A method and device for detecting image sticking level of a display screen, and an electronic device

Abstract

The application provides a display screen image sticking level detection method and device and electronic equipment; the method comprises the following steps: acquiring an image sticking image corresponding to a residual image test image; the image sticking image is divided into a plurality of sub-regions, the frequency domain image, the spatial domain image and the shape feature of each sub-region are determined; based on the frequency domain image, the spatial domain image and the shape feature of all the sub-regions, the image sticking level of the display screen is determined. The method provided by the application fully considers the special properties of the human visual system, can realize image sticking detection similar to human judgment effect, thereby meeting the display screen performance evaluation requirements facing the actual needs of users, and improving the accuracy of display screen image sticking level detection.

Description

Technical Field

[0001] This application relates to the field of display technology, and in particular to a method, apparatus and electronic device for detecting the image retention level of a display screen. Background Technology

[0002] Liquid crystal display (LCD) (LED) is one of the most widely used display technologies, commonly found in home appliances and consumer electronics. To improve the quality of LCD displays and enhance user experience, image quality testing is necessary during the manufacturing process. Image sticking (IS) is a phenomenon that significantly impacts display quality. When an LCD displays a fixed image for an extended period, if the displayed content is changed, faint outlines of the original image may remain on the screen for a period, resulting in image sticking and affecting the display's performance. Image sticking is related to factors such as residual electric field, voltage holding ratio, and tilt angle, and is difficult to completely eliminate during LCD manufacturing.

[0003] Currently, existing technologies mainly employ manual inspection or testing equipment inspection. Manual inspection relies on the human eye to directly judge the degree of image persistence; this method is highly subjective, difficult to quantify, and easily affected by individual differences and environmental factors, resulting in low reliability. Testing equipment inspection, on the other hand, does not fully consider the unique characteristics of the human visual system, making it difficult to achieve image persistence detection results similar to human judgment, thus failing to meet the performance evaluation requirements of displays that cater to actual user needs. Summary of the Invention

[0004] This application provides a method, apparatus, and electronic device for detecting the image persistence level of a display screen. It can fully take into account the special properties of the human visual system and achieve image persistence detection with a similar effect to human judgment, thereby meeting the display screen performance evaluation requirements for actual user needs and improving the accuracy of display screen image persistence level detection.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a method for detecting the image persistence level of a display screen, comprising:

[0007] Obtain the residual image corresponding to the afterimage test image;

[0008] The residual image is segmented into multiple sub-regions, and the frequency domain image, spatial domain image, and shape features of each sub-region are determined.

[0009] Based on the frequency domain image, spatial domain image, and shape features of all the sub-regions, the image persistence level of the display screen is determined.

[0010] In the above scheme, segmenting the image residual into multiple sub-regions and determining the frequency domain image, spatial domain image, and shape features of each sub-region includes:

[0011] For each sub-region, the sub-region is preprocessed and high-frequency noise is removed to obtain the frequency domain image of the sub-region;

[0012] The two-dimensional digital image obtained after performing equalization operation on the sub-region is determined as the spatial domain image of the sub-region.

[0013] The sub-region is matched with an image to obtain its shape features.

[0014] In the above scheme, determining the image persistence level of the display screen based on the frequency domain image, spatial domain image, and shape features of all the sub-regions includes:

[0015] For each sub-region, based on the frequency domain image, spatial domain image, and shape features of the sub-region, the edge enhancement feature value, contrast sensitivity feature value, and neighborhood sharpness feature value of the sub-region are determined;

[0016] Based on the edge enhancement feature value, contrast sensitivity feature value, and neighborhood sharpness feature value of the sub-region, the overall evaluation value of the afterimage of the sub-region is determined;

[0017] Based on the comprehensive evaluation value of the afterimage in all the sub-regions, the image retention level of the display screen is determined.

[0018] In the above scheme, determining the overall evaluation value of the afterimage of the sub-region based on the edge enhancement feature value, contrast sensitivity feature value, and neighborhood sharpness feature value of the sub-region includes:

[0019] For each sub-region, determine the first value of the product of the first regression coefficient and the edge enhancement feature value, the second value of the product of the second regression coefficient and the contrast sensitivity feature value, and the third value of the product of the third regression coefficient and the neighborhood sharpness.

[0020] The sum of the first value, the second value, and the third value corresponding to the sub-region is determined as the comprehensive evaluation value of the afterimage of the sub-region.

[0021] In the above scheme, determining the edge enhancement feature value, contrast sensitivity feature value, and neighborhood sharpness feature value for each sub-region based on the frequency domain image, spatial domain image, and shape features of the sub-region includes:

[0022] Based on the shape features of the sub-region, determine the position of the afterimage geometric contour in the spatial domain image of the corresponding sub-region in the image residue image;

[0023] Based on the edge points on the afterimage geometric contour of the sub-region, determine the first pixel point corresponding to each edge point within the first region, perform a first processing on the first pixel point, and determine the edge enhancement feature value of the sub-region based on the first processing result.

[0024] The target window is determined with each edge point on the afterimage geometric contour of the sub-region as the center. The pixels within the target window are subjected to a second processing, and the contrast sensitivity feature value of the sub-region is determined based on the result of the second processing.

