Methods, systems, devices, and storage media of detecting a display device

Abstract

This invention provides a method, system, apparatus, and storage medium for detecting a display device. The method includes: acquiring feature values ​​of each point in a grayscale image displayed by the display device; the feature values ​​of each point forming an image matrix of the grayscale image; summing the feature values ​​of each column in the image matrix to obtain the feature sum of each column; determining the number of grayscale abrupt changes in the feature sums of all columns based on the feature sums of each column; determining the number of grayscale bars in the grayscale image based on the determined number; and determining the detection result of the display device based on the number of grayscale bars. The grayscale detection process is automated, eliminating the need for manual inspection. Furthermore, by superimposing the feature values ​​of each column as the data basis for the entire detection method, interference caused by angular deviations between the actual image and the image itself, as well as errors caused by uneven light emission from the display device, can be corrected, while simultaneously improving the overall detection speed.

Description

Technical Field

[0001] This invention relates to the field of display device testing, and more specifically, to methods, systems, apparatus, and storage media for testing display devices. Background Technology

[0002] Display screens and other display devices are becoming increasingly important in people's lives and work. At the same time, the demand for display devices is also growing, and people are demanding higher quality from these products.

[0003] In the manufacturing process of display devices, grayscale testing is particularly important. Each digital image is composed of many points, called pixels. The light source behind each pixel can display different brightness levels, and grayscale represents the different brightness levels from the darkest to the brightest. The more grayscale levels there are, the more delicate the image effect and the clearer the lines and contours. Grayscale testing mainly detects whether a display device can display a certain number of grayscale levels, that is, whether it has a certain grayscale display capability, thereby detecting whether the display device can display a clear image. The testing process requires the use of grayscale test images, which are tools specifically used to test the color reproduction performance of display devices and consist of several grayscale bars. Traditional grayscale detection methods first display a grayscale test image on the display device, and then rely on the human eye to identify the number of grayscale bars in the image displayed by the display device. The more grayscale bars identified, or the closer they are to the actual number of grayscale bars in the grayscale test image, the better the grayscale display capability of the display device, and the smoother and softer the trend of the image changes, which is more in line with the standard.

[0004] However, traditional grayscale detection methods rely solely on human eyes to view the grayscale levels / number of grayscale levels displayed on the display device, which requires a lot of manpower and resources, and the control over detection accuracy is relatively low. Summary of the Invention

[0005] The present invention aims to overcome at least one of the defects of the prior art and provides a method, system, device and storage medium for detecting display devices, which solves the problem that traditional grayscale detection methods consume a lot of manpower and resources and have low detection accuracy.

[0006] The technical solution adopted in this invention includes:

[0007] A method for detecting a display device includes: acquiring feature values ​​of each point in a grayscale image displayed by the display device; the feature values ​​of each point forming an image matrix of the grayscale image; summing the feature values ​​of each column in the image matrix to obtain the feature sum of each column; determining the number of grayscale abrupt changes in the feature sums of all columns based on the feature sums of each column; determining the number of grayscale bars in the grayscale image based on the determined number; and determining the detection result of the display device based on the number of grayscale bars.

[0008] The method for detecting a display device provided by this invention uses a grayscale image to test the grayscale display of the display device, obtaining the feature values ​​of each point in the image displayed by the display device to construct an image matrix. Since the grayscale image used for testing is generally composed of vertical grayscale bars, the feature values ​​of each column in the image matrix are summed to obtain the feature sum of each column, which serves as the data basis for grayscale detection. The number of grayscale abrupt changes is identified from the feature sum of each column. The occurrence of a grayscale abrupt change indicates a transition from one grayscale bar to another. Therefore, the number of grayscale abrupt changes can determine the number of grayscale bars in the grayscale image. Based on the determined number of grayscale bars in the grayscale image, it can be determined whether the display device can reproduce the true number of grayscale bars in the grayscale image. The method provided by this invention automates the grayscale detection process, eliminating the need for manual inspection. By superimposing the feature values ​​of each column as the data basis for the entire detection method, it can correct interference caused by angular deviations between the actual image and the image, as well as errors caused by uneven illumination of the display device itself. Simultaneously, it avoids processing the feature values ​​of each point, thereby improving the overall detection speed.

