Display compensation method, apparatus, device, and readable storage medium

Abstract

The application relates to a display compensation method, device, equipment and readable storage medium. The method comprises the following steps: acquiring a first image and a second image of a display module respectively; determining an image similarity and a spot compensation distribution difference based on the first image and the second image; determining a target pre-processing parameter of the second image according to the similarity and / or the spot compensation distribution difference; and performing display compensation on the display module based on the target pre-processing parameter of the second image. The method can improve the compensation effect of an actual production line.

Description

Technical Field

[0001] This application relates to the field of organic light-emitting diode display technology, and in particular to a display compensation method, apparatus, device, and readable storage medium. Background Technology

[0002] In Organic Light-Emitting Diode (OLED) display technology, a mura refers to uneven brightness or color on the screen. To improve display quality, mura compensation is required.

[0003] The Demura process includes: 1) acquiring pixel brightness data using an image acquisition device; 2) driving an integrated circuit to generate compensation data and applying it to the display screen.

[0004] Traditional debugging is carried out in the laboratory, but due to the differences between laboratory and production line equipment, the compensation effect is not good in actual production, which in turn affects the consistency and quality of the display screen. Summary of the Invention

[0005] Therefore, it is necessary to provide a display compensation method, apparatus, equipment, and readable storage medium that can improve the compensation effect of actual production lines, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for display compensation, including:

[0007] Acquire the first and second images of the display module respectively;

[0008] Based on the first and second images, determine the image similarity and differences in speckle compensation distribution;

[0009] Based on similarity and / or differences in speckle compensation distribution, determine the target preprocessing parameters for the second image;

[0010] Display compensation is performed on the display module based on the target preprocessing parameters of the second image.

[0011] In one embodiment, determining the target preprocessing parameters of the second image based on similarity and / or speckle compensation distribution differences includes:

[0012] If the similarity and / or speckle compensation distribution differences meet the optimization triggering conditions, the current preprocessing parameters of the second image are optimized to determine the target preprocessing parameters of the second image.

[0013] In one embodiment, the above-described optimization of the current preprocessing parameters of the second image includes:

[0014] Adjust the parameters of the current image acquisition equipment in the production line, and / or adjust the current image processing algorithm in the production line.

[0015] In one embodiment, determining the target preprocessing parameters of the second image based on similarity and / or speckle compensation distribution differences includes:

[0016] If the similarity and / or speckle compensation distribution differences do not meet the optimization triggering conditions, the current preprocessing parameters of the second image are determined as the target preprocessing parameters of the second image.

[0017] In one embodiment, the process of determining the difference in spot compensation distribution described above includes:

[0018] The first image and the second image are fitted with speckle compensation respectively to obtain the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0019] The difference in speckle compensation distribution is determined based on the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0020] In one embodiment, the process of determining image similarity described above includes:

[0021] The first image and the second image are processed by data conversion to obtain the converted first image and the converted second image respectively;

[0022] Feature extraction is performed on the first and second converted images respectively to obtain the blob feature images corresponding to the first and second images.

[0023] The similarity between the blob feature images corresponding to the first image and the blob feature images corresponding to the second image is calculated using a preset similarity algorithm to obtain the image similarity.

[0024] In one embodiment, the first image is a laboratory image and the second image is a production line image.

[0025] In one embodiment, the process of acquiring the first image includes:

[0026] After adjusting the speckle compensation effect of the display module in a laboratory environment, the first image of the display module is obtained.

[0027] In one embodiment, the process of acquiring the second image includes:

[0028] After synchronizing the speckle compensation algorithm to the production line, a second image of the display module is obtained; the speckle compensation algorithm was obtained after debugging the speckle compensation effect of the display module in a laboratory environment.

[0029] Secondly, this application also provides a display compensation device, comprising:

[0030] The image acquisition module is used to acquire the first and second images of the display module, respectively.

[0031] The difference determination module is used to determine the image similarity and the difference in speckle compensation distribution based on the first image and the second image;

[0032] The parameter determination module is used to determine the target preprocessing parameters of the second image based on similarity and / or differences in speckle compensation distribution;

[0033] The compensation module is used to perform display compensation on the display module based on the target preprocessing parameters of the second image.

[0034] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0035] Acquire the first and second images of the display module respectively;

[0036] Based on the first and second images, determine the image similarity and differences in speckle compensation distribution;

[0037] Based on similarity and / or differences in speckle compensation distribution, determine the target preprocessing parameters for the second image;

[0038] Display compensation is performed on the display module based on the target preprocessing parameters of the second image.

[0039] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0040] Acquire the first and second images of the display module respectively;

[0041] Based on the first and second images, determine the image similarity and differences in speckle compensation distribution;

[0042] Based on similarity and / or differences in speckle compensation distribution, determine the target preprocessing parameters for the second image;

[0043] Display compensation is performed on the display module based on the target preprocessing parameters of the second image.

[0044] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0045] Acquire the first and second images of the display module respectively;

[0046] Based on the first and second images, determine the image similarity and differences in speckle compensation distribution;

[0047] Based on similarity and / or differences in speckle compensation distribution, determine the target preprocessing parameters for the second image;

[0048] Display compensation is performed on the display module based on the target preprocessing parameters of the second image.

