Color correction method and system for display screen and medium

Abstract

The invention discloses a color correction method and system for a display screen and a medium, and relates to the related field of display screen color correction, and the method comprises the steps: obtaining a target image; establishing a historical image display database which is made 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 demand; displaying the target image through a target display screen to obtain a target output color, and analyzing to obtain a target color presentation state; judging whether the target color presentation state meets a first color presentation requirement or not; 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. The technical problem that the pertinence and accuracy of correction are insufficient due to the fact that image color presentation requirements cannot be accurately matched in color correction of an existing display screen is solved, and the technical effects of accurately matching the image color presentation requirements and improving the pertinence and accuracy of correction are achieved.

Description

Technical Field

[0001] This application relates to the field of display screen color correction, and particularly to a color correction method, system and medium for a display screen. Background Art

[0002] In the field of display screen image display, accurately presenting the image color to meet user needs is crucial, which is directly related to the user experience and the accuracy of image information transmission. Currently, to solve the problem of accurate color presentation of the display screen, color calibration of the display screen is mainly relied on, and the display effect is optimized by adjusting the color parameters of the display screen. Most of the existing color calibration methods are based on general standards or single material samples. Since there are differences in the color requirements of different material images presented on the display screen, this general 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 screen and unable to meet the user's expectations for the color of specific material images.

[0003] In the related technologies at the present stage, there are technical problems in the color correction of the display screen that it cannot accurately match the color presentation requirements of the image, resulting in insufficient pertinence and accuracy of the correction. Summary of the Invention

[0004] By providing a color correction method, system and medium for a display screen, this application adopts technical means such as obtaining a target image corresponding to a first material, constructing a historical image database of the same type of material and analyzing to obtain the first color presentation requirement, making the target display screen display the target image to obtain the target color presentation state, judging whether this state meets the first color presentation requirement, and if not, introducing a color correction plan for correction to obtain the corrected color presentation state, etc., solves the technical problem that the existing color correction of the display screen cannot accurately match the color presentation requirements of the image, resulting in insufficient pertinence and accuracy of the correction, and achieves the technical effect of accurately matching the color presentation requirements of the image and improving the pertinence and accuracy of the correction.

[0005] This application provides a color correction method for a display screen, including: obtaining a target image, where the target image refers to an image to be displayed by a target display screen, and the target image corresponds to a first material in a predetermined type of material; constructing a historical image display database of the same type of material as the first material, and invoking a demand recognition 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; judging 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.

[0006] In a possible implementation, a historical image display database of the same material as the first material is established, and a requirement recognition mechanism is invoked 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 a first historical image from the historical image display database; obtaining 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, using the first historical output color as a requirement benchmark; analyzing the requirement benchmark according to the requirement recognition mechanism to obtain the first color presentation requirement.

[0007] In a possible implementation, when 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, the following processing is performed: invoking a predetermined single-item index set, where the predetermined single-item index set includes a first index; using the first index as a constraint, sequentially matching a first historical index value and a first predetermined index value corresponding to the first index in the first historical output color and the first predetermined target color; comparing the first historical index value with the first predetermined index value to obtain a first deviation value of the first index; performing a variation weighted analysis 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-item index set includes a hue index set, a lightness index, and a saturation index, and the hue index set further includes a red index, a green index, and a blue index.

[0009] In a possible implementation, when analyzing the requirement benchmark according to the requirement recognition mechanism to obtain the first color presentation requirement, the following processing is performed: extracting a second index from the predetermined single-item index set; combining the requirement benchmark to obtain a second historical index value of the second index; according to the requirement recognition mechanism, calculating a ratio of the first historical index value to the second historical index value 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 invoking a requirement recognition mechanism to analyze the historical image display database to obtain a first color presentation requirement, the following processing is further performed: performing a discrete cosine transform preprocessing on the target image to obtain a preprocessing result; extracting an AC coefficient from the preprocessing result, and calibrating the first color presentation requirement with the AC coefficient as a weight.

[0011] In a possible implementation, if 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: obtaining the 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 operation duration of the target display screen according to the color correction plan, and matching a predetermined aging feedback coefficient corresponding to the total operation duration; using the ambient light feedback coefficient and the predetermined aging feedback coefficient as weights to perform color presentation control adjustment on the target display screen to obtain the corrected color presentation state.

[0012] In a possible implementation, after introducing a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state if not satisfied, the following processing is further performed: obtaining the reference color presentation state of the target image; sequentially performing characterization processing on the corrected color presentation state and the reference color presentation state to respectively obtain a corrected color feature and a reference color feature; sequentially performing curve processing on the corrected color feature and the reference color feature to respectively obtain a corrected curve and a reference curve; obtaining the curve similarity between the corrected 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.

[0013] The present application also provides a color correction system for a display screen, including: a target image acquisition module for acquiring a target image, where 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 constructing a historical image display database of the same type of material as the first material, and invoking a requirement recognition 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 to obtain a target color presentation state; a judgment module for judging whether the target color presentation state meets the first color presentation requirement; a color correction module for, if not satisfied, introducing a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state.

