A white screen brightness compensation color cast correction method and device and storage medium

Abstract

The application discloses a white screen brightness compensation color deviation correction method and device and a storage medium, which are used for double correction of a display screen after Demura compensation based on a white screen. The application comprises the following steps: collecting regional images of a target screen under different exposure times, and calculating mean values of brightness data of the regional images; establishing a mapping relationship according to the mean values of the brightness data and actual brightness data of the regional images; calculating Euclidean distances from centers of the regional images to a center of the target screen; constructing a brightness-exposure response function according to the mapping relationship and the Euclidean distances, and obtaining a brightness-exposure characteristic matrix; performing first correction on the mean values of the brightness data by using the brightness-exposure characteristic matrix, and obtaining first corrected brightness data; constructing a chroma-brightness correction function of each pixel point in the target screen; and performing second correction on the first corrected brightness data by using the chroma-brightness correction function, and obtaining second corrected brightness data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of display, in particular to a white screen brightness compensation color deviation correction method and device and storage medium. BACKGROUND

[0002] The rapid development of display technology, especially the wide application of OLED and MicroLED in small display panels such as smart phones, tablet computers and high-end televisions, puts forward higher requirements for the color accuracy, peak brightness and picture uniformity of the display screen. However, the response of the photosensitive element (CMOS / CCD) of the industrial camera under different exposure times is not completely linear, especially under low illumination or high exposure conditions, which is prone to saturation or nonlinear response. If accurate modeling and correction are not performed, systematic errors will exist between the collected brightness data and the actual luminous intensity, affecting the Demura compensation accuracy. Due to the non-uniformity of the camera imaging angle (such as perspective distortion and light attenuation in the edge area), as well as the viewing angle dependent characteristics of the display panel itself, the RGB three-channel collected brightness of the image edge area is distorted, and even if the actual screen brightness is consistent, it may be misjudged as color unevenness, resulting in "pseudo Mura" or color deviation phenomenon after compensation.

[0003] Based on the white screen, the existing technology usually directly collects the synthesized brightness of the full white screen as the compensation basis or uses an independent brightness compensation model. However, white is a mixture of R, G, and B three-color sub-pixels. Due to differences in pixel arrangement, microlens design or driving circuit, there is obvious RGB light crosstalk (Crosstalk) phenomenon. Direct compensation based on the synthesized white screen brightness ignores the non-ideal coupling effects of R, G, and B three channels in spatial distribution, response characteristics and acquisition process, and cannot distinguish the true contribution of each channel, resulting in color temperature drift or color unevenness in local area after compensation. Using an independent brightness compensation model ignores the coupling relationship between RGB three channels, lacks a joint correction function for the systematic deviation between the collected data and the true brightness, resulting in differences between the compensation result and the human eye perception, especially more obvious in dark field or low gray scale area. SUMMARY

[0004] The present application discloses a white screen brightness compensation color deviation correction method, device and storage medium, which is used for double correction of the display screen after Demura compensation based on the white screen.

[0005] The first aspect of the present application discloses a white screen brightness compensation color deviation correction method, comprising:

[0006] Under different exposure times, the brightness data of a plurality of region images in the target screen is collected, and the brightness data mean values of the plurality of region images are calculated respectively.

[0007] establishing a mapping relationship according to the average luminance data and the actual luminance data of the area images;

[0008] respectively calculating Euclidean distances from centers of a plurality of the area images to a center of the target screen;

[0009] constructing a luminance-exposure response function according to the mapping relationship and the Euclidean distances, to obtain a luminance-exposure characteristic matrix;

[0010] performing a first correction on the average luminance data by using the luminance-exposure characteristic matrix, to obtain first corrected luminance data;

[0011] calculating an amplitude gain according to the first corrected luminance data and the actual luminance data;

[0012] constructing a chroma-luminance correction function of each pixel point in the target screen according to the amplitude gain;

[0013] performing a second correction on the first corrected luminance data by using the chroma-luminance correction function, to obtain second corrected luminance data.

[0014] Optionally, the step of constructing the luminance-exposure response function according to the mapping relationship and the Euclidean distances, to obtain the luminance-exposure characteristic matrix, specifically comprises:

[0015] calculating an image center standard coefficient according to the mapping relationship of the four area images of the center of the target screen;

[0016] calculating a trend parameter according to the image center standard coefficient and the Euclidean distances;

[0017] constructing the luminance-exposure response function according to the trend parameter and the center coordinates of the target screen, to obtain the luminance-exposure characteristic matrix.

