Anomaly detection method, device and equipment of display system and storage medium

Abstract

The application is suitable for the technical field of display, and provides an abnormality detection method, device and equipment of a display system and a storage medium. The method comprises the following steps: in response to a standard test target being received by a to-be-detected display system, output image data of a first preset frame number output by the to-be-detected display system is acquired; the output image data is a continuous grid image after gamma correction processing. According to a current energy value of each grid in the output image data and a reference energy value corresponding to each grid, an energy difference value of each grid is calculated; the current energy value is determined based on all pixel values in each grid, and the reference energy value is determined based on the standard test target and a normal display system. If the energy difference value of the grid in the output image data of a second preset frame number is greater than a preset adaptive threshold value, it is determined that the to-be-detected display system has a real gamma input value abnormality. The gamma input value abnormality is quickly detected and positioned.

Description

Technical Field

[0001] This application belongs to the field of display technology, and in particular relates to a method, apparatus, device and storage medium for detecting anomalies in a display system. Background Technology

[0002] In display systems, gamma correction compensates for the non-linear response characteristics of display devices through non-linear functions, enabling input signals to be presented according to the expected brightness relationship.

[0003] When the display system starts up, the Gamma lookup table or calibration curve may fail to load randomly due to software vulnerabilities, memory errors, data loading conflicts, or other reasons. This failure is unpredictable and difficult to reproduce. Gamma loading failure usually manifests as errors at one or a few data points on the curve, rather than the entire curve failing, causing a specific input brightness to be mapped to an incorrect output brightness. Existing detection methods struggle to capture and reproduce this random anomaly, lacking effective automated detection tools, which greatly hinders problem localization and debugging. Moreover, existing detection methods typically only detect overall system display anomalies, failing to pinpoint the specific Gamma input value, further increasing the difficulty of debugging. Summary of the Invention

[0004] This application provides an anomaly detection method, apparatus, device, and storage medium for a display system, which can solve the technical problems of detection difficulties and low positioning accuracy of abnormal pixels in the gamma correction anomaly detection process of display systems in the prior art.

[0005] In a first aspect, embodiments of this application provide an anomaly detection method for a display system, the method comprising:

[0006] In response to the display system under test receiving a standard test target image, the system acquires output image data of a first preset number of frames from the display system under test; wherein the output image data is a continuous grid image after gamma correction processing;

[0007] The energy difference of each grid is calculated based on the current energy value of each grid in the output image data and the reference energy value corresponding to each grid; wherein, the current energy value is determined based on all pixel values ​​in each grid, and the reference energy value is determined based on the standard test target image and the normal display system;

[0008] If the energy difference of the grid in the output image data for a consecutive second preset frame number is greater than a preset adaptive threshold, it is determined that the display system to be detected has an abnormal real gamma input value; wherein, the preset adaptive threshold is determined based on the standard deviation of the reference energy value corresponding to the grid and a preset confidence factor, and the second preset frame number is less than or equal to the first preset frame number.

[0009] In one possible implementation of the first aspect, the process of creating the standard test target map includes:

[0010] Based on the resolution and bit depth of the display system to be tested, an initial test image is created, and the total number of grid cells in the initial test image is determined;

[0011] Based on the total number of grid cells, the initial test image is divided into a grid with a preset number of rows and a preset number of columns; wherein, the product of the preset number of rows and the preset number of columns is the total number of grid cells;

[0012] Based on the index number of each grid, grayscale values ​​are filled into each grid in the initial test image according to the raster scan order to obtain the standard test target image.

[0013] In one possible implementation of the first aspect, the process of determining the reference energy value includes:

[0014] The standard test target image is input into the normal display system to obtain normal output image data after gamma correction processing;

[0015] The reference energy value of each grid is calculated based on all pixel values ​​within each grid in the normal output image data.

[0016] In one possible implementation of the first aspect, after determining that the display system to be detected has an abnormal real gamma input value, the method includes:

[0017] Based on the index number of the grid in the output image data where there is an abnormal gamma input value, the corresponding gamma input value is located, and abnormal location information is generated.

