optimal brightness value is calculated, and the expression is: ; in, Represents the error between the brightness of the d-th pixel after adjustment and the predicted brightness value, Indicates the actual brightness of the d-th pixel, is the predicted optimal brightness value, The adjustment factor between brightness and color temperature difference, is the color saturation of the d-th pixel, is the predicted optimal color saturation value, and d is the index of the pixel; Through the brightness error detection algorithm on the edge computing device, the brightness error between each pixel and the adjacent pixel is calculated to determine the brightness gradient error of the LED display. The expression is: ; in, Represents the brightness gradient error between the dth pixel and the previous pixel, Indicates the brightness of the pixel at d-1; Define a maximum allowed brightness gradient error , by comparing the brightness gradient error of each pixel The maximum allowed brightness gradient error The size of , evaluates the brightness gradient error; It should be noted that The definition of is determined through user surveys and feedback to understand the user's acceptance of brightness changes; when ≤ When , it means that the brightness gradient error of the current pixel is within the normal range and no correction is required; when > When , it means that the brightness gradient error of the current pixel exceeds the normal range and needs to be corrected. The brightness value of the current pixel should be adjusted to reduce the brightness gradient error. The expression is: ; in, is the brightness value of the d-th pixel after correction, is the brightness error of the dth pixel, k is the brightness amplitude correction coefficient; According to the corrected brightness value ,Through the correction algorithm on the edge computing device, the LED driving signal corresponding to each pixel is adjusted to correct the brightness gradient error.

[0035] It should also be noted that the brightness gradient error detection algorithm specifically calculates the difference between the actual brightness value of each pixel and the predicted optimal brightness setting value by analyzing the image data collected in real time. , and then determine the brightness error of each pixel. In addition, the algorithm also calculates the brightness error difference between adjacent pixels , to determine the brightness gradient error. Through these calculations, the system can promptly detect and identify the brightness inconsistency problems on the display.

[0036] Brightness gradient error correction algorithm Specifically, this algorithm is based on the brightness error of each pixel and brightness gradient error , calculate the correction The brightness value of each pixel is corrected by adjusting the LED drive signal to reduce the brightness error and brightness gradient error. The corrected brightness value It will be used to update the display status of the LED display to ensure that the brightness of the display is more uniform and accurate.

[0037] This embodiment also provides a brightness gradient error determination system for an LED display screen, including: a data acquisition and preprocessing module, a brightness prediction and adjustment module, an edge computing device deployment module, and a brightness gradient error detection and correction module; The data acquisition and preprocessing module is used to collect the real-time image of the ambient light intensity and the LED display screen, and preprocess the collected ambient light data and image data; The brightness prediction and adjustment module is used to predict the optimal brightness value of the LED display under different ambient light intensities, and to make preliminary adjustments to the brightness of the LED display based on the predicted optimal brightness value; The edge computing device deployment module is used to deploy the brightness error detection and correction algorithm on the edge computing device; The brightness gradient error detection and correction module is used to detect and correct the brightness gradient error of the adjusted LED display using the brightness error detection algorithm deployed on the edge computing device.

[0038] This embodiment also provides a computer device, which is suitable for the case of a method for determining a brightness gradient error of an LED display screen, comprising: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the method for determining a brightness gradient error of an LED display screen proposed in the above embodiment.

[0039] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0040] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for determining the brightness gradient error of an LED display screen proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable red-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a disk or an optical disk.

[0041] In summary, the present invention ensures the quality of the subsequent brightness prediction model input data by: preprocessing the ambient light intensity and the real-time image data of the LED display screen with high quality. An adaptive brightness prediction model including a convolutional neural network and a fully connected layer is constructed to realize the function of predicting the optimal brightness value according to different ambient light intensities and image contents. By evaluating the predicted optimal brightness value and preliminarily adjusting the brightness of the LED display screen according to the evaluation result, a dynamic brightness adjustment function based on the predicted brightness value is realized. The brightness error detection and correction algorithm is deployed on the edge computing device, and the adjusted LED display screen image data is collected in real time, so that the real-time detection and correction of the brightness gradient error of the adjusted LED display screen is realized, which effectively improves the display effect of the LED display screen under different ambient light conditions, avoids the visual discomfort caused by local over-brightness or over-darkness, and significantly enhances the realism and viewing of the displayed content.