[0009] Furthermore, after summing the eigenvalues ​​of each column in the image matrix to obtain the eigensum of each column, the sum of the eigenvalues ​​of each column is differentiated by order n, where n≥1. The sum of the eigenvalues ​​of each column after the nth derivative is taken as the eigensum of each column.

[0010] Taking the nth derivative of the sum of the feature values ​​of each column is more helpful in determining the location of gray-level abrupt changes in the grayscale image. This makes the changes between the feature sums of each column obtained after the nth derivative more significant and makes it easier to determine the number of gray-level abrupt changes in subsequent steps.

[0011] Furthermore, based on the characteristics of each column, the number of gray-scale abrupt changes in the characteristics of all columns is determined. Specifically, it is determined whether the signs of the sums of features of each two adjacent columns are opposite. If the signs of the sums of features of two adjacent columns are opposite, the number of gray-scale abrupt changes is incremented by one.

[0012] Furthermore, based on the features of each column, the number of gray-scale mutations occurring in the feature sums of all columns is determined. Specifically, the feature sums of each column are compared with a preset gray-scale mutation threshold. If the feature sum of any column is greater than the gray-scale mutation threshold, the number of gray-scale mutations is incremented by one.

[0013] Further, based on the characteristics of each column, the number of gray-scale abrupt changes in the characteristics of all columns is determined, specifically: a first judgment and a second judgment are performed on the characteristics of each column; the first judgment is to determine whether the characteristics of each column are greater than the gray-scale abrupt change threshold; the second judgment is to determine whether the signs of the characteristics of each pair of adjacent columns are opposite; if the first judgment determines that the characteristics of one column are greater than the gray-scale abrupt change threshold, and the second judgment determines that the signs of the characteristics of the same column and its adjacent characteristics are opposite, the number of gray-scale abrupt changes is incremented by one.

[0014] Furthermore, the feature values ​​are either the grayscale value or the brightness value of each point. Grayscale and brightness values ​​are commonly used image feature values, which are beneficial for calculation and processing.

[0015] Furthermore, before taking the nth derivative of the sum of the eigenvalues ​​of each column, the sum of the eigenvalues ​​of each column obtained by the summation is smoothed.

[0016] Smoothing the sum of eigenvalues ​​is mainly for processing noise in the display device. There are many factors that cause noise, such as noise caused by the characteristics of the display device itself, or noise caused by vignetting in the image acquisition device itself when acquiring the grayscale image displayed by the display device. Smoothing can remove interference factors caused by the display device itself and the tools used in the detection.

[0017] Furthermore, the test result of the display device is determined based on the number of grayscale bars, specifically by determining whether the number of grayscale bars is the same as the preset standard number of grayscale bars. If yes, the test result of the display device is qualified; otherwise, the test result of the display device is unqualified.

[0018] A system for detecting a display device includes a feature value acquisition module for acquiring feature values ​​of each point in a grayscale image displayed by the display device; the feature values ​​of each point constitute an image matrix of the grayscale image; a feature sum determination module for summing the feature values ​​of each column in the image matrix to obtain the feature sum of each column; a grayscale change determination module for determining the number of grayscale changes occurring in the feature sums of all columns based on the feature sums of each column; and a detection module for determining the number of grayscale bars in the grayscale image based on the determined number, and further for determining the detection result of the display device based on the number of grayscale bars.

[0019] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method of the above-described detection and display device.

[0020] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for the detection display device described above.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] This invention provides a detection method that achieves fully automated grayscale detection of display devices, eliminating the need for manual inspection and reducing manpower and material resources. It also significantly improves detection accuracy. Furthermore, the detection method of this invention superimposes the feature values ​​of each column as the data basis for the entire detection process. This can correct interference caused by angular deviations between the actual image and the picture, as well as errors caused by uneven light emission from the display device itself. It also avoids processing the feature values ​​of each point, thereby improving the overall detection speed. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating method steps S1 to S4 of an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of a grayscale test image composed of vertical grayscale bars used in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram illustrating the transformation of an m*n image matrix into a 1*n matrix in an embodiment of the present invention.

[0026] Figure 4 This is a flowchart illustrating the method steps S21 to S23 of an embodiment of the present invention.

[0027] Figure 5 This is a flowchart illustrating steps S231a to S3 in the first optional scheme of method step S23 of an embodiment of the present invention.