[0018] In one possible implementation of the first aspect, after determining that the display system to be detected has an abnormal real gamma input value, the method includes:

[0019] Acquire and record the current operating status data of the display system under test;

[0020] Based on the current operating status data, the reasons for the abnormal real gamma input value of the display system under test are analyzed, and the cause analysis results are obtained.

[0021] In one possible implementation of the first aspect, when the display system to be detected is an RGB display system, the method includes:

[0022] The standard test target image is input into the RGB display system, and the first output image data of the red channel, the second output image data of the green channel, and the third output image data of the blue channel are extracted respectively.

[0023] The gamma input values ​​corresponding to the grids in the first output image data, the second output image data, and the third output image data are respectively checked for abnormalities to obtain a first detection result corresponding to the first output image data, a second detection result corresponding to the second output image data, and a third detection result corresponding to the third output image data.

[0024] The detection results of the RGB display system are obtained based on the first detection result, the second detection result, and the third detection result.

[0025] Secondly, embodiments of this application provide an anomaly detection device for a display system, comprising:

[0026] The acquisition module is used to acquire output image data of a first preset number of frames output by the display system under test in response to the display system under test receiving a standard test target image; wherein the output image data is a continuous grid image after gamma correction processing;

[0027] The calculation module is used to calculate the energy difference of each grid based on the current energy value of each grid in the output image data and the reference energy value corresponding to each grid; wherein, the current energy value is determined based on all pixel values ​​in each grid, and the reference energy value is determined based on the standard test target image and the normal display system;

[0028] The determination module is used to determine that the display system under test has an abnormal real gamma input value if the energy difference of the grid in the output image data of a second consecutive preset frame number is greater than a preset adaptive threshold; wherein the preset adaptive threshold is determined based on the standard deviation of the reference energy value corresponding to the grid and a preset confidence factor, and the second preset frame number is less than or equal to the first preset frame number.

[0029] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the abnormal detection method of the display system described in any of the above claims.

[0030] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the anomaly detection method for the display system described in any of the preceding claims.

[0031] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the abnormality detection method of the display system described in any of the first aspects.

[0032] The beneficial effects of the embodiments in this application compared with the prior art are:

[0033] This application provides an anomaly detection method for a display system. The method includes: First, in response to the display system under test receiving a standard test target image, acquiring output image data of a first preset number of frames from the display system under test; wherein the output image data is a continuous grid image after gamma correction processing. Then, calculating the energy difference of each grid based on the current energy value of each grid in the output image data and the reference energy value corresponding to each grid; wherein the current energy value is determined based on all pixel values ​​within each grid, and the reference energy value is determined based on the standard test target image and a normal display system. Finally, if the energy difference of the grids in the output image data of a second preset number of consecutive frames is greater than a preset adaptive threshold, it is determined that the display system under test has an abnormal true gamma input value; wherein the preset adaptive threshold is determined based on the standard deviation of the reference energy value corresponding to the grid and a preset confidence factor, and the second preset number of frames is less than or equal to the first preset number of frames. This method uses a standard test target image as input image to calculate the energy difference between the output image data of the display system under test and the output image of a normal system. This quantifies the performance deviation of the display system under test in each grid region and determines whether the display system under test has abnormal gamma input values. It achieves rapid detection and localization of abnormal gamma input values. Furthermore, by employing a multi-frame verification mechanism and an adaptive threshold algorithm, it effectively reduces the false alarm rate and improves detection accuracy. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating an anomaly detection method for a display system according to an embodiment of this application;

[0036] Figure 2 This is a schematic diagram illustrating the principle of an anomaly detection method for a display system provided in an embodiment of this application;

[0037] Figure 3 This is a schematic diagram of the structure of an anomaly detection device for a display system provided in an embodiment of this application;

[0038] Figure 4 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0039] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0040] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0041] It should also be 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.

[0042] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0043] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0044] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0045] In display systems, gamma correction is achieved through a nonlinear function (usually a power function). ,in, (Gamma value) to compensate for the non-linear response characteristics of the display device, so that the input signal can be presented according to the expected brightness relationship.