[0042] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of a method for determining the brightness gradient error of an LED display screen is provided.

[0043] An ambient light sensor was used to detect the ambient light intensity, and a high-precision camera was used to capture real-time images of the LED display. The laboratory simulated different light intensities from early morning to night, ranging from 100 lux to 1000 lux. In addition, 100 sets of ambient light intensity data were collected and 100 real-time images of the LED display were captured.

[0044] First, a laboratory environment was set up, using an ambient light sensor to detect the ambient light intensity, and a high-precision camera to capture real-time images of the LED display; Secondly, the collected ambient light intensity data and real-time image data were preprocessed, including standardization and smoothing filtering, as well as denoising, color correction, image enhancement and size standardization operations; Next, an adaptive brightness prediction model was constructed based on the preprocessed data, and model training and evaluation were performed; Finally, a brightness error detection and correction algorithm was deployed on the edge computing device to collect the adjusted LED display image data in real time, and detect and correct the brightness gradient error.

[0045] The details are shown in Table 1: Table 1 LED display brightness adjustment experimental data table

[0046] By analyzing the data in the above table, it is clear that the method of the present invention is effective and superior. The present invention significantly reduces the problem of uneven local brightness by real-time detection and correction of brightness gradient errors. For example, in the early morning environment, the brightness gradient error is only 2 cd / m², which is much lower than the maximum allowable error of 5 cd / m². This shows that the present invention can effectively reduce the brightness gradient error and improve the realism and viewing quality of the displayed content.

[0047] The brightness gradient error determination method of the LED display screen of the present invention can not only effectively reduce the phenomenon of uneven local brightness, but also maintain a stable display effect under different light intensities. Especially when the ambient light intensity changes greatly, such as from 100 lux in the early morning to 800 lux at noon, the present invention can still maintain a low brightness gradient error, which reflects the significant advantages of the present invention in terms of brightness prediction accuracy, brightness gradient error detection and correction, etc., and can effectively improve the display effect and user experience of the LED display screen under different ambient light conditions, and has high practical value.

[0048] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for determining brightness gradient error of an LED display screen, characterized in that: include, Collect the real-time images of ambient light intensity and LED display screen and perform pre-processing; Based on the pre-processed ambient light intensity and real-time image data, an adaptive brightness prediction model is constructed to predict the optimal brightness value of the LED display under different ambient light intensities; Evaluate the predicted optimal brightness value and preliminarily adjust the brightness of the LED display screen based on the evaluation results; Deploy brightness error detection and correction algorithms on edge computing devices and collect adjusted LED display image data in real time; Through brightness error detection on the edge computing device, the brightness gradient error of the adjusted LED display is detected, the detection results are evaluated and corrected.

2. The method for determining the brightness gradient error of an LED display screen according to claim 1, wherein: The specific steps of collecting the ambient light intensity and the real-time image of the LED display screen and preprocessing are as follows: Use ambient light sensor to detect the surrounding light intensity; Use high-precision cameras to capture real-time images of LED displays; Standardize the ambient light intensity data and perform smoothing filtering on the ambient light intensity data; Perform denoising, color correction, image enhancement, and size normalization on real-time image data.

3. The method for determining the brightness gradient error of an LED display screen as claimed in claim 2, characterized in that: Based on the pre-processed ambient light intensity and real-time image data, an adaptive brightness prediction model is constructed to predict the optimal brightness value of the LED display screen under different ambient light intensities. The specific steps are as follows: Based on the preprocessed ambient light intensity and real-time image data, an adaptive brightness prediction model consisting of a convolutional neural network and a fully connected layer is constructed; The pre-processed ambient light intensity data and real-time image data are input into the adaptive brightness prediction model for training and optimization of model parameters; The mean square error is used during training to measure the difference between the predicted brightness and the actual brightness; Define a comprehensive brightness evaluation function based on the preprocessed ambient light intensity data and real-time image data , comprehensively evaluate the ambient light intensity and image data, and predict the optimal brightness value, the expression is: ； in, Represents the optimal brightness value predicted under given ambient light intensity and image data conditions, is the i-th data point in the ambient light intensity data, is real-time image data, is the ambient light intensity data The weight coefficient of Is the image data The weight coefficient of is the ambient light intensity The nonlinear transformation function is Is the image data The nonlinear transformation function is: n is the length of the ambient light intensity data, m is the length of the image data, i is the index of the ambient light intensity data, j is the index of the image data, It is the adjustment factor to prevent the predicted brightness value from being too low. is an adjustment factor to prevent the predicted brightness value from being too high, is the comprehensive brightness evaluation function.