[0028] Figure 6 This is a flowchart illustrating steps S231b to S3 in the second optional scheme of method step S23 of an embodiment of the present invention.

[0029] Figure 7 This is a schematic diagram of a curve drawn based on the features of each column in an embodiment of the present invention.

[0030] Figure 8 This is a flowchart illustrating steps S231c to S3 in the third optional scheme of method step S23 of an embodiment of the present invention.

[0031] Figure 9 This is a flowchart illustrating the method steps S41 to S43 of an embodiment of the present invention.

[0032] Figure 10 This is a flowchart illustrating the method steps T1 to T10 of an embodiment of the present invention.

[0033] Figure 11 This is a schematic diagram of the system module composition of an embodiment of the present invention. Detailed Implementation

[0034] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the invention. To better illustrate the following embodiments, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; it is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0035] This embodiment provides a method for testing a display device, used to perform grayscale detection on the display device to test its grayscale display capability. The display device may specifically be an electronic display screen, an LED display screen, etc.

[0036] like Figure 1 As shown, the method for detecting the display device in this embodiment includes steps S1 to S4:

[0037] S1: Obtain the feature values ​​of each point in the grayscale image displayed by the display device;

[0038] Specifically, in step S1, the grayscale image displayed by the display device can be acquired by an image acquisition device, such as by taking a picture of the grayscale image displayed by the display device with a camera. In order to reduce the error that the image acquisition device may bring in the whole detection process, it is preferable to use a high-precision image acquisition device to acquire the grayscale image displayed by the display device.

[0039] A grayscale image refers to a grayscale test image acquired by an image acquisition device and displayed on a display device. The grayscale test image can be composed of several gray-scale gradient squares or several gray-scale gradient bars. The gray-scale bars can be arranged vertically or horizontally. In this embodiment, the grayscale test image used is composed of several vertical gray-scale bars arranged in the column direction, such as... Figure 2 The image shown is one type of grayscale test image used in this embodiment. The grayscale values ​​of several grayscale bars in the image increase continuously from left to right. Alternatively, the grayscale values ​​of several grayscale bars in the grayscale test image used in this embodiment can also increase continuously from right to left.

[0040] Each point in a grayscale image is a pixel. The feature value of a point refers to a value that can reflect the image properties of the pixel. Specifically, the feature value of a point can be the gray value of the pixel or the brightness value of the pixel. In this embodiment, the feature value of a point is preferably the brightness value of the pixel.

[0041] The pixels in an image are arranged in rows and columns. After obtaining the feature values ​​of each point in the image, the feature values ​​of each point form an m*n image matrix of grayscale image. This means that there are a total of m*n elements in the matrix, arranged in m rows and n columns. Each element in the matrix is ​​the feature value of each point in the image, that is, the brightness value of each pixel.

[0042] S2: Summing the eigenvalues ​​of each column in the image matrix yields the eigensum of each column. Based on the eigensum of each column, the number of gray-level abrupt changes in the eigensum of all columns is determined.

[0043] In step S2, the eigenvalues ​​of each column in the m*n image matrix are summed to obtain the eigensum of each column. The eigensum of each column together constitutes a new 1*n matrix, such as... Figure 3 As shown, step S2 actually transforms the m*n image matrix into a 1*n matrix, where each element of the 1*n matrix is ​​the feature sum of each column of the m*n image matrix.

[0044] A grayscale abrupt change refers to a sudden numerical shift in the grayscale value between adjacent pixels in an image. When a grayscale abrupt change occurs between adjacent pixels, this point can be considered an edge or contour line in the image. When the object of detection is a grayscale test image, the grayscale abrupt change is manifested as the transition between two adjacent grayscale bars.

[0045] Because display devices generally suffer from uneven light emission, manifesting as brighter central areas and slightly darker edges, and because slight angular deviations inevitably occur between the image acquired by the image acquisition device and the displayed image, the feature values ​​of each point obtained from the image will have certain errors compared to the actual feature values ​​on the image, making them unsuitable as the data basis for determining the number of grayscale abrupt changes. Step S2, by summing the feature values ​​of each point in each column before subsequent data processing, can correct for interference caused by angular deviations between the actual image and the displayed image, as well as errors caused by uneven light emission from the display device itself. Furthermore, since the grayscale test images used in this embodiment have grayscale bars arranged in the column direction, step S2 sums the feature values ​​of each column in the m*n image matrix, and the resulting feature sum serves as the total feature value for each column, reflecting the characteristics of each column. The features of all columns can also reflect the characteristics of the individual grayscale bars arranged in the column direction.