[0018] Optionally, the step of constructing the chroma-luminance correction function of each pixel point in the target screen according to the amplitude gain, specifically comprises:

[0019] constructing a two-dimensional curved surface model of the target screen according to the amplitude gain;

[0020] constructing the chroma-luminance correction function of each pixel point in the target screen according to the two-dimensional curved surface model.

[0021] Optionally, before the step of calculating the amplitude gain according to the first corrected luminance data and the actual luminance data, the color cast correction method further comprises:

[0022] The first correction luminance data and the actual luminance data are normalized respectively to obtain normalized first correction luminance data and normalized actual luminance data.

[0023] Optionally, the step of calculating the amplitude gain according to the first correction luminance data and the actual luminance data specifically comprises:

[0024] The amplitude gain is calculated according to the normalized first correction luminance data and the normalized actual luminance data.

[0025] Optionally, before the step of establishing the mapping relationship according to the luminance data mean and the actual luminance data of the area image, the color cast correction method further comprises:

[0026] The actual luminance data of the area image is collected by a colorimeter.

[0027] Optionally, the step of constructing the luminance-exposure response function according to the trend parameter and the target screen center coordinates to obtain the luminance-exposure characteristic matrix specifically comprises:

[0028] A luminance-exposure response function suitable for each pixel point is constructed according to the trend parameter and the target screen center coordinates to obtain an RGB corresponding luminance-exposure characteristic matrix according to the following formula 4:

[0029] , Formula 4;

[0030] Wherein, is a trend parameter, (Row_C, Col_C) is a target screen center coordinate, (r, c) is a coordinate position of a pixel point, R , G and B are RGB corresponding luminance-exposure characteristic matrices.

[0031] Optionally, the step of constructing the two-dimensional curved surface model of the target screen according to the amplitude gain specifically comprises:

[0032] A two-dimensional curved surface model of the target screen is constructed according to the following formula 6, assuming that the amplitude gain changes smoothly with the spatial position:

[0033]

[0034] , Formula 6;

[0035] Wherein, Let (Row_C, Col_C) be the amplitude gain, (Row_C, Col_C) be the coordinates of the target screen center, and (RowM*N, ColM*N) be the coordinates of the center of each region of the image. For the error term, The coefficients to be solved are denoted as .

[0036] A second aspect of this application provides a color shift correction device for white screen brightness compensation, comprising:

[0037] The first acquisition unit is used to acquire brightness data of several regions of the target screen at different exposure times, and to calculate the average brightness data of the several regions of the image respectively.

[0038] The processing unit is used to establish a mapping relationship based on the average brightness data and the actual brightness data of the region image;

[0039] The first calculation unit is used to calculate the Euclidean distance from the center of several of the regional images to the center of the target screen.

[0040] The first construction unit is used to construct a brightness-exposure response function based on the mapping relationship and the Euclidean distance, and obtain a brightness-exposure characteristic matrix;

[0041] The correction unit is used to perform a first correction on the mean value of the brightness data using the brightness-exposure characteristic matrix to obtain the first corrected brightness data;

[0042] The second calculation unit is used to calculate the amplitude gain based on the first corrected brightness data and the actual brightness data;

[0043] The second construction unit is used to construct a chromaticity-luminance correction function for each pixel in the target screen based on the amplitude gain.

[0044] The correction unit is used to perform a second correction on the first corrected luminance data using the chromaticity-luminance correction function to obtain the second corrected luminance data.

[0045] Optionally, prior to the first calculation unit, the color shift correction device further includes:

[0046] The normalization unit is used to normalize the first corrected brightness data and the actual brightness data respectively to obtain normalized first corrected brightness data and normalized actual brightness data.

[0047] Optionally, before the processing unit, the color shift correction device further includes:

[0048] The second acquisition unit is used to acquire actual brightness data of several regions of the image through a colorimeter.

[0049] A third aspect of this application provides a computer-readable storage medium on which a program is stored, which, when executed on a computer, performs the methods described in the first aspect and any optional methods of the first aspect.