[0049] The aforementioned display compensation method, apparatus, device, and readable storage medium acquire a first image and a second image, respectively, allowing for image comparison under different environments to more accurately assess compensation requirements. Subsequently, based on image similarity and differences in speckle compensation distribution, brightness and color non-uniformity in specific areas can be effectively identified. By determining the target preprocessing parameters of the second image, display compensation can be performed on the display module in the production line, ensuring the compensation effect closely matches the results of laboratory debugging. This method reduces unsatisfactory compensation effects caused by differences in parameters such as equipment model, image acquisition device height, and exposure time, thereby improving the consistency and overall quality of the display screen. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is an internal structural diagram of a computer device in one embodiment;

[0052] Figure 2 This is a flowchart illustrating the compensation method in one embodiment;

[0053] Figure 3 This is a flowchart illustrating the compensation method in another embodiment;

[0054] Figure 4a This is a compensation distribution map corresponding to the first image in a laboratory environment in another embodiment;

[0055] Figure 4b This is a compensation distribution map corresponding to the second image in a production line environment, as shown in another embodiment.

[0056] Figure 5 This is a flowchart illustrating the compensation method in another embodiment;

[0057] Figure 6 This is a structural block diagram showing the compensation device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] In organic light-emitting diode (OLED) display technology, image quality and consistency are key performance indicators. Mura, or uneven brightness or color on a display screen, severely impacts user experience and reduces the overall quality of the display device. Mura formation stems from multiple factors, including material properties, manufacturing processes, and the design of the driving circuitry. Therefore, to improve the image quality and consistency of OLED displays, effective compensation for mura is necessary; the technique employed is called speckle compensation.

[0060] The Mura process typically involves two main steps. First, a high-precision camera is used to acquire brightness data for each pixel of the display screen, generating a brightness calibration file. This calibration file records the specific brightness information for each pixel, laying the foundation for subsequent compensation work. Second, the driver IC generates compensation data based on this calibration file and writes the compensation data into the IC, thereby compensating for the brightness of each pixel, eliminating the Mura phenomenon, and improving the display effect.

[0061] However, in the process of developing this invention, the inventors discovered the following problem in the related technology: the traditional Demura debugging process is usually carried out in a laboratory environment. The model of the laboratory equipment, camera height, exposure time, and other parameters may differ significantly from those of the equipment on the actual production line. This difference leads to a significant inconsistency between the Demura effect confirmed in the laboratory and the compensation effect on the actual production line, thus affecting the overall consistency and quality of the display screen.

[0062] To address the aforementioned issues, this application provides a display compensation scheme. It acquires a first image and a second image, allowing for comparison under different environments to more accurately assess compensation requirements. Then, based on image similarity and differences in speckle compensation distribution, it effectively identifies brightness and color non-uniformity in specific areas. By determining the preprocessing parameters of the second image, display compensation can be performed on the display module on the production line, ensuring the compensation effect closely matches the results of laboratory debugging. This method reduces unsatisfactory compensation effects caused by differences in parameters such as equipment model, image acquisition device height, and exposure time, thereby improving the consistency and overall quality of the display screen.

[0063] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 1 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores relevant data during the display compensation process. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a display compensation method.

[0064] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0065] In one exemplary embodiment, refer to Figure 2 As shown, a display compensation method is provided, which is applied to... Figure 1 The following steps are used as an example of computer equipment, including steps 201 to 204.

[0066] in:

[0067] Step 201: Obtain the first image and the second image of the display module respectively.

[0068] A display module is a component consisting of multiple display units used to display images or information. Display modules typically integrate driving circuits and other related electronic components to achieve image display functionality. The first image can be a laboratory image, taken in a laboratory environment. The second image can be a production line image, obtained during actual production.

[0069] In this embodiment, firstly, in a laboratory, an image acquisition device and lighting equipment need to be prepared and set up in appropriate locations. Next, the shooting parameters of the image acquisition device, including height, focal length, and exposure time, are adjusted to match the device settings with the characteristics of the display module. Subsequently, the lighting conditions are calibrated to ensure uniform and stable illumination within the laboratory, avoiding shadows or reflections that could affect image quality. Under these conditions, images of the display module are captured using the laboratory image acquisition device; multiple shots can be taken to improve data accuracy. Finally, the first image acquired by the laboratory image acquisition device and the associated shooting parameters are sent to a computer. The image acquisition device can be a camera.

[0070] During the acquisition of the second image, the height, focal length, and exposure gain of the production line image acquisition equipment are adjusted based on the actual production environment requirements and the relevant shooting parameters collected by the laboratory image acquisition equipment to adapt to the lighting and background conditions of the production line. Simultaneously, the production environment is monitored to ensure stable lighting conditions and avoid affecting image quality. Next, images of the display module are captured using the production line image acquisition equipment; multiple shots can be taken to improve data accuracy. Finally, the second image acquired by the production line image acquisition equipment and the relevant shooting parameters are sent to a computer.

[0071] Step 202: Based on the first image and the second image, determine the image similarity and the difference in speckle compensation distribution.

[0072] Image similarity is an indicator used to measure the degree of similarity between the first image and the second image.

[0073] The difference in speckle compensation distribution refers to the varying degrees and distributions of compensation requirements for the Mura phenomenon between the first and second images. This difference in speckle compensation distribution indicates variations in the compensation effect of the display module under different environmental conditions.

[0074] In this embodiment, the process of determining image similarity can be as follows: First, the first image and the second image need to be preprocessed. This includes denoising and standardizing the first and second images to eliminate the influence of environmental factors and device differences on image quality. Next, various methods, such as mean squared error or structural similarity index, can be used to determine the similarity between the first image and the second image.