[0046] In one specific implementation, when determining the number of grayscale abrupt changes in the sum of features across all columns based on the features of each column, step S2 can involve calculating the difference between two adjacent feature sums and setting a preset threshold for judging grayscale abrupt changes. This threshold is the minimum difference between two grayscale values ​​when a grayscale abrupt change is detected, and is a preset value. When the difference between two adjacent feature sums is greater than or equal to the threshold, a grayscale abrupt change is considered to have occurred, and subsequent steps are performed to count the number of grayscale abrupt changes. When the difference between two adjacent feature sums is less than the threshold, no grayscale abrupt change is considered to have occurred.

[0047] In a preferred embodiment, the feature sum of each column obtained in step S2 is obtained by summing the feature values ​​of each column in the m*n image matrix and then taking the derivative, such as... Figure 4 As shown, the specific execution process of step S2 includes the following steps:

[0048] S21: Summing the eigenvalues ​​of each column in the image matrix to obtain the sum of the eigenvalues ​​of each column;

[0049] S22: Take the nth derivative of the sum of the eigenvalues ​​of each column, where n≥1, and use the sum of the eigenvalues ​​of each column after the nth derivative as the eigensum of each column.

[0050] Taking the nth derivative of the sum of the eigenvalues ​​of each column obtained by summing makes the changes between the sums of the eigenvalues ​​of each column more obvious. Therefore, the sum of the eigenvalues ​​of each column after the nth derivative is taken as the eigensum of each column, which is helpful for identifying the eigensum of gray-scale abrupt changes in subsequent steps.

[0051] Generally, first-order differentiation of an image is used to detect image edges, i.e., the locations of gray-level abrupt changes. Second-order differentiation alters the sign of the feature values ​​at image edges, i.e., the locations of gray-level abrupt changes, thus making it easier to identify edges and edge transitions. Preferably, in this embodiment, the value of n in step S22 is 2, meaning that the second-order differentiation is performed on the sum of the feature values ​​of each column obtained by summation. The sum of the feature values ​​of each column after second-order differentiation serves as the feature sum of each column and also as each element of the 1*n matrix after the m*n image matrix is ​​transformed. The changes between the feature sums in the 1*n matrix after second-order differentiation are more significant, making it easier to determine the number of gray-level abrupt changes in subsequent steps.

[0052] Preferably, before performing the nth-order derivative of the sum of the eigenvalues ​​of each column in step S22, the sum of the eigenvalues ​​of each column is first smoothed. The smoothing method can be determined according to the actual situation, such as mean filtering or Gaussian filtering, etc. Smoothing the sum of the eigenvalues ​​is mainly to reduce noise in the display device. There are many factors that cause noise, such as noise generated by the characteristics of the display device itself, or noise caused by vignetting in the image acquisition device itself when acquiring the grayscale image displayed by the image acquisition device. Smoothing can remove interference factors generated by the display device itself and the tools used in the detection.

[0053] S23: Determine the number of gray-scale abrupt changes in the features of all columns based on the features of each column;

[0054] There are three possible execution methods for step S23:

[0055] like Figure 5 As shown, the first optional scheme includes steps S231a to S233a:

[0056] S231a: Determine whether the sum of features in each column is greater than the grayscale mutation threshold. If yes, proceed to step S232a; otherwise, proceed to step S233a.

[0057] After taking the second derivative of the sum of the feature values ​​in each column, the derivative of the gray value of one pixel at the gray-level abrupt change will be relatively large. The gray-level abrupt change threshold is the minimum value of the larger of the two gray values ​​at the point of the gray-level abrupt change; it is a preset value. By comparing the sum of the feature values ​​in each column with the gray-level abrupt change threshold, the location of the gray-level abrupt change can be identified, and the number of gray-level abrupt changes can be recorded in subsequent steps by counting.

