Color correction method, system and medium for display screen

Abstract

The present invention discloses a color correction method, system and medium for a display screen, and relates to the field related to color correction of display screens. The method comprises: obtaining a target image; establishing a historical image display database of the same material as a first material, and calling a demand identification mechanism to analyze the historical image display database to obtain a first color presentation requirement; displaying the target image on a target display screen to obtain a target output color, and analyzing to obtain a target color presentation state; determining whether the target color presentation state meets the first color presentation requirement; if not, introducing a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state. The method solves the technical problem that the color correction of existing display screens cannot accurately match the image color presentation requirement, resulting in insufficient pertinence and accuracy of the correction, and achieves the technical effect of accurately matching the image color presentation requirement and improving the pertinence and accuracy of the correction.

Description

Technical Field

[0001] The present application relates to the field of display screen color correction, and in particular to a display screen color correction method, system, and medium. Background Art

[0002] In the field of display image display, accurately presenting image colors to meet user needs is crucial and directly related to the user experience and the accuracy of image information transmission. Currently, the solution to the problem of accurate display color presentation mainly relies on color calibration of the display, and the display effect is optimized by adjusting the color parameters of the display. Most existing color calibration methods are based on universal standards or single material samples. Due to the differences in the color requirements of different material images presented on the display, this universal calibration method is difficult to accurately match the color presentation requirements of various material images, resulting in inaccurate color presentation of some images on the display, which cannot meet the user's expectations for the color of specific material images.

[0003] In the current related technologies, the color correction of display screens cannot accurately match the image color presentation requirements, resulting in technical problems such as insufficient targeting and accuracy of the correction. Summary of the Invention

[0004] The present application provides a color correction method, system and medium for a display screen, which adopts the method of obtaining a target image corresponding to a first material, establishing a historical image database of similar materials and analyzing and obtaining a first color presentation requirement, allowing a target display screen to display the target image to obtain a target color presentation state, judging whether the state meets the first color presentation requirement, and if not, introducing a color correction plan to perform correction to obtain a corrected color presentation state. These technical means solve the technical problem that the color correction of existing display screens cannot accurately match the image color presentation requirement, resulting in insufficient pertinence and accuracy of the correction, and achieve the technical effect of accurately matching the image color presentation requirement and improving the pertinence and accuracy of the correction.

[0005] The present application provides a color correction method for a display screen, comprising: acquiring a target image, wherein the target image refers to an image to be displayed through a target display screen, and the target image corresponds to a first material of a predetermined type of material; establishing a historical image display database of the same material as the first material, and calling a demand identification mechanism to analyze the historical image display database to obtain a first color presentation requirement; displaying the target image through the target display screen to obtain a target output color, and analyzing to obtain a target color presentation state; determining whether the target color presentation state meets the first color presentation requirement; and if not, introducing a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state.

[0006] In a possible implementation, a historical image display database of the same material as the first material is established, and a demand identification mechanism is called to analyze the historical image display database to obtain a first color presentation requirement, and the following processing is performed: extracting a first historical display data group of the first historical image in the historical image display database; obtaining a first predetermined target color of the first historical image; comparing the first historical output color in the first historical display data group with the first predetermined target color to obtain a first color deviation index; if the first color deviation index is within a predetermined deviation limit, taking the first historical output color as a demand benchmark; analyzing the demand benchmark according to the demand identification mechanism to obtain the first color presentation requirement.

[0007] In a possible implementation, the first historical output color in the first historical display data group is compared with the first predetermined target color to obtain a first color deviation index, and the following processing is performed: a predetermined single indicator set is retrieved, wherein the predetermined single indicator set includes a first indicator; with the first indicator as a constraint, the first historical indicator value and the first predetermined indicator value corresponding to the first indicator are matched in the first historical output color and the first predetermined target color in turn; the first historical indicator value is compared with the first predetermined indicator value to obtain a first deviation value of the first indicator; and a variation weighted analysis is performed on the first deviation value to obtain the first color deviation index.

[0008] In a possible implementation, the following processing is performed: the predetermined single index set includes a hue index set, a brightness index, and a saturation index, and the hue index set also includes a red index, a green index, and a blue index.

[0009] In a possible implementation, the demand benchmark is analyzed according to the demand identification mechanism to obtain the first color presentation requirement, and the following processing is performed: extracting the second indicator from the predetermined single indicator set; obtaining the second historical indicator value of the second indicator in combination with the demand benchmark; and calculating the ratio of the first historical indicator value to the second historical indicator value according to the demand identification mechanism to obtain the first color presentation requirement.

[0010] In a possible implementation, after establishing a historical image display database of the same material as the first material, and calling the demand identification mechanism to analyze the historical image display database to obtain the first color presentation requirement, the following processing is also performed: discrete cosine transform preprocessing is performed on the target image to obtain a preprocessing result; the AC coefficient in the preprocessing result is extracted, and the first color presentation requirement is calibrated using the AC coefficient as a weight.

[0011] In a possible implementation, if it is not satisfied, a color correction plan is introduced to perform presentation correction on the target color presentation state to obtain a corrected color presentation state, and the following processing is performed: the actual ambient light information of the target image is obtained according to the color correction plan; the actual ambient light information is compared and analyzed with the ideal ambient light information to obtain an ambient light feedback coefficient; the total operating time of the target display screen is obtained according to the color correction plan, and a predetermined aging feedback coefficient corresponding to the total operating time is matched; the color presentation control adjustment of the target display screen is performed with the ambient light feedback coefficient and the predetermined aging feedback coefficient as weights to obtain the corrected color presentation state.

[0012] In a possible implementation, if the conditions are not met, a color correction plan is introduced to correct the target color presentation state. After the corrected color presentation state is obtained, the following processing is also performed: obtaining a reference color presentation state of the target image; characterizing the corrected color presentation state and the reference color presentation state in turn, and obtaining corrected color features and reference color features respectively; curve-forming the corrected color features and the reference color features in turn, and obtaining a correction curve and a reference curve respectively; obtaining the curve similarity between the correction curve and the reference curve, and using the curve similarity to characterize the color presentation quality of the target image on the target display screen.