[0075] The process of determining the difference in speckle compensation distribution can be as follows: First, the first and second images need to be preprocessed to eliminate the influence of environmental factors. For example, denoising and normalization processes can be performed to improve image quality. Next, brightness and color information are extracted from the first and second images respectively. For brightness extraction, a grayscale conversion algorithm can be used to convert the color image into a grayscale image, and the brightness value of each pixel can be calculated using formula (1).

[0076] Y = 0.299R + 0.587G + 0.114B (1)

[0077] In equation (1), Y represents the image brightness, B represents the pixel value of the blue channel, R represents the pixel value of the red channel, and G represents the pixel value of the green channel.

[0078] Meanwhile, the data from the red, green, and blue channels are retained, the brightness distribution of each channel is analyzed, and the average value and standard deviation are calculated for subsequent comparisons.

[0079] After extracting the brightness and color information, the next step is to compare the first and second images pixel by pixel. This step involves constructing a comparison matrix to quantify the differences in brightness and color. During the comparison, the mean and standard deviation obtained in the above process can be used to calculate the brightness difference of each pixel using absolute difference or difference of squares. Simultaneously, the red, green, and blue channel values ​​are compared channel by channel to calculate the differences in color distribution.

[0080] Finally, based on the differences in brightness and color distribution of each pixel, the difference in speckle compensation distribution is determined. For example, suppose that some areas of the first image taken in a laboratory environment have uniform brightness, while the same areas in the second image show significantly uneven brightness. In this case, the brightness differences in these areas are calculated using the above method, and the color histogram shows that the color distribution of the second image is also significantly different from that of the first image. Ultimately, the speckle compensation distribution difference indicates a significant difference in the compensation distribution.

[0081] Step 203: Determine the target preprocessing parameters of the second image based on similarity and / or differences in speckle compensation distribution.

[0082] Among them, target preprocessing parameters refer to various factors set or adjusted during image acquisition and processing to optimize image quality.

[0083] In this embodiment, firstly, the specific difference regions and compensation requirements between the first and second images can be identified based on similarity and / or differences in speckle compensation distribution. Next, preprocessing parameters are determined based on the specific difference regions and compensation requirements between the first and second images. These target preprocessing parameters typically include the image acquisition device height, exposure gain value, and mapping value. Adjusting the image acquisition device height aims to ensure consistent viewing angles in the acquired images; the exposure gain value is set based on the brightness differences in the second image to improve its brightness uniformity; and the mapping value can correct the brightness and color of the second image.

[0084] Step 204: Based on the target preprocessing parameters of the second image, perform display compensation on the display module.

[0085] In this embodiment, pre-determined target preprocessing parameters, such as the image acquisition device height, exposure gain value, and mapping value, are first applied to the production line. Next, the configured image acquisition device is used on the production line to acquire real-time images of the display module. To ensure image quality, images should be captured under consistent environmental conditions to minimize the influence of external factors. The acquired images should cover as much of the display module's surface as possible for comprehensive analysis. Subsequently, the acquired second image is analyzed for brightness and color information, including calculating the brightness value and color distribution of each pixel, identifying areas of difference, and comparing it with the first image.

[0086] Based on the results of brightness and color analysis, compensation data is generated for each area requiring compensation. This compensation data includes the specific pixel values ​​that need adjustment and their corresponding compensation values. The generated compensation data is then input into the display module's driver IC to implement actual display compensation, adjusting each pixel according to its specific compensation value to eliminate the Mura phenomenon and other brightness unevenness issues.

[0087] During the compensation process, real-time monitoring can be used to verify the compensation effect. For example, by continuously acquiring images and analyzing their brightness and color information, compensation parameters can be adjusted in a timely manner to ensure that the final effect meets expectations. Finally, the compensated display module is evaluated, and the compensation effect and related data are recorded.

[0088] In the aforementioned display compensation method, acquiring a first image and a second image allows for comparison under different environments, leading to a more accurate assessment of compensation requirements. Subsequently, analysis based on image similarity and differences in speckle compensation distribution effectively identifies brightness and color non-uniformity in specific areas. By determining the target preprocessing parameters for the second image, display compensation can be performed on the display module on the production line, ensuring the compensation effect closely matches the results of laboratory debugging. This method reduces unsatisfactory compensation effects caused by differences in parameters such as equipment model, image acquisition device height, and exposure time, thereby improving the consistency and overall quality of the display screen.

[0089] In an exemplary embodiment, the above-mentioned "determining the target preprocessing parameters of the second image based on similarity and / or blob compensation distribution differences" includes:

[0090] If the similarity and / or speckle compensation distribution differences meet the optimization triggering conditions, the current preprocessing parameters of the second image are optimized to determine the target preprocessing parameters of the second image.

[0091] Optimization trigger conditions refer to the standards or criteria by which computer equipment initiates the optimization process under specific circumstances. Optimization trigger conditions typically include the following aspects:

[0092] (1) Similarity index: When the similarity index between the first image and the second image is lower than the preset threshold, it indicates that there is a significant difference in image quality, which will trigger the optimization process.

[0093] (2) Difference in speckle compensation distribution: If the difference in speckle compensation distribution exceeds the predetermined range when comparing the first image and the second image, it can be considered that the current compensation strategy needs to be adjusted, thereby triggering the optimization process.

[0094] In this embodiment, when the similarity and / or speckle compensation distribution differences meet the optimization triggering conditions, the computer device analyzes the current second image data and its current preprocessing parameters to evaluate the effect of the current preprocessing parameters, compares them with the first image, identifies factors affecting image quality, and generates suggestions for adjusting the preprocessing parameters. For example, if the current exposure gain value is insufficient to achieve image brightness uniformity, the computer device will suggest appropriately increasing the exposure gain value; if the height of the image acquisition device significantly affects the consistency of the image viewing angle, the computer device will suggest readjusting the position of the image acquisition device.