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

[0015] A color correction method, system and medium for a display screen proposed in this application first obtain a target image, where the target image refers to an image to be displayed by 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 type of material as the first material is established, and a requirement recognition mechanism is called to analyze the historical image display database to obtain a first color presentation requirement. Next, the target image is displayed on the target display screen to obtain a target output color, and the target color presentation state is analyzed. Then, it is determined whether the target color presentation state meets the first color presentation requirement. If it does not meet, a color correction plan is introduced to perform presentation correction on the target color presentation state to obtain a corrected color presentation state. The technical effect of accurately matching the image color presentation requirement and improving the pertinence and accuracy of correction is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments of the present invention will be 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 operations before or below do not necessarily need to be executed precisely in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0017] Figure 1 It is a schematic flowchart of a color correction method for a display screen provided by an embodiment of the present application.

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

[0019] Description of 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 OF THE EMBODIMENTS

[0020] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following will further describe this application in detail with reference to the accompanying drawings. The described embodiments should not be construed as limitations on this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0022] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first / second" merely distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or 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 technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0023] An embodiment of this application provides a method for color correction of a display screen, as Figure 1 shown, the method includes: Step S100, obtain a target image, where the target image refers to an image to be displayed by a target display screen, and the target image corresponds to a first material in a predetermined type of material.

[0024] Specifically, the target image refers to an image to be displayed by a target display screen, and its material type is known and belongs to a certain type (such as metal, fabric, liquid, etc.) in the predetermined type of material. The predetermined type of material is a set of pre-defined material categories used to classify the material of the target image so as to perform corresponding color correction operations according to the material type subsequently. 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 is 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 metadata of the image (such as the shooting scene recorded in the file attributes, the object material label, etc.) or the pre-set image classification rules. <�

[0025] For example, when the target image is a photo of a metal object, after the image acquisition device captures it, it is stored as an image file. If the metadata of the image file clearly indicates "Material: Metal", it can be directly recognized as the target image corresponding to the first material being metal; or in the case of no metadata annotation, a pre-set image classification rule is used to analyze features such as the texture and reflection of the object in the image to determine that it is a metal material, thereby determining that the material of the target image is metal. Another example is to read the target image from an image database that stores images of various materials. Through the classification index in the database, the first material corresponding to the target image is found to be fabric.

[0026] Step S200: Build a historical image display database with the same type of material as the first material, and retrieve the requirement recognition mechanism to analyze the historical image display database to obtain the first color presentation requirement.

[0027] Specifically, from the database storing historical images and their display records, filter out the images with the same type of material as the first material and their display color data (such as RGB values, color space coordinates, etc.) on the display screen, and store them in a dedicated database. These data are used to analyze the color presentation rules of images of the same type of material, thereby obtaining the color presentation requirement. The filtering process can be achieved through database query statements based on the material classification field.

[0028] Analyze the data in the historical image display database based on methods such as statistical analysis, machine learning (such as clustering analysis, neural network, etc.). Taking clustering analysis as an example, cluster according to the characteristic indicators (such as brightness, saturation, color temperature, etc.) of the colors presented by the historical images on the display screen to find the color presentation rules of images with the same type of material as the first material, thereby obtaining the first color presentation requirement, including the required values of each indicator in the predetermined single - item index set, such as the brightness required value, saturation required value, color temperature required value, etc.

[0029] For example, the historical image display database stores a large number of display records of metal - material images, including the RGB values of the images, the parameters of the display screen (such as resolution, color gamut, etc.), and information such as user evaluations of the display effects. Through the clustering analysis algorithm, the display color data of these metal - material images are divided into several categories, and each category corresponds to a typical color presentation requirement. Suppose the analysis result shows that for metal - material images, users prefer a color presentation with higher brightness and moderate saturation. Then, the brightness required value in the first color presentation requirement can be set within a higher numerical range, and the saturation required value can be set within a moderate numerical range.

[0030] For another example, a neural network algorithm is used to analyze the historical image display database. The input of the neural network is the display color data of the metal material image, and the output is the corresponding color presentation requirement. After being trained with a large number of training samples, the neural network can learn the color presentation rules of the metal material image. When new display data of the metal material image is input, the neural network can output the first color presentation requirement. For example, for a certain metal material image, the color presentation requirement output by the neural network is: the brightness requirement value is 180 - 220 (assuming the brightness value range is 0 - 255), and the saturation requirement value is 80 - 120 (assuming the saturation value range is 0 - 255).

[0031] In a possible implementation, a historical image display database of the same type of material as the first material is established, and a requirement recognition mechanism is called to analyze the historical image display database to obtain the first color presentation requirement. Step S200 further includes step S210 of extracting the first historical display data group of the first historical image in the historical image display database. Specifically, an image of the same material as the target image (referred to as the first historical image) is randomly selected from the historical image display database, and its display data group is extracted. The first historical display data group includes the actual color data (such as RGB values, brightness, saturation, color temperature, etc.) when the first historical image is displayed on the target display screen.

[0032] 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 group of this image includes its RGB values of (200, 180, 160), brightness of 180 cd / m², saturation of 70, and color temperature of 6500K when it is displayed on the target display screen.

[0033] Step S220, obtaining the first predetermined target color of the first historical image. Specifically, through a predefined standard color model (such as sRGB, Adobe RGB, etc.) or the ideal color value obtained through user feedback, the predetermined target color of the first historical image under ideal display conditions is obtained. For example, assuming the predetermined target color of the first historical image under ideal display conditions is RGB values (210, 190, 170), brightness of 190 cd / m², saturation of 75, and color temperature of 6500K.