[0025] In the above scheme, the step of determining a first pixel point corresponding to each edge point within a first region based on the edge points on the afterimage geometric contour of the sub-region, performing a first processing on the first pixel point, and determining the edge enhancement feature value of the sub-region based on the first processing result includes:

[0026] Perform convolution calculation on the first pixel to determine the first gradient value of the first pixel;

[0027] The first gradient value is transformed exponentially to obtain the second gradient value;

[0028] After accumulating the second gradient values ​​of the first pixel corresponding to each edge point, a normalization operation is performed to obtain the edge enhancement feature value of the sub-region.

[0029] In the above scheme, the step of determining a target window centered on each edge point of the afterimage geometric contour of the sub-region, performing a second processing on the pixels within the target window, and determining the contrast sensitivity feature value of the sub-region based on the result of the second processing includes:

[0030] Determine the target window within a second region centered on each of the edge points;

[0031] Determine the value of the difference quantization parameter between each region and the central region in the first number of regions of the target window;

[0032] The target gradient of the target window is determined by summing all the difference quantization parameters greater than or equal to 0 in the target window and then dividing the sum by the second quantity. The difference between the first quantity and the second quantity is 1.

[0033] The target gradients of all target windows in the sub-region are summed and then normalized to obtain the contrast sensitivity feature value corresponding to the sub-region.

[0034] In the above scheme, determining the value of the difference quantization parameter between each region and the central region in the first number of regions of the target window includes:

[0035] Determine the central region within the third region centered on each of the aforementioned edge points;

[0036] The first number of regions centered on the central region and the central region are defined as the target window, and each region in the target window is the same size;

[0037] The difference between the average gradient value of pixels in each region of the target window and the average gradient value of pixels in the central region is determined as the value of the difference quantization parameter.

[0038] In the above scheme, determining the edge enhancement feature value, contrast sensitivity feature value, and neighborhood sharpness feature value for each sub-region based on the frequency domain image, spatial domain image, and shape features of the sub-region includes:

[0039] Based on the sub-regions in the afterimage test image and the corresponding sub-regions in the image retention image, the spatial domain characteristics of the sub-regions in the afterimage test image and the frequency domain characteristics of each sub-region are determined.

[0040] Based on the data fitting method, the spatial domain features and frequency domain features of the sub-region are subjected to data fitting processing to obtain the neighborhood sharpness feature value of the sub-region.

[0041] In the above scheme, determining the residual effect level of the display screen based on the comprehensive evaluation value of the afterimage across all sub-regions includes:

[0042] The target image comprehensive evaluation value is obtained by summing the comprehensive evaluation values ​​of the image retention of all the sub-regions and then averaging them.

[0043] Based on the relationship table between the comprehensive evaluation value of image retention and the image retention level of the display screen, the image retention level of the display screen corresponding to the target comprehensive evaluation value of image retention is determined.

[0044] Secondly, embodiments of this application provide a device for detecting the image retention level of a display screen, the device comprising:

[0045] The image retention image acquisition module is used to acquire the image retention image corresponding to the image retention test image;

[0046] The sub-region image feature determination module is used to segment the image residual image into multiple sub-regions and determine the frequency domain image, spatial domain image and shape features of each sub-region.

[0047] The image persistence level determination module is used to determine the image persistence level of the display screen based on the frequency domain image, spatial domain image, and shape features of all the sub-regions.

[0048] Thirdly, embodiments of this application provide an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the image retention level detection method for a display screen provided in embodiments of this application.

[0049] Fourthly, embodiments of this application provide a computer-readable storage medium, the storage medium including a set of computer-executable instructions, which, when executed, are used to perform the image retention level detection method for a display screen provided in embodiments of this application.

[0050] The image retention level detection method for a display screen provided in this application involves acquiring an image retention image corresponding to an image retention test image; dividing the image retention image into multiple sub-regions; determining the frequency domain image, spatial domain image, and shape features of each sub-region; and determining the image retention level of the display screen based on the frequency domain image, spatial domain image, and shape features of all sub-regions. The method determines the frequency domain image, spatial domain image, and shape features of each sub-region in the image retention image, and for each sub-region, determines the edge enhancement feature value, contrast sensitivity feature value, and neighborhood sharpness feature value based on the frequency domain image, spatial domain image, and shape features of the sub-region; determines the comprehensive image retention evaluation value of the sub-region based on the edge enhancement feature value, contrast sensitivity feature value, and neighborhood sharpness feature value of the sub-region; and determines the image retention level of the display screen based on the comprehensive image retention evaluation value of all sub-regions. This technical solution can simulate the human eye's visual system formed by perceiving light to detect image retention, meeting the display screen performance evaluation requirements for actual user needs and improving the accuracy of image retention level detection. Attached Figure Description

[0051] The accompanying drawings are provided for a better understanding of this solution and do not constitute a limitation of this application. Wherein:

[0052] Figure 1This is a schematic diagram of an optional processing flow for a method of detecting the image retention level of a display screen provided in an embodiment of this application;

[0053] Figure 2 This is a ghosting test image provided in the embodiments of this application for a method of detecting the level of image retention on a display screen;

[0054] Figure 3 This is another image retention test image for the method of detecting image retention level of a display screen provided in the embodiments of this application;

[0055] Figure 4 This is a schematic diagram of an optional structure of the image retention level detection device for the display screen provided in the embodiments of this application;

[0056] Figure 5 This is a schematic diagram of another optional structure of the image retention level detection system for the display screen provided in the embodiments of this application;

[0057] Figure 6 This is a schematic block diagram of an optional electronic device provided in an embodiment of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0060] In the following description, the terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0062] The following describes a method for detecting the image persistence level of a display screen according to an embodiment of this application. See also... Figure 1 , Figure 1 This is a schematic diagram of an optional processing flow of the image retention level detection method for a display screen provided in this application embodiment. The following will be combined with... Figure 1 Steps S101-S103 shown will be explained.

