Method, device and readable storage medium for detecting residual image of display panel

Abstract

The application discloses a residual image detection method, device and equipment of a display panel and a readable storage medium, and belongs to the technical field of display. The method comprises the following steps: acquiring an original gray scale image of the display panel, wherein the original gray scale image is an image obtained by inputting a signal of a preset gray scale to all pixel points of the display panel at a first moment; acquiring a test gray scale image of the display panel, wherein the test gray scale image is an image obtained by inputting the signal of the preset gray scale to all pixel points of the display panel after controlling the display panel to display a black-and-white checkerboard picture according to a plurality of checkerboard regions and continuously for a preset time length; and performing gray scale change analysis and edge matching analysis on the original gray scale image and the test gray scale image to determine whether the test gray scale image contains residual images of the black-and-white checkerboard picture. The method can accurately detect the residual images of the display panel.

Description

Technical Field

[0001] This application relates to the field of display technology, and in particular to a method, apparatus, device, and readable storage medium for detecting image retention on a display panel. Background Technology

[0002] With societal progress, display panel technology has been widely applied in the electronic display industry, improving the quality of image and video displays.

[0003] Due to the characteristics and operational limitations of display panels, image retention is a frequent problem. Currently, image retention is typically detected through subjective assessment using the naked eye, which cannot accurately detect the image retention issue. Summary of the Invention

[0004] This application provides a method, apparatus, device, and readable storage medium for detecting image retention on a display panel, which can accurately detect image retention on the display panel. The technical solution is as follows:

[0005] In a first aspect, a method for detecting image retention in a display panel is provided. The method includes: acquiring an original grayscale image of the display panel, wherein the original grayscale image refers to an image obtained by inputting a preset grayscale signal to all pixels of the display panel at a first moment; acquiring a test grayscale image of the display panel, wherein the test grayscale image refers to an image obtained by controlling the display panel to display a black and white checkerboard pattern in multiple checkerboard areas after the first moment, and continuing for a preset time, and then inputting a preset grayscale signal to all pixels of the display panel; and performing grayscale change analysis and edge matching analysis on the original grayscale image and the test grayscale image to determine whether there is an image retention of the black and white checkerboard pattern in the test grayscale image.

[0006] In some possible implementations, the method further includes: performing grayscale change analysis on the original grayscale image and the test grayscale image to determine whether each checkerboard region in the test grayscale image satisfies the surface afterimage condition; performing edge matching analysis on the original grayscale image and the test grayscale image to determine whether each checkerboard region in the test grayscale image satisfies the edge afterimage condition; if the first checkerboard region satisfies both the surface afterimage condition and the edge afterimage condition, then it is determined that the first checkerboard region has a surface afterimage; wherein, the first checkerboard region is any one of multiple checkerboard regions; if the first checkerboard region satisfies the edge afterimage condition but does not satisfy the surface afterimage condition, then it is determined that the first checkerboard region has an edge afterimage.

[0007] In some possible implementations, the method further includes: determining the grayscale change amount of the first checkerboard region based on the difference between the test grayscale value and the original grayscale value; wherein the test grayscale value is the average grayscale value of the first checkerboard region in the test grayscale image, and the original grayscale value is the average grayscale value of the first checkerboard region in the original grayscale image; determining the surface retention parameter based on the ratio of the grayscale change amount to the original grayscale value; determining whether the surface retention parameter is greater than a preset surface retention threshold; if the surface retention parameter is greater than the preset surface retention threshold, then determining that the first checkerboard region meets the surface retention condition; if the surface retention parameter is less than or equal to the preset surface retention threshold, then determining that the first checkerboard region does not meet the surface retention condition.

[0008] In some possible implementations, the method further includes: if there is a face afterimage in the first chessboard area, then determining the face afterimage level of the first chessboard area according to the face afterimage parameters and the preset level correspondence.

[0009] In some possible implementations, the method further includes: determining the surface residual parameters according to the following formula:

[0010]

[0011] Where K is the afterimage coefficient, ΔL A(m,n) L represents the grayscale variation of the first chessboard area. A(m,n) is the original grayscale value, and b is the color perception coefficient.

[0012] In some possible implementations, the method further includes: determining a structural similarity index between a first edge and a second edge based on a test grayscale value and an original grayscale value; wherein the test grayscale value is the average grayscale value of the first checkerboard region in the test grayscale image, and the original grayscale value is the average grayscale value of the first checkerboard region in the original grayscale image; the first edge is the edge of the first checkerboard region in the original grayscale image; the second edge is the edge of the first checkerboard region in the test grayscale image; determining whether the structural similarity index is greater than a preset similarity threshold; if the structural similarity index is greater than the preset similarity threshold, then determining that the first checkerboard region satisfies the edge-line afterimage condition; if the structural similarity index is less than or equal to the preset similarity threshold, then determining that the first checkerboard region does not satisfy the edge-line afterimage condition.