[0050] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0051] To address the response problem of industrial cameras, this study acquires regional images at different exposure times and calculates the average brightness data of several regional images. This approach considers the non-linear response of industrial camera sensors at different exposure times, reducing systematic errors caused by camera response issues and providing a standardized data foundation for subsequent analysis. Based on the average brightness data and the actual brightness data of the regional images, a mapping relationship is established, revealing the logical relationship between exposure time and true brightness. This clarifies the connection between the brightness data acquired by the camera at different exposure times and the actual luminous intensity, providing a basis for subsequent brightness data correction.

[0052] The Euclidean distance from the center of the region image to the center of the target screen is calculated, taking into account the non-uniformity of the camera's imaging viewpoint. Combining the mapping relationship between exposure time and true brightness with the spatial location information (Euclidean distance) of the region image, a brightness-exposure response function is constructed, and a characteristic matrix is ​​obtained. This comprehensively considers both camera response nonlinearity and imaging viewpoint nonuniformity, enabling a more accurate description of the relationship between brightness and true luminous intensity at different exposure times and spatial locations. The brightness-exposure characteristic matrix is ​​used to correct the mean brightness data, effectively solving the systematic error problem between the mean brightness data and the true luminous intensity. Furthermore, it eliminates errors caused by camera response nonlinearity and imaging viewpoint nonuniformity, making the obtained first corrected brightness data closer to the true brightness.

[0053] Based on the first calibrated brightness data and the actual brightness data, the amplitude gain was calculated to clarify the degree of difference between the first calibrated brightness data and the actual brightness data. Based on the amplitude gain, a chromaticity-luminance correction function was constructed for each pixel in the target screen. Considering the spatial distribution, response characteristics, and non-ideal coupling effects during the acquisition process of the RGB three channels, the true contribution of each channel could be distinguished by constructing a chromaticity-luminance correction function for each pixel. A secondary correction was performed on the first calibrated brightness data using the chromaticity-luminance correction function, effectively solving the problem of color temperature drift or color unevenness in local areas after compensation. This made the compensation result more consistent with human visual perception, improving the color accuracy and image uniformity of the display panel. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of an embodiment of the color shift correction method for white screen brightness compensation in this application;

[0056] Figure 2 This is a schematic diagram of an embodiment of constructing the luminance-exposure response function in this application;

[0057] Figure 3 This is a schematic diagram of an embodiment for calculating amplitude gain in this application;

[0058] Figure 4 This is a schematic diagram of an embodiment of constructing the chromaticity-luminance correction function in this application;

[0059] Figure 5 This is a schematic diagram of the color shift correction device for white screen brightness compensation in this application. Detailed Implementation

[0060] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0061] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0062] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0063] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0064] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0065] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not an embodiment," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0066] This application discloses a color deviation correction method, apparatus and storage medium for white screen brightness compensation, which is used to perform dual correction on the display screen after Demura compensation based on the white screen.

[0067] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0068] The method of this application can be applied to servers, devices, terminals, or other devices with logical processing capabilities; therefore, this application does not limit its application. For ease of description, the following description uses a system as the executing entity.

[0069] Please see Figure 1 This application provides an embodiment of a color shift correction method for white screen brightness compensation, comprising:

[0070] 101. Under different exposure times, collect brightness data of several regions of the target screen, and calculate the average brightness data of the several regions respectively;

[0071] First, under the same ambient lighting conditions, take a uniform reference image using the camera. This can typically be done by photographing a uniformly illuminated plane or capturing a uniformly lit environment without obstructions. This reference image will serve as a reference for planar correction, used to eliminate inhomogeneities in the camera imaging system, such as lens vignetting and uneven sensor response.

[0072] The captured reference image is analyzed to calculate the correction coefficient for each pixel position. The calculated correction coefficients are saved as a correction matrix for subsequent flat-field correction of the acquired screen image.

[0073] Based on the camera's performance and the brightness range of the display screen, determine a set of progressively increasing exposure times, Exp_Ti. For example, you can start with a shorter exposure time and gradually increase it in increments until you reach the camera's maximum allowable exposure time or a suitable exposure time that allows for clear image capture without overexposure. The specific exposure time steps can be adjusted and optimized based on actual experimental conditions. Following the determined exposure time steps, set the camera's exposure times sequentially as Exp_T1, Exp_T2, ..., Exp_Tn.