[0095] Next, the computer equipment will implement actual optimization of the current preprocessing parameters based on these adjustment suggestions, modifying the image acquisition device height, exposure gain value, and mapping value, and ensuring that all target preprocessing parameters meet the new optimization requirements. After completing the parameter adjustments, the computer equipment will conduct tests to verify the optimization effect, capturing a new second image and analyzing its brightness and color information, comparing it with the second image before optimization to ensure that the optimized image has a significant improvement in quality and consistency.

[0096] In the above embodiments, the optimized preprocessing parameters are better adapted to the actual conditions of the production line, thereby effectively reducing the problem of unsatisfactory compensation effect caused by differences. This not only improves the display consistency of the display module, but also improves the overall quality of the final product.

[0097] In an exemplary embodiment, the above-mentioned "optimization of the current preprocessing parameters of the second image" includes:

[0098] Adjust the parameters of the current image acquisition equipment in the production line, and / or adjust the current image processing algorithm in the production line.

[0099] The parameters of the image acquisition device may include:

[0100] (1) Image acquisition device height: The vertical distance between the image acquisition device and the display module. Maintaining a consistent image acquisition device height helps ensure consistent image viewing angle and size.

[0101] (2) Exposure: The amount of light received by the photosensitive element of the image acquisition device during the shooting process. Appropriate exposure values ​​can improve the brightness and clarity of the image.

[0102] (3) Gain: The degree to which amplifies a signal in an image sensor. Adjusting the gain value can improve image quality under low-light conditions.

[0103] Image processing algorithms may include:

[0104] (1) Feature map value: Feature information extracted from the image, which helps in subsequent image processing and analysis.

[0105] (2) Notch filling: In image processing, fill in blank areas caused by interference or defects to restore the integrity of the image.

[0106] (3) Color deviation suppression: Eliminate color deviation in the image caused by light source or shooting conditions to ensure color accuracy.

[0107] (4) Surface brightness processing: Adjust the brightness of the image according to the shape of the surface and the lighting conditions to improve the visual effect.

[0108] (5) Filter parameters: Settings for various filters used in image processing to remove noise or enhance specific features.

[0109] In this embodiment, when adjusting the parameters of the current image acquisition device in the production line, the computer device can send an adjustment command to a device for adjusting the height of the image acquisition device to adjust the height of the image acquisition device. Subsequently, the computer device can also send corresponding commands for exposure and gain values ​​to optimize the brightness of the image.

[0110] The computer device can also send instructions to the image processing module of the image acquisition device to adjust the Notch area filling, color shift suppression, surface brightness processing, and filter parameters.

[0111] In the above embodiments, optimizing image acquisition device parameters (such as exposure, focal length, and height) can significantly improve image sharpness and color reproduction. Secondly, adjusting image processing algorithms (such as noise reduction, color correction, and brightness adjustment) can effectively improve the efficiency and accuracy of image processing. Optimized algorithms can reduce image non-uniformity caused by changes in lighting and environmental interference, enabling the production line to maintain high-standard display performance under various conditions. Furthermore, by dynamically adjusting image acquisition device parameters and image processing algorithms, the production line possesses greater adaptability and flexibility. This improves production efficiency when facing different batches of products or changing production environments.

[0112] In an exemplary embodiment, the above-mentioned "determining the target preprocessing parameters of the second image based on similarity and / or blob compensation distribution differences" includes:

[0113] If the similarity and / or speckle compensation distribution differences do not meet the optimization triggering conditions, the current preprocessing parameters of the second image are determined as the target preprocessing parameters of the second image.

[0114] In this embodiment of the application, if the similarity and / or the difference in the distribution of spot compensation does not meet the optimization triggering conditions, the computer device determines the preprocessing parameters of the current second image as the target preprocessing parameters of the second image.

[0115] In some embodiments, the computer device first reads the preprocessing parameters of the current second image, including the image acquisition device height, exposure gain value, mapping value, and related image processing algorithm settings. Next, the computer device performs a similarity assessment between the current second image and the first image, and analyzes the differences in speckle compensation distribution to confirm whether these differences meet preset optimization trigger conditions. If the similarity and / or speckle compensation distribution differences do not meet the optimization trigger conditions, the computer device will not perform parameter optimization, but will directly confirm the current preprocessing parameters as valid parameters.

[0116] During the application process, the computer device applies the confirmed preprocessing parameters to the current second image processing. Finally, the computer device continues to monitor the quality of the processed second image and periodically evaluates the similarity and compensation distribution so that appropriate adjustments can be made when optimization conditions are met in the future.

[0117] In the above embodiments, when the differences in similarity and compensation distribution are not significant, the preprocessing parameters of the current second image have adapted to the actual production environment. This indicates that the preprocessing parameters of the current second image are effective in practical applications. Therefore, keeping the parameters unchanged can reduce errors and unnecessary adjustments during the production process, thereby reducing operational risks.

[0118] In one exemplary embodiment, refer to Figure 3 As shown, the above-mentioned "process for determining the difference in spot compensation distribution" includes steps 301 to 302, wherein:

[0119] Step 301: Perform blob compensation fitting processing on the first image and the second image respectively to obtain the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0120] In this embodiment, during the blot compensation fitting process of the first image, the computer device first reads the first image and preprocesses it. Next, the computer device applies a preset blot compensation algorithm to identify brightness and color non-uniformity in the image and locate the blot regions that need compensation. By analyzing the pixel values ​​of these regions, the computer device records the required compensation value (e.g., grayscale) for each pixel. Specifically, the blot compensation algorithm records the brightness and color information of each pixel in the first image. Then, image processing techniques are used to identify the blot regions present in the first image.