[0013] The present application also provides a color correction system for a display screen, comprising: a target image acquisition module, for acquiring a target image, wherein the target image refers to an image to be displayed through a target display screen, and the target image corresponds to a first material in a predetermined type of material; a first color presentation requirement acquisition module, for establishing a historical image display database of the same material as the first material, and calling a requirement identification mechanism to analyze the historical image display database to obtain a first color presentation requirement; a target color presentation state acquisition module, for displaying the target image through the target display screen to obtain a target output color, and analyzing the target color presentation state; a judgment module, for judging whether the target color presentation state meets the first color presentation requirement; and a color correction module, for introducing a color correction plan to perform presentation correction on the target color presentation state if it does not meet the requirement, to obtain a corrected color presentation state.

[0014] The present application also provides a computer-readable storage medium, comprising: a computer program stored thereon, which implements a color correction method for a display screen when the program is executed by a processor.

[0015] The color correction method, system and medium for a display screen proposed in this application first obtain a target image, wherein the target image refers to an image to be displayed on a target display screen, and the target image corresponds to a first material in a predetermined type of material. Then, a historical image display database of the same material as the first material is established, and a demand identification mechanism is called to analyze the historical image display database to obtain a first color presentation requirement. Then, the target image is displayed on the target display screen to obtain a target output color, and a target color presentation state is obtained by analysis. Then, it is determined whether the target color presentation state meets the first color presentation requirement. If not, a color correction plan is introduced to correct the target color presentation state to obtain a corrected color presentation state. This achieves the technical effect of accurately matching image color presentation requirements and improving the pertinence and accuracy of correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A schematic flow chart of a color correction method for a display screen provided in an embodiment of the present application.

[0018] Figure 2 A schematic structural diagram of a color correction system for a display screen provided in an embodiment of the present application.

[0019] Description of the reference numerals: target image acquisition module 10 , first color presentation requirement acquisition module 20 , target color presentation state acquisition module 30 , judgment module 40 , color correction module 50 . DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, 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 limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be 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, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The present application provides a method for color correction of a display screen. Figure 1 As shown, the method includes:

[0024] Step S100 : Acquire a target image, wherein the target image refers to an image to be displayed on a target display screen, and the target image corresponds to a first material among predetermined types of materials.

[0025] Specifically, the target image refers to an image to be displayed on the target display screen, and its material type is known and belongs to one of the predetermined types of materials (such as metal, fabric, liquid, etc.). The predetermined type of material is a set of predefined material categories used to classify the material of the target image so that corresponding color correction operations can be performed based on the material type. The target image data is read through an image acquisition device such as a camera, scanner, etc., or from an image storage medium such as a hard disk, network storage device, etc. The type of material corresponding to the target image refers to the first material. For the first material in the predetermined type of material corresponding to the target image, its material type can be identified through the image metadata (such as the shooting scene and object material label recorded in the file properties) or pre-set image classification rules.

[0026] For example, if the target image is a photograph of a metal product, the image capture device captures it and stores it as an image file. If the image file's metadata clearly states "Material: Metal," it can be directly identified as a target image corresponding to a first material of metal. Alternatively, in the absence of metadata annotation, a pre-defined image classification rule can be used to analyze the texture, reflectivity, and other features of the object in the image to determine that it is a metal material, thereby determining that the target image's material is metal. For another example, the target image can be read from an image database that stores images of various materials. Using the classification index in the database, it can be determined that the first material corresponding to the target image is fabric.

[0027] Step S200 , establishing a historical image display database of the same material as the first material, and calling a demand identification mechanism to analyze the historical image display database to obtain a first color presentation requirement.

[0028] Specifically, images of similar materials to the first material and their displayed color data (e.g., RGB values, color space coordinates, etc.) are selected from a database storing historical images and their display records. This data is then stored in a dedicated database. This data is then used to analyze the color rendering patterns of images of similar materials, thereby determining color rendering requirements. This selection process can be implemented using database query statements based on the material classification field.

[0029] The data in the historical image display database is analyzed using statistical analysis, machine learning (such as cluster analysis and neural networks). Taking cluster analysis as an example, clustering is performed based on the characteristic indicators of the color displayed on the display screen (such as brightness, saturation, and color temperature). The color rendering patterns of images of the same material as the first material are identified, thereby obtaining the first color rendering requirements, including the required values ​​of each indicator in the predetermined single indicator set, such as the required brightness value, the required saturation value, and the required color temperature value.

[0030] For example, a historical image display database stores a large number of display records of metal images, including information such as the image's RGB values, display parameters (such as resolution and color gamut), and user evaluations of the display effects. Using a cluster analysis algorithm, the display color data for these metal images is divided into several categories, each corresponding to a typical color rendering requirement. Assuming the analysis results indicate that users prefer colors with high brightness and moderate saturation for metal images, the brightness requirement in the first color rendering requirement can be set to a high range, and the saturation requirement to a moderate range.

[0031] Another example is using a neural network algorithm to analyze a historical image display database. The neural network's input is the display color data of metal images, and its output is the corresponding color rendering requirements. After training with a large number of training samples, the neural network can learn the color rendering patterns of metal images. When fed with new metal image display data, the neural network can output the first color rendering requirements. For example, for a metal image, the neural network outputs the color rendering requirements: a brightness requirement of 180-220 (assuming the brightness range is 0-255), and a saturation requirement of 80-120 (assuming the saturation range is 0-255).

[0032] In one possible implementation, a historical image display database containing the same material as the first material is established, and a requirement identification mechanism is invoked to analyze the historical image display database to obtain a first color rendering requirement. Step S200 further includes step S210, extracting a first historical display data set for the first historical image in the historical image display database. Specifically, an image (referred to as the first historical image) containing the same material as the target image is randomly selected from the historical image display database, and its display data set is extracted. The first historical display data set includes the actual color data (e.g., RGB values, brightness, saturation, color temperature, etc.) of the first historical image when displayed on the target display screen.