[0046] When the display system starts up, the Gamma lookup table or calibration curve may fail to load randomly due to software vulnerabilities, memory errors, data loading conflicts, or other reasons. This failure is unpredictable and difficult to reproduce. Gamma loading failure usually manifests as errors at one or a few data points on the curve, rather than the entire curve failing, causing a specific input brightness to be mapped to an incorrect output brightness. Existing detection methods struggle to capture and reproduce this random anomaly, lacking effective automated detection tools, which greatly hinders problem localization and debugging. Moreover, existing detection methods typically only detect overall system display anomalies, failing to pinpoint the specific Gamma input value, further increasing the difficulty of debugging.

[0047] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating an anomaly detection method for a display system according to an embodiment of this application. As an example and not a limitation, this method is applied to or operates in a terminal device for the automatic detection of gamma correction anomalies in the display system. The method includes:

[0048] S11. In response to the display system under test receiving the standard test target image, acquire the output image data of the first preset number of frames output by the display system under test. The output image data is a continuous grid image after gamma correction processing.

[0049] S12. Calculate the energy difference for each grid based on the current energy value and the reference energy value for each grid in the output image data. The current energy value is determined based on all pixel values ​​within each grid, and the reference energy value is determined based on the standard test target image and the normal display system.

[0050] S13. If the energy difference of the grids in the output image data of a second consecutive preset frame number is greater than a preset adaptive threshold, then it is determined that the display system to be detected has an abnormal real gamma input value. The preset adaptive threshold is determined based on the standard deviation of the reference energy value corresponding to the grid and a preset confidence factor, and the second preset frame number is less than or equal to the first preset frame number.

[0051] Gamma correction anomaly refers to the phenomenon where the brightness, contrast, or color performance of an image does not match expectations due to incorrect parameter settings, algorithm defects, or equipment malfunctions during the gamma correction process.

[0052] In this embodiment, before detecting gamma correction anomalies in the display system under test, a standard test target image and a benchmark database need to be established. The display system under test is the display device to be tested, such as a monitor, television, or projector. The standard test target image is a pre-designed image with specific patterns and parameters. The standard test target image serves as a benchmark for testing the performance of the display system, allowing for accurate evaluation of whether the display system's output meets expectations during the testing process. The benchmark database is a set of data on the images output after inputting the standard test target image into a normal display system, and may include the energy value, brightness, color, etc., of each grid.

[0053] In one possible implementation, the process of creating a standard test target map includes:

[0054] Based on the resolution and bit depth of the display system to be tested, an initial test image is created, and the total number of grid cells in the initial test image is determined.

[0055] Based on the total number of grid cells, the initial test image is divided into a grid with a preset number of rows and columns; where the product of the preset number of rows and columns is the total number of grid cells.

[0056] Based on the index number of each grid, grayscale values ​​are filled into each grid in the initial test image according to the raster scanning order to obtain a standard test target image.

[0057] First, create a resolution matching the display system to be tested (resolution is W×H, e.g., ...). A completely identical initial test image is used. This initial test image is divided into multiple grids, which are small regions, each containing pixels with a specific set of data. By analyzing each grid, the performance of the display system in different regions can be examined more precisely, improving the accuracy of anomaly detection.

[0058] Next, the total number of grid cells in the initial test image is determined based on the bit depth of the display system to be tested. Where B represents the bit depth of the display system under test. Bit depth is the number of binary bits used to describe the color information stored in each pixel. Bit depth directly determines the number of colors and grayscale precision that an image or display system can represent. For example, an 8-bit bit depth can display... Gray level.

[0059] Then, the initial test image is uniformly divided into a grid with a preset number of rows and columns, satisfying the following conditions: .

[0060] Finally, each grid is filled with grayscale values ​​according to the raster scan order to obtain the standard test target image. That is, grid i (the grid index number) is filled with grayscale value i, where... The raster scanning order is from left to right and from top to bottom. The grayscale value represents the brightness of the grid, and its range can be determined by the total number of grid cells.

[0061] For example, the bit depth of the display system to be tested is: The system resolution is W×H=1920×1080. Therefore, the total number of grid cells in the standard test target image is: The grid is divided into: The standard test target image is divided into 256 grids, filled with grayscale values ​​from 0 to 255 using raster scanning sequence. Each grid is 120 pixels × 67 pixels (rounded down). The index of each grid ranges from 0 to 255, and the pixel grayscale value of each grid corresponds to its index. It's also important to note that when dividing the grid, to visually approximate a square, a pair of factors closest to the square root is typically chosen (e.g., a 16×16 grid for N=256, a 32×32 grid for N=1024, and a 64×64 grid for N=4096). Figure 2 As shown, Figure 2 This is a schematic diagram illustrating the principle of an anomaly detection method for a display system according to an embodiment of this application. Figure 2 In the standard test target image, the grid division is similar to the grid of Go.