[0058] S232a: Increment the number of grayscale mutations by one;

[0059] The initial value for the number of grayscale mutations is 0. In step S232a, incrementing the number of grayscale mutations by one indicates that the number of grayscale mutations has increased by one.

[0060] S233a: Determine whether the sum of features of all columns has been compared and whether it is greater than the gray-scale mutation threshold. If not, repeat step S231a until the judgment of the sum of features of all columns is completed. If yes, execute step S3.

[0061] like Figure 6 As shown, the second optional scheme includes steps S231b to S233b:

[0062] S231b: Determine whether the signs of the sums of features of any two adjacent columns in each column are opposite. If yes, proceed to step S232b; otherwise, proceed to step S233b.

[0063] The feature sum of each column is obtained by taking the second derivative of the sum of the feature values ​​of each column. As mentioned above, taking the second derivative of the image will cause the sign of the feature values ​​at the edge of the image, that is, at the point of gray-level change. Therefore, the feature sum obtained after the second derivative will have one positive and one negative sign at the gray-level change, that is, at the transition between two adjacent gray level bars.

[0064] In this alternative approach, the values ​​of the feature sums for each class in the 1*n matrix can be presented as a graph, such as... Figure 7 As shown, if the signs of the sums of features in adjacent columns are opposite and there is a large difference between the two values, it will be represented by a consecutive peak and a trough on the curve. In step S231b, when determining whether the signs of the sums of features in adjacent columns are opposite, a curve can be drawn based on the values ​​of the sums of features in each column. If a position with a consecutive peak and a trough is found on the curve, the signs of the two sums of features corresponding to the peak and the trough must be opposite.

[0065] S232b: Increment the number of grayscale mutations by one;

[0066] S233b: Determine whether the sign of the sum of features of all adjacent columns has been determined and whether it is positive or negative. If not, repeat step S231b until the sign of the sum of features of all adjacent columns is determined. If yes, proceed to step S3.

[0067] The third alternative includes steps S231c to S233c:

[0068] S231c: Perform a first judgment and a second judgment on the feature sum of each column; if the first judgment determines that the feature sum of a column is greater than the gray-scale mutation threshold, and the second judgment determines that the value of the feature sum of the same column has the opposite sign to the value of the feature sum of its neighboring column, execute step S232c; otherwise, execute step S233c.

[0069] Specifically, the first judgment is to determine whether the sum of features in any column of each column is greater than the grayscale mutation threshold. The specific judgment process of the first judgment can be referred to the content of step S231a above, and will not be repeated here.

[0070] The second judgment involves determining whether the signs of the sums of the characteristics of any two adjacent columns in each column are opposite. For a detailed explanation of the second judgment process, please refer to step S231b above; it will not be repeated here.

[0071] In step S231c, the two judgments can be executed in parallel or in sequence.

[0072] Preferably, such as Figure 8 As shown, in step S231c, the first judgment is executed first. If the first judgment determines that the sum of features in a column is less than or equal to the gray-scale change threshold, step S233c is executed. If the first judgment determines that the sum of features in a column is greater than the gray-scale change threshold, the second judgment is executed. If the second judgment determines that the value of the sum of features in the same column has the opposite sign to the value of the sum of features adjacent to it, step S232c is executed. If the second judgment determines that the value of the sum of features in the same column has the opposite sign to the value of the sum of features adjacent to it, step S233c is executed.

[0073] S232c: Increment the number of grayscale mutations by one;

[0074] S233c: Determine whether the first judgment has been made on the feature sum of all columns. If not, repeat step S231c until the first judgment on the feature sum of all columns is completed. If yes, execute step S3.

[0075] Compared with the execution processes of S231a to S233a in the first alternative scheme and S231b to S233b in the second alternative scheme, since two judgments are performed in step S231c, S232c will only be called if both judgments are satisfied in step S231c. Therefore, the possibility of being affected by extreme cases in steps S231c to S233c is lower, and the error in the whole process is smaller.

[0076] S3: Determine the number of grayscale bars in the grayscale image based on the determined number;

[0077] Specifically, the number of gray-level abrupt changes represents the number of transition points between two adjacent gray-level bars. Therefore, in step S233a, the number of gray-level bars in the determined gray-level image should be equal to the number of gray-level abrupt changes plus one.

[0078] S4: Determine the detection result of the display device based on the number of grayscale bars.