[0034] Step S230, comparing the first historical output color in the first historical display data group with the first predetermined target color to obtain the first color deviation index. Specifically, a variety of calculation methods, such as Euclidean distance, color difference formula (such as CIEDE2000), etc., are used to calculate the difference between the actual color data (the first historical output color) in the first historical display data group and the predetermined target color to obtain the color deviation index.

[0035] For example, the deviation of RGB values is calculated using the Euclidean distance: RGB deviation The deviations of color temperature, brightness, and saturation are calculated using the color difference formula: 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 RGB deviation, brightness deviation, saturation deviation, and color temperature deviation can be weighted and summed: First color deviation index = 0.5×17.32 + 0.3×10 + 0.1×5 + 0.1×0 = 12.16.

[0036] In step S240, if the first color deviation index is within a predetermined deviation limit, the first historical output color is used as the demand benchmark. Specifically, a predetermined deviation limit (such as 15) is set. If the first color deviation index is less than or equal to this limit, it is considered that the first historical output color is close to the ideal color and can be used as the demand benchmark.

[0037] For example, assume that the predetermined deviation limit is 15 and the first color deviation index is 12.16, which is less than the predetermined deviation limit. Therefore, the first historical output color (RGB values of 200, 180, 160, brightness of 180 cd / m², saturation of 70, and color temperature of 6500 K) is used as the demand benchmark.

[0038] In step S250, the demand benchmark is analyzed according to the demand recognition mechanism to obtain the first color presentation demand. Specifically, the demand benchmark is analyzed through a demand recognition mechanism (such as a machine learning algorithm, statistical analysis, etc.), and the demand values in a predetermined single index set (such as brightness, saturation, color temperature, etc.) are extracted to form the first color presentation demand.

[0039] This implementation method obtains an accurate color deviation index by comparing the actual displayed color of historical images with the predetermined target color, and determines the requirement baseline based on this, which can more accurately reflect the color presentation effect that the target image should have on the display screen. Compared with directly using the ideal color model or fixed standard, this method can better adapt to the changes in the actual display environment and improve the accuracy of color correction. Different display screens and display environments may lead to differences in color presentation. By analyzing the historical image display data and combining the color deviation under the actual display conditions, the color presentation requirements can be dynamically adjusted, making the correction scheme more adaptable. For example, for different brands or models of display screens, even if their display characteristics are different, the most suitable correction requirements can be found through historical data. Using the historical image display data as a reference avoids complex color measurement and analysis for each target image. Through the pre-constructed historical database and requirement recognition mechanism, the correction requirements can be quickly generated, reducing the dependence on professional equipment and manual intervention during the correction process and lowering the correction cost.

[0040] In a possible implementation, the first historical output color in the first historical display data set is compared with the first predetermined target color to obtain a first color deviation index. Step S230 further includes step S231 of retrieving a predetermined single-index set, where the predetermined single-index set includes a hue index set, a lightness index, and a saturation index, and the hue index set further includes a red index, a green index, and a blue index. Among them, the predetermined single-index set includes a first index. Specifically, the predetermined single-index set is a set of indexes for measuring the color presentation effect, used to determine whether the target color presentation state meets the color presentation requirements. A set of single indexes is selected from the pre-defined index set to evaluate the color deviation. These indexes include the hue index set (red index, green index, blue index), the lightness index, and the saturation index.

[0041] In step S232, with the first index as a constraint, the first historical index value and the first predetermined index value corresponding to the first index are sequentially matched in the first historical output color and the first predetermined target color. Specifically, one of the indexes is selected as the first index (for example, the red index is selected as the first index), and the values of this index are extracted from the first historical output color and the first predetermined target color. For example, assuming that the first index is the red index (i.e., the value of the red component), the red index value of the first historical output color is 200 (the first historical index value), and the red index value of the first predetermined target color is 210 (the first predetermined index value).

[0042] Step S233: Compare the first historical index value with the first predetermined index value to obtain the first deviation value of the first index. Specifically, calculate the difference between the first historical index value and the first predetermined index value, and use the absolute value to represent the deviation. For example, the first deviation value = |210 - 200| = 10.

[0043] Step S234: Perform variant weighted analysis on the first deviation value to obtain the first color deviation index. Specifically, perform weighted analysis on the deviation values of each index, and the weights are preset according to the importance and variation of the index. Variant weighted analysis is used to consider the coefficient of variation of the index (the ratio of the standard deviation to the mean) to more accurately reflect the impact of the deviation.

[0044] This implementation method can more comprehensively evaluate color deviation by introducing multi-dimensional indexes such as the hue index set (red, green, blue), lightness, and saturation. Traditional color deviation evaluations only focus on the deviation of brightness or RGB values, while ignoring other important attributes of colors. Through the comprehensive evaluation of multi-dimensional indexes, the actual color deviation can be more accurately reflected. Variant weighted analysis can adjust the weights according to the actual variation of the indexes, so as to more accurately reflect the actual impact of each index on color deviation. For example, if the coefficient of variation of the red index is relatively high, it indicates that the deviation of the red component has a greater impact on the overall color, so a higher weight can be given. This weighted method can improve the accuracy of color correction and make the corrected color closer to the target color. Different materials and display environments may lead to different color deviation characteristics. Through the comprehensive evaluation of multi-dimensional indexes and variant weighted analysis, different color deviation situations can be adapted, and more targeted correction requirements can be generated. For example, for metal material images, the deviation of the red and green components may be more critical, while for fabric material images, the deviation of saturation and lightness may be more important. By dynamically adjusting the weights, the requirements of different materials and display environments can be better adapted.