[0062] It should be noted that after generating the standard test target image, it is also necessary to establish a precise mapping relationship between the grid index number i and the image coordinate region. Specifically, this involves calculating the width and height of each grid cell, i.e. , This may require rounding. Indicates the width of the grid. The height of the grid is represented by W, the number of pixels in the horizontal direction of the display system to be tested is represented by H, the number of columns is represented by cols, and the number of rows is represented by rows.

[0063] For any grid index i, its row number and column number can be calculated, i.e. (Integer division) (Modular operation), where, Indicates the row number of the grid. This indicates the column number of the grid. The corresponding image coordinate region for that grid is represented as:

[0064] X-axis range: ;

[0065] Y-axis range: .

[0066] In some examples, a benchmark database needs to be established after the standard test target image is generated.

[0067] In one possible implementation, the process of determining the reference energy value includes:

[0068] The standard test target image is input into the normal display system to obtain the normal output image data after gamma correction.

[0069] The baseline energy value of each grid is calculated based on all pixel values ​​within each grid in the normal output image data.

[0070] Specifically, input the standard test target image into a normal display system (i.e., a system where Gamma loading is confirmed to be normal). Here, a normal display system can be understood as a display device that is in normal working condition.

[0071] After Gamma correction, normal output image data from a normal display system is captured, and frame buffer data is directly obtained. Then, the reference energy value of grid i in each normal output image data is calculated. That is, the sum of the pixel values ​​(grayscale values) of all pixels within grid i. Where (x, y) are pixels within the image region corresponding to grid i, x represents the x-coordinate of the pixel within the image region corresponding to grid i, y represents the y-coordinate of the pixel within the image region corresponding to grid i, and i represents the grid index. This represents the number of pixels within the image region corresponding to grid i in the normal output image data. In this embodiment, the pixel value (grayscale value) is... Additionally, it can calculate the statistical characteristics of the baseline energy value, including the mean. and standard deviation The standard deviation reflects the normal fluctuation range of the grid's energy values ​​(e.g., due to noise). An array of reference energy values... The statistical characteristics of these characteristics serve as the benchmark for detection, thereby obtaining a benchmark database.

[0072] It should be noted that this application embodiment also includes a dynamic benchmark update mechanism, which can automatically update the benchmark data in the benchmark database when the display system is confirmed to be normal.

[0073] In some examples, after generating standard test target images and benchmark databases, real-time anomaly detection methods from the display system can be used, specifically:

[0074] First, a standard test target image is input into the display system under test, and then multiple frames (i.e., the first preset number of frames) of output image data are obtained after Gamma correction in the display system under test. At this time, the output image data is a continuous grid image. The first preset number of frames M is a pre-set number of continuous frames.

[0075] Then, the current energy value of each grid in the output image data is calculated. The current energy value reflects the energy characteristics of the image within that grid. The energy difference for each grid is calculated by subtracting the current energy value of the corresponding grid in the benchmark database from the current energy value of that grid. ,in, Indicates the energy difference. This represents the baseline energy value. It should be understood that gridded analysis allows a global problem to be decomposed into local problems, enabling localization to specific grid regions.

[0076] Finally, the presence of anomalies in each grid is determined based on the energy difference of each grid and a preset adaptive threshold. The preset adaptive threshold is a dynamically adjusted threshold set according to the grid index number, used to determine whether the energy difference of the grids exceeds the normal range. Let the preset adaptive threshold be... , ; As the confidence factor, This represents the standard deviation of the baseline energy values. The confidence factor k can be set based on statistical principles (such as the normal distribution). Typically, k=3 is a commonly used standard, indicating that if the energy difference... Exceeding the normal fluctuation range (e.g.) If 99.7% of the noise level is detected, it is considered not random noise but a systematic anomaly, thus achieving a balance between detection sensitivity and false alarm rate. In this embodiment, the k value can be adjusted according to the actual system noise level. It should be understood that by setting a dynamic preset adaptive threshold, it is possible to adapt to the energy fluctuation range of different grids and avoid misjudgments in low signal-to-noise ratio scenarios with a fixed threshold.