[0033] For example, assuming the target image material is metal, a historical image of the metal material (the first historical image) is extracted from the historical image display database. The historical display data set for this image includes its RGB value (200, 180, 160) when displayed on the target display screen, with a brightness of 180 cd / m², a saturation of 70, and a color temperature of 6500K.

[0034] Step S220: Obtain a first predetermined target color for the first historical image. Specifically, the predetermined target color for the first historical image under ideal display conditions is obtained using a predefined standard color model (such as sRGB, Adobe RGB, etc.) or ideal color values ​​obtained through user feedback. For example, assume that the predetermined target color for the first historical image under ideal display conditions is an RGB value of (210, 190, 170), a brightness of 190 cd / m², a saturation of 75, and a color temperature of 6500K.

[0035] Step S230 compares the first historical output color in the first historical display data set with the first predetermined target color to obtain a first color deviation index. Specifically, various calculation methods, such as Euclidean distance and color difference formulas (e.g., CIEDE2000), are used to calculate the difference between the actual color data (the first historical output color) in the first historical display data set and the predetermined target color to obtain the color deviation index.

[0036] For example, the deviation of RGB values ​​is calculated using Euclidean distance: RGB deviation The color difference formulas are used to calculate the deviations in color temperature, brightness, and saturation: Brightness deviation = |190−180| = 10 (cd / m²); Saturation deviation = |75−70| = 5; Color temperature deviation = |6500−6500| = 0 (K). These deviations are combined into a color deviation index. For example, the weighted sum of the RGB deviation, brightness deviation, saturation deviation, and color temperature deviation can be: First Color Deviation Index = 0.5 × 17.32 + 0.3 × 10 + 0.1 × 5 + 0.1 × 0 = 12.16.

[0037] In step S240, if the first color deviation index is within a predetermined deviation limit, the first historical output color is used as a desired benchmark. Specifically, a predetermined deviation limit (e.g., 15) is set. If the first color deviation index is less than or equal to the limit, the first historical output color is considered close to the ideal color and can be used as the desired benchmark.

[0038] For example, assuming the predetermined deviation limit is 15, the first color deviation index is 12.16, which is less than the predetermined deviation limit. Therefore, the first historical output color (RGB values ​​200, 180, 160, brightness 180 cd / m², saturation 70, and color temperature 6500K) is used as the demand benchmark.

[0039] Step S250: Analyze the requirement benchmark using the requirement identification mechanism to obtain the first color rendering requirement. Specifically, the requirement benchmark is analyzed using a requirement identification mechanism (e.g., a machine learning algorithm, statistical analysis, etc.) to extract the required values ​​for a predetermined set of individual indicators (e.g., brightness, saturation, color temperature, etc.) to form the first color rendering requirement.

[0040] This implementation compares the actual display colors of historical images with predetermined target colors to obtain an accurate color deviation index, which is then used as a basis for determining the required benchmark. This method can more accurately reflect the color rendering effect of the target image on the display screen. Compared with directly using an ideal color model or fixed standard, this method is more adaptable to changes in the actual display environment and improves the accuracy of color correction. Different display screens and display environments may cause differences in color rendering. By analyzing historical image display data and combining it with color deviations under actual display conditions, color rendering requirements can be dynamically adjusted, making the correction scheme more adaptable. For example, for display screens of different brands or models, even if their display characteristics vary, the most suitable correction requirements can be found through historical data. Using historical image display data as a reference avoids complex color measurement and analysis of each target image. Through a pre-built historical database and requirement identification mechanism, correction requirements can be quickly generated, reducing the reliance on specialized equipment and manual intervention during the correction process and lowering the correction cost.

[0041] In one possible implementation, the first historical output color in the first historical display data group is compared with the first predetermined target color to obtain a first color deviation index. Step S230 further includes step S231, calling a predetermined single indicator set, wherein the predetermined single indicator set includes a hue indicator set, a lightness indicator, and a saturation indicator, and the hue indicator set also includes a red indicator, a green indicator, and a blue indicator, wherein the predetermined single indicator set includes the first indicator. Specifically, the predetermined single indicator set is a set of indicators for measuring the color rendering effect, and is used to determine whether the target color rendering state meets the color rendering requirements. A set of single indicators is selected from the predefined indicator set to evaluate color deviation. These indicators include a hue indicator set (red indicator, green indicator, blue indicator), a lightness indicator, and a saturation indicator.

[0042] Step S232 uses the first indicator as a constraint, sequentially matching the first historical indicator value and the first predetermined indicator value corresponding to the first indicator in the first historical output color and the first predetermined target color. Specifically, one of the indicators is selected as the first indicator (for example, the red indicator is selected as the first indicator), and the value of this indicator is extracted from the first historical output color and the first predetermined target color. For example, assuming the first indicator is the red indicator (i.e., the value of the red component), the red indicator value of the first historical output color is 200 (the first historical indicator value), and the red indicator value of the first predetermined target color is 210 (the first predetermined indicator value).

[0043] Step S233: Compare the first historical indicator value with the first predetermined indicator value to obtain a first deviation value of the first indicator. Specifically, the difference between the first historical indicator value and the first predetermined indicator value is calculated, and the deviation is expressed as an absolute value. For example, the first deviation value = |210 - 200| = 10.

[0044] Step S234: A weighted variation analysis is performed on the first deviation value to obtain the first color deviation index. Specifically, a weighted analysis is performed on the deviation value of each indicator, with the weights pre-set based on the indicator's importance and variation. Weighted variation analysis considers the indicator's coefficient of variation (the ratio of the standard deviation to the mean) to more accurately reflect the impact of deviation.

[0045] This implementation approach, by introducing multi-dimensional metrics such as hue (red, green, and blue), lightness, and saturation, enables a more comprehensive assessment of color deviation. Traditional color deviation assessments focus solely on brightness or RGB value deviations, overlooking other important color attributes. This comprehensive assessment of multi-dimensional metrics more accurately reflects actual color deviations. Weighted variation analysis adjusts weights based on the actual variation of each metric, more accurately reflecting the actual impact of each metric on color deviation. For example, a high coefficient of variation for the red metric indicates that deviations in the red component have a greater impact on the overall color, and therefore can be given a higher weight. This weighting approach improves color correction accuracy, bringing the corrected color closer to the target color. Different materials and display environments can result in different color deviation characteristics. Comprehensive evaluation of multi-dimensional metrics and weighted variation analysis can adapt to different color deviations and generate more targeted correction requirements. For example, for metallic images, deviations in the red and green components may be more critical, while for textile images, deviations in saturation and lightness may be more important. Dynamic weighting allows for better adaptation to the needs of different materials and display environments.