[0063] Step S101: Obtain the residual image corresponding to the afterimage test image.

[0064] In some embodiments, the display screen can be a standalone liquid crystal display screen or an electronic product that includes a display screen, such as a laptop computer, mobile phone, or television screen. After the image of the afterimage on the display screen is held for a short period, the display screen is changed to a grayscale image. At this time, the afterimage test image will leave an afterimage on the grayscale image, forming an image retention image. This image retention image is then acquired by a detection device.

[0065] The residual image includes two parts: the afterimage and the grayscale image. The grayscale image is preferably an image where the values ​​of red, green, and blue (RGB) are all 127.

[0066] The detection device for acquiring residual images can preferably be a charge-coupled device (CCD) imaging luminance meter. Using standard photographic techniques, the CCD imaging luminance meter is used to take pictures at the orthographic projection angle and at a distance of 30-50cm from the display screen to obtain residual images in a dark room environment.

[0067] The afterimage test image can be presented in the following two ways:

[0068] Method 1: The image for the afterimage test can be as follows Figure 2 The image shown is an n*m black and white checkerboard pattern. Figure 2 In the image, the afterimage test image consists of black and white squares arranged alternately.

[0069] Method 2: The image for the afterimage test can be as follows Figure 3 The image shows grid images at different gray levels. Figure 3 In the image shown, each distinct cell has a background of a specific grayscale value, and within each cell is a base graphic that is either pure black or pure white. The base graphic can be... Figure 3 Each cell in the grid represents a square, but it can also be a triangle, a circle, or other more complex shapes. Each cell represents a sub-region.

[0070] Step S102: The residual image is segmented into multiple sub-regions, and the frequency domain image, spatial domain image, and shape features of each sub-region are determined.

[0071] In some embodiments, the residual image can be segmented into corresponding sub-regions based on the sub-regions defined in the original residual image test image. The sub-regions of the residual image correspond one-to-one with the sub-regions of the residual image test image.

[0072] If the afterimage test image is as follows Figure 2 The black and white checkerboard shown has each sub-region containing two white squares and two black squares, that is, each sub-region contains four squares. The white squares and black squares are arranged alternately, with white squares opposite each other and black squares opposite each other.

[0073] If the afterimage test image is as follows Figure 3 The grid images with different gray levels shown are such that each sub-region is a gray level grid.

[0074] In some embodiments, after obtaining the segmented sub-regions, processing can be performed on each sub-region to obtain the frequency domain image, spatial domain image, and shape features corresponding to each sub-region of the residual image.

[0075] The frequency domain image can be used to describe the energy spectrum characteristics of each sub-region in the image persistence image. The method for determining the frequency domain image of a sub-region is as follows: Each sub-region of the image persistence image is preprocessed, and its corresponding frequency domain information is obtained through Fourier transform. High-frequency signals and noise are removed, and the resulting two-dimensional amplitude map is determined as the frequency domain image. The frequency domain image of the i-th sub-region can be represented as F... i .

[0076] Spatial domain images can be used to describe the spatial domain enhancement characteristics of each sub-region in an image persistence image. The method for determining the spatial domain image of a sub-region is as follows: Each sub-region of the image persistence image undergoes equalization operations such as histogram processing or gamma transformation to enhance the image persistence features within the sub-region. The resulting two-dimensional digital image is then defined as the spatial domain image. The spatial domain image of the i-th sub-region can be represented as S. i .

[0077] Shape features can be used to determine the geometric contour of each sub-region in the afterimage test image. The method for determining the shape features of a sub-region is as follows: using the afterimage test image as a matching template, an image matching method, such as Normalized Cross-Correlation (NCC), is employed to obtain the shape features of each sub-region in the afterimage. The shape feature of the i-th sub-region can be represented as C. i .

[0078] Step S103: Based on the frequency domain image, spatial domain image, and shape features of all the sub-regions, determine the image persistence level of the display screen.

[0079] In some embodiments, edge enhancement feature values, contrast sensitivity feature values, and neighborhood sharpness feature values ​​for each sub-region can be determined based on the frequency domain image, spatial domain image, and shape features of each sub-region.

[0080] In some embodiments, the edge enhancement feature value of each sub-region can be determined based on the shape features and spatial domain image corresponding to each sub-region. The edge enhancement feature value can reflect the attention of the human visual system to the edge information of the sub-region and can represent the brightness gradient at the shape contour of each sub-region in the image persistence image. The edge enhancement feature value can be represented by EE. i express.

[0081] The calculation process of the edge enhancement feature value of each sub-region in the residual image can be shown in steps 201-202.

[0082] Step 201: Determine the position of the geometric contour of the afterimage in the image residue image by using the shape features of the sub-region.

[0083] Step 202: Based on the edge points on the afterimage geometric contour of each sub-region, determine the first pixel point corresponding to each edge point within the first region, perform a first processing on the first pixel point, and determine the edge enhancement feature value of each sub-region based on the first processing result.

[0084] Step 202 specifically includes steps 202a-202c.