[0079] The test result of the display device refers to the test result that reflects whether the display device can normally display the grayscale in the grayscale test image. This test result also reflects whether the display device has good grayscale display capabilities. Specifically, such as... Figure 9 As shown, the specific execution process of step S4 includes the following steps:

[0080] S41: Determine whether the number of grayscale bars is the same as the preset standard number of grayscale bars. If yes, proceed to step S42; if no, proceed to step S43.

[0081] The standard grayscale count refers to the number of grayscale lines presented in the grayscale test image selected in this embodiment.

[0082] S42: Determine that the test result of the display device is qualified;

[0083] S43: The test result of the display device is determined to be unqualified.

[0084] In this embodiment, the test results of the display device are divided into two categories: qualified and unqualified. When the number of grayscale bars in the image displayed by the display device is consistent with the number of grayscale bars in the standard image, the test result of the display device is qualified, indicating that the display device can normally display the grayscale in the grayscale test image, and also indicating that the grayscale display capability of the display device is good. When the number of grayscale bars in the image displayed by the display device is inconsistent with the number of grayscale bars in the standard image, the test result of the display device is unqualified, indicating that the display device cannot normally display the grayscale in the grayscale test image, and also indicating that the grayscale display capability of the display device is poor.

[0085] Since the detection of a display device depends on the number of grayscale bars presented in the grayscale test image, the more grayscale bars in the grayscale test image and the smaller the difference in grayscale values ​​between the bars, the more layers the grayscale test image divides into. If the display device needs to reproduce the grayscale in the image, it needs to have excellent grayscale display capabilities. Based on this, this embodiment can replace different grayscale test images according to actual needs to further test the grayscale display capabilities of the display device. For example, after a display device determines its test result is qualified in step S42, the original grayscale test image is replaced with a grayscale test image that has more grayscale bars and smaller grayscale differences between the bars. Then, steps S1 to S4 of the method provided in this embodiment are executed again. If the number of grayscale bars in the grayscale image displayed by the display device is inconsistent with the standard number of grayscale bars in the replaced grayscale test image, it indicates that the grayscale display capability of the display device is not excellent and can only be considered qualified.

[0086] The test results of display devices can be divided into more than just qualified and unqualified. It should be determined according to the actual situation. For example, when the number of grayscale bars in the image displayed by the display device is inconsistent with the standard number of grayscale bars, the grayscale display capability of the display device can be rated as excellent or good based on the difference between the number of grayscale bars and the standard number of grayscale bars.

[0087] In a preferred embodiment, such as Figure 10 As shown, the overall execution process of the method provided in this embodiment includes the following steps:

[0088] T1: Acquire the brightness values ​​of each point in the grayscale image displayed by the display device through a high-precision image acquisition device;

[0089] The pixels in the image are arranged in rows and columns. After obtaining the brightness value of each point in the image, the brightness values ​​of each point constitute an m*n image matrix of the grayscale image.

[0090] T2: Sum the brightness values ​​of each column in the m*n image matrix to obtain the sum of the brightness values ​​of each column;

[0091] T3: Smooth the sum of brightness values ​​in each column, take the second derivative of the sum of brightness values ​​in each column, and use the sum of brightness values ​​in each column after the second derivative as the brightness sum of each column.

[0092] The brightness of each column forms a new 1*n matrix.

[0093] T4: Perform the first judgment and the second judgment on the brightness sum of each column in a 1*n matrix; when it is determined in the first judgment that the brightness sum of a column is less than or equal to the gray-level mutation threshold, execute step T6; when it is determined in the first judgment that the brightness sum of a column is greater than the gray-level mutation threshold, execute the second judgment. When it is determined in the second judgment that the signs of the brightness sum values of the same column and its adjacent brightness sum values are opposite in sign, execute step T5; when it is determined in the second judgment that the signs of the brightness sum values of the same column and its adjacent brightness sum values are not opposite in sign, execute step T6;

[0094] The first judgment is to determine whether the feature sum of any column in the feature sums of each column is greater than the gray-level mutation threshold.

[0095] The second judgment is to determine whether the signs of the brightness sum values of any two adjacent columns in the feature sums of each column are opposite in sign.