[0077] The second preset frame number N is the number of consecutive frames set to determine whether the display system under test has an abnormal true gamma input value. In this case, the second preset frame number is less than or equal to the first preset frame number (N≤M). In this embodiment, the first preset frame number M≥3 and the second preset frame number N≥2 can be set. An abnormal gamma input value refers to a situation where, during the process of receiving input signals and performing gamma correction, the gamma input value deviates from the normal value due to various reasons (such as hardware failure, software error, etc.), thus affecting the display effect.

[0078] Specifically, if the energy difference between the grids is greater than a preset adaptive threshold, then it is determined that the gamma input value corresponding to the grid in the output image data is abnormal, that is, if If so, it is determined that the gamma input value corresponding to grid i is abnormal, where, This represents the preset adaptive threshold, that is, the preset adaptive threshold corresponding to grid i is... .like Figure 2 In the process, after the standard test target image (N=256, using a 16×16 grid, grayscale value range 0~255, filled according to the raster scan order, i.e. grid number 0~255) is input into the 8-bit deep display system, the display system performs Gamma processing (lookup table conversion) on the standard test target image and outputs the detection results. Among them, four abnormal grids were detected, namely grid number 127, grid number 128, grid number 129 and grid number 200, and their positions were also marked. Figure 2 In this context, LUT stands for lookup table, which is a structure that pre-computes and stores data to quickly map input values ​​to output values. LUT[0] represents the initial version of the lookup table, and LUT

[255] represents the transformed lookup table.

[0079] If the gamma input value corresponding to the grid in the output image data of the second consecutive preset number of frames is abnormal, then it is determined that the display system under test has an abnormal real gamma input value. That is, in the output image data of M consecutively detected frames, it is determined that the grid appears in N consecutive frames. If this is the case, it can be determined that the display system under test has an abnormal real gamma input value. It should be understood that by employing a multi-frame verification mechanism, false judgments caused by transient interference can be eliminated, thereby improving detection robustness.

[0080] It is understood that this application provides an anomaly detection method for a display system. The method includes: first, in response to the display system under test receiving a standard test target image, acquiring output image data for a first preset number of frames from the display system under test; wherein the output image data is a continuous grid image after gamma correction processing. Then, calculating the energy difference for each grid based on the current energy value of each grid in the output image data and the reference energy value corresponding to each grid; wherein the current energy value is determined based on all pixel values ​​within each grid, and the reference energy value is determined based on the standard test target image and a normal display system. Finally, if the energy difference of the grids in the output image data for a second preset number of consecutive frames is greater than a preset adaptive threshold, it is determined that the display system under test has an abnormal real gamma input value; wherein the preset adaptive threshold is determined based on the standard deviation of the reference energy value corresponding to the grid and a preset confidence factor, and the second preset number of frames is less than or equal to the first preset number of frames. This method uses a standard test target image as input image to calculate the energy difference between the output image data of the display system under test and the output image of a normal system. This quantifies the performance deviation of the display system under test in each grid region and determines whether the display system under test has abnormal gamma input values. It achieves rapid detection and localization of abnormal gamma input values. Furthermore, by employing a multi-frame verification mechanism and an adaptive threshold algorithm, it effectively reduces the false alarm rate and improves detection accuracy.

[0081] In one possible implementation, after determining that the display system to be detected has an anomaly in the true gamma input value, the method includes:

[0082] Based on the index number of the grid with abnormal gamma input values ​​in the output image data, the corresponding gamma input values ​​are located, and abnormal location information is generated.

[0083] In some examples, if an abnormal gamma input value is determined in the display system under test, the corresponding gamma input value can be located based on the index number of the grid with the abnormal gamma value, triggering system status capture. Here, the index number is a unique identifier for each grid in the output image data. The gamma input value is the raw parameter value used for gamma correction in the display system, typically configured by hardware registers or set by software algorithms. The gamma input value controls the non-linear brightness transformation of the output image, directly affecting the display effect.