[0085] Step 202a: Based on a first-order differential operator, such as the Sobel operator or the Gaussian operator, perform convolution calculation on the first pixel within a first region near the edge point on the afterimage geometric contour of the sub-region to obtain the first gradient value of the first pixel. The first pixel within the first region can be a pixel within a rectangle with a side length of 3 pixels near the pixel corresponding to the pixel on the afterimage geometric contour in the spatial domain image; the first gradient value can be the grayscale gradient value or the brightness gradient value of the pixel.

[0086] Step 202b: Let the first gradient distribution function of the first pixel be f(x,y), where f(x,y)∈[0,f m The first gradient value of the first pixel can be transformed using an exponential function to obtain the corresponding second gradient value, as shown in formula (1):

[0087]

[0088] In formula (1), f mThis represents the maximum value in the first gradient, and g(x,y) is the second gradient value corresponding to the original first gradient value of the first pixel after the exponential function transformation.

[0089] Step 202c: After accumulating the second gradient values ​​of the first pixel corresponding to each edge point on the afterimage geometric contour of each sub-region, perform normalization operation to obtain the edge enhancement feature value of each sub-region.

[0090] If there are N edge points on the geometric contour line of the afterimage of the sub-region, then the edge enhancement feature value of the sub-region is calculated as shown in formula (2):

[0091]

[0092] In formula (2), N is the number of edge points, f m This represents the maximum value in the first gradient. g(x,y) is the second gradient value corresponding to the original first gradient value of the first pixel after the exponential function transformation.

[0093] In some embodiments, the contrast sensitivity feature value of each sub-region can be determined based on the shape features and spatial domain image corresponding to each sub-region. The contrast sensitivity feature value can reflect the sensitivity of the human visual system to differences in edge brightness, representing the rate of change of brightness gradient at the shape contour in the afterimage image. The contrast sensitivity feature value can be represented by CS. i express.

[0094] The calculation process of the contrast sensitivity feature value of each sub-region in the residual image can be shown in steps 203-204 below.

[0095] Step 203: Determine the position of the geometric contour of the afterimage in the image residue image by using the shape features of the sub-region.

[0096] Step 204: Determine the target window centered on each edge point on the afterimage geometric contour of each sub-region, perform a second processing on the pixels within the target window, and determine the contrast sensitivity feature value of each sub-region based on the result of the second processing.

[0097] Specifically, step 204 includes steps 204a-204d.

[0098] Step 204a: Based on first-order differential operators, such as the Sobel operator or the Gaussian operator, perform convolution calculations on the pixels within a second region near the edge points on the afterimage geometric contour of the sub-region to obtain the third gradient value of the pixels. The pixels within the second region can be pixels within a rectangular region of length 3n pixels centered on the pixels on the afterimage geometric contour in the spatial domain image; the third gradient value can be the grayscale gradient value or the brightness gradient value of the pixel; and n is a pre-set integer parameter.

[0099] Step 204b: For edge points on the afterimage outline, such as edge point p k , with edge point p k Centered on the edge point p k The target window is defined as the central region within the third region centered on the target window. This third region is a rectangular area with sides of n pixels. The second number of regions centered on the target window, along with the central region located at the center, constitute the target window encompassing the first number of regions. Each region is an n*n (pixel) rectangular area. The central region is located at the center of the target window. The regions near the central region can be selected from the eight surrounding areas: upper left, upper, upper right, left, right, lower left, and lower right. Including the central region, the target window comprises nine regions, hence the first number is nine. Let the target window be labeled W1, W2, ..., W9 from upper left to lower right, then the central region is represented as W5.

[0100] Step 204c: Determine the difference quantization parameter between the average gradient value of pixels in each region of the target window and the average gradient value of pixels in the central region. If the central region is represented by W5, then the difference quantization parameter between the central region and the k-th region can be d(W5, W...). k The difference quantification parameter is calculated as shown in formula (3).

[0101] d(W5,W k ) = G5 - G k (3)

[0102] In formula (3), G k Used to represent the k-th region W in the target window k G5 represents the average gradient value of all pixels in the central region, i.e., W5.

[0103] Step 204d: Based on step 204c, sum the values ​​of all differential quantization parameters greater than or equal to 0 in the target window corresponding to all edge points, and then sum the quotient of this sum with the second quantity to obtain the target gradient. This gradient is then normalized to obtain the contrast sensitivity feature value corresponding to the sub-region. If the first quantity is 9, then the second quantity is the number of all regions in the target window excluding the central region, which is 8.

[0104] Let the second gradient distribution function of the second pixel be f'(x,y), where f'(x,y)∈[0,f' m ],f' m Let N represent the maximum value in the third gradient. If there are N edge points on the geometric contour line of the afterimage of the sub-region, then the calculation of the contrast sensitivity feature value of the sub-region is shown in formula (4).

[0105]

[0106] In formula (4), N is the number of edge points within the sub-region, and f′ m This represents the maximum gradient value among the third gradient values. d(W5, W) k ) represents the value of the difference quantification parameter between the central region and the kth region as shown in formula (3).

[0107] In some embodiments, the neighborhood sharpness feature value of each sub-region can be determined based on the spatial domain image and frequency domain image corresponding to each sub-region. The neighborhood sharpness feature value can be used to reflect the local masking effect of the human visual system, representing the overall brightness distribution within a certain area of ​​the residual image. The neighborhood sharpness feature value can be represented by NC... i express.