[0045] In a possible implementation method, analyze the requirement benchmark according to the requirement recognition mechanism to obtain the first color presentation requirement. Step S250 further includes step S251: Extract the second index from the predetermined single-item index set. Specifically, select another index from the predetermined single-item index set as the second index. This index can be one of the hue index set (such as the green index, blue index), the lightness index, or the saturation index. For example, assume that in step S230, the first index selects the red index. Now select the green index from the predetermined single-item index set as the second index.

[0046] Step S252: Obtain the second historical index value of the second index in combination with the demand benchmark. Specifically, according to the demand benchmark (i.e., the first historical output color), extract the value corresponding to the second index. The demand benchmark is the color data that is considered to be close to the ideal color presentation after screening. For example, assume that the red index value of the demand benchmark (the first historical output color) is 200, the green index value is 180, and the blue index value is 160. The second index is the green index, so the second historical index value is 180.

[0047] Step S253: According to the demand recognition mechanism, calculate the ratio of the first historical index value to the second historical index value to obtain the first color presentation demand. Specifically, use the demand recognition mechanism to calculate the ratio of the first historical index value to the second historical index value, and use this ratio as the reference value of the first color presentation demand. This ratio is used to adjust the color of the target image to make it closer to the demand benchmark. For example, assume that the first historical index value (red index value) is 200 and the second historical index value (green index value) is 180. Calculate the ratio: Ratio = First historical index value ÷ Second historical index value = 200 ÷ 180 ≈ 1.11. This ratio 1.11 is used as part of the first color presentation demand to adjust the ratio of the red and green components of the target image.

[0048] This implementation method can more accurately evaluate the color demand by calculating the ratio of the first historical index value to the second historical index value. It not only considers the absolute value of a single index but also the relative relationship between different indexes. For example, the ratio of the red and green components is particularly important for the color presentation of certain materials (such as metals). The ratio calculation can more accurately reflect this relationship. Images of different materials have different color ratio requirements. For example, images of metal materials may require a higher ratio of red components to green components, while images of fabric materials may require a more balanced ratio. By calculating the ratio, the color correction scheme can be dynamically adjusted according to the demand benchmarks of different materials to make it more adaptable.

[0049] In a possible implementation method, after constructing a historical image display database of the same type of material as the first material, and invoking the demand recognition mechanism to analyze the historical image display database to obtain the first color presentation demand, the method further includes: performing discrete cosine transform preprocessing on the target image to obtain a preprocessing result; extracting the AC coefficients from the preprocessing result, and calibrating the first color presentation demand with the AC coefficients as weights.

[0050] 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, and the AC component represents the details and variations of the image. For example, assume the target image is a picture of a metal material. After performing the DCT transformation on this picture, a DCT coefficient matrix is obtained. The first element (top left) in the matrix is the DC component, and the remaining elements are the AC components.

[0051] Extract the AC coefficients from the DCT coefficient matrix. The AC coefficients reflect the detail and texture information of the image. For example, assume 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 is 1 to serve as weights. Then use these weights to perform weighted adjustment on each index in the first color rendering requirement.

[0052] For example, assume the first color rendering requirement includes a brightness requirement value of 180 cd / m², a saturation requirement value of 70, and a color temperature requirement value of 6500 K. The extracted AC coefficients are: 50, 30, 20, -10, 5, 10, -5. First, take the absolute value of these coefficients and normalize them: |50| = 50, |30| = 30, |20| = 20, |-10| = 10, |5| = 5, |10| = 10, |-5| = 5. Sum = 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. Use these weights to calibrate the first color rendering 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 = 4940 K.

[0053] This implementation method calibrates the first color rendering requirement by using the AC components in the DCT coefficient matrix, which can more precisely reflect the detailed features of the target image. This method takes into account the high-frequency information of the image and helps improve the matching degree between the corrected color and the target image. Different images have different detailed features. By calibrating according to the AC components in the DCT coefficient matrix, the color correction scheme can be dynamically adjusted to better adapt to the specific features of the target image.

[0054] Step S300: Display the target image through the target display screen to obtain the target output color, and analyze to obtain the target color rendering state.

[0055] Specifically, the target output color refers to the color presented when the target display screen actually displays the target image, and its color characteristics can be obtained through measurement or acquisition. The target color rendering state refers to the color rendering situation reflected by the actual values of each index in the predetermined single-item index set (such as brightness, saturation, color temperature, etc.) when the target display screen displays the target image.

[0056] Transmit the data of the target image to the display driving circuit of the target display screen. The display driving circuit controls the pixel points of the display screen to emit light according to the color information of the target image, so as to display the target image on the display screen and obtain the target output color. Use a color analysis instrument (such as a spectrophotometer, color analyzer, etc.) to measure the target image displayed on the target display screen, and obtain parameters such as the RGB values, color temperature, and brightness of the target output color, or collect the data of the target output color through the built-in color sensor of the display screen. Then, according to the predetermined single-item index set (such as brightness, saturation, color temperature, etc.), extract the actual values of each index from the measured or collected data.