[0096] T5: Increment the number of occurrences of gray-level mutation by one;

[0097] T6: Determine whether the first judgment has been performed on the feature sums of all columns. If not, repeat step T4 until the first judgment on the feature sums of all columns is completed. If so, execute step T7;

[0098] T7: Determine the number of gray levels of the grayscale image based on the determined number;

[0099] Specifically, the determined number of gray levels of the grayscale image should be equal to the determined number of gray-level mutations plus one.

[0100] T8: Determine whether the number of gray levels is the same as the preset standard number of gray levels. If so, execute step T9; if not, execute step T10;

[0101] The standard number of gray levels refers to the number of gray levels presented in the gray-level test picture.

[0102] T9: Determine that the detection result of the display device is qualified;

[0103] T10: Determine that the detection result of the display device is unqualified.

[0104] The method for detecting a display device provided in this embodiment uses a grayscale image to test the grayscale display of the display device. The grayscale detection process is fully automated, without the need for manual visual inspection. Moreover, the feature values of each column are superimposed as the data basis for the entire method of detection. It can not only correct the interference caused by the angular deviation between the actual image and the picture, but also correct the error caused by uneven light emission of the display device itself. At the same time, it avoids processing the feature values of each point, and can improve the speed of the entire detection.

[0105] Based on the same idea as the above method for detecting a display device, as Figure 11 As shown, this embodiment also provides a system for detecting a display device, including:

[0106] The feature value acquisition module 100 is used to acquire the feature values ​​of each point in the grayscale image displayed by the display device;

[0107] The feature values ​​of each point constitute the image matrix of the grayscale image; specifically, the feature values ​​are the grayscale values ​​or the brightness values ​​of each point.

[0108] The feature determination module 200 is used to sum the feature values ​​of each column in the image matrix to obtain the feature sum of each column;

[0109] The gray-scale mutation determination module 300 is used to determine the number of gray-scale mutations that occur in the features of all columns based on the features of each column.

[0110] The detection module 400 is used to determine the number of grayscale bars in the grayscale image based on the determined number, and also to determine the detection result of the display device based on the number of grayscale bars.

[0111] Specifically, the feature and determination module 200 includes:

[0112] The first feature determination submodule 210 is used to sum the feature values ​​of each column in the image matrix to obtain the sum of the feature values ​​of each column.

[0113] The second feature and determination submodule 220 is used to perform an nth-order derivative on the sum of the feature values ​​of each column, where n≥1, and to use the sum of the feature values ​​of each column after the nth-order derivative as the feature sum of each column.

[0114] Preferably, before performing an nth-order derivative on the sum of the feature values ​​of each column obtained by the first feature and determination submodule 210, the second feature and determination submodule 220 performs a smoothing process on the sum of the feature values ​​of each column obtained by the first feature and determination submodule 210.

[0115] Specifically, the grayscale mutation determination module 300 includes:

[0116] The first gray-scale mutation determination submodule 310a is used to determine whether the signs of the sums of features of each column and each pair of adjacent columns are opposite.

[0117] The second gray-scale mutation determination submodule 320a is used to increment the number of gray-scale mutations when the first gray-scale mutation determination submodule 310b determines that the signs of the sum of features of two adjacent columns are opposite.

[0118] Optionally, the grayscale mutation determination module 300 specifically includes:

[0119] The first gray-scale mutation determination submodule 310b is used to compare the features of each column with a preset gray-scale mutation threshold.

[0120] The second gray-scale mutation determination submodule 320b is used to increment the number of gray-scale mutations when the first gray-scale mutation determination submodule 310b determines that the sum of the features of a column is greater than the gray-scale mutation threshold.

[0121] Optionally, the grayscale mutation determination module 300 specifically includes:

[0122] The first grayscale mutation determination submodule 310c performs a first judgment and a second judgment on the features of each column;

[0123] The first determination is to determine whether the sum of the features of each column is greater than the grayscale mutation threshold;

[0124] The second determination is to determine whether the signs of the sums of features of each column and every two adjacent columns are opposite.

[0125] The second grayscale mutation determination submodule 320c is used to increment the number of grayscale mutations by one when the first grayscale mutation determination submodule 310c determines in the first judgment that the sum of features of a column is greater than the grayscale mutation threshold, and in the second judgment that the value of the sum of features of the same column has opposite signs to the value of the sum of features of its adjacent columns.