[0108] The calculation process of the neighborhood sharpness feature value of each sub-region in the residual image can be shown in steps 205-206 below.

[0109] Step 205: Based on each sub-region in the afterimage test image and the corresponding sub-region in the afterimage residual image, calculate the spatial domain features of each sub-region in the afterimage test image. A preferred method is to calculate the structural information of each sub-region based on the SSIM model (Structural Similarity Index Model), as shown in formulas (5) and (6).

[0110] SSIM = l α +c β +s γ (5)

[0111]

[0112] In formula (5), l, c, and s represent the comparison functions of gray level or brightness, contrast, and structure of the sub-region, respectively.

[0113] In formula (6), t1, t2, and t3 are parameters to prevent division by zero, and the values ​​of these parameters can be determined according to the actual situation. α, β, and γ are parameters, respectively. Subscript 1 represents the sub-region of the afterimage test image, and subscript 2 represents the sub-region corresponding to the spatial domain of the afterimage. u1 represents the mean gray level or brightness of the sub-region of the test image; u2 represents the mean gray level or brightness of the sub-region of the afterimage; σ1 represents the variance of the gray level or brightness of the sub-region of the test image; σ2 represents the variance of the gray level or brightness of the sub-region of the afterimage; σ 12 This represents the grayscale or brightness covariance between a sub-region of the test image and a sub-region of the residual image.

[0114] Step 206: Based on each sub-region in the afterimage test image and the corresponding sub-region in the afterimage residual image, calculate the frequency domain features of each sub-region in the afterimage test image. A preferred method is to calculate the SE feature (Spectral Entropy), as shown in formulas (7) and (8).

[0115] FDE=-∑ (i,j)∈D f norm (i,j)·log(f norm (i,j)) (7)

[0116]

[0117] In formula (7), f(i,j) represents the frequency domain image F. i Two-dimensional distribution,

[0118] In formula (8), f nor, (i,j) represents the normalized amplitude spectrum, which is equivalent to normalizing the amplitude of each two-dimensional coordinate (i,j) on the amplitude graph to between 0 and 1.

[0119] Step 207: Based on data fitting methods, such as SVM (Support Vector Machine), perform data fitting processing on the spatial domain characteristics and frequency domain features of each sub-region, and calculate the contrast sensitivity feature value of the sub-region in the afterimage test image. As shown in formula (9).

[0120] NC i =SVM[(SSIM1,…,SSIM N ),(FDE1,…,FDE N (9)

[0121] In formula (9), NC i This represents the contrast sensitivity feature value of the i-th sub-region.

[0122] In some embodiments, the overall evaluation value of the afterimage corresponding to each sub-region is determined based on the edge enhancement feature value, contrast sensitivity feature value, and neighborhood sharpness feature value that affect each sub-region in the afterimage.

[0123] The determination of the comprehensive evaluation value of the afterimage can be based on a linear regression model. For a certain sub-region A... i The calculated three feature values ​​are edge enhancement feature value, contrast sensitivity feature value, and neighborhood sharpness feature value. The model for the comprehensive evaluation value of the afterimage of the sub-region can be defined as shown in formula (10).

[0124] ECC i =C ee ·EE i +C cs CS i +C nc NC i (10)

[0125] Among them, ECC i C represents the overall evaluation value of the afterimage in the i-th sub-region. ee C represents the first regression coefficient. cs C represents the second regression coefficient. nc Represents the third regression coefficient, EE i CS represents the edge enhancement feature value of the i-th sub-region. i NC represents the contrast sensitivity feature value of the i-th sub-region. i This represents the neighborhood sharpness feature value of the i-th sub-region.

[0126] Image retention levels can be categorized from low to high based on the degree of image retention as perceived by the human eye. There are five levels of image retention: L1, L2, L3, L4, and L5. L1 indicates that the human eye cannot distinguish the retained image; L2 indicates that the human eye can slightly distinguish the retained image; L3 indicates that the human eye can distinguish the retained image; L4 indicates that the human eye can clearly distinguish the retained image; and L5 indicates that the human eye can very clearly distinguish the retained image.

[0127] Instance data of residual images can be collected through a large number of experiments. A portion of this data can be used as the training set, and the remaining data as the test set. The first regression coefficient, the second regression coefficient, and the third regression coefficient can be obtained through multiple linear regression.

[0128] In some embodiments, if Level represents the target comprehensive evaluation value of the afterimage of the display screen, a relationship table between the target comprehensive evaluation value of the afterimage and the afterimage residue level of the display screen is constructed, as shown in formula (12).

[0129]

[0130] Among them, when 0 ≤ Level < K1, it is level L1, indicating that the human eye cannot distinguish the afterimage residual image; when K1 ≤ Level < K2, it is level L2, indicating that the human eye can slightly distinguish the afterimage residual image; when K2 ≤ Level < K3, it is level L3, indicating that the human eye can distinguish the afterimage residual image; when K3 ≤ Level < K4, it is level L4, indicating that the human eye can relatively obviously distinguish the afterimage residual image; when K4 ≤ Level < K5, it is level L5, indicating that the human eye can very obviously distinguish the afterimage residual image. K1, K2, K3, K4, and K5 are constants within the range of 0 to 255, which are determined according to the actual situation.

[0131] In formula (12), Level ∈ [0, 255], L ∈ {L1, L2, L3, L4, L5}.