[0046] In one possible implementation, the requirement benchmark is analyzed according to the requirement identification mechanism to obtain the first color rendering requirement. Step S250 further includes step S251 of extracting a second indicator from the predetermined set of individual indicators. Specifically, another indicator is selected from the predetermined set of individual indicators as the second indicator. This indicator can be a hue indicator (e.g., a green indicator, a blue indicator), a lightness indicator, or a saturation indicator. For example, suppose in step S230, a red indicator was selected as the first indicator. Now, a green indicator is selected from the predetermined set of individual indicators as the second indicator.

[0047] Step S252 combines the desired benchmark with the second historical indicator value to obtain a second historical indicator value for the second indicator. Specifically, the value corresponding to the second indicator is extracted based on the desired benchmark (i.e., the first historical output color). The desired benchmark is color data that has been screened and deemed close to the ideal color presentation. For example, assume the desired benchmark (the first historical output color) has a red index value of 200, a green index value of 180, and a blue index value of 160. Since the second indicator is the green index, the second historical index value is therefore 180.

[0048] Step S253, according to the requirement identification mechanism, calculate the ratio of the first historical indicator value to the second historical indicator value to obtain the first color presentation requirement. Specifically, the requirement identification mechanism is used to calculate the ratio of the first historical indicator value to the second historical indicator value, and the ratio is used as a reference value for the first color presentation requirement. This ratio is used to adjust the color of the target image to make it closer to the requirement benchmark. For example, assume that the first historical indicator value (red indicator value) is 200 and the second historical indicator value (green indicator value) is 180. Calculate the ratio: Ratio = first historical indicator value ÷ second historical indicator value = 200 ÷ 180 ≈ 1.11. This ratio of 1.11 is used as part of the first color presentation requirement to adjust the proportion of the red and green components of the target image.

[0049] This implementation, by calculating the ratio of the first historical indicator value to the second historical indicator value, enables a more accurate assessment of color requirements. This approach considers not only the absolute value of a single indicator, but also the relative relationship between different indicators. For example, the ratio of red and green components is particularly important for the color rendering of certain materials, such as metal, and this ratio calculation can more accurately reflect this relationship. Images of different materials have different requirements for color ratios. For example, images of metal may require a higher ratio of red to green components, while images of fabric may require a more balanced ratio. By calculating the ratio, the color correction scheme can be dynamically adjusted based on the requirements of different materials, making it more adaptable.

[0050] In one possible implementation, after establishing a historical image display database of the same material as the first material, and calling a demand identification mechanism to analyze the historical image display database to obtain a first color presentation requirement, the method further includes: performing discrete cosine transform preprocessing on the target image to obtain a preprocessing result; extracting the AC coefficient in the preprocessing result, and calibrating the first color presentation requirement using the AC coefficient as a weight.

[0051] Specifically, the discrete cosine transform (DCT) is a mathematical transformation method that converts an image from the spatial domain to the frequency domain. It decomposes the image into a direct current (DC) component and an alternating current (AC) component. The DC component represents the average brightness of the image, while the AC component represents image detail and variation. For example, suppose the target image is a picture of metal. After performing a DCT transform on this image, a DCT coefficient matrix is ​​obtained. The first element in the matrix (top left corner) is the DC component, and the remaining elements are the AC components.

[0052] Extract the AC coefficients from the DCT coefficient matrix, which reflects the details and texture information of the image. For example, suppose the DCT coefficient matrix is ​​as follows: Where 100 is the DC component and the rest are AC components. Extract all non-zero AC components: 50, 30, 20, -10, 5, 10, -5. Normalize the extracted AC coefficients so that their sum equals 1, and use these as weights. These weights are then used to adjust the various indicators in the first color rendering requirement.

[0053] For example, suppose the first color rendering requirement includes a brightness requirement of 180 cd / m², a saturation requirement of 70, and a color temperature requirement of 6500K. The extracted AC coefficients are: 50, 30, 20, -10, 5, 10, and -5. First, take the absolute values ​​of these coefficients and normalize them: |50| = 50, |30| = 30, |20| = 20, |-10| = 10, |5| = 5, |10| = 10, and |-5| = 5. The total is 50 + 30 + 20 + 10 + 5 + 10 + 5 = 130. Normalized weights: 50 / 130≈0.38, 30 / 130≈0.23, 20 / 130≈0.15, -10 / 130≈-0.08, 5 / 130≈0.04, 10 / 130≈0.08, -5 / 130≈-0.04. These weights are used to calibrate the first color presentation requirement. The calibrated brightness requirement value = 180×(0.38+0.23+0.15-0.08+0.04+0.08-0.04) = 180×0.76 = 136.8 cd / m², the calibrated saturation requirement value = 70×(0.38+0.23+0.15-0.08+0.04+0.08-0.04) = 70×0.76 = 53.2, and the calibrated color temperature requirement value = 6500×(0.38+0.23+0.15-0.08+0.04+0.08-0.04) = 6500×0.76 = 4940K.

[0054] This implementation uses the AC components in the DCT coefficient matrix to calibrate the primary color rendering requirement, more accurately reflecting the detailed features of the target image. This approach considers the image's high-frequency information, helping to improve the match between the corrected colors and the target image. Different images have different detailed features, and by calibrating based on the AC components in the DCT coefficient matrix, the color correction scheme can be dynamically adjusted to better suit the specific characteristics of the target image.

[0055] Step S300: Display the target image on the target display screen to obtain a target output color, and analyze to obtain a target color presentation state.