[0013] In some possible implementations, the method further includes: determining the luminance factor and luminance covariance of the first checkerboard region based on the test grayscale value and the original grayscale value; determining the contrast factor of the first checkerboard region based on the test luminance standard deviation and the original luminance standard deviation; wherein the test luminance standard deviation is the luminance standard deviation of the first checkerboard region in the test grayscale image, and the original grayscale value is the luminance standard deviation of the first checkerboard region in the original grayscale image; determining the structure factor of the first checkerboard region based on the test luminance standard deviation, the original luminance standard deviation, and the luminance covariance; and determining the structural similarity index based on the luminance factor, the contrast factor, and the structure factor.

[0014] The image retention detection method for display panels provided in this application acquires an original grayscale image and a test grayscale image of the display panel, performs grayscale change analysis and edge matching analysis on the original and test grayscale images, and determines whether a black-and-white checkerboard image retention exists in the test grayscale image. First, this method can objectively determine whether image retention occurs on the display panel, thereby improving the accuracy of image retention detection. Second, this method can implement image retention detection of the display panel through a software program, eliminating the need for expensive equipment and specific detection environments to evaluate image retention, thus saving costs and resources. Third, this method can achieve image retention detection of the display panel solely through grayscale change analysis and edge matching analysis, simplifying algorithm complexity and saving data resource costs and computation time costs.

[0015] Secondly, a display panel image retention detection device is provided, the display panel image retention detection device including an acquisition module and a determination module.

[0016] Thirdly, a computer device is provided, the computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the above-described method for detecting image retention on a display panel.

[0017] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described screen detection method.

[0018] It is understood that the beneficial effects of the second, third, and fourth aspects mentioned above can be found in the relevant descriptions in the first aspect above, and will not be repeated here. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0020] Figure 1 This is a flowchart of a display panel image retention detection method provided in an embodiment of this application;

[0021] Figure 2 This is a schematic diagram of an example of a black and white checkerboard pattern provided in an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of an example of a display panel afterimage provided in an embodiment of this application;

[0023] Figure 4 This is a flowchart illustrating an example of a process for determining whether each checkerboard region in a test grayscale image satisfies the surface afterimage condition, as provided in an embodiment of this application.

[0024] Figure 5 This is a flowchart illustrating an example of a process for determining whether each checkerboard region in a test grayscale image satisfies the edge-line afterimage condition, provided in an embodiment of this application.

[0025] Figure 6 This is a schematic diagram of the structure of a display panel image retention detection device provided in an embodiment of this application;

[0026] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0028] It should be understood that "multiple" as mentioned in this application refers to two or more. In the description of this application, unless otherwise stated, " / " indicates "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist, for example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, to facilitate a clear description of the technical solutions of this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and that "first," "second," etc., do not necessarily imply differences.

[0029] Before providing a detailed explanation of the embodiments of this application, the application scenarios of these embodiments will be described first.

[0030] With societal progress, display panel technology has been widely applied in the electronic display industry, improving the quality of image and video displays.

[0031] Due to the characteristics and operational limitations of display panels, image retention is a frequent problem. Image retention refers to the image persistence effect that occurs when a display panel rapidly switches between images or videos, caused by the slow pixel response time. Currently, the following methods are commonly used to detect image retention on display panels:

[0032] A. Visual observation: Subjective evaluation is conducted by directly observing the image retention effect on the display panel during switching. However, this method is affected by subjective factors and individual differences, and cannot accurately detect image retention on the display panel.

[0033] B. Image retention can be assessed by measuring the response time of the display panel when switching images using specialized equipment. However, specialized equipment is usually expensive and requires a high-quality testing environment, resulting in high costs.

[0034] C. Image retention can be detected by analyzing the differences between image sequences or pixels on the display panel using image processing algorithms. These algorithms can include differential image processing algorithms, moving object detection algorithms, and optical flow algorithms. However, image processing algorithms require large amounts of image data and complex computational steps, increasing both data resource costs and computation time costs.

[0035] Therefore, this application provides a method for detecting image retention in display panels. By performing grayscale change analysis and edge matching analysis on the original grayscale image and the test grayscale image, the method determines whether image retention exists in the display panel. This method can accurately detect image retention in display panels and saves resources and costs.

[0036] The following is a detailed explanation of a method for detecting image retention on a display panel provided in the embodiments of this application. The execution subject of the method provided in the embodiments of this application can be a computer device, a processor, or any device including a processor. The following description will use a computer device as the execution subject as an example.

[0037] Example 1:

[0038] Figure 1 This is a flowchart of a method for detecting image retention in a display panel according to an embodiment of this application. See also... Figure 1 The method includes the following steps.

[0039] S101: Obtain the original grayscale image of the display panel. The original grayscale image refers to the image obtained by inputting a preset grayscale signal to all pixels of the display panel at the first moment.

[0040] The first moment can be any moment before the black and white checkerboard pattern is illuminated. The preset grayscale can be set as needed, with a value range of 0 to 255. For example, the preset grayscale can be 127, 0, or 255. It should be noted that the original grayscale image is acquired before the black and white checkerboard pattern is illuminated. At this point, the preset grayscale signal is input to all pixels on the display panel, resulting in an image where the grayscale values ​​do not change abruptly; that is, all pixels in the original grayscale image have the same grayscale value.