[0132] In some embodiments, there are the following two schemes for determining the afterimage residue level of the display screen based on the comprehensive evaluation values of the afterimages of all sub-regions.

[0133] Scheme 1: Suppose there are N sub-regions in total. After summing up the comprehensive evaluation values of the afterimages calculated for all sub-regions, the average value is taken to obtain the target comprehensive evaluation value of the afterimage, as shown in formula (11).

[0134]

[0135] In formula (10), the symbol [] represents the rounding function, and ECC i represents the comprehensive evaluation value of the afterimage of the i-th sub-region.

[0136] Scheme 2: Group and divide the sub-regions with the same gray value or brightness value. After summing up the comprehensive evaluation values of the afterimages corresponding to the sub-regions in each group with the same gray value or brightness value, the average value is taken to obtain the afterimage residue level of the display screen corresponding to each group or each gray value or brightness value of the sub-regions.

[0137] Figure 4 FIG. 30 is a schematic structural diagram of an optional device for detecting the afterimage residue level of the display screen provided by the embodiments of the present application; the device 400 for detecting the afterimage residue level of the display screen includes a control module 401, an afterimage residual image acquisition module 402, a sub-region image feature determination module 403, an afterimage residue level determination module 404, and a storage module 405.

[0138] Among them, the control module 401 is used to control the display, realize the lighting and switching of the screen, and control the image retention acquisition module 402, the sub-region image feature determination module 403 and the image retention level determination module 404 to perform corresponding calculation operations.

[0139] The image retention image acquisition module 402 is used to acquire the image retention image corresponding to the image retention test image.

[0140] The sub-region image feature determination module 403 is used to segment the residual image into multiple sub-regions and determine the frequency domain image, spatial domain image and shape features of each sub-region.

[0141] The image persistence level determination module 404 is used to determine the image persistence level of the display screen based on the frequency domain image, spatial domain image and shape features of all the sub-regions.

[0142] Storage module 405 is used to store data from the image retention level detection device 400 during the detection process.

[0143] In some embodiments, the sub-region image feature determination module 403 is configured to: preprocess the sub-region for each sub-region and remove high-frequency noise to obtain a frequency domain image of the sub-region; determine the two-dimensional digital image obtained after equalization operation of the sub-region as the spatial domain image of the sub-region; and obtain the shape features of the sub-region by image matching.

[0144] In some embodiments, the image persistence level determination module 404 is configured to: for each sub-region, determine a first value of the product of a first regression coefficient and the edge enhancement feature value, a second value of the product of a second regression coefficient and the contrast sensitivity feature value, and a third value of the product of a third regression coefficient and the neighborhood sharpness; and determine the sum of the first value, the second value, and the third value corresponding to the sub-region as the comprehensive image persistence evaluation value of the sub-region.

[0145] In some embodiments, the image persistence level determination module 404 is further configured to: determine the position of the afterimage geometric contour in the spatial domain image of the corresponding sub-region in the image persistence image based on the shape features of the sub-region; determine the first pixel point corresponding to each edge point within a first region based on the edge points on the afterimage geometric contour of the sub-region; perform a first processing on the first pixel point; determine the edge enhancement feature value of the sub-region based on the first processing result; determine a target window centered on each edge point on the afterimage geometric contour of the sub-region; perform a second processing on the pixel points within the target window; and determine the contrast sensitivity feature value of the sub-region based on the second processing result.

[0146] In some embodiments, the image persistence level determination module 404 is further configured to: perform convolution calculation on the first pixel to determine the first gradient value of the first pixel; perform exponential transformation on the first gradient value to obtain a second gradient value; accumulate the second gradient values ​​of the first pixel corresponding to each edge point and then perform normalization operation to obtain the edge enhancement feature value of the sub-region.

[0147] In some embodiments, the image persistence level determination module 404 is further configured to: determine a target window within a second region centered on each edge point; determine the value of the difference quantization parameter between each region and the central region in a first number of regions of the target window; determine the quotient between the sum of all difference quantization parameter values ​​greater than or equal to 0 in the target window and the second number, and determine the target gradient of the target window, wherein the difference between the first number and the second number is 1; and accumulate and normalize the target gradients of all target windows in the sub-region to obtain the contrast sensitivity feature value corresponding to the sub-region.

[0148] In some embodiments, the image persistence level determination module 404 is further configured to: determine a central region within a third region centered on each edge point; determine a first number of regions centered on the central region and the central region as a target window, wherein each region in the target window is of the same size; and determine the difference between the average gradient value of the pixels in each region in the target window and the average gradient value of the pixels in the central region as the value of the difference quantization parameter.

[0149] In some embodiments, the image persistence level determination module 404 is further configured to: determine the spatial domain characteristics of the sub-region in the image persistence test image and the frequency domain characteristics of each sub-region based on the sub-region in the image persistence test image and the corresponding sub-region in the image persistence image; and perform data fitting processing on the spatial domain characteristics and the frequency domain characteristics of the sub-region based on a data fitting method to obtain the neighborhood sharpness feature value of the sub-region.

[0150] In some embodiments, the image retention level determination module 404 is further configured to: sum the image retention comprehensive evaluation values ​​of all sub-regions and obtain an average value to obtain a target image retention comprehensive evaluation value; and determine the image retention level of the display screen corresponding to the target image retention comprehensive evaluation value based on the relationship table between the image retention comprehensive evaluation value and the image retention level of the display screen.