[0084] Specifically, the abnormal grid index number can be extracted from the grid set where the true gamma input value is confirmed to be abnormal; based on the abnormal grid index number, the grid region can be determined, and the gamma correction mapping table of the display system can be queried to obtain the gamma input value of the corresponding region.

[0085] It should be understood that the specific fault area can be located by the abnormal grid index number, thus realizing the location of abnormal gamma values ​​and improving detection accuracy.

[0086] In one possible implementation, after determining that the display system to be detected has an anomaly in the true gamma input value, the method includes:

[0087] Acquire and record the current operating status data of the display system to be tested.

[0088] Based on the current operating status data, the reasons for the abnormal real gamma input value of the system under test are analyzed, and the cause analysis results are obtained.

[0089] In some examples, when it is determined that the display system under test has an abnormal real gamma input value, the current operating status data of the display system under test can be obtained and recorded. This current operating status data is a collection of real-time operating parameters of the display system under test at the time the abnormality occurs, and may include hardware status, software configuration, environmental conditions, etc.

[0090] Then, based on the current operating status data, the reasons for the abnormal true gamma input value in the display system under test can be analyzed, thus obtaining the cause analysis results. For example, hardware fault diagnosis can be performed based on the hardware status, such as checking whether the gamma-related register values ​​in the driver chip are consistent with the design values; software defect diagnosis can be performed based on the software configuration, such as confirming whether the gamma parameters in the display settings file have been mistakenly modified; environmental interference can be eliminated based on environmental conditions, such as comparing the stability of gamma parameters at high temperatures and normal temperatures. The cause analysis results can provide long-term improvement suggestions for the future.

[0091] It should be understood that by acquiring the current operating status data of the display system and generating cause analysis results, the efficiency and accuracy of fault detection can be improved, and maintenance costs and the risk of misoperation can be reduced.

[0092] In one possible implementation, when the display system to be detected is an RGB display system, the method includes:

[0093] The standard test target image is input into the RGB display system, and the first output image data of the red channel, the second output image data of the green channel, and the third output image data of the blue channel are extracted respectively.

[0094] The gamma input values ​​corresponding to the grids in the first output image data, the second output image data, and the third output image data are respectively checked for abnormalities, and the first detection result, the second detection result, and the third detection result are obtained for the first output image data, the second output image data, and the third output image data.

[0095] Based on the first, second, and third test results, the test results of the RGB display system are obtained.

[0096] In some examples, when the display system to be tested is an RGB display system, the three color channels (red, green, and blue) of the RGB display system can be detected separately. Image data for the red, green, and blue channels are extracted separately, corresponding to the first, second, and third output image data. Then, anomaly detection is performed independently for each channel, that is, the gamma input values ​​corresponding to the grids in the first, second, and third output image data are checked for anomalies, yielding the corresponding first, second, and third detection results. Finally, the detection results of the three channels (i.e., the first, second, and third detection results) are comprehensively analyzed to obtain the detection result of the RGB display system. It should be understood that by detecting each channel independently, anomalies in the red, green, and blue channels can be located, avoiding false positives and thus improving detection accuracy.

[0097] It should be noted that, in this embodiment of the application, an environmental compensation mechanism can also be set up to further improve the detection accuracy by combining environmental sensor data for compensation analysis.

[0098] Compared with the prior art, the anomaly detection method for a display system provided in this application has the following significant advantages:

[0099] (1) Precise quantitative positioning. It can directly locate abnormal Gamma input values, narrowing the debugging scope from the entire system to specific data points, and the positioning accuracy can reach a single gray level.

[0100] (2) Fully automatic and unattended operation. It solves the problem of capturing random anomalies and realizes 24-hour uninterrupted automatic detection without human intervention.

[0101] (3) Wide adaptability. It can support display systems with different bit depths such as 8-bit, 10-bit, and 12-bit and various resolutions, and has good versatility.

[0102] (4) Strong engineering practicality. The algorithm has low complexity and small amount of computation, making it easy to implement in embedded systems. It has a fast detection speed, and 4K resolution can be detected in seconds.

[0103] (5) High reliability. By employing multi-frame verification mechanisms, adaptive threshold algorithms, or environmental compensation techniques, the false alarm rate can be effectively reduced and the detection accuracy improved.