[0121] After the compensation values ​​for all spotted areas are determined, the computer system will aggregate these compensation values ​​to generate a compensation distribution map. (Refer to...) Figure 4a As shown in the compensation distribution map corresponding to the first image in a laboratory environment, the horizontal axis represents different compensation values, while the vertical axis represents the proportion of pixels occupied by these compensation values. For example, if a certain compensation value is 1 and its proportion is 0.25, it means that in a 100×100 image, 100×100×0.25 pixels need to be compensated by one gray level.

[0122] The compensation distribution map corresponding to the second image was determined using the same method. (Refer to...) Figure 4b As shown, in the compensation distribution map corresponding to the second image in the production line environment, the horizontal axis represents different compensation values, while the vertical axis represents the proportion of pixels occupied by these compensation values.

[0123] Step 302: Determine the difference in speckle compensation distribution based on the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0124] In this embodiment, the computer device extracts each compensation value and its corresponding pixel percentage based on the compensation distribution map of the first image. Subsequently, the computer device similarly processes the compensation distribution map of the second image, extracting the corresponding compensation values ​​and pixel percentage data. Next, the computer device calculates the pixel percentage differences under the same compensation value, thereby determining the speckle compensation distribution differences.

[0125] For example, suppose the compensation distribution map of the first image shows that 30% of the pixels have a compensation value of 2, while the compensation distribution map of the second image shows that 20% of the pixels have a compensation value of 2. The computer device will recognize this difference and conclude that when the compensation value is 2, the difference in pixel percentage between the first and second images is 10%, that is, the difference in speckle compensation distribution is 10%.

[0126] In the above embodiments, by performing speckle compensation fitting processing on the first and second images, the resulting compensation distribution map can effectively identify and quantify the compensation differences between the two. This not only helps to clarify the performance of speckle compensation under different environments but also provides data support for optimizing compensation strategies on the production line. By analyzing the differences in speckle compensation distribution, the compensation algorithm can be better adjusted to improve the overall display quality of the display module, thereby enhancing product consistency and reliability.

[0127] In one exemplary embodiment, refer to Figure 5 As shown, the above "process for determining image similarity" includes steps 401 to 403, wherein:

[0128] Step 401: Perform data conversion processing on the first image and the second image respectively to obtain the converted first image and the converted second image.

[0129] In this embodiment, the computer device first uses dedicated image processing software, such as Adobe Photoshop or the open-source computer vision library OpenCV, to perform data conversion processing on the first and second images. The image processing software can convert raw image data (such as raw image file (RAW)) into a standard bitmap image file (BMP) format. During the conversion process, the computer device reads the raw image and applies a preset conversion algorithm to ensure accurate preservation of color and brightness information. After the conversion is completed, the converted first and second images are saved in BMP format. BMP has good image quality and broad compatibility, facilitating subsequent feature extraction and analysis.

[0130] Step 402: Perform feature extraction processing on the converted first image and the converted second image respectively to obtain the blob feature image corresponding to the first image and the blob feature image corresponding to the second image.

[0131] In this embodiment of the application, the computer device performs feature extraction processing on the converted first image and the converted second image to extract the blob features therein.

[0132] First, for the converted first image, the computer device analyzes its brightness and color distribution, extracts vertical mura (S-axis mura) features, and identifies vertically uneven brightness areas in the image. Next, horizontal mura (G-axis mura) features are extracted to identify horizontal brightness differences.

[0133] Subsequently, the computer equipment performs the same analysis on the converted second image. Through these processes, the final blob feature images corresponding to the first image and the second image are obtained.

[0134] Step 403: Calculate the similarity between the blob feature image corresponding to the first image and the blob feature image corresponding to the second image using a preset similarity algorithm to obtain the image similarity.

[0135] In this embodiment of the application, the computer device will use a preset similarity algorithm to calculate the similarity between the blob feature image corresponding to the first image and the blob feature image corresponding to the second image.

[0136] First, the computer device reads the blob feature images corresponding to the first image and the second image, and converts them into a format suitable for similarity analysis. Next, algorithms such as structural similarity index or mean squared error are applied to calculate the differences in brightness and contrast between the blob feature images corresponding to the first and second images, obtaining a similarity value. By analyzing the obtained similarity value, the computer device assesses the consistency of the blob features between the first and second images.

[0137] For example, a computer device can use a structural similarity index algorithm to calculate the similarity between a blob feature image corresponding to a first image and a blob feature image corresponding to a second image. First, the computer device extracts the brightness, contrast, and structural information of the two feature images, and then inputs this information into the structural similarity index algorithm. Assuming a similarity value of 0.85 is obtained, this indicates that the two images are visually very similar, and the blob feature images have good consistency. A similarity value close to 1 indicates high consistency, while a value close to 0 indicates significant differences.

[0138] In the above embodiments, by performing data conversion processing on the first and second images, the converted images can be compared under the same standard, thereby eliminating errors caused by different shooting conditions. Based on this, feature extraction processing can extract important speckle feature information from the images, facilitating subsequent analysis. Using a preset similarity algorithm to calculate the similarity between the two images can effectively identify differences between laboratory debugging and actual production, thereby quickly adjusting and optimizing speckle compensation of the display module during production, thus improving the quality and consistency of the final product.