[0151] It should be noted that the image retention level detection device for the display screen in this application embodiment is similar to the method embodiment for detecting the image retention level of the display screen described above, and has similar beneficial effects to the same method embodiment, therefore, it will not be described again. For any technical details not covered in the image retention level detection device for the display screen provided in this application embodiment, please refer to... Figures 1 to 3 The meaning is understood in accordance with the description of any of the accompanying drawings.

[0152] Figure 5 A schematic diagram of an optional structure of the image persistence level detection system for a display screen provided in this embodiment is shown. The image persistence level detection system 500 for the display screen includes a control module 501, a display module 502, an image persistence image acquisition module 503, a calculation module 504, a storage module 507, and an output device 508. The calculation module 502 includes a sub-region image feature determination module 505 and an image persistence level determination module 506.

[0153] The control module 501 is used to control the display module to turn on and switch the display screen, and to control the calculation module 502 to perform corresponding calculation operations.

[0154] The display module 502 is used to display a specified image of a ghosting test, keep it still for a period of time, and then change the grayscale of the screen to display the image of the ghosting.

[0155] Image retention image acquisition module 503 is used to capture digital images of image retention using standard photographic techniques.

[0156] The calculation module 504 is used to determine the image persistence level of the display screen, and includes two sub-modules. The sub-region image feature determination module 505 is used to divide the image persistence image into multiple sub-regions and determine the frequency domain image, spatial domain image and shape features of each sub-region.

[0157] The image persistence level determination module 506 is used to determine the image persistence level of the display screen based on the frequency domain image, spatial domain image and shape features of all the sub-regions.

[0158] The storage module 507 is used to store the frequency domain image, spatial domain image and shape features of each sub-region calculated by the sub-region image feature determination module 505, as well as the image persistence level of the display screen determined by the image persistence level determination module 507.

[0159] Output module 508 is used to output the detected image persistence level of the display screen.

[0160] In some embodiments, the control module 501 controls the display module 502 to display a specified image of the afterimage test, holds it still for a period of time, and then changes the grayscale of the screen to display the image of the afterimage. Subsequently, the control module 501's image afterimage acquisition module 503 captures the image of the afterimage displayed by the display module 502 using a CCD imaging luminance meter and standard photographic techniques. After acquiring the image, the control module 501 controls the calculation module 504 to sequentially execute the sub-region image feature determination module 505 and the image afterimage level determination module 506, and stores all the calculated results from the sub-region image feature determination module 505 and the image afterimage level determination module 506 in the storage module 507. The output device outputs the image afterimage level of the display screen stored in the storage module 507 to the test engineer in the form of an image or a signal.

[0161] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device 600 is used to implement a method for detecting display screen image retention levels according to embodiments of the present disclosure. In some alternative embodiments, the electronic device 600 can implement the display screen image retention level detection method provided in the embodiments of this application by running a computer program. For example, the computer program can be a software module in an operating system; it can be a native application (APP), i.e., a program that needs to be installed in the operating system to run; it can also be a small program, i.e., a program that only needs to be downloaded to a browser environment to run; or it can be a small program that can be embedded in any APP. In short, the above-mentioned computer program can be any form of application, module, or plugin.

[0162] In practical applications, electronic device 600 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Cloud technology refers to a hosting technology that unifies hardware, software, and network resources within a wide area network (WAN) or local area network (LAN) to achieve data computation, storage, processing, and sharing. Electronic device 600 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smart TV, smartwatch, etc., but is not limited to these.

[0163] Electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, in-vehicle terminals, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0164] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the electronic device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0165] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of displays, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0166] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the method for detecting the level of display image retention. For example, in some alternative embodiments, the method for detecting the level of display image retention may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some alternative embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method for detecting the level of display image retention described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured by any other suitable means (e.g., by means of firmware) as a method for detecting the level of image persistence on the display screen.

[0167] This application provides a computer-readable storage medium storing executable instructions. When the executable instructions are executed by a processor, the processor will execute the display screen image retention level detection method provided in this application.

[0168] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a variety of devices including one or any combination of the above-mentioned memories.

[0169] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0170] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0171] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0172] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0173] It should be understood that in the various embodiments of this application, the sequence number of each implementation process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0174] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of this application are included within the scope of protection of this application.

Claims

1. A method of detecting the image sticking level of a display screen, characterized by, The method comprises: acquiring an image sticking test image corresponding to an image sticking image; segmenting the image sticking image into a plurality of sub-regions, and determining a frequency domain image, a spatial domain image, and a shape feature of each of the sub-regions; determining an image sticking level of the display screen based on the frequency domain images, the spatial domain images, and the shape features of all the sub-regions; wherein the segmentation of the image sticking image into the plurality of sub-regions, and the determination of the frequency domain image, the spatial domain image, and the shape feature of each of the sub-regions comprise: for each sub-region, pre-processing the sub-region, and removing high-frequency noise to obtain the frequency domain image of the sub-region; and determining a two-dimensional digital image obtained after equalization operation of the sub-region as the spatial domain image of the sub-region; the sub-region is subjected to image matching to obtain the shape feature of the sub-region.

2. The method of claim 1, wherein, The determination of the image sticking level of the display screen based on the frequency domain images, the spatial domain images, and the shape features of all the sub-regions comprises: for each sub-region, determining an edge enhancement feature value, a contrast sensitivity feature value, and a neighborhood clarity feature value of the sub-region based on the frequency domain image, the spatial domain image, and the shape feature of the sub-region; determining a comprehensive evaluation value of image sticking of the sub-region based on the edge enhancement feature value, the contrast sensitivity feature value, and the neighborhood clarity feature value of the sub-region; determining the image sticking level of the display screen based on the comprehensive evaluation values of image sticking of all the sub-regions.