[0074] The target screen is divided into multiple regular regions, which can be squares, rectangles, or other regular shapes of uniform size. For example, the screen can be evenly divided into 10×10 small square regions. At each set exposure time, an image acquisition device (such as a camera) is used to acquire images of each regular region of the target screen, obtaining region images at different exposure times. After acquiring each region image, the image quality is checked to ensure that there is no overexposure or underexposure. If overexposure or underexposure occurs, the exposure time or other camera parameters can be adjusted appropriately, and then the image can be acquired again.

[0075] For each acquired region image, its average brightness data is calculated using an image processing algorithm. For example, for a 100×100 pixel region image, the average brightness data of the region image is obtained by summing the gray values ​​of all 10,000 pixels and dividing by 10,000.

[0076] 102. Establish a mapping relationship based on the average brightness data and the actual brightness data of the region image;

[0077] The mean and actual brightness data of each region obtained in step 101 are organized and recorded. Statistical analysis is performed on the collected data according to Formula 1. For each region, a polynomial fit is performed on the RGB three channels using the least squares method to establish a mapping relationship. This mapping relationship represents the logical relationship between exposure time and true brightness.

[0078] , Formula 1;

[0079] in, This represents the mapping relationship, where n is the order of the polynomial fitting. is the k-th order fitting coefficient of the image in region (M, N) on channel C.

[0080] 103. Calculate the Euclidean distance from the center of the image in several regions to the center of the target screen;

[0081] A two-dimensional rectangular coordinate system is established with the center of the target screen as the origin (0,0), and the coordinates of the center of each region of the image are determined by an algorithm.

[0082] According to Formula 2 (Euclidean distance formula), calculate the Euclidean distance from the center of the image in each region to the center of the target screen:

[0083] Formula 2;

[0084] Among them, Dis M*N For Euclidean distance, (Row) M*N Col M*N (Row_C, Col_C) represents the coordinates of the center of the region image, and (Row_C, Col_C) represents the coordinates of the center of the target screen.

[0085] 104. Based on the mapping relationship and Euclidean distance, construct the luminance-exposure response function to obtain the luminance-exposure characteristic matrix;

[0086] By combining the mapping relationship between exposure time and true brightness established in step 102 with the Euclidean distance from the center of the region image to the center of the target screen calculated in step 103, a brightness-exposure response function is constructed. This function can describe the relationship between exposure time and brightness at different Euclidean distances.

[0087] The constructed luminance-exposure response function is represented in matrix form, resulting in the luminance-exposure characteristic matrix. This matrix reflects the luminance values ​​corresponding to different exposure times and Euclidean distances, providing a basis for subsequent luminance correction.

[0088] 105. The mean value of the brightness data is corrected for the first time using the brightness-exposure characteristic matrix to obtain the first corrected brightness data;

[0089] Based on the exposure time corresponding to the average brightness data obtained in step 101, and the Euclidean distance from the center of the regional image to the center of the target screen, the corresponding parameters are found in the brightness-exposure characteristic matrix. These parameters are then used to correct the average brightness data. After the above calculations, the first corrected brightness data for each regional image is obtained. This data can more accurately reflect the true brightness of the target screen at different positions and exposure times.

[0090] 106. Calculate the amplitude gain based on the first corrected brightness data and the actual brightness data;

[0091] The amplitude gain is calculated based on the difference between the initial calibrated brightness data of the target screen and the actual measured brightness. By comparing the two, the required brightness adjustment ratio for each pixel or area can be determined.

[0092] Because there are numerous pixels on the screen, calculating the amplitude gain requires calculating the gain for each pixel individually. Alternatively, the screen can be divided into several regions, and the average amplitude gain for each region can be calculated. By iterating through all pixels, the amplitude gain value for each pixel is obtained, forming an amplitude gain matrix with the same number of pixels as the target screen.

[0093] 107. Based on the amplitude gain, construct the chromaticity-luminance correction function for each pixel in the target screen;

[0094] Color cast is essentially an imbalance in the brightness ratio of the RGB three channels. For example, if the R channel is too strong in a pure white image, it will cause a reddish tint. A correction function needs to be constructed by combining amplitude gain and chromaticity information (RGB channel ratio).

[0095] For each pixel, the RGB chromaticity value of the first corrected luminance data is used as input, and a chromaticity-luminance correction function is constructed based on the amplitude gain and the target chromaticity ratio (such as RGB=1:1:1 for standard pure white).