[0041] Optionally, a preset grayscale signal can be input to the display panel, which can then display the original grayscale image. At this point, the original grayscale image can be captured by an industrial camera and stored in a computer device.

[0042] S102: Obtain a test grayscale image of the display panel. The test grayscale image refers to the image obtained by controlling the display panel to display a black and white checkerboard pattern in multiple checkerboard areas after the first moment, and continuing for a preset time, and then inputting a preset grayscale signal to all pixels of the display panel.

[0043] After acquiring the original grayscale image, the display panel's image is switched from the original grayscale image to a black-and-white checkerboard image, which is maintained for a preset duration. Optionally, the preset duration can be set according to needs, such as 30 minutes, 60 minutes, etc. Then, the preset grayscale signal is input to all pixels of the display panel to obtain a test grayscale image.

[0044] Optionally, after maintaining the black and white checkerboard pattern for a preset time, a preset grayscale signal is input to the display panel, which can then display a test grayscale image. At this time, the test grayscale image can be captured by an industrial camera and stored in a computer device.

[0045] Taking a preset grayscale of 127 as an example, after switching the original grayscale image to a black and white checkerboard image and keeping it for 30 minutes, a signal with a grayscale value of 127 is input to all pixels of the display panel to obtain a test grayscale image.

[0046] Optionally, the size of the checkerboard area can be set according to the aspect ratio of the display panel, with each checkerboard area having the same size, meaning each checkerboard area has the same number of pixels. For example, Figure 2 Here is a schematic diagram of an example of a black and white checkerboard pattern, such as... Figure 2As shown, the display panel includes an M-row N-column checkerboard area. By controlling the M-row N-column checkerboard area to display black and white in alternating patterns, a black and white checkerboard image is obtained.

[0047] It should be noted that when switching from the black and white checkerboard image to the test grayscale image, due to the slow pixel response speed, the grayscale values ​​of pixels in some checkerboard areas of the black and white checkerboard image may change abruptly, resulting in image artifacts on the test grayscale image. For example, Figure 3 This is a schematic diagram illustrating an example of display panel afterimages, such as... Figure 3 As shown, when a signal with a preset grayscale of 127 is input to the test grayscale image, after switching from the black and white checkerboard image to the test grayscale image, there are afterimages of the black and white checkerboard image at the edges of the checkerboard areas A(2,2) and A(4,3) and the adjacent edges of the checkerboard areas A(2,2) and A(2,4) in the test grayscale image.

[0048] S103: Perform grayscale change analysis and edge matching analysis on the original grayscale image and the test grayscale image to determine whether there is a black and white checkerboard pattern afterimage in the test grayscale image.

[0049] Grayscale change analysis compares the grayscale values ​​of the original grayscale image and the test grayscale image. In other words, both the original and test grayscale images are input signals with the same preset grayscale level. When the black and white checkerboard image is switched to the test grayscale image, due to the slower pixel response speed, the grayscale values ​​of pixels in some checkerboard areas of the original image change abruptly. This results in image retention on the test grayscale image, causing a change in the grayscale values ​​of the test grayscale image. Consequently, the grayscale values ​​of the test grayscale image differ from those of the original grayscale image. By comparing the grayscale values ​​of the original and test grayscale images, it can be determined whether image retention exists.

[0050] Edge matching analysis compares the edges of each checkerboard region in the original grayscale image and the test grayscale image to determine if any afterimages exist. See also Figure 3 When the black and white checkerboard screen is switched to the test grayscale screen, line afterimages are likely to occur at the edges of adjacent black and white checkerboard squares in the black and white checkerboard screen. Edge matching analysis can accurately detect whether afterimages appear in the display panel.

[0051] The image retention detection method for display panels provided in this application acquires an original grayscale image of the display panel and a test grayscale image of the display panel. Grayscale change analysis and edge matching analysis are performed on the original and test grayscale images to determine whether a black-and-white checkerboard pattern image retention exists in the test grayscale image. First, this method can objectively determine whether image retention occurs on the display panel, thereby improving the accuracy of image retention detection. Second, this method can achieve image retention detection of the display panel through a software program, eliminating the need for expensive equipment and specific detection environments to evaluate image retention, thus saving costs and resources. Third, this method can achieve image retention detection of the display panel solely through grayscale change analysis and edge matching analysis, simplifying algorithm complexity and saving data resource costs and computation time costs.

[0052] Example 2:

[0053] The grayscale change analysis and edge matching analysis provided in the embodiments of this application will be explained in detail below.

[0054] In one embodiment, step S103 above: performing grayscale change analysis and edge matching analysis on the original grayscale image and the test grayscale image to determine whether there is a black and white checkerboard pattern afterimage in the test grayscale image, including:

[0055] A. Perform grayscale change analysis on the original grayscale image and the test grayscale image to determine whether each checkerboard region in the test grayscale image meets the surface afterimage condition.