[0057] For example, the target display screen is a liquid crystal display, and the target image is a picture of a fabric material. Transmit the target image to the display driving circuit of the liquid crystal display through a data cable. The display driving circuit controls the arrangement of liquid crystal molecules and the brightness of the backlight according to the color information of the target image, so that the display screen displays the target image. At this time, use a spectrophotometer to measure the target image on the display screen. The measured RGB values of the target output color are (150, 100, 80), the color temperature is 6500K, and the brightness is 120 cd / m². Assuming that the predetermined single-item index set includes brightness, saturation, and color temperature, and the saturation corresponding to the calculated RGB value is 60, then the target color rendering state is: the actual value of brightness is 120 cd / m², the actual value of saturation is 60, and the actual value of color temperature is 6500K.

[0058] For another example, the target display screen is an OLED display with a built-in color sensor, and the target image is a picture of a liquid material. While the target image is being displayed, the built-in color sensor collects data on the target output color, obtaining an RGB value of (100, 120, 140), a color temperature of 7000K, and a brightness of 150 cd / m². According to the predetermined single-index set, the actual saturation value is calculated to be 70. Then the target color presentation state is: the actual brightness value is 150 cd / m², the actual saturation value is 70, and the actual color temperature value is 7000K.

[0059] Step S400, determine whether the target color presentation state meets the first color presentation requirement.

[0060] Specifically, through a comparison algorithm, each actual index value in the target color presentation state is compared one by one with each index requirement value in the first color presentation requirement. The comparison algorithm can be a simple numerical range judgment or a complex weighted scoring algorithm. If the actual values of all indexes are within the corresponding requirement value ranges, or the weighted score reaches the set threshold, it is determined that the target color presentation state meets the first color presentation requirement; otherwise, it is determined that it does not meet.

[0061] For example, the first color presentation requirement is: the brightness requirement value range is 180 - 220, the saturation requirement value range is 80 - 120, and the color temperature requirement value range is 6000K - 7000K. The target color presentation 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 numerical range judgment, it is found that the actual brightness value of 190 is within the requirement value range of 180 - 220, the actual saturation value of 90 is within the requirement value range of 80 - 120, and the actual color temperature value of 6500K is within the requirement value range of 6000K - 7000K. Therefore, it is determined that the target color presentation state meets the first color presentation requirement.

[0062] For another example, using a weighted scoring algorithm, assume the weight of brightness is 0.4, the weight of saturation is 0.3, and the weight of color temperature is 0.3. The first color presentation requirement is: the brightness requirement value range is 180 - 220, the saturation requirement value range is 80 - 120, and the color temperature requirement value range is 6000K - 7000K. The target color presentation state is: the actual brightness value is 170, the actual saturation value is 110, and the actual color temperature value is 6200K. Calculate the weighted score: 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 set threshold is 0.2, then since the total weighted score of 0.185 is less than 0.2, it is determined that the target color presentation state does not meet the first color presentation requirement.

[0063] Step S500, if not satisfied, introduce a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state.

[0064] Specifically, a color correction plan is a set of pre - set correction plans for specific color deviation situations, including specific correction parameters and methods, used to adjust the color of the target image so that its color presentation state meets the color presentation requirements.

[0065] If the target color presentation state does not meet the first color presentation requirement, according to the pre - set color correction rule library, select a color correction plan that matches the material of the target image and the deviation situation of the current color presentation state. Among them, the color correction rule library is constructed based on historical correction data and expert experience, and contains correction parameters (such as RGB value adjustment amounts, color space conversion parameters, etc.) for images of different materials under different color deviation situations. Apply the correction parameters in the selected color correction plan to the color data of the target image to adjust the color of the target image. After the adjustment is completed, display the adjusted image on the target display screen again to obtain a corrected color presentation state, and the actual values of its color characteristic indicators are closer to the first color presentation requirement.

[0066] For example, if the target image material is liquid and the target color presentation state does not meet the first color presentation requirement, specifically manifested as low brightness and high saturation. According to the color correction rule library, select the corresponding color correction plan. This plan stipulates that for liquid material images, when the brightness is low, increase the R, G, and B components in the RGB value by 10 respectively; when the saturation is high, convert the RGB value to the HSV color space and reduce the saturation S by 0.1. Adjust the color data of the target image according to this plan. Suppose the original RGB value of the target image is (100, 120, 140), the adjusted RGB value is (110, 130, 150), then convert it to the HSV color space, reduce the saturation S by 0.1, and then convert it back to the RGB color space to obtain the corrected RGB value of (105, 125, 145). Transmit the adjusted image data to the target display for display to obtain the corrected color presentation state.

[0067] Another example is that the target image material is metal and the color temperature of the target color presentation state is too high. The corresponding plan in the color correction rule library stipulates that for metal material images, when the color temperature is too high, reduce the color temperature by adjusting the balance of the RGB value. The specific method is to increase the B component in the RGB value and appropriately reduce the R component. Suppose the original RGB value of the target image is (200, 180, 160), after adjustment according to the plan, the RGB value becomes (190, 180, 170). Display the adjusted image on the target display to obtain the corrected color presentation state.