3. The method of claim 2, wherein the determination of the comprehensive evaluation value of image sticking of the sub-region based on the edge enhancement feature value, the contrast sensitivity feature value, and the neighborhood clarity feature value of the sub-region comprises: for each sub-region, determining a first value of a product of a first regression coefficient and the edge enhancement feature value, a second value of a product of a second regression coefficient and the contrast sensitivity feature value, and a third value of a product of a third regression coefficient and the neighborhood clarity feature value; determining a sum of the first value, the second value, and the third value corresponding to the sub-region as the comprehensive evaluation value of image sticking of the sub-region.

4. The method of claim 2, wherein, The determination of the edge enhancement feature value, the contrast sensitivity feature value, and the neighborhood clarity feature value of the sub-region based on the frequency domain image, the spatial domain image, and the shape feature of the sub-region for each sub-region comprises: determining a position of a geometric contour of image sticking in the image sticking image in the spatial domain image of the corresponding sub-region based on the shape feature of the sub-region; determining a first pixel point corresponding to each edge point in a first region range on the geometric contour of image sticking of the sub-region based on the edge point, performing a first processing on the first pixel point, and determining the edge enhancement feature value of the sub-region based on a first processing result; determining a target window with each edge point on the geometric contour of image sticking of the sub-region as a center, performing a second processing on pixel points in the target window, and determining the contrast sensitivity feature value of the sub-region based on a second processing result.

5. The method of claim 3, wherein, The edge points on the residual image geometric profile of the sub-region are used to determine corresponding first pixel points of each edge point within a first region range, the first pixel points are subjected to first processing, and an edge enhancement characteristic value of the sub-region is determined based on a first processing result. The first pixel points are subjected to convolution calculation to determine first gradient values of the first pixel points. The first gradient values are subjected to exponential transformation to obtain second gradient values. The second gradient values of the first pixel points corresponding to each edge point are accumulated and then subjected to normalization operation to obtain the edge enhancement characteristic value of the sub-region.

6. The method of claim 3, wherein, Each edge point on the residual image geometric profile of the sub-region is used to determine a target window centered on the edge point, the pixel points in the target window are subjected to second processing, and a contrast sensitivity characteristic value of the sub-region is determined based on a second processing result. A target window within a second region range centered on the edge point is determined. A difference quantization parameter value of each region relative to a center region in a first number of regions of the target window is determined. A target gradient of the target window is determined by summing the difference quantization parameter values greater than or equal to 0 in the target window and dividing the sum by a second number, and the difference between the first number and the second number is 1. The target gradients of all target windows in the sub-region are accumulated and then subjected to normalization to obtain a contrast sensitivity characteristic value corresponding to the sub-region.

7. The method of claim 6, wherein, The difference quantization parameter value of each region relative to the center region in the first number of regions of the target window is determined. A center region within a third region range centered on the edge point is determined. A second number of regions centered on the center region and the center region are determined as a target window, and each region in the target window has the same size. A difference between an average gradient value of the pixel points in each region and an average gradient value of the pixel points in the center region is determined as the difference quantization parameter value.

8. The method of claim 2, wherein, For each sub-region, an edge enhancement characteristic value, a contrast sensitivity characteristic value, and a neighborhood sharpness characteristic value of the sub-region are determined based on a frequency domain image, a spatial domain image, and a shape feature of the sub-region. Based on the sub-region in the residual image test image and the corresponding sub-region in the image residual image, spatial domain characteristics of the sub-region in the residual image test image and frequency domain characteristics of each sub-region are determined. Based on a data fitting method, the spatial domain characteristics of the sub-region and the frequency domain characteristics of the sub-region are subjected to data fitting processing to obtain a neighborhood sharpness characteristic value of the sub-region.

9. The method of claim 2, wherein, Based on the residual comprehensive evaluation values of all sub-regions, an image residual level of the display screen is determined. An average value of the residual comprehensive evaluation values of all sub-regions is obtained to obtain a target residual comprehensive evaluation value. Based on a relationship table of residual comprehensive evaluation values and display screen image residual levels, an image residual level of the display screen corresponding to the target residual comprehensive evaluation value is determined.

10. An apparatus for detecting image sticking level of a display screen, characterized by, The device comprises: An image residual image acquisition module is configured to acquire an image residual image corresponding to a residual image test image; A sub-region image feature determination module is configured to segment the image residual image into a plurality of sub-regions, and determine a frequency domain image, a spatial domain image, and a shape feature of each of the sub-regions; An image residual level determination module is configured to determine an image residual level of the display screen based on the frequency domain image, the spatial domain image, and the shape feature of all the sub-regions. The sub-region image feature determination module is specifically configured to: for each sub-region, pre-process the sub-region and remove high-frequency noise to obtain a frequency domain image of the sub-region; determine a two-dimensional digital image obtained after equalization operation on the sub-region as a spatial domain image of the sub-region; and obtain a shape feature of the sub-region through image matching.

11. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The storage medium includes a set of computer executable instructions that, when executed, perform the method of detecting the image residual level of the display screen according to any one of claims 1-9.

Images