[0104] (6) Strong robustness. Through noise suppression and outlier handling algorithms, the system’s resistance to environmental interference is enhanced.

[0105] (7) Good scalability. It can be extended to anomaly detection in other color processing modules, such as color space conversion and white balance correction.

[0106] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0107] Corresponding to the above embodiment of an anomaly detection method for a display system, Figure 3 This illustration shows a schematic diagram of an anomaly detection device for a display system according to an embodiment of this application. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0108] Reference Figure 3 The anomaly detection device 3 of the display system in this embodiment includes:

[0109] The acquisition module 31 is used to acquire the output image data of the first preset number of frames output by the display system under test in response to the display system under test receiving the standard test target image. The output image data is a continuous grid image after gamma correction processing.

[0110] The calculation module 32 is used to calculate the energy difference of each grid based on the current energy value of each grid in the output image data and the reference energy value corresponding to each grid. The current energy value is determined based on the pixel values ​​within each grid, and the reference energy value is determined based on the standard test target image and the normal display system.

[0111] The determination module 33 is used to determine that the display system under test has an abnormal real gamma input value if the energy difference of the grids in the output image data of a consecutive second preset frame number is greater than a preset adaptive threshold. The preset adaptive threshold is determined based on the standard deviation of the reference energy value corresponding to the grid and a preset confidence factor, and the second preset frame number is less than or equal to the first preset frame number.

[0112] Furthermore, the anomaly detection device 3 of the display system includes a standard target image generation module.

[0113] The standard target image generation module includes:

[0114] The initial image generation unit is used to create an initial test image based on the resolution and bit depth of the display system to be tested, and to determine the total number of grid cells in the initial test image.

[0115] A grid division unit is used to divide the initial test image into a grid with a preset number of rows and a preset number of columns based on the total number of grids; wherein the product of the preset number of rows and the preset number of columns is the total number of grids.

[0116] The target image generation unit is used to fill grayscale values ​​for each grid in the initial test image according to the index number of each grid and in the raster scanning order to obtain a standard test target image.

[0117] Furthermore, the anomaly detection device 3 of the display system includes a benchmark database generation module.

[0118] The benchmark database generation module includes:

[0119] The data output unit is used to input the standard test target image into the normal display system and obtain the normal output image data after gamma correction processing.

[0120] The reference value calculation unit is used to calculate the reference energy value of each grid based on all pixel values ​​in each grid in the normal output image data.

[0121] Furthermore, the anomaly detection device 3 of the display system includes:

[0122] The anomaly location information generation module is used to locate the corresponding gamma input value based on the index number of the grid with anomaly gamma input value in the output image data, and generate anomaly location information.

[0123] Furthermore, the anomaly detection device 3 of the display system includes:

[0124] The running status acquisition module is used to acquire and record the current running status data of the display system under test.

[0125] The cause analysis result generation module is used to analyze the reasons for the abnormal real gamma input value of the system under test based on the current operating status data, and obtain the cause analysis results.

[0126] Furthermore, the anomaly detection device 3 of the display system includes an RGB detection module.

[0127] The RGB detection module includes:

[0128] The RGB image output unit is used to input a standard test target image into the RGB display system and extract the first output image data of the red channel, the second output image data of the green channel, and the third output image data of the blue channel, respectively.

[0129] The RGB image detection unit is used to detect whether there are any abnormalities in the gamma input values ​​corresponding to the grids in the first output image data, the second output image data, and the third output image data, respectively, and to obtain the first detection result corresponding to the first output image data, the second detection result corresponding to the second output image data, and the third detection result corresponding to the third output image data.

[0130] The RGB result generation unit is used to obtain the detection results of the RGB display system based on the first detection result, the second detection result, and the third detection result.

[0131] It should be noted that the information interaction and execution process between the modules in the above-mentioned abnormality detection device 3 of the display system are based on the same concept as the method embodiment of this application. For details on their specific functions and the resulting technical effects, please refer to the method embodiment section, which will not be repeated here.

[0132] This application also provides a terminal device, such as... Figure 4 As shown, Figure 4 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. (Refer to...) Figure 4 The terminal device 4 in this embodiment includes a memory 41, a processor 42, and a computer program stored in the memory 41 and executable on the processor 42. When the processor 42 executes the computer program, it implements the steps in the abnormal detection method embodiment of the display system described above.