[0068] In a possible implementation manner, if it is not satisfied, introduce a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state. Step S500 further includes step S510 of obtaining the actual ambient light information of the target image according to the color correction plan. Specifically, obtain the actual ambient light information of the target image through an ambient light sensor or a preset ambient light model. The ambient light information includes parameters such as light intensity and color temperature. For example, suppose the actual ambient light information of the target image is: light intensity: 500 lux, color temperature: 5500 K.

[0069] Step S520: Compare and analyze the actual ambient light information with the ideal ambient light information to obtain the ambient light feedback coefficient. Specifically, the ideal ambient light information is predefined according to the display requirements. Compare the actual ambient light information with the preset ideal ambient light information and calculate the ambient light feedback coefficient. For example, the ideal light intensity is 600 lux, the ideal color temperature is 6500 K, the light intensity deviation = (500 - 600) / 600 = -0.167, and the color temperature deviation = (5500 - 6500) / 6500 = -0.154. The ambient light feedback coefficient = 1 - (|light intensity deviation| + |color temperature deviation|) = 1 - (0.167 + 0.154) = 0.679.

[0070] Step S530: Obtain the total operation duration of the target display screen according to the color correction plan and match the predefined aging feedback coefficient corresponding to the total operation duration. Specifically, obtain the total operation duration of the display screen through its usage record and match the aging feedback coefficient 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, assume that the total operation duration of the target display screen is 2000 hours, and the preset aging model is that the aging feedback coefficient decreases by 0.1 every 1000 hours. Then the aging feedback coefficient = 1 - (2000 / 1000)×0.1 = 0.8.

[0071] Step S540: Use the ambient light feedback coefficient and the predefined aging feedback coefficient as weights to perform color rendering control adjustment on the target display screen to obtain the corrected color rendering state. Specifically, use the ambient light feedback coefficient and the aging feedback coefficient as weights to adjust the color rendering of the target display screen. The adjusted color rendering state is closer to the ideal state.

[0072] For example, assume that the original color rendering state of the target display screen is: brightness: 180 cd / m², saturation: 70, color temperature: 6500 K, ambient light feedback coefficient: 0.679, aging feedback coefficient: 0.8. Assume that the ambient light feedback coefficient weight is 0.6 and the aging feedback coefficient weight is 0.4. The comprehensive 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. Use the comprehensive weight to correct the original color rendering state: the corrected brightness = 180×0.7274 ≈ 130.93 cd / m², the corrected saturation = 70×0.7274 ≈ 50.92, and the corrected color temperature = 6500×0.7274 ≈ 4728.1 K.

[0073] In this implementation method, by considering the effects of actual ambient light and display screen aging, the corrected color presentation state can more accurately reflect the actual display conditions. This method not only takes into account color deviations, but also considers the effects of ambient light and aging on the display effect, thereby improving the accuracy of correction. Different ambient light conditions and degrees of display screen aging will result in different display effects. By dynamically adjusting the ambient light feedback coefficient and aging feedback coefficient, it is possible to better adapt to different display conditions and make the correction scheme more adaptable.

[0074] In a possible implementation method, if it is not satisfied, after introducing a color correction plan to perform presentation correction on the target color presentation state and obtaining the corrected color presentation state, the method further includes: obtaining the reference color presentation state of the target image; sequentially performing characterization processing on the corrected color presentation state and the reference color presentation state to respectively obtain a corrected color feature and a reference color feature; sequentially performing curve fitting on the corrected color feature and the reference color feature to respectively obtain a corrected curve and a reference curve; obtaining the curve similarity between the corrected 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.

[0075] Specifically, the reference color presentation state refers to the color presentation state of the target image under ideal display conditions, which can be obtained by measuring with professional equipment or using a preset standard color model. For example, assume the reference color presentation state is: reference brightness: 200 cd / m², reference saturation: 80, reference color temperature: 6500K. The characterization process is to convert the color presentation state into a set of feature vectors, and these feature vectors are used for curve fitting. The characterization process includes color space conversion (such as from RGB to CIELAB) and feature extraction (such as brightness, saturation, color temperature, etc.). For example, assume the corrected color presentation state: corrected brightness: 130.93 cd / m², corrected saturation: 50.92, corrected color temperature: 4728.1K. After the characterization process, the corrected color features are: [130.93, 50.92, 4728.1], and the reference color features are: [200, 80, 6500]. The curve fitting process is to convert the feature vectors into curves for more intuitive comparison of the color presentation states. The curve fitting process includes interpolation and fitting to generate continuous curves. For example, assume brightness, saturation, and color temperature are selected as features and converted into curves respectively. The corrected curves are: brightness curve: from 0 to 130.93; saturation curve: from 0 to 50.92; color temperature curve: from 0 to 4728.1. The reference curves are: brightness curve: from 0 to 200; saturation curve: from 0 to 80; color temperature curve: from 0 to 6500. The curve similarity can be evaluated by calculating the distance between two curves, and the 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 .

[0076] This implementation method intuitively compares the corrected color presentation state with the reference color presentation state through the characterization process and the curve fitting process. This method not only considers the absolute values of colors but also the changing trends of colors, thus improving the accuracy of color presentation quality assessment.