[0056] Specifically, the target output color refers to the color presented when the target display screen actually displays the target image. Its color characteristics can be measured or acquired. The target color rendering state refers to the color rendering of the target output color as reflected by the actual values ​​of each indicator in a predetermined set of individual indicators (such as brightness, saturation, and color temperature) when the target display screen displays the target image.

[0057] The target image data is transmitted to the display driver circuit of the target display. The display driver circuit controls the display's pixels to emit light according to the target image's color information, thereby displaying the target image on the display and obtaining the target output color. A color analysis instrument (such as a spectrophotometer or color analyzer) is used to measure the target image displayed on the target display to obtain parameters such as the RGB values, color temperature, and brightness of the target output color. Alternatively, the target output color data is collected using the display's built-in color sensor. Then, based on a predetermined set of individual indicators (such as brightness, saturation, and color temperature), the actual values ​​of each indicator are extracted from the measured or collected data.

[0058] For example, the target display is an LCD, and the target image is a picture of a fabric. The target image is transmitted via a data line to the LCD's display driver circuit. The display driver circuit controls the arrangement of the liquid crystal molecules and the brightness of the backlight based on the target image's color information, causing the display to display the target image. A spectrophotometer is then used to measure the target image on the display. The measured RGB values ​​for the target output color are (150, 100, 80), the color temperature is 6500K, and the brightness is 120 cd / m². Assuming the predetermined individual indicator set includes brightness, saturation, and color temperature, and the saturation corresponding to the calculated RGB values ​​is 60, the target color rendering state is: the actual brightness value is 120 cd / m², the actual saturation value is 60, and the actual color temperature is 6500K.

[0059] For example, suppose the target display is an OLED display with a built-in color sensor, and the target image is an image of a liquid. While the target image is displayed, the built-in color sensor collects data on the target's output color, obtaining RGB values ​​of (100, 120, 140), a color temperature of 7000K, and a brightness of 150 cd / m². Based on the predetermined set of individual indicators, the actual saturation value is calculated to be 70. Therefore, the target color rendering status is: the actual brightness value is 150 cd / m², the actual saturation value is 70, and the actual color temperature value is 7000K.

[0060] Step S400: Determine whether the target color presentation state meets the first color presentation requirement.

[0061] Specifically, a comparison algorithm is used to compare the actual values ​​of each indicator in the target color rendering state with the required values ​​of each indicator in the first color rendering requirement. This comparison algorithm can be a simple value range judgment or a complex weighted scoring algorithm. If the actual values ​​of all indicators are within the corresponding required value range, or if the weighted score reaches the set threshold, the target color rendering state is judged to meet the first color rendering requirement; otherwise, it is judged not to meet the requirement.

[0062] For example, the first color rendering requirement is: the required brightness value range is 180-220, the required saturation value range is 80-120, and the required color temperature value range is 6000K-7000K. The target color rendering state is: the actual brightness value is 190, the actual saturation value is 90, and the actual color temperature value is 6500K. Through a simple value range judgment, it is found that the actual brightness value of 190 is within the required value range of 180-220, the actual saturation value of 90 is within the required value range of 80-120, and the actual color temperature value of 6500K is within the required value range of 6000K-7000K. Therefore, it is determined that the target color rendering state meets the first color rendering requirement.

[0063] For example, using a weighted scoring algorithm, assuming a weight of 0.4 for brightness, 0.3 for saturation, and 0.3 for color temperature, the first color rendering requirement is: a brightness range of 180-220, a saturation range of 80-120, and a color temperature range of 6000K-7000K. The target color rendering state is: actual brightness of 170, actual saturation of 110, and actual color temperature of 6200K. Calculating the weighted scores: Brightness score = (170-180) / (220-180) × 0.4 = -0.1, Saturation score = (110-80) / (120-80) × 0.3 = 0.225, Color temperature score = (6200-6000) / (7000-6000) × 0.3 = 0.06. The total weighted score is -0.1+0.225+0.06=0.185. If the threshold is set to 0.2, then since the total weighted score 0.185 is less than 0.2, it is determined that the target color rendering state does not meet the first color rendering requirement.

[0064] Step S500: If the conditions are not met, a color correction plan is introduced to correct the target color presentation state to obtain a corrected color presentation state.

[0065] Specifically, a color correction plan is a pre-set correction scheme for specific color deviation situations, which includes specific correction parameters and methods, and is used to adjust the color of the target image so that its color presentation status meets the color presentation requirements.

[0066] If the target color rendering state does not meet the first color rendering requirement, a color correction plan is selected based on a pre-defined color correction rule library that matches the target image material and the deviation between the current color rendering state. This color correction rule library is constructed based on historical correction data and expert experience and contains correction parameters (such as RGB value adjustments and color space conversion parameters) for images of different materials and different color deviations. The correction parameters in the selected color correction plan are applied to the color data of the target image to adjust the target image's color. Once the adjustment is complete, the adjusted image is displayed again on the target display screen to obtain the corrected color rendering state, with the actual values ​​of its color characteristic indicators closer to the first color rendering requirement.

[0067] For example, if the target image material is liquid, the target color rendering state does not meet the first color rendering requirement, specifically manifested as low brightness and high saturation. Based on the color correction rule library, a corresponding color correction plan is selected. This plan stipulates that for liquid material images, when the brightness is low, the R, G, and B components of the RGB value are increased by 10 respectively; when the saturation is high, the RGB value is converted to the HSV color space and the saturation S is reduced by 0.1. According to this plan, the color data of the target image is adjusted. Assuming that the original target image has RGB values ​​of (100, 120, 140), the adjusted RGB values ​​are (110, 130, 150). These values ​​are then converted to the HSV color space, the saturation S is reduced by 0.1, and then converted back to the RGB color space, resulting in the corrected RGB values ​​of (105, 125, 145). The adjusted image data is transmitted to the target display screen for display, resulting in the corrected color rendering state.

[0068] For example, if the target image material is metal, the color temperature of the target color rendering state is too high. The corresponding plan in the color correction rule library stipulates that for metallic images, when the color temperature is too high, the color temperature is lowered by adjusting the RGB values. The specific method is to increase the B component in the RGB value while appropriately reducing the R component. Suppose the RGB values ​​of the original target image are (200, 180, 160). After adjusting according to the plan, the RGB values ​​become (190, 180, 170). Displaying the adjusted image on the target display will obtain the corrected color rendering state.