[0126] In the above-described implementation of the detection and display device system, the logical division of each functional module is merely illustrative. In practical applications, the functions can be assigned to different functional modules as needed, for example, due to hardware configuration requirements or software implementation considerations. This would allow the internal structure of the detection and display device implementation system to be divided into functional modules different from those described above, while still fulfilling all the functions described. Furthermore, the execution process of the modules in the above-described example of the detection and display device implementation system is based on the same concept as the method of the detection and display device described in this embodiment. Its principle and the resulting technical effects are the same as those of the aforementioned detection and display device method. For details, please refer to the description of the method implementation method; further elaboration is not provided here.

[0127] Based on the same idea as the above-described detection and display device method, this embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the above-described detection and display device method, possessing corresponding functions and beneficial effects.

[0128] Based on the same idea as the method of the above-described detection and display device, this embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the method of the above-described detection and display device and has corresponding functions and beneficial effects.

[0129] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A method for detecting a display device, characterized in that, include: Obtain the feature values ​​of each point in the grayscale image displayed by the display device; The feature values ​​of each point constitute the image matrix of the grayscale image; The feature sum of each column is obtained by summing the feature values ​​of each column in the image matrix. Based on the characteristics of each column, determine the number of gray-scale abrupt changes in the characteristics of all columns; The number of grayscale bars in the grayscale image is determined based on the determined number; The detection result of the display device is determined based on the number of grayscale bars; After summing the eigenvalues ​​of each column in the image matrix to obtain the eigensum of each column, the nth derivative of the eigensum of each column is taken, where n≥1. The eigensum of each column after the nth derivative is taken as the eigensum of each column. Before taking the nth derivative of the feature sum of each column, the feature sum of each column obtained by summation is smoothed.

2. The method for detecting a display device according to claim 1, characterized in that, Based on the characteristics of each column, determine the number of gray-scale abrupt changes in the characteristics of all columns. Specifically, determine whether the signs of the sums of characteristics of each pair of adjacent columns are opposite. If the signs of the sums of characteristics of adjacent columns are opposite, increment the number of gray-scale abrupt changes by one.

3. The method for detecting a display device according to claim 1, characterized in that, Based on the features of each column, determine the number of gray-scale mutations that occur in the feature sums of all columns. Specifically, compare the feature sum of each column with a preset gray-scale mutation threshold. If the feature sum of any column is greater than the gray-scale mutation threshold, increment the number of gray-scale mutations by one.

4. The method for detecting a display device according to claim 1, characterized in that, Based on the characteristics of each column, determine the number of gray-scale abrupt changes in the characteristics of all columns, specifically as follows: For each column, perform a first judgment and a second judgment on its features; The first determination is to determine whether the sum of the features of each column is greater than the grayscale mutation threshold; The second determination is to determine whether the signs of the sums of features of each column and every two adjacent columns are opposite. If the first determination determines that the sum of features in one column is greater than the gray-scale mutation threshold, and the second determination determines that the sum of features in the same column has opposite signs to the sum of features in its adjacent columns, then the number of gray-scale mutations is incremented by one.

5. The method for detecting a display device according to any one of claims 1 to 4, characterized in that, The feature value is either the gray value or the brightness value of each point.

6. The method for detecting a display device according to any one of claims 1 to 4, characterized in that, The test result of the display device is determined based on the number of grayscale bars. Specifically, it is determined whether the number of grayscale bars is the same as the preset standard number of grayscale bars. If so, the test result of the display device is qualified; otherwise, the test result of the display device is unqualified.

7. A system for detecting a display device according to claim 1, characterized in that, The feature value acquisition module is used to acquire the feature values ​​of each point in the grayscale image displayed by the display device; the feature values ​​of each point constitute the image matrix of the grayscale image; The feature and determination module is used to sum the feature values ​​of each column in the image matrix to obtain the feature sum of each column; A gray-scale mutation determination module is used to determine the number of gray-scale mutations that occur in the features of all columns based on the features of each column. The detection module is used to determine the number of grayscale bars in the grayscale image based on the determined number, and also to determine the detection result of the display device based on the number of grayscale bars.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of the detection display device according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of the detection display device according to any one of claims 1 to 6.

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