[0056] In other words, grayscale change analysis analyzes the changes in grayscale values ​​of the original grayscale image and the grayscale image to determine whether the changes in grayscale values ​​of each checkerboard region in the test grayscale image meet the preset surface afterimage conditions.

[0057] Meeting the conditions for surface image retention indicates the possible existence of surface image retention; not meeting the conditions indicates the absence of surface image retention. See also... Figure 3 Afterimages occur when switching from a black-and-white checkerboard image to a test grayscale image. The black-and-white checkerboard image remains in the test grayscale image. Area afterimages are instances where the corresponding black-and-white checkerboard area in the test grayscale image retains traces of the checkerboard image, and these traces also appear at the edges of the corresponding checkerboard area in the test grayscale image. Line afterimages are instances where the edges of adjacent checkerboard areas in the corresponding black-and-white checkerboard area in the test grayscale image retain traces of the black-and-white checkerboard image.

[0058] B. Perform edge matching analysis on the original grayscale image and the test grayscale image to determine whether each checkerboard region in the test grayscale image satisfies the edge line afterimage condition.

[0059] At this point, edge matching analysis is performed on the original grayscale image and the test grayscale image to determine whether each corresponding checkerboard region in the test grayscale image satisfies the edge line ghosting condition. Satisfying the line ghosting condition indicates that the display panel has line ghosting; not satisfying the line ghosting condition indicates that the display panel does not have line ghosting.

[0060] C. If the first chessboard region satisfies both the surface afterimage condition and the edge afterimage condition, then it is determined that the first chessboard region contains a surface afterimage, where the first chessboard region is any one of multiple chessboard regions. If the first chessboard region satisfies the edge afterimage condition but does not satisfy the surface afterimage condition, then it is determined that the first chessboard region contains an edge afterimage.

[0061] The first checkerboard area is any one of the checkerboard areas in the black-and-white checkerboard image. In other words, if any corresponding checkerboard area in the test grayscale image simultaneously satisfies both the surface afterimage condition and the edge line afterimage condition, then the checkerboard area exhibits surface afterimage. If any corresponding checkerboard area in the test grayscale image only satisfies the edge line afterimage condition, then the checkerboard area exhibits line afterimage.

[0062] The grayscale change analysis and edge matching analysis provided in this application embodiment determine whether each checkerboard region in the test grayscale image meets the conditions for surface image retention and line image retention. If the first checkerboard region meets both the surface image retention condition and the edge line image retention condition, then it is determined that the first checkerboard region has surface image retention. If the first checkerboard region meets the edge line image retention condition but does not meet the surface image retention condition, then it is determined that the first checkerboard region has edge line image retention. First, this method can not only determine whether image retention exists, but also determine whether the image retention type is surface image retention or line image retention, improving the practicality of image retention detection for display panels. Second, by combining the judgment results of surface image retention and line image retention conditions, the existence of surface image retention is determined, fully considering the characteristics of surface image retention, and thus more accurately identifying surface image retention.

[0063] The process of determining whether each checkerboard region in a test grayscale image meets the surface afterimage condition, as provided in the embodiments of this application, will be further explained below with reference to the accompanying drawings and embodiments.

[0064] Example 3:

[0065] Figure 4 This is a flowchart illustrating an example of a process for determining whether each checkerboard region in a test grayscale image satisfies the surface afterimage condition, as provided in an embodiment of this application. See also... Figure 4 Taking the first chessboard area as an example, the method includes the following steps:

[0066] S201: Determine the grayscale change of the first checkerboard region based on the difference between the test grayscale value and the original grayscale value; wherein, the test grayscale value is the average grayscale value of the first checkerboard region in the test grayscale image, and the original grayscale value is the average grayscale value of the first checkerboard region in the original grayscale image.

[0067] The first checkerboard area comprises multiple pixels, each with a corresponding grayscale value. A computer device can acquire the grayscale values ​​of all pixels in the first checkerboard area, thereby determining the average grayscale value of the first checkerboard area. See also... Figure 2 For example, if the first checkerboard area is A(1,1), and A contains 30 pixels, the average gray level of the first checkerboard area can be calculated based on the gray level value of each pixel.

[0068] Based on this, the average grayscale value of the first checkerboard region in the original grayscale image can be calculated to obtain the original grayscale value; the average grayscale value of the first checkerboard region in the test grayscale image can be calculated to obtain the test grayscale value. Therefore, the difference between the test grayscale value and the original grayscale value can be calculated. For example, if the original grayscale value is 127 and the test grayscale value is 150, the difference between the test grayscale value and the original grayscale value can be calculated as 23.

[0069] S202: Determine the surface afterimage parameters based on the ratio of the grayscale change to the original grayscale value.

[0070] The computer equipment can determine the grayscale change in the first checkerboard area based on the test grayscale value and the original grayscale value. Then, based on the grayscale change and the original grayscale value, the surface afterimage parameter can be determined. For example, if the grayscale change is 23 and the original grayscale value is 127, the surface afterimage parameter can be calculated to be 0.181.