[0069] In one possible implementation, if the conditions are not met, a color correction scheme is introduced to correct the target color presentation state to obtain a corrected color presentation state. Step S500 further includes step S510, in which actual ambient light information of the target image is obtained according to the color correction scheme. Specifically, the actual ambient light information of the target image is obtained using an ambient light sensor or a preset ambient light model. Ambient light information includes parameters such as light intensity and color temperature. For example, assuming the actual ambient light information of the target image is: light intensity: 500 lux, color temperature: 5500K.

[0070] Step S520: Compare and analyze the actual ambient light information with the ideal ambient light information to obtain an ambient light feedback coefficient. Specifically, the ideal ambient light information is predefined based on display requirements. The actual ambient light information is compared with the preset ideal ambient light information to calculate the ambient light feedback coefficient. For example: ideal light intensity: 600 lux, ideal color temperature: 6500K, light intensity deviation = (500-600) / 600 = -0.167, color temperature deviation = (5500-6500) / 6500 = -0.154. Ambient light feedback coefficient = 1-(|light intensity deviation|+|color temperature deviation|) = 1-(0.167+0.154) = 0.679.

[0071] Step S530, obtain the total operating time of the target display screen according to the color correction plan, and match the predetermined aging feedback coefficient corresponding to the total operating time. Specifically, the total operating time of the display screen is obtained through its usage record, and the aging feedback coefficient is matched according to the preset aging model. The aging feedback coefficient reflects the performance degradation of the display screen due to the increase in usage time. For example, assuming that the total operating time of the target display screen is 2000 hours, the preset aging model is: the aging feedback coefficient decreases by 0.1 every 1000 hours, then the aging feedback coefficient = 1-(2000 / 1000)×0.1=0.8.

[0072] Step S540 uses the ambient light feedback coefficient and the predetermined aging feedback coefficient as weights to perform color control adjustments on the target display screen to obtain the corrected color rendering state. Specifically, the ambient light feedback coefficient and the predetermined aging feedback coefficient are used as weights to adjust the color rendering of the target display screen, so that the adjusted color rendering state is closer to the ideal state.

[0073] For example, assume the target display's original color rendering state is: luminance: 180 cd / m², saturation: 70, color temperature: 6500K, ambient light feedback coefficient: 0.679, and aging feedback coefficient: 0.8. Assume the ambient light feedback coefficient weight is 0.6, and the aging feedback coefficient weight is 0.4. The combined weight = (ambient light feedback coefficient × ambient light weight) + (aging feedback coefficient × aging weight) = (0.679 × 0.6) + (0.8 × 0.4) = 0.4074 + 0.32 = 0.7274. Using the combined weight, the original color rendering state is corrected: Corrected luminance = 180 × 0.7274 ≈ 130.93 cd / m², Corrected saturation = 70 × 0.7274 ≈ 50.92, and Corrected color temperature = 6500 × 0.7274 ≈ 4728.1K.

[0074] This approach takes into account the effects of actual ambient light and display aging, resulting in a more accurate reflection of actual display conditions after correction. This approach not only accounts for color deviation but also the effects of ambient light and aging on the display, thereby improving calibration accuracy. Different ambient light conditions and display aging can lead to different display effects. By dynamically adjusting the ambient light feedback coefficient and aging feedback coefficient, we can better adapt to different display conditions, making the calibration solution more adaptable.

[0075] In one possible implementation, if the conditions are not met, a color correction plan is introduced to correct the target color presentation state. After obtaining the corrected color presentation state, the method further includes: obtaining a reference color presentation state of the target image; characterizing the corrected color presentation state and the reference color presentation state in turn, and obtaining corrected color features and reference color features respectively; curve-processing the corrected color features and the reference color features in turn, and obtaining a correction curve and a reference curve respectively; obtaining the curve similarity between the correction curve and the reference curve, and using the curve similarity to characterize the color presentation quality of the target image on the target display screen.

[0076] Specifically, the benchmark color rendering state refers to the color rendering state of the target image under ideal display conditions. This can be obtained by measuring with professional equipment or using a preset standard color model. For example, assume the benchmark color rendering state is: benchmark brightness: 200 cd / m², benchmark saturation: 80, and benchmark color temperature: 6500K. Characterization processing converts the color rendering state into a set of feature vectors, which are used for curve processing. Characterization processing includes color space conversion (such as from RGB to CIELAB) and feature extraction (such as brightness, saturation, and color temperature). For example, assume the corrected color rendering state is: corrected brightness: 130.93 cd / m², corrected saturation: 50.92, and corrected color temperature: 4728.1K. After characterization processing, the corrected color features are: [130.93, 50.92, 4728.1], and the benchmark color features are: [200, 80, 6500]. Curve processing is to convert feature vectors into curves so that the color presentation status can be compared more intuitively. Curve processing includes interpolation and fitting to generate continuous curves. For example, suppose brightness, saturation, and color temperature are selected as features and converted into curves respectively. The correction curves are: brightness curve: from 0 to 130.93; saturation curve: from 0 to 50.92; color temperature curve: from 0 to 4728.1. Baseline curve: brightness curve: from 0 to 200; saturation curve: from 0 to 80; color temperature curve: from 0 to 6500. Curve similarity can be evaluated by calculating the distance between two curves. Methods include Euclidean distance, dynamic time warping (DTW), etc. For example, the calculation is as follows: brightness curve similarity Saturation curve similarity Color temperature curve similarity Comprehensive similarity .

[0077] This approach uses characterization and curve processing to intuitively compare the corrected color rendering state with the reference color rendering state. This method considers not only the absolute value of the color but also the color change trend, thereby improving the accuracy of color rendering quality assessment.