[0077] The embodiment of this application adopts technical means such as obtaining the target image corresponding to the first material, establishing a historical image database of the same type of material and analyzing to obtain the first color presentation requirement, making the target display screen display the target image to obtain the target color presentation state, judging whether this state meets the first color presentation requirement, and if not, introducing a color correction plan for correction to obtain the corrected color presentation state, etc., to solve the technical problem that the color correction of the existing display screen cannot accurately match the color presentation requirement of the image, resulting in insufficient pertinence and accuracy of the correction, and achieving the technical effect of accurately matching the color presentation requirement of the image and improving the pertinence and accuracy of the correction.

[0078] In the foregoing, reference is made to Figure 1 A method for color calibration of a display screen according to an embodiment of the present invention is described in detail. Next, reference will be made to Figure 2 Describe a color calibration system for a display screen according to an embodiment of the present invention.

[0079] A color calibration system for a display screen according to an embodiment of the present invention is used to solve the technical problem that the color calibration of the existing display screen cannot accurately match the color presentation requirements of the image, resulting in insufficient pertinence and accuracy of the calibration, and achieves the technical effect of accurately matching the color presentation requirements of the image and improving the pertinence and accuracy of the calibration. A color calibration system for a display screen includes: a target image acquisition module 10, a first color presentation requirement acquisition module 20, a target color presentation state acquisition module 30, a judgment module 40, and a color calibration module 50.

[0080] The target image acquisition module 10 is used to acquire a target image, where 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; the first color presentation requirement acquisition module 20 is used to construct a historical image display database of materials of the same type as the first material, and retrieve a requirement recognition mechanism to analyze the historical image display database to obtain a first color presentation requirement; the target color presentation state acquisition module 30 is used to display the target image on the target display screen to obtain a target output color, and analyze to obtain a target color presentation state; the judgment module 40 is used to judge whether the target color presentation state meets the first color presentation requirement; the color calibration module 50 is used to, if it does not meet, introduce a color calibration plan to perform presentation calibration on the target color presentation state to obtain a calibrated color presentation state.

[0081] Next, the specific configuration of the first color presentation requirement acquisition module 20 will be described in detail. As described above, a historical image display database of materials of the same type as the first material is constructed, and a requirement recognition mechanism is retrieved to analyze the historical image display database to obtain a first color presentation requirement. The first color presentation requirement acquisition module 20 may further include: a first historical display data group extraction unit for extracting a first historical display data group of a first historical image in the historical image display database; a first predetermined target color acquisition unit for acquiring a first predetermined target color of the first historical image; a color comparison unit for 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; a requirement benchmark acquisition unit for, if the first color deviation index is within a predetermined deviation limit, using the first historical output color as a requirement benchmark; a requirement benchmark analysis unit for analyzing the requirement benchmark according to the requirement recognition mechanism to obtain the first color presentation requirement.

[0082] Among them, by comparing the first historical output color in the first historical display data group with the first predetermined target color, a first color deviation index is obtained. The color comparison unit may further include: a predetermined single-index set retrieval subunit for retrieving a predetermined single-index set, where the predetermined single-index set includes a first index; a matching subunit for, with the first index as a constraint, sequentially matching the first historical index value and the first predetermined index value corresponding to the first index in the first historical output color and the first predetermined target color; a first deviation value obtaining subunit for comparing the first historical index value with the first predetermined index value to obtain a first deviation value of the first index; and a variation weighted analysis subunit for performing variation weighted analysis on the first deviation value to obtain the first color deviation index.

[0083] Among them, the predetermined single-index set retrieval subunit may further include: the predetermined single-index set includes a hue index set, a lightness index, and a saturation index, and the hue index set further includes a red index, a green index, and a blue index.

[0084] Among them, by analyzing the requirement benchmark according to the requirement recognition mechanism to obtain the first color presentation requirement, the requirement benchmark analysis unit may further include: a second index extraction subunit for extracting a second index in the predetermined single-index set; a second historical index value obtaining subunit for obtaining a second historical index value of the second index in combination with the requirement benchmark; and a ratio calculation subunit for calculating a ratio of the first historical index value to the second historical index value according to the requirement recognition mechanism to obtain the first color presentation requirement.

[0085] Among them, after constructing a historical image display database of the same type of material as the first material and retrieving the requirement recognition mechanism to analyze the historical image display database to obtain the first color presentation requirement, the system may further include: a discrete cosine transform preprocessing module for performing discrete cosine transform preprocessing on the target image to obtain a preprocessing result; and a first color presentation requirement calibration module for extracting the AC coefficients in the preprocessing result and calibrating the first color presentation requirement with the AC coefficients as weights.

[0086] Next, the specific configuration of the color correction module 50 will be described in detail. As described above, if the condition is not met, a color correction plan is introduced to perform presentation correction on the target color presentation state to obtain a corrected color presentation 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 operation duration of the target display screen according to the color correction plan and matching the predetermined aging feedback coefficient corresponding to the total operation duration; and a color presentation control and adjustment unit for performing color presentation control and adjustment on the target display screen with the ambient light feedback coefficient and the predetermined aging feedback coefficient as weights to obtain the corrected color presentation state.