[0071] Optionally, the surface residual parameters can be obtained by formula (1) or by deformation of formula (1).

[0072]

[0073] Where K is the afterimage coefficient, ΔL A(m,n) L represents the grayscale variation of the first chessboard area. A(m,n) is the original grayscale value, and b is the color perception coefficient.

[0074] Optionally, K and b can be set according to requirements. For example, with K = 1000, b = 2.2, and ΔL... A(m,n ) is 23, L A(m,n Taking 127 as an example, the surface residual parameter S can be calculated to be 23.275.

[0075] S203: Determine whether the surface image retention parameter is greater than the preset surface image retention threshold. If the surface image retention parameter is greater than the preset surface image retention threshold, then execute S204; if the surface image retention parameter is less than or equal to the preset surface image retention threshold, then execute S205.

[0076] The threshold for surface afterimages can be set according to requirements, for example, it can be 0.1.

[0077] S204: Determine that the first chessboard area satisfies the surface residual image condition.

[0078] For example, if the surface afterimage threshold is 0.1 and the surface afterimage parameter is 23.275, then the first chessboard area is determined to meet the surface afterimage condition.

[0079] S205: Determine that the first chessboard area does not meet the condition for a face image.

[0080] In one implementation, if it is determined that surface afterimages exist in the first checkerboard area, the level of the surface afterimages can be further determined based on the surface afterimage parameters and a preset level correspondence. The level of the surface afterimages is used to characterize the severity of the surface afterimages.

[0081] For example, the preset levels can be A, B, C, and D, where level A represents the highest degree of image retention, level B represents a relatively high degree of image retention, level C represents a moderate degree of image retention, and level D represents a relatively low degree of image retention. The area image retention parameter range corresponding to level A is greater than 10, level B is greater than 5 and less than or equal to 10, level C is greater than 1 and less than or equal to 5, and level D is greater than 0.1 and less than or equal to 1. The image retention level can be determined based on the level range corresponding to the area image retention parameters.

[0082] The method provided in this application determines the grayscale change in the first checkerboard area based on the difference between the test grayscale value and the original grayscale value. It then determines the surface retention parameter based on the ratio of the grayscale change to the original grayscale value. When the surface retention parameter is greater than a preset surface retention threshold, the surface retention condition is determined to be met. By calculating the difference between the test grayscale value and the original grayscale value, it is possible to specifically and quantitatively determine whether the grayscale value of the pixels in the first checkerboard area of ​​the display panel has undergone abrupt changes, thus improving the accuracy of display panel retention detection. Furthermore, when surface retention exists in the first checkerboard area, the surface retention level can be further determined, allowing users to directly understand the severity of the surface retention, providing a reference standard for subsequent display panel maintenance, and improving the practicality of retention detection.

[0083] Example 3:

[0084] The process of determining whether each checkerboard region in a test grayscale image meets the edge afterimage condition, as provided in the embodiments of this application, will be further explained below with reference to the accompanying drawings and embodiments.

[0085] Figure 5 This is a flowchart illustrating an example of a process for determining whether each checkerboard region in a test grayscale image satisfies the edge-line afterimage condition, as provided in an embodiment of this application. See also... Figure 5 The method includes the following steps:

[0086] S301: Determine the structural similarity index between the first edge and the second edge based on the test grayscale value and the original grayscale value; wherein, the test grayscale value is the average grayscale value of the first checkerboard region in the test grayscale image, and the original grayscale value is the average grayscale value of the first checkerboard region in the original grayscale image; the first edge is the edge of the first checkerboard region in the original grayscale image; and the second edge is the edge of the first checkerboard region in the test grayscale image.

[0087] It is understandable that the first edge and the second edge are positioned exactly the same on the display panel. For example, refer to... Figure 2 The first edge is the right edge of the checkerboard region A(1,1) in the original grayscale image (i.e., the left edge of the checkerboard region A(1,2)), and the second edge is the right edge of the checkerboard region A(1,1) in the test grayscale image (i.e., the left edge of the checkerboard region A(1,2)).

[0088] In one embodiment, step S301 can be implemented through the following process:

[0089] A. Determine the luminance factor and luminance covariance of the first checkerboard area based on the test grayscale value and the original grayscale value.

[0090] The brightness factor I can be calculated using formula (2).

[0091]

[0092] Where, μ x μ is the average grayscale value of the first checkerboard region in the original grayscale image. y C1 is a constant used to test the average grayscale value of the first checkerboard region in the grayscale image.

[0093] The brightness covariance of the first checkerboard area can be determined based on the test grayscale value and the original grayscale value.

[0094] B. Determine the contrast factor of the first checkerboard region based on the standard deviation of the test brightness and the standard deviation of the original brightness; wherein, the standard deviation of the test brightness is the standard deviation of the brightness of the first checkerboard region in the test grayscale image, and the original grayscale value is the standard deviation of the brightness of the first checkerboard region in the original grayscale image.

[0095] The contrast factor C can be calculated using formula (3).