[0133] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the various method embodiments above.

[0134] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0135] 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 computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographic device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0136] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0137] 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, or a combination of computer software and electronic hardware. 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 implementation should not be considered beyond the scope of this application.

[0138] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0139] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0140] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An abnormality detection method of a display system, characterized by, The method comprises the following steps: In response to the standard test target received by the display system to be detected, the output image data of a first preset number of frames output by the display system to be detected is acquired; wherein the output image data is a continuous grid image after gamma correction processing; According to the current energy value of each grid in the output image data and the reference energy value corresponding to each grid, the energy difference value of each grid is calculated; wherein the current energy value is determined based on all pixel values in each grid, and the reference energy value is determined based on the standard test target and a normal display system; If the energy difference value of the grid in the output image data of a second preset number of continuous frames is greater than a preset adaptive threshold, it is determined that the display system to be detected has a real gamma input value abnormality; wherein the preset adaptive threshold is determined based on the standard deviation of the reference energy value corresponding to the grid and a preset confidence factor, and the second preset number of frames is less than or equal to the first preset number of frames.

2. The abnormality detection method of a display system according to claim 1, characterized by, The creation process of the standard test target comprises the following steps: According to the resolution and bit depth of the display system to be detected, an initial test image is created, and the total number of grids in the initial test image is determined; According to the total number of grids, the initial test image is divided into a preset number of rows and a preset number of columns of grids; wherein the product of the preset number of rows and the preset number of columns is the total number of grids; According to the index number of each grid, the gray value of each grid in the initial test image is filled in the raster scan order to obtain the standard test target.

3. The abnormality detection method of a display system according to claim 2, characterized in that, The determination process of the reference energy value comprises the following steps: The standard test target is input into the normal display system to obtain normal output image data after gamma correction processing; According to all pixel values in each grid in the normal output image data, the reference energy value of each grid is calculated.

4. The abnormality detection method of a display system according to claim 3, characterized in that, After it is determined that the display system to be detected has a real gamma input value abnormality, the method comprises the following steps: According to the index number of the grid with a gamma input value abnormality in the output image data, the corresponding gamma input value is located to generate abnormality positioning information.

5. The abnormality detection method of a display system according to claim 1, characterized in that, After it is determined that the display system to be detected has a real gamma input value abnormality, the method comprises the following steps: Current running state data of the display system to be detected is acquired and recorded; According to the current running state data, the cause of the real gamma input value abnormality of the display system to be detected is analyzed to obtain a cause analysis result.

6. The abnormality detection method of a display system according to claim 1, characterized by, In the case that the display system to be detected is an RGB display system, the method comprises the following steps: The standard test target is input into the RGB display system to extract first output image data of a red channel, second output image data of a green channel and third output image data of a blue channel respectively; Detect whether there is an abnormality in the gamma input value corresponding to the grid in the first output image data, the second output image data, and the third output image data, respectively, to obtain a first detection result corresponding to the first output image data, a second detection result corresponding to the second output image data, and a third detection result corresponding to the third output image data; Obtain a detection result of the RGB display system according to the first detection result, the second detection result, and the third detection result.

7. An abnormality detection device of a display system characterized by comprising: Comprise: An acquisition module is configured to acquire output image data output by a to-be-detected display system for a first preset number of frames in response to the to-be-detected display system receiving a standard test target; wherein the output image data is a continuous grid image after gamma correction processing; A calculation module is configured to calculate an energy difference value of each grid according to a current energy value of each grid in the output image data and a reference energy value corresponding to each grid; wherein the current energy value is determined based on all pixel values in each grid, and the reference energy value is determined based on the standard test target and a normal display system; A determination module is configured to determine that there is a real gamma input value abnormality in the to-be-detected display system if the energy difference value of the grid in the output image data for a second preset number of continuous frames is greater than a preset adaptive threshold; wherein the preset adaptive threshold is determined based on a standard deviation of the reference energy value corresponding to the grid and a preset confidence factor, and the second preset number of frames is less than or equal to the first preset number of frames.

8. A terminal device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer program is executed to cause the method in any one of claims 1 to 6 to be executed.

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