[0087] Among them, after introducing a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state if the condition is not met, the system may 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 performing characterization processing on the corrected color presentation state and the reference color presentation state to respectively obtain a corrected color feature and a reference color feature; a curve processing module for sequentially performing curve processing on the corrected color feature and the reference color feature to respectively obtain a corrected curve and a reference curve; and a color presentation quality acquisition module for acquiring the curve similarity between the corrected curve and the reference curve and characterizing the color presentation quality of the target display screen for the target image with the curve similarity.

[0088] The color correction system for a display screen provided by an embodiment of the present invention can execute the color correction method for a display screen provided by any embodiment of the present invention, and has corresponding function modules and beneficial effects for executing the method.

[0089] Although various references are made to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on a user terminal and / or a server. The included respective units and modules are only divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, the specific names of the respective functional units are only for facilitating mutual distinction and do not limit the protection scope of the present invention.

[0090] Based on the foregoing embodiments, an embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor of an electronic device, it can implement the method described in any previous embodiment.

[0091] The above specific embodiments do not constitute a limitation on the protection scope of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the protection scope of this application. In some cases, the actions or steps recited in this application can be executed in a sequence different from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A color calibration method for a display screen, characterized in that, Including: Obtain a target image, where 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; Construct a historical image display database of the same type of material as the first material, and retrieve a demand recognition mechanism to analyze the historical image display database to obtain a first color presentation demand; Display the target image on the target display screen to obtain a target output color, and analyze to obtain a target color presentation state; Determine whether the target color presentation state meets the first color presentation demand; If it does not meet the requirement, introduce a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state.

2. The color correction method of a display screen according to claim 1, characterized in that, Construct a historical image display database of the same type of material as the first material, and retrieve a demand recognition mechanism to analyze the historical image display database to obtain a first color presentation demand, including: Extract the first historical display data group of the first historical image in the historical image display database; Obtain the first predetermined target color of the first historical image; Compare 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, use the first historical output color as the demand benchmark; Analyze the demand benchmark according to the demand recognition mechanism to obtain the first color presentation demand.

3. The color calibration method of a display screen according to claim 2, characterized in that, Compare 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, including: Retrieve a predetermined single-index set, where the predetermined single-index set includes a first index; With the first index as a constraint, sequentially match the first historical index value and the first predetermined index value corresponding to the first index in the first historical output color and the first predetermined target color; Compare the first historical index value with the first predetermined index value to obtain a first deviation value of the first index; Perform variant weighted analysis on the first deviation value to obtain the first color deviation index.

4. The color calibration method of a display screen according to claim 3, characterized in that, The predetermined single-index set includes a hue index set, a lightness index, and a saturation index, and the hue index set further includes a red index, a green index, and a blue index.

5. The color calibration method of a display screen according to claim 3, characterized in that, Analyze the demand benchmark according to the demand recognition mechanism to obtain the first color presentation demand, including: Extract a second index in the predetermined single-index set; Combine the demand benchmark to obtain a second historical index value of the second index; According to the demand recognition mechanism, calculate the ratio of the first historical index value to the second historical index value to obtain the first color presentation demand.

6. The color correction method of a display screen according to claim 1, characterized in that After constructing a historical image display database of the same type of material as the first material, and retrieving a demand recognition mechanism to analyze the historical image display database to obtain a first color presentation demand, it further includes: Perform discrete cosine transform preprocessing on the target image to obtain a preprocessing result; Extract the alternating current coefficients in the preprocessing result, and calibrate the first color presentation demand with the alternating current coefficients as weights.

7. The color calibration method of a display screen according to claim 1, characterized in that, If not satisfied, introduce a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state, including: Obtain the actual ambient light information of the target image according to the color correction plan; Compare and analyze the actual ambient light information with the ideal ambient light information to obtain an ambient light feedback coefficient; Obtain the total operation duration of the target display screen according to the color correction plan, and match the predetermined aging feedback coefficient corresponding to the total operation duration; Use the ambient light feedback coefficient and the predetermined aging feedback coefficient as weights to perform color presentation control adjustment on the target display screen to obtain the corrected color presentation state.

8. The color calibration method of a display screen according to claim 1, wherein, After introducing a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state when not satisfied, it further includes: Obtain the reference color presentation state of the target image; Perform characterization processing on the corrected color presentation state and the reference color presentation state in sequence, and respectively obtain a corrected color feature and a reference color feature; Perform curve processing on the corrected color feature and the reference color feature in sequence, and respectively obtain a corrected curve and a reference curve; Obtain the curve similarity between the corrected curve and the reference curve, and use the curve similarity to characterize the color presentation quality of the target display screen for the target image.

9. A color correction system for a display screen, characterized in that, The system is used to implement a color correction method for a display screen according to any one of claims 1-8, and the system includes: A target image acquisition module for acquiring a target image, where the target image refers to an image to be displayed by a target display screen, and the target image corresponds to the first material in a predetermined type of material; A first color presentation requirement acquisition module for constructing a historical image display database of the same type of material as the first material, and invoking a requirement recognition 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 to obtain a target color presentation state; A judgment module for judging whether the target color presentation state meets the first color presentation requirement; A color correction module for, if not satisfied, introducing a color correction plan to perform presentation correction on the target color presentation state to obtain a corrected color presentation state.

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

Images