[0096]

[0097] Where, σ x σ represents the original standard deviation of brightness. y C2 is a constant used to measure the standard deviation of brightness.

[0098] C. Determine the structure factor of the first checkerboard region based on the standard deviation of the measured brightness, the standard deviation of the original brightness, and the brightness covariance.

[0099] The structural factor S can be calculated using formula (4).

[0100]

[0101] Where, σ xy For the brightness covariance, σ x σ represents the original standard deviation of brightness. y C3 is a constant used to test the standard deviation of brightness.

[0102] D. Determine the structural similarity index based on the brightness factor, contrast factor, and structure factor.

[0103] The structural similarity index M can be calculated using formula (5).

[0104] M = I α C β S λ (5)

[0105] Where I is the luminance factor, C is the contrast factor, S is the structure factor, and α, β, and λ are weighting coefficients.

[0106] S302: Determine whether the structural similarity index is greater than the preset similarity threshold. If the structural similarity index is greater than the preset similarity threshold, then execute S303. If the structural similarity index is less than or equal to the preset similarity threshold, then execute S304.

[0107] It should be noted that the structural similarity index calculated according to the above formula (5) has a value range greater than -1 and less than 1. If the structural similarity index is closer to 1, it means that the edges of the first edge and the second edge are more similar, that is, the original grayscale image and the test grayscale image are more similar; if the structural similarity index is closer to -1, it means that the edges of the first edge and the second edge are less similar, that is, the original grayscale image and the test grayscale image are less similar; if the structural similarity index is 0, the original grayscale image and the test grayscale image are moderately similar.

[0108] The similarity threshold can be set manually according to requirements, and can be 0.8, 0.7, 0.6, etc. For example, if the similarity threshold is 0.8 and the structural similarity index is 0.9, it can be determined that the first chessboard area meets the edge line afterimage condition. If the calculated structural similarity index is greater than -1 and less than or equal to 0.8, it can be determined that the first chessboard area does not meet the edge line afterimage condition.

[0109] S303: Determine that the first chessboard area satisfies the edge line afterimage condition.

[0110] S304: Determine that the first chessboard area does not meet the edge line afterimage condition.

[0111] In this embodiment, the luminance factor and luminance covariance of the first checkerboard region are determined based on the test grayscale value and the original grayscale value. The contrast factor of the first checkerboard region is determined based on the test luminance standard deviation and the original luminance standard deviation. The structure factor of the first checkerboard region is determined based on the test luminance standard deviation, the original luminance standard deviation, and the luminance covariance. A structural similarity index is determined based on the luminance factor, contrast factor, and structure factor. When the structural similarity index is greater than a preset similarity threshold, the first checkerboard region is determined to meet the edge-line afterimage condition. This method, by calculating the structural similarity index through the luminance factor, contrast factor, and structure factor, can accurately determine the similarity between the first edge and the second edge, thereby accurately detecting afterimages on the display panel.

[0112] Figure 6 A schematic diagram of the structure of an image detection device provided in this application is shown. The device 600 includes:

[0113] The acquisition module 601 is used to acquire the original grayscale image of the display panel, which refers to the image obtained by inputting a preset grayscale signal to all pixels of the display panel at the first moment; it is also used to acquire the test grayscale image of the display panel, which refers to the image obtained by controlling the display panel to display a black and white checkerboard pattern according to multiple checkerboard areas after the first moment, and continuing for a preset time, and then inputting a preset grayscale signal to all pixels of the display panel.

[0114] The determination module 602 is used to perform grayscale change analysis and edge matching analysis on the original grayscale image and the test grayscale image to determine whether there is a black and white checkerboard pattern afterimage in the test grayscale image.

[0115] In some embodiments, the determining module 602 is further configured to perform grayscale change analysis on the original grayscale image and the test grayscale image to determine whether each checkerboard region in the test grayscale image satisfies the surface afterimage condition; perform edge matching analysis on the original grayscale image and the test grayscale image to determine whether each checkerboard region in the test grayscale image satisfies the edge afterimage condition; if the first checkerboard region satisfies both the surface afterimage condition and the edge afterimage condition, then it is determined that the first checkerboard region has a surface afterimage; wherein, the first checkerboard region is any one of multiple checkerboard regions; if the first checkerboard region satisfies the edge afterimage condition but does not satisfy the surface afterimage condition, then it is determined that the first checkerboard region has an edge afterimage.

[0116] In some embodiments, the determining module 602 is further configured to determine the grayscale change amount of the first checkerboard region based on the difference between the test grayscale value and the original grayscale value; wherein, the test grayscale value is the average grayscale value of the first checkerboard region in the test grayscale image, and the original grayscale value is the average grayscale value of the first checkerboard region in the original grayscale image; determine the surface retention parameter based on the ratio of the grayscale change amount to the original grayscale value; determine whether the surface retention parameter is greater than a preset surface retention threshold; if the surface retention parameter is greater than the preset surface retention threshold, then determine that the first checkerboard region meets the surface retention condition; if the surface retention parameter is less than or equal to the preset surface retention threshold, then determine that the first checkerboard region does not meet the surface retention condition.