[0139] In an exemplary embodiment, the above-mentioned "acquisition process of the first image" includes:

[0140] After adjusting the speckle compensation effect of the display module in a laboratory environment, the first image of the display module is obtained.

[0141] In this embodiment of the application, in a laboratory environment, an initial image of the display module is acquired using a laboratory image acquisition device, and the first image is sent to a computer device. The computer device processes the acquired initial image using a pre-defined speckle compensation algorithm. The speckle compensation algorithm analyzes the uneven brightness and color differences existing in the display module and adjusts the corresponding pixel values ​​according to a pre-defined compensation rule to achieve a uniform display effect.

[0142] After multiple rounds of debugging and optimization, the computer equipment records the compensation results after each debugging session and evaluates the compensation effect. Once debugging is complete, the computer equipment generates the final first image, which reflects the actual performance of the display module after speckle compensation, ensuring that the expected standards are met in terms of display quality.

[0143] In the above embodiments, through laboratory debugging, brightness and color unevenness issues in the display module can be identified and resolved under controlled conditions, thereby enabling the display module to achieve optimal display performance. Secondly, the acquired first image provides a benchmark for subsequent production line applications, serving as a reference for subsequent display compensation.

[0144] In an exemplary embodiment, the above-mentioned "second image acquisition process" includes:

[0145] After synchronizing the speckle compensation algorithm to the production line, a second image of the display module is obtained.

[0146] The speckle compensation algorithm was obtained after debugging the speckle compensation effect of the display module in a laboratory environment.

[0147] In this embodiment, after synchronizing the speckle compensation algorithm to the production line, the computer equipment will acquire a second image of the display module. At the start of this process, the production line image acquisition equipment photographs the display module in the actual working environment, maintaining the same lighting conditions and shooting angle as the laboratory environment during the acquisition process, and sends the acquired initial image to the computer equipment.

[0148] The computer equipment will apply a speckle compensation algorithm, developed and tested in a laboratory environment, to adjust the brightness and color uniformity of the display module in real time, optimizing the corresponding pixel values. After processing by the speckle compensation algorithm, a second image of the display module will be captured using production line image acquisition equipment, and then sent to the computer equipment.

[0149] In the above embodiments, the speckle compensation algorithm obtained by debugging in a laboratory environment can enable the display modules on the production line to obtain optimized display effects in actual operation. This method reduces the problem of unsatisfactory compensation effects caused by differences in equipment or environment, and improves the consistency and quality of display.

[0150] According to some embodiments of this application, a display compensation method is provided. Taking the application of this method to a computer device as an example, it may include the following steps:

[0151] Step 1: After adjusting the speckle compensation effect of the display module in a laboratory environment, obtain the first image of the display module. The first image is a laboratory image.

[0152] Step 2: After synchronizing the speckle compensation algorithm to the production line, acquire the second image of the display module. The speckle compensation algorithm was obtained after debugging the speckle compensation effect of the display module in a laboratory environment. The second image is a production line image.

[0153] Step 3: Based on the first image and the second image, determine the image similarity and the difference in speckle compensation distribution. The process of determining the difference in speckle compensation distribution includes steps 4 and 5; the process of determining image similarity includes steps 6 to 8.

[0154] Step 4: Perform blob compensation fitting on the first image and the second image respectively to obtain the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0155] Step 5: Determine the difference in speckle compensation distribution based on the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0156] Step 6: Perform data conversion processing on the first image and the second image respectively to obtain the converted first image and the converted second image.

[0157] Step 7: Perform feature extraction processing on the converted first image and the converted second image respectively to obtain the blob feature image corresponding to the first image and the blob feature image corresponding to the second image.

[0158] Step 8: Calculate the similarity between the blob feature image corresponding to the first image and the blob feature image corresponding to the second image using a preset similarity algorithm to obtain the image similarity.

[0159] Step 9: If the similarity and / or speckle compensation distribution difference meets the optimization triggering conditions, adjust the parameters of the current image acquisition equipment in the production line, and / or adjust the current image processing algorithm in the production line to determine the target preprocessing parameters for the second image. If the similarity and / or speckle compensation distribution difference does not meet the optimization triggering conditions, determine the current preprocessing parameters of the second image as the target preprocessing parameters for the second image.

[0160] Step 10: Perform display compensation on the display module based on the target preprocessing parameters of the second image.

[0161] In the above embodiments, acquiring the first and second images respectively allows for comparison under different environments, thereby more accurately assessing compensation requirements. Then, based on the analysis of image similarity and differences in speckle compensation distribution, brightness and color uniformity in specific areas can be effectively identified. By determining the target preprocessing parameters of the second image, display compensation can be performed on the display module in the production line, ensuring the compensation effect closely matches the results of laboratory debugging. This method can reduce unsatisfactory compensation effects caused by differences in parameters such as equipment model, image acquisition device height, and exposure time, thereby improving the consistency and overall quality of the display screen.

[0162] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0163] Based on the same inventive concept, this application also provides a display compensation device for implementing the display compensation method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more display compensation device embodiments provided below can be found in the limitations of the display compensation method described above, and will not be repeated here.

[0164] In one exemplary embodiment, such as Figure 6 As shown, a display compensation device is provided, including: an image acquisition module 501, a difference determination module 502, a parameter determination module 503, and a compensation module 504, wherein:

[0165] Image acquisition module 501 is used to acquire the first image and the second image of the display module respectively;

[0166] The difference determination module 502 is used to determine the image similarity and the difference in speckle compensation distribution based on the first image and the second image;

[0167] The parameter determination module 503 is used to determine the target preprocessing parameters of the second image based on similarity and / or differences in speckle compensation distribution;

[0168] The compensation module 504 is used to perform display compensation on the display module based on the target preprocessing parameters of the second image.