[0078] The embodiment of the present application adopts technical means such as obtaining a target image corresponding to a first material, establishing a historical image database of similar materials and analyzing it to obtain a first color presentation requirement, allowing a target display screen to display the target image to obtain a target color presentation state, judging whether the state meets the first color presentation requirement, and if not, introducing a color correction plan to perform correction to obtain a corrected color presentation state. This solves the technical problem that the color correction of existing display screens cannot accurately match the image color presentation requirement, resulting in insufficient pertinence and accuracy of the correction, and achieves the technical effect of accurately matching the image color presentation requirement and improving the pertinence and accuracy of the correction.

[0079] In the above, refer to Figure 1 A color correction method for a display screen according to an embodiment of the present invention is described in detail. Figure 2 A color correction system for a display screen according to an embodiment of the present invention is described.

[0080] A color correction system for a display screen according to an embodiment of the present invention is designed to address the technical problem that existing color correction systems for display screens cannot accurately match image color rendering requirements, resulting in insufficient correction targeting and accuracy. The system achieves the technical effect of accurately matching image color rendering requirements and improving the targeting and accuracy of correction. The system includes a target image acquisition module 10, a first color rendering requirement acquisition module 20, a target color rendering state acquisition module 30, a determination module 40, and a color correction module 50.

[0081] A target image acquisition module 10 is used to acquire a target image, wherein the target image refers to an image to be displayed through a target display screen, and the target image corresponds to a first material in a predetermined type of material; a first color presentation requirement acquisition module 20 is used to establish a historical image display database of the same material as the first material, and call a requirement identification mechanism to analyze the historical image display database to obtain a first color presentation requirement; a target color presentation state acquisition module 30 is used to display the target image through the target display screen to obtain a target output color, and analyze the target color presentation state; a judgment module 40 is used to judge whether the target color presentation state meets the first color presentation requirement; a color correction module 50 is used to introduce a color correction plan to correct the target color presentation state if it does not meet the requirement, and obtain a corrected color presentation state.

[0082] The specific configuration of the first color rendering requirement acquisition module 20 will be described in detail below. As described above, a historical image display database of the same material as the first material is established, and the requirement identification mechanism is called to analyze the historical image display database to obtain the first color rendering requirement. The first color rendering requirement acquisition module 20 may further include: a first historical display data group extraction unit for extracting the first historical display data group of the first historical image in the historical image display database; a first predetermined target color acquisition unit for acquiring the first predetermined target color of the first historical image; a color comparison unit for comparing the first historical output color in the first historical display data group with the first predetermined target color to obtain a first color deviation index; a requirement benchmark acquisition unit for using the first historical output color as a requirement benchmark if the first color deviation index is within a predetermined deviation limit; and a requirement benchmark analysis unit for analyzing the requirement benchmark according to the requirement identification mechanism to obtain the first color rendering requirement.

[0083] Among them, the first historical output color in the first historical display data group is compared with the first predetermined target color to obtain a first color deviation index. The color comparison unit may further include: a predetermined single indicator set calling subunit for calling a predetermined single indicator set, wherein the predetermined single indicator set includes a first indicator; a matching subunit for matching the first historical indicator value and the first predetermined indicator value corresponding to the first indicator in the first historical output color and the first predetermined target color in turn with the first indicator as a constraint; a first deviation value acquisition subunit for comparing the first historical indicator value with the first predetermined indicator value to obtain a first deviation value of the first indicator; and a variation weighted analysis subunit for performing variation weighted analysis on the first deviation value to obtain the first color deviation index.

[0084] Among them, the predetermined single indicator set calling subunit may further include: the predetermined single indicator set includes a hue indicator set, a brightness indicator and a saturation indicator, and the hue indicator set also includes a red indicator, a green indicator and a blue indicator.

[0085] Among them, the demand benchmark is analyzed according to the demand identification mechanism to obtain the first color presentation requirement, and the demand benchmark analysis unit may further include: a second indicator extraction subunit for extracting the second indicator from the predetermined single indicator set; a second historical indicator value acquisition subunit for obtaining the second historical indicator value of the second indicator in combination with the demand benchmark; a ratio calculation subunit for calculating the ratio of the first historical indicator value to the second historical indicator value according to the demand identification mechanism to obtain the first color presentation requirement.

[0086] Among them, after establishing a historical image display database of the same material as the first material, and calling the demand identification mechanism to analyze the historical image display database to obtain the first color presentation requirement, the system can further include: a discrete cosine transform preprocessing module for performing discrete cosine transform preprocessing on the target image to obtain a preprocessing result; a first color presentation requirement calibration module for extracting the AC coefficient in the preprocessing result, and calibrating the first color presentation requirement with the AC coefficient as the weight.

[0087] The specific configuration of the color correction module 50 will be described in detail below. As described above, if the target color rendering state is not satisfied, a color correction plan is introduced to perform rendering correction on the target color rendering state to obtain the corrected color rendering state. The color correction module 50 may further include: an actual ambient light information acquisition unit for acquiring the actual ambient light information of the target image according to the color correction plan; a light information comparison and analysis unit for comparing and analyzing the actual ambient light information with the ideal ambient light information to obtain an ambient light feedback coefficient; a predetermined aging feedback coefficient acquisition unit for acquiring the total operating time of the target display screen according to the color correction plan and matching the predetermined aging feedback coefficient corresponding to the total operating time; and a color rendering control adjustment unit for performing color rendering control adjustment on the target display screen using the ambient light feedback coefficient and the predetermined aging feedback coefficient as weights to obtain the corrected color rendering state.

[0088] Among them, if it is not satisfied, a color correction plan is introduced to correct the target color presentation state. After obtaining the corrected color presentation state, the system can further include: a reference color presentation state acquisition module for acquiring the reference color presentation state of the target image; a characterization processing module for sequentially characterizing the corrected color presentation state and the reference color presentation state, and obtaining corrected color features and reference color features respectively; a curve processing module for sequentially curve processing the corrected color features and the reference color features, and obtaining a correction curve and a reference curve respectively; a color presentation quality acquisition module for obtaining the curve similarity between the correction curve and the reference curve, and using the curve similarity to characterize the color presentation quality of the target display screen for the target image.