4. The method for determining brightness gradient error of an LED display screen as claimed in claim 3, characterized in that: The predicted optimal brightness value is evaluated, and the brightness of the LED display is preliminarily adjusted according to the evaluation result. The specific steps are as follows: Define a brightness adaptation threshold T, and evaluate the adaptability of the current optimal brightness value by comparing the predicted optimal brightness value B with the brightness adaptation threshold T; When B<T, it means that the current brightness is too low, and the brightness of the LED display should be reduced according to the predicted brightness value B; When B>T, it means that the current brightness is too high, and the brightness of the LED display should be increased according to the predicted brightness value B.

5. The method for determining brightness gradient error of an LED display screen as claimed in claim 4, characterized in that: The brightness error detection and correction algorithm is deployed on the edge computing device, and the adjusted LED display image data is collected in real time. The specific steps are as follows: Select an embedded computer as the edge computing device and deploy a brightness error detection and correction algorithm on the embedded computer; A high-precision camera is used to collect the LED display image data in real time after preliminary adjustment, and the real-time image data is denoised, color corrected, enhanced and size standardized.

6. The method for determining brightness gradient error of an LED display screen as claimed in claim 5, characterized in that: The brightness gradient error of the LED display screen after adjustment is calculated by the brightness error detection algorithm on the edge computing device. The specific steps are as follows: Calculate the difference between the actual brightness value of each pixel in each LED display and the predicted optimal brightness value. The expression is: ； in, Represents the error between the brightness of the d-th pixel after adjustment and the predicted brightness value, Indicates the actual brightness of the d-th pixel, is the predicted optimal brightness value, The adjustment factor between brightness and color temperature difference, is the color saturation of the d-th pixel, is the predicted optimal color saturation value, and d is the index of the pixel; Through the brightness error detection algorithm on the edge computing device, the brightness error between each pixel and the adjacent pixel is calculated to determine the brightness gradient error of the LED display. The expression is: ； in, Represents the brightness gradient error between the dth pixel and the previous pixel, Indicates the brightness of the d-1th pixel.

7. The method for determining brightness gradient error of an LED display screen as claimed in claim 6, characterized in that: The specific steps of evaluating the test results and making corrections are as follows: Define a maximum allowed brightness gradient error , by comparing the brightness gradient error of each pixel The maximum allowed brightness gradient error The size of , evaluates the brightness gradient error; when ≤ When , it means that the brightness gradient error of the current pixel is within the normal range and no correction is required; when > When , it means that the brightness gradient error of the current pixel exceeds the normal range and needs to be corrected. The brightness value of the current pixel should be adjusted to reduce the brightness gradient error. The expression is: ； in, is the brightness value of the d-th pixel after correction, is the brightness error of the dth pixel, k is the brightness amplitude correction coefficient; According to the corrected brightness value ,Through the correction algorithm on the edge computing device, the LED driving signal corresponding to each pixel is adjusted to correct the brightness gradient error.

8. A system for determining brightness gradient error of an LED display screen, based on the method for determining brightness gradient error of an LED display screen according to any one of claims 1 to 7, characterized in that: Including data acquisition and preprocessing module, brightness prediction and adjustment module, edge computing device deployment module and brightness gradient error detection and correction module; The data acquisition and preprocessing module is used to collect the real-time image of the ambient light intensity and the LED display screen, and preprocess the collected ambient light data and image data; The brightness prediction and adjustment module is used to predict the optimal brightness value of the LED display under different ambient light intensities, and to make preliminary adjustments to the brightness of the LED display based on the predicted optimal brightness value; The edge computing device deployment module is used to deploy the brightness error detection and correction algorithm on the edge computing device; The brightness gradient error detection and correction module is used to detect and correct the brightness gradient error of the adjusted LED display using the brightness error detection algorithm deployed on the edge computing device.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for determining the brightness gradient error of the LED display screen described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for determining the brightness gradient error of an LED display screen according to any one of claims 1 to 7 are implemented.

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