[0096] 108. Use the chromaticity-luminance correction function to perform a second correction on the first corrected luminance data to obtain the second corrected luminance data.

[0097] The first corrected luminance data is used as input and substituted into the chroma-luminance correction function corresponding to each pixel. For each pixel, its first corrected luminance data is processed by the correction function.

[0098] According to the calculation rules of the correction function, the input brightness data is processed to obtain the corrected brightness and chromaticity information, i.e., the second corrected brightness data of the pixel. The data obtained after correction of all pixels are integrated to form the complete second corrected brightness data. The corrected second corrected brightness data eliminates exposure and spatial differences, while ensuring the chromaticity accuracy of the white image (pure white without color cast).

[0099] In this embodiment, by acquiring regional images at different exposure times, the brightness performance of the target screen under different exposure conditions can be obtained, providing a comprehensive data foundation for subsequent accurate analysis of screen brightness characteristics. Dividing the target screen into images of predetermined regular area sizes and calculating the average brightness data reduces the amount of data while improving data processing efficiency compared to processing each pixel individually. The established mapping relationship clarifies the logical connection between exposure time and true brightness, enabling accurate inference of the screen's true brightness under different exposure conditions. This mapping relationship provides the theoretical basis for the entire color shift correction process.

[0100] The Euclidean distance from the center of the regional image to the center of the target screen is calculated, taking into account the spatial relationship between different screen locations and the center location. A luminance-exposure response function is constructed by combining the mapping relationship and the Euclidean distance, comprehensively considering the influence of exposure time and spatial location on screen brightness. The resulting luminance-exposure characteristic matrix provides an accurate mathematical model for subsequent brightness correction. The luminance-exposure characteristic matrix can adapt to different exposure conditions and brightness variations at different screen locations, making the correction method more adaptable. The first correction of the mean luminance data using the luminance-exposure characteristic matrix can initially eliminate brightness deviations caused by exposure time and spatial location, making the corrected luminance data closer to the true screen brightness. The first corrected luminance data obtained after the first correction is more accurate and uniform, reducing the amount of deviation that needs to be processed in subsequent corrections.

[0101] Calculating the amplitude gain between the first corrected brightness data and the actual brightness data allows for precise quantification of the required brightness adjustment. The amplitude gain reflects the difference between the brightness data after the first correction and the actual brightness. Based on the amplitude gain, a chromaticity-luminance correction function is constructed for each pixel, fully considering the individual differences between different pixels. Each pixel may have slight differences in chromaticity and luminance due to factors such as manufacturing process and optical characteristics. By constructing a dedicated correction function for each pixel, precise correction can be achieved for each pixel.

[0102] A second correction is applied to the first calibrated luminance data using a chroma-luminance correction function. This function comprehensively considers the relationship between chroma and luminance, allowing for simultaneous chroma correction while adjusting luminance. This helps resolve potential color cast issues in white screen displays, resulting in a more harmonious balance between chroma and luminance, and a purer, more uniform white. The second calibrated luminance data obtained after these two corrections is more accurate and uniform, significantly improving screen display quality.

[0103] Please see Figure 2 This application provides an embodiment for constructing a luminance-exposure response function, including:

[0104] 201. Collect actual brightness data of several regions of the image using a colorimeter;

[0105] According to the colorimeter's instruction manual and measurement requirements, set appropriate measurement parameters, such as measurement time, integration time, and measurement mode. Point the colorimeter's measurement probe at various areas of the target screen image and collect actual brightness data according to the set measurement parameters. During the measurement process, keep the colorimeter stable and avoid probe movement or interference from external light. Multiple measurements can be taken and averaged to improve the reliability of the measurement results.

[0106] 202. Based on the average brightness data and the actual brightness data of the regional image, establish a mapping relationship. The mapping relationship is the logical relationship between exposure time and true brightness in the regional image.

[0107] 203. Calculate the Euclidean distance from the center of several regions of the image to the center of the target screen;

[0108] Steps 202 and 203 are similar to steps 102 and 103 in the above embodiments, and can be referred to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0109] 204. Based on the mapping relationship of the four regions of the target screen center image, calculate the standard coefficient of the image center;

[0110] Based on the target screen's size and pre-defined zoning rules, four image regions located at the center of the target screen are precisely identified. These four image regions should be distributed as evenly as possible around the screen center to comprehensively reflect the brightness characteristics of the central area. For example, if the screen is divided into regular grid regions, four adjacent and symmetrically distributed regions can be selected by calculating the center coordinates.