[0169] In some embodiments, the parameter determination module 503 is specifically used to optimize the current preprocessing parameters of the second image and determine the target preprocessing parameters of the second image when the similarity and / or speckle compensation distribution difference meets the optimization triggering conditions.

[0170] In some embodiments, the parameter determination module 503 is specifically used to adjust the parameters of the current image acquisition device in the production line, and / or to adjust the current image processing algorithm in the production line.

[0171] In some embodiments, the parameter determination module 503 is specifically used to determine the current preprocessing parameters of the second image as the target preprocessing parameters of the second image when the similarity and / or the difference in the distribution of spot compensation does not meet the optimization triggering conditions.

[0172] In some embodiments, the difference determination module 502 is specifically used to perform blob compensation fitting processing on the first image and the second image respectively to obtain the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image; and to determine the blob compensation distribution difference based on the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0173] In some embodiments, the difference determination module 502 is specifically used to perform data conversion processing on the first image and the second image respectively to obtain the converted first image and the converted second image; to perform feature extraction processing on the converted first image and the converted second image respectively to obtain the blob feature image corresponding to the first image and the blob feature image corresponding to the second image; and to calculate the similarity between the blob feature image corresponding to the first image and the blob feature image corresponding to the second image using a preset similarity algorithm to obtain the image similarity.

[0174] In some embodiments, the image acquisition module 501 is specifically used to acquire a second image of the display module after synchronizing the speckle compensation algorithm to the production line; the speckle compensation algorithm is obtained after debugging the speckle compensation effect of the display module in a laboratory environment.

[0175] In some embodiments, the image acquisition module 501 is specifically used to acquire a first image of the display module after debugging the speckle compensation effect of the display module in a laboratory environment.

[0176] Each module in the aforementioned display compensation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0177] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0178] Acquire the first and second images of the display module respectively;

[0179] Based on the first and second images, determine the image similarity and differences in speckle compensation distribution;

[0180] Based on similarity and / or differences in speckle compensation distribution, determine the target preprocessing parameters for the second image;

[0181] Display compensation is performed on the display module based on the target preprocessing parameters of the second image.

[0182] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0183] If the similarity and / or speckle compensation distribution differences meet the optimization triggering conditions, the current preprocessing parameters of the second image are optimized to determine the target preprocessing parameters of the second image.

[0184] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0185] Adjust the parameters of the current image acquisition equipment in the production line, and / or adjust the current image processing algorithm in the production line.

[0186] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0187] If the similarity and / or speckle compensation distribution differences do not meet the optimization triggering conditions, the current preprocessing parameters of the second image are determined as the target preprocessing parameters of the second image.

[0188] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0189] The first image and the second image are fitted with speckle compensation respectively to obtain the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0190] The difference in speckle compensation distribution is determined based on the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0191] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0192] The first image and the second image are processed by data conversion to obtain the converted first image and the converted second image respectively;

[0193] Feature extraction is performed on the first and second converted images respectively to obtain the blob feature images corresponding to the first and second images.

[0194] The similarity between the blob feature images corresponding to the first image and the blob feature images corresponding to the second image is calculated using a preset similarity algorithm to obtain the image similarity.

[0195] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0196] After adjusting the speckle compensation effect of the display module in a laboratory environment, the first image of the display module is obtained.

[0197] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0198] After synchronizing the speckle compensation algorithm to the production line, a second image of the display module is obtained; the speckle compensation algorithm was obtained after debugging the speckle compensation effect of the display module in a laboratory environment.

[0199] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0200] Acquire the first and second images of the display module respectively;

[0201] Based on the first and second images, determine the image similarity and differences in speckle compensation distribution;

[0202] Based on similarity and / or differences in speckle compensation distribution, determine the target preprocessing parameters for the second image;

[0203] Display compensation is performed on the display module based on the target preprocessing parameters of the second image.

[0204] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0205] If the similarity and / or speckle compensation distribution differences meet the optimization triggering conditions, the current preprocessing parameters of the second image are optimized to determine the target preprocessing parameters of the second image.

[0206] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0207] Adjust the parameters of the current image acquisition equipment in the production line, and / or adjust the current image processing algorithm in the production line.

[0208] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0209] If the similarity and / or speckle compensation distribution differences do not meet the optimization triggering conditions, the current preprocessing parameters of the second image are determined as the target preprocessing parameters of the second image.

[0210] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0211] The first image and the second image are fitted with speckle compensation respectively to obtain the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0212] The difference in speckle compensation distribution is determined based on the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0213] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0214] The first image and the second image are processed by data conversion to obtain the converted first image and the converted second image respectively;

[0215] Feature extraction is performed on the first and second converted images respectively to obtain the blob feature images corresponding to the first and second images.

[0216] The similarity between the blob feature images corresponding to the first image and the blob feature images corresponding to the second image is calculated using a preset similarity algorithm to obtain the image similarity.

[0217] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0218] After adjusting the speckle compensation effect of the display module in a laboratory environment, the first image of the display module is obtained.

[0219] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0220] After synchronizing the speckle compensation algorithm to the production line, a second image of the display module is obtained; the speckle compensation algorithm was obtained after debugging the speckle compensation effect of the display module in a laboratory environment.

[0221] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0222] Acquire the first and second images of the display module respectively;

[0223] Based on the first and second images, determine the image similarity and differences in speckle compensation distribution;

[0224] Based on similarity and / or differences in speckle compensation distribution, determine the target preprocessing parameters for the second image;

[0225] Display compensation is performed on the display module based on the target preprocessing parameters of the second image.

[0226] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0227] If the similarity and / or speckle compensation distribution differences meet the optimization triggering conditions, the current preprocessing parameters of the second image are optimized to determine the target preprocessing parameters of the second image.

[0228] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0229] Adjust the parameters of the current image acquisition equipment in the production line, and / or adjust the current image processing algorithm in the production line.

[0230] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0231] If the similarity and / or speckle compensation distribution differences do not meet the optimization triggering conditions, the current preprocessing parameters of the second image are determined as the target preprocessing parameters of the second image.

[0232] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0233] The first image and the second image are fitted with speckle compensation respectively to obtain the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0234] The difference in speckle compensation distribution is determined based on the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image.

[0235] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0236] The first image and the second image are processed by data conversion to obtain the converted first image and the converted second image respectively;

[0237] Feature extraction is performed on the first and second converted images respectively to obtain the blob feature images corresponding to the first and second images.

[0238] The similarity between the blob feature images corresponding to the first image and the blob feature images corresponding to the second image is calculated using a preset similarity algorithm to obtain the image similarity.

[0239] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0240] After adjusting the speckle compensation effect of the display module in a laboratory environment, the first image of the display module is obtained.

[0241] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0242] After synchronizing the speckle compensation algorithm to the production line, a second image of the display module is obtained; the speckle compensation algorithm was obtained after debugging the speckle compensation effect of the display module in a laboratory environment.

[0243] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0244] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0245] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A display compensation method, characterized in that, The method includes: A first image and a second image of the display module are acquired respectively; wherein, the first image is a laboratory image; and the second image is a production line image; Based on the first image and the second image, determine the image similarity and the difference in speckle compensation distribution; Based on the similarity and the difference in the spot compensation distribution, the target preprocessing parameters of the second image are determined; the target preprocessing parameters include the image acquisition device height, exposure gain value, and mapping value; the mapping value is used to perform brightness and color correction on the second image; Based on the target preprocessing parameters of the second image, display compensation is performed on the display module; The process of determining the difference in the spot compensation distribution includes: The first image and the second image are fitted with speckle compensation respectively to obtain the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image. Based on the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image, the difference in the spot compensation distribution is determined; The process of determining image similarity includes: The first image and the second image are respectively subjected to data conversion processing to obtain the converted first image and the converted second image; Feature extraction processing is performed on the converted first image and the converted second image respectively to obtain the blob feature image corresponding to the first image and the blob feature image corresponding to the second image; The similarity between the blob feature images corresponding to the first image and the blob feature images corresponding to the second image is calculated using a preset similarity algorithm to obtain the image similarity.

2. The method according to claim 1, characterized in that, The step of determining the target preprocessing parameters of the second image based on the similarity and the difference in speckle compensation distribution includes: If the similarity and the difference in the speckle compensation distribution meet the optimization triggering conditions, the current preprocessing parameters of the second image are optimized to determine the target preprocessing parameters of the second image.

3. The method according to claim 2, characterized in that, The optimization of the current preprocessing parameters of the second image includes: The parameters of the current image acquisition equipment in the production line are adjusted, and the current image processing algorithm in the production line is also adjusted.

4. The method according to claim 2, characterized in that, The step of determining the target preprocessing parameters of the second image based on the similarity and the difference in speckle compensation distribution includes: If the similarity and the difference in the speckle compensation distribution do not meet the optimization triggering conditions, the current preprocessing parameters of the second image are determined as the target preprocessing parameters of the second image.

5. The method according to claim 1, characterized in that, The process of acquiring the first image includes: After adjusting the speckle compensation effect of the display module in a laboratory environment, the first image of the display module is obtained.

6. The method according to claim 5, characterized in that, The process of acquiring the second image includes: After synchronizing the speckle compensation algorithm to the production line, a second image of the display module is obtained; the speckle compensation algorithm was obtained after debugging the speckle compensation effect of the display module in a laboratory environment.

7. A display compensation device, characterized in that, The device includes: The image acquisition module is used to acquire a first image and a second image of the display module, respectively; wherein, the first image is a laboratory image; and the second image is a production line image; The difference determination module is used to determine the image similarity and the difference in speckle compensation distribution based on the first image and the second image; The parameter determination module is used to determine the target preprocessing parameters of the second image based on the similarity and the difference in the spot compensation distribution; the target preprocessing parameters include the image acquisition device height, exposure gain value, and mapping value; the mapping value is used to perform brightness and color correction on the second image; The compensation module is used to perform display compensation on the display module based on the target preprocessing parameters of the second image; The difference determination module is specifically used to perform blob compensation fitting processing on the first image and the second image respectively to obtain a compensation distribution map corresponding to the first image and a compensation distribution map corresponding to the second image; and to determine the blob compensation distribution difference based on the compensation distribution map corresponding to the first image and the compensation distribution map corresponding to the second image. The difference determination module is specifically used to perform blob compensation fitting processing on the first image and the second image respectively, to obtain the first image and the second image respectively; to perform data conversion processing on the first image and the second image respectively, to obtain the converted first image and the converted second image; to perform feature extraction processing on the converted first image and the converted second image respectively, to obtain the blob feature image corresponding to the first image and the blob feature image corresponding to the second image; and to calculate the similarity between the blob feature image corresponding to the first image and the blob feature image corresponding to the second image using a preset similarity algorithm, to obtain the image similarity.

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

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

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