[0117] In some embodiments, the determining module 602 is further configured to determine the surface afterimage level of the first chessboard area based on the surface afterimage parameters and a preset level correspondence if surface afterimage exists in the first chessboard area.

[0118] In some embodiments, the determining module 602 is further configured to determine the surface residual parameters according to the following formula:

[0119]

[0120] Where K is the afterimage coefficient, ΔL A(m,n) L represents the grayscale variation of the first chessboard area. A(m,n) is the original grayscale value, and b is the color perception coefficient.

[0121] In some embodiments, the determining module 602 is further configured to determine a structural similarity index between the first edge and the second edge based on the test grayscale value and the original grayscale value; wherein, the test grayscale value is the average grayscale value of the first checkerboard region in the test grayscale image, and the original grayscale value is the average grayscale value of the first checkerboard region in the original grayscale image; the first edge is the edge of the first checkerboard region in the original grayscale image; the second edge is the edge of the first checkerboard region in the test grayscale image; determine whether the structural similarity index is greater than a preset similarity threshold; if the structural similarity index is greater than the preset similarity threshold, then determine that the first checkerboard region satisfies the edge line afterimage condition; if the structural similarity index is less than or equal to the preset similarity threshold, then determine that the first checkerboard region does not satisfy the edge line afterimage condition.

[0122] In some embodiments, the determining module 602 is further configured to: determine the luminance factor and luminance covariance of the first checkerboard region based on the test grayscale value and the original grayscale value; determine the contrast factor of the first checkerboard region based on the test luminance standard deviation and the original luminance standard deviation; wherein the test luminance standard deviation is the luminance standard deviation of the first checkerboard region in the test grayscale image, and the original grayscale value is the luminance standard deviation of the first checkerboard region in the original grayscale image; determine the structure factor of the first checkerboard region based on the test luminance standard deviation, the original luminance standard deviation, and the luminance covariance; and determine the structural similarity index based on the luminance factor, the contrast factor, and the structure factor.

[0123] The specific manner in which the device 600 performs the image retention detection method for the display panel and the beneficial effects thereof can be found in the relevant descriptions in the method embodiments, and will not be repeated here.

[0124] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 7 As shown, the computer device 700 includes: a processor 710, a memory 720, and a computer program 721 stored in the memory 720 and executable on the processor 710. When the processor 710 executes the computer program 721, it implements the steps in the display panel afterimage detection method in the above embodiments.

[0125] The computer device 700 can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device 700 can be a desktop computer, a portable computer, a network server, a handheld computer, a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device 700. Those skilled in the art will understand that... Figure 7The computer device 700 is merely an example and does not constitute a limitation on the computer device 700. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0126] The processor 710 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0127] In some embodiments, memory 720 may be an internal storage unit of computer device 700, such as a hard disk or memory of computer device 700. In other embodiments, memory 720 may be an external storage device of computer device TH, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on computer device TH. Furthermore, memory 720 may include both internal and external storage units of computer device 700. Memory 720 is used to store operating systems, applications, boot loaders, data, and other programs. Memory 720 may also be used to temporarily store data that has been output or will be output.

[0128] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the various method embodiments described above.

[0129] 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 above method embodiments of this application can be implemented by a computer program instructing related hardware. This 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 some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage devices. The computer-readable storage medium mentioned in this application can be a non-volatile storage medium; in other words, it can be a non-transient storage medium.

[0130] It should be understood that all or part of the steps of the above embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. The computer instructions can be stored in the above-described computer-readable storage medium.

[0131] 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.

[0132] 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.

[0133] In the embodiments provided in this application, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer 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.

[0134] 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.

[0135] The above-described 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. A method for detecting image retention on a display panel, characterized in that, The display panel includes multiple checkerboard areas, and the method includes: The original grayscale image of the display panel is obtained, wherein the original grayscale image refers to the image obtained by inputting a preset grayscale signal to all pixels of the display panel at the first moment; Acquire a test grayscale image of the display panel. The test grayscale image refers to the image obtained after the first moment, by controlling the display panel to display a black and white checkerboard pattern according to the multiple checkerboard areas, and after a preset duration, by inputting the preset grayscale signal to all pixels of the display panel. Perform grayscale change analysis and edge matching analysis on the original grayscale image and the test grayscale image to determine whether there is a residual image of the black and white checkerboard pattern in the test grayscale image, including: Gray-level change analysis is performed on the original grayscale image and the test grayscale image to determine whether each of the checkerboard regions in the test grayscale image satisfies the surface afterimage condition; the gray-level change analysis is a comparison of the gray levels of the original grayscale image and the test grayscale image; Edge matching analysis is performed on the original grayscale image and the test grayscale image to determine whether each of the checkerboard regions in the test grayscale image satisfies the edge line afterimage condition. If the first chessboard area satisfies both the surface afterimage condition and the edge line afterimage condition, then it is determined that the first chessboard area has a surface afterimage; wherein, the first chessboard area is any one of the plurality of chessboard areas; If the first chessboard area satisfies the edge line afterimage condition but does not satisfy the surface afterimage condition, then it is determined that the first chessboard area has an edge line afterimage.