[0089] A color correction system for a display screen provided by an embodiment of the present invention can execute a color correction method for a display screen provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0090] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0091] Based on the foregoing embodiments, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor of an electronic device, it can implement the method described in any of the foregoing embodiments.

[0092] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A color correction method for a display screen, characterized in that: include: Acquire a target image, wherein the target image refers to an image to be displayed on a target display screen, and the target image corresponds to a first material among predetermined types of materials, wherein the first material refers to a material type corresponding to image content in the target image; Establishing a historical image display database of the same material as the first material, and calling a demand identification mechanism to analyze the historical image display database to obtain a first color presentation requirement; Displaying the target image on the target display screen to obtain a target output color, and analyzing to obtain a target color presentation state; Determining whether the target color presentation state meets the first color presentation requirement; If it is not satisfied, a color correction plan is introduced to correct the target color presentation state to obtain a corrected color presentation state; The process of establishing a historical image display database of the same material as the first material and invoking a demand identification mechanism to analyze the historical image display database to obtain a first color presentation requirement includes: Extracting a first historical display data group of a first historical image from the historical image display database; Acquiring a first predetermined target color of the first historical image; Comparing a first historical output color in the first historical display data group with the first predetermined target color to obtain a first color deviation index; If the first color deviation index is within a predetermined deviation limit, taking the first historical output color as a demand benchmark; Analyze the demand benchmark according to the demand identification mechanism to obtain the first color rendering requirement; If the conditions are not met, a color correction plan is introduced to correct the target color presentation state to obtain a corrected color presentation state, including: Acquiring actual ambient light information of the target image according to the color correction plan; Comparing and analyzing the actual ambient light information with the ideal ambient light information to obtain an ambient light feedback coefficient; Obtaining the total operating time of the target display screen according to the color correction plan, and matching a predetermined aging feedback coefficient corresponding to the total operating time; The ambient light feedback coefficient and the predetermined aging feedback coefficient are used as weights to perform color presentation control adjustment on the target display screen to obtain the corrected color presentation state.

2. The color correction method for a display screen according to claim 1, wherein: Comparing the first historical output color in the first historical display data group with the first predetermined target color to obtain a first color deviation index includes: Retrieving a predetermined single indicator set, wherein the predetermined single indicator set includes a first indicator; Using the first indicator as a constraint, matching the first historical indicator value and the first predetermined indicator value corresponding to the first indicator in the first historical output color and the first predetermined target color in sequence; Comparing the first historical indicator value with the first predetermined indicator value to obtain a first deviation value of the first indicator; Performing a weighted variation analysis on the first deviation value to obtain the first color deviation index.

3. The color correction method for a display screen according to claim 2, wherein: The predetermined single index set includes a hue index set, a brightness index and a saturation index, and the hue index set also includes a red index, a green index and a blue index.

4. The color correction method for a display screen according to claim 2, wherein: Analyzing the requirement benchmark according to the requirement identification mechanism to obtain the first color rendering requirement includes: Extracting a second indicator from the predetermined single indicator set; obtaining a second historical indicator value of the second indicator in combination with the demand benchmark; According to the demand identification mechanism, a ratio of the first historical indicator value to the second historical indicator value is calculated to obtain the first color presentation demand.

5. The color correction method for a display screen according to claim 1, characterized in that: After establishing a historical image display database of the same material as the first material and invoking a demand identification mechanism to analyze the historical image display database to obtain a first color presentation requirement, the method further includes: Performing discrete cosine transform preprocessing on the target image to obtain a preprocessing result; An AC coefficient is extracted from the preprocessing result, and the first color presentation requirement is calibrated using the AC coefficient as a weight.

6. The color correction method for a display screen according to claim 1, wherein: If the color is not satisfied, a color correction plan is introduced to correct the target color presentation state. After obtaining the corrected color presentation state, the method further includes: Acquiring a reference color presentation state of the target image; Performing characterization processing on the correction color presentation state and the reference color presentation state in sequence to obtain correction color features and reference color features respectively; The correction color feature and the reference color feature are sequentially processed into curves to obtain a correction curve and a reference curve respectively; The curve similarity between the correction curve and the reference curve is obtained, and the color presentation quality of the target image on the target display screen is characterized by the curve similarity.

7. A color correction system for a display screen, characterized in that: The system is used to implement the color correction method for a display screen according to any one of claims 1 to 6, and the system comprises: a target image acquisition module, configured to acquire a target image, wherein the target image refers to an image to be displayed on a target display screen, and the target image corresponds to a first material among predetermined types of materials, wherein the first material refers to a material type corresponding to image content in the target image; A first color rendering requirement acquisition module is configured to establish a historical image display database of the same material as the first material, and to call a requirement identification mechanism to analyze the historical image display database to obtain a first color rendering requirement; a target color presentation state acquisition module, configured to display the target image on the target display screen to obtain a target output color, and to analyze and obtain a target color presentation state; A judgment module, configured to judge whether the target color presentation state meets the first color presentation requirement; A color correction module is used to introduce a color correction plan to correct the target color presentation state if the target color presentation state is not satisfied, so as to obtain a corrected color presentation state; The first color rendering requirement acquisition module is further configured to: Extracting a first historical display data group of a first historical image from the historical image display database; Acquiring a first predetermined target color of the first historical image; Comparing a first historical output color in the first historical display data group with the first predetermined target color to obtain a first color deviation index; If the first color deviation index is within a predetermined deviation limit, taking the first historical output color as a demand benchmark; Analyze the demand benchmark according to the demand identification mechanism to obtain the first color rendering requirement; Wherein, the color correction module is further used for: Acquiring actual ambient light information of the target image according to the color correction plan; Comparing and analyzing the actual ambient light information with the ideal ambient light information to obtain an ambient light feedback coefficient; Obtaining the total operating time of the target display screen according to the color correction plan, and matching a predetermined aging feedback coefficient corresponding to the total operating time; The ambient light feedback coefficient and the predetermined aging feedback coefficient are used as weights to perform color presentation control adjustment on the target display screen to obtain the corrected color presentation state.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a color correction method for a display screen according to any one of claims 1 to 6 is implemented.

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