[0111] From the mapping relationship, key parameters related to the four image regions at the center of the target screen are extracted. According to Formula 3, the extracted key parameters of the mapping relationship for the four image regions are calculated to obtain the standard coefficients of the image center:

[0112] , Formula 3;

[0113] in, The standard coefficient for the image center is... This represents the mapping relationship between image regions, where C represents the color channel (one of the three RGB channels).

[0114] 205. Calculate the trend parameters based on the standard coefficient of the image center and the Euclidean distance;

[0115] The Euclidean distances from the centers of several regional images to the center of the target screen are calculated in step 203. These Euclidean distances reflect the spatial position information of each regional image relative to the center of the screen.

[0116] Using known image center standard coefficients and corresponding Euclidean distance data, the parameters in the computational model are determined through least squares or other parameter estimation methods. Then, the Euclidean distances of each region of the image are substituted into the computational model to obtain the corresponding trend parameters.

[0117] 206. Based on the trend parameters and the center coordinates of the target screen, construct the brightness-exposure response function to obtain the brightness-exposure characteristic matrix.

[0118] Based on Formula 4, combined with trend parameters and the center coordinates of the target screen, a luminance-exposure response function suitable for each pixel is constructed, yielding the RGB corresponding luminance-exposure characteristic matrix R. G and B This function describes the brightness variation at various locations on the target screen under different exposure times:

[0119] , Formula 4;

[0120] in, Here is the brightness-exposure characteristic matrix, where (Row_C, Col_C) are the coordinates of the target screen center. Here, (r, c) represents the trend parameter, and (r, c) represents the pixel coordinates.

[0121] In this embodiment, the image center standard coefficient is calculated by focusing on four regions of the target screen's center image, comprehensively considering the brightness information of different directions and positions in the center region, thus avoiding the one-sidedness of a single region. The trend parameter is calculated, fully considering the influence of the spatial position of different locations on the target screen relative to the screen center on the brightness. The brightness of the screen at different positions may vary due to factors such as optical characteristics and manufacturing processes. This spatial difference can be quantified using Euclidean distance, enabling the trend parameter to accurately reflect the trend of brightness change with spatial position.

[0122] The constructed brightness-exposure response function combines trend parameters and the center coordinates of the target screen, comprehensively describing the brightness variation patterns at various locations on the target screen under different exposure times. This function considers not only the impact of exposure time on brightness but also spatial location factors, making the relationship between brightness and exposure more accurate and complete.

[0123] Please see Figure 3 This application provides an embodiment for calculating amplitude gain, including:

[0124] 301. Normalize the first corrected brightness data and the actual brightness data respectively to obtain normalized first corrected brightness data and normalized actual brightness data;

[0125] The average value of the first corrected brightness data of the region image after the first correction is taken to obtain the first corrected brightness RCM*N, GCM*N, and BCM*N. Then, the first corrected brightness RCM*N, GCM*N, and BCM*N of the region image are subjected to brightness centering normalization processing to obtain normalized first corrected brightness data RCnormM*N, GCnormM*N, and BCnormM*N.

[0126] Similarly, the actual brightness data LRM*N, LGM*N, and LBM*N are normalized to obtain normalized actual brightness data LRnormM*N, LGnormM*N, and LBnormM*N.

[0127] 302. The amplitude gain is calculated based on the normalized first correction brightness data and the normalized actual brightness data.

[0128] According to Formula 5, by combining the normalized first corrected luminance data RCnormM*N, GCnormM*N, and BCnormM*N with the normalized actual luminance data LRnormM*N, LGnormM*N, and LBnormM*N, the amplitude gain T is calculated:

[0129] , Formula 5;

[0130] in, For amplitude gain, The normalized actual luminance data (i.e., one of LRnormM*N, LGnormM*N, and LBnormM*N). The first normalized luminance data is one of the three: RCnormM*N, GCnormM*N, and BCnormM*N.

[0131] In this embodiment, normalization ensures that the first calibrated luminance data and the actual luminance data are within the same numerical range. The actual luminance data may differ from the first calibrated luminance data due to variations in measuring equipment and environment; normalization allows both to be analyzed and processed under a unified standard.

[0132] By calculating the amplitude gain, the ratio between the initial corrected brightness and the actual brightness can be accurately reflected. This ratio is crucial for evaluating the effectiveness of the initial correction and for further optimizing the screen display. If the amplitude gain is close to 1, it indicates that the initial corrected brightness is close to the actual brightness, and the initial correction effect is good; if the amplitude gain deviates significantly from 1, it indicates that there is a large brightness difference at the corresponding pixel or area, requiring more in-depth adjustments.

[0133] Please see Figure 4 This application provides an embodiment for constructing a chromaticity-luminance correction function, including:

[0134] 401. Based on the amplitude gain, construct a two-dimensional curved surface model of the target screen;

[0135] First, assume that the target amplitude gain TargetCM*N varies smoothly with spatial location. This means that the change in amplitude gain is continuous and smooth at different locations on the screen, without abrupt jumps.

[0136] Based on the following formula 6, construct a two-dimensional surface model of the target screen:

[0137]

[0138] , Formula 6;

[0139] in, Let (Row_C, Col_C) be the amplitude gain, (Row_C, Col_C) be the coordinates of the target screen center, and (RowM*N, ColM*N) be the coordinates of the center of each region of the image. For the error term, The coefficients to be solved are denoted as .

[0140] The coefficients are generally solved using the least squares method. The least squares method finds the best function match for the data by minimizing the sum of squared errors, thereby determining these coefficients so that the constructed two-dimensional surface model can best fit the target amplitude gain as a function of spatial location.

[0141] 402. Based on the two-dimensional surface model, construct the chromaticity-luminance correction function for each pixel in the target screen.

[0142] Using formulas 7 and 8, the global amplitude surface obtained from step 301 is fitted, i.e., the coefficients and model structure are determined, and the RGB chromaticity-luminance correction functions Rcor(r,c), Gcor(r,c), and Bcor(r,c) for each pixel are constructed:

[0143] Formula 7;

[0144] Formula 8;

[0145] Where (r,c) represents the coordinates of the RGB pixel, (Row_C, Col_C) represents the coordinates of the center of the target screen, and Rraw(r,c) represents the first corrected brightness data.

[0146] By multiplying with the correction function TR(r,c), the corrected RGB data Rcor(r,c) is obtained. Similarly, Gcor(r,c) and Bcor(r,c) can be obtained, thus realizing the correction of the chromaticity and brightness of each pixel.

[0147] In this embodiment, a two-dimensional curved surface model is constructed to describe the change of the target amplitude gain with spatial position, and a chromaticity-luminance correction function is further constructed for each pixel, enabling precise correction for the characteristics of different locations on the screen. This effectively solves the problem of uneven brightness and chromaticity that may exist in different areas of the screen, making the display of the entire screen more uniform and consistent, and improving the visual experience.

[0148] Please see Figure 5 This application provides an embodiment of a color shift correction device for white screen brightness compensation, comprising:

[0149] The first acquisition unit 501 is used to acquire brightness data of several regions of the target screen at different exposure times, and calculate the average brightness data of the several regions of the image respectively.

[0150] Processing unit 502 is used to establish a mapping relationship based on the average brightness data and the actual brightness data of the region image;

[0151] The first calculation unit 503 is used to calculate the Euclidean distance from the center of several regional images to the center of the target screen, respectively.

[0152] The first construction unit 504 is used to construct a brightness-exposure response function based on the mapping relationship and the Euclidean distance, and obtain a brightness-exposure characteristic matrix;

[0153] Correction unit 505 is used to perform a first correction on the mean value of the brightness data using the brightness-exposure characteristic matrix to obtain first corrected brightness data;

[0154] The second calculation unit 506 is used to calculate the amplitude gain based on the first corrected brightness data and the actual brightness data;

[0155] The second construction unit 507 is used to construct a chromaticity-luminance correction function for each pixel in the target screen based on the amplitude gain.

[0156] The correction unit 508 is used to perform a second correction on the first corrected luminance data using the chromaticity-luminance correction function to obtain the second corrected luminance data.

[0157] Optionally, prior to the first calculation unit 506, the color shift correction device further includes:

[0158] The normalization unit 509 is used to normalize the first corrected brightness data and the