2. The method according to claim 1, characterized in that, The step of performing grayscale change analysis on the original grayscale image and the test grayscale image to determine whether each of the chessboard regions satisfies the surface afterimage condition includes: The grayscale change of the first checkerboard area is determined based on the difference between the test grayscale value and the original grayscale value; wherein, the test grayscale value is the average grayscale value of the first checkerboard area in the test grayscale image, and the original grayscale value is the average grayscale value of the first checkerboard area in the original grayscale image. The surface afterimage parameters are determined based on the ratio of the grayscale change to the original grayscale value; Determine whether the surface image parameter is greater than a preset surface image threshold; If the surface ghosting parameter is greater than the preset surface ghosting threshold, then the first chessboard area is determined to meet the surface ghosting condition; If the surface ghosting parameter is less than or equal to the preset surface ghosting threshold, then the first chessboard area is determined not to meet the surface ghosting condition.

3. The method according to claim 2, characterized in that, The method further includes: If there is a face afterimage in the first chessboard area, the face afterimage level of the first chessboard area is determined according to the face afterimage parameters and the preset level correspondence.

4. The method according to claim 2, characterized in that, The step of determining the surface afterimage parameters based on the ratio of the grayscale change to the original grayscale value includes: The surface afterimage parameters are determined according to the following formula: Where K is the afterimage coefficient, ΔL A(m,n) L represents the grayscale variation of the first chessboard area. A(m,n) is the original grayscale value, and b is the color perception coefficient.

5. The method according to claim 1, characterized in that, The step of performing edge matching analysis on the original grayscale image and the test grayscale image to determine whether each checkerboard region in the test grayscale image satisfies the edge afterimage condition includes: Based on the test grayscale value and the original grayscale value, the structural similarity index between the first edge and the second edge is determined; wherein, the test grayscale value is the average grayscale value of the first checkerboard region in the test grayscale image, the original grayscale value is the average grayscale value of the first checkerboard region in the original grayscale image, the first edge is the edge of the first checkerboard region in the original grayscale image, and the second edge is the edge of the first checkerboard region in the test grayscale image; Determine whether the structural similarity index is greater than a preset similarity threshold; If the structural similarity index is greater than the preset similarity threshold, then the first chessboard region is determined to satisfy the edge line afterimage condition. If the structural similarity index is less than or equal to a preset similarity threshold, then the first chessboard region is determined not to meet the edge line afterimage condition.

6. The method according to claim 5, characterized in that, The step of determining the structural similarity index between the first edge and the second edge based on the test grayscale value and the original grayscale value includes: The brightness factor and brightness covariance of the first checkerboard area are determined based on the test grayscale value and the original grayscale value. The contrast factor of the first checkerboard region is determined based on the standard deviation of the test brightness and the standard deviation of the original brightness; wherein, the standard deviation of the test brightness is the standard deviation of the brightness of the first checkerboard region in the test grayscale image, and the original grayscale value is the standard deviation of the brightness of the first checkerboard region in the original grayscale image. The structure factor of the first checkerboard region is determined based on the test brightness standard deviation, the original brightness standard deviation, and the brightness covariance. The structural similarity index is determined based on the luminance factor, the contrast factor, and the structure factor.

7. A device for detecting image retention on a display panel, characterized in that, The display panel includes multiple checkerboard areas, and the afterimage detection device for the display panel includes: The acquisition module is used for: The original grayscale image of the display panel is obtained, wherein the original grayscale image refers to the image obtained by inputting a preset grayscale signal to all pixels of the display panel at the first moment; Acquire a test grayscale image of the display panel. The test grayscale image refers to the image obtained after the first moment, by controlling the display panel to display a black and white checkerboard pattern according to the multiple checkerboard areas, and after a preset duration, by inputting the preset grayscale signal to all pixels of the display panel. The determination module is used to perform grayscale change analysis and edge matching analysis on the original grayscale image and the test grayscale image to determine whether there is a residual image of the black and white checkerboard pattern in the test grayscale image, including: Gray-level change analysis is performed on the original grayscale image and the test grayscale image to determine whether each of the checkerboard regions in the test grayscale image satisfies the surface afterimage condition; the gray-level change analysis is a comparison of the gray levels of the original grayscale image and the test grayscale image; Edge matching analysis is performed on the original grayscale image and the test grayscale image to determine whether each of the checkerboard regions in the test grayscale image satisfies the edge line afterimage condition. If the first chessboard grid region satisfies both the surface afterimage condition and the edge line afterimage condition, then it is determined that the first chessboard grid region has a surface afterimage; wherein, the first chessboard grid region is any one of the plurality of chessboard grid regions; If the first chessboard area satisfies the edge line afterimage condition but does not satisfy the surface afterimage condition, then it is determined that the first chessboard area has an edge line afterimage.

8. A computer device, characterized in that, The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method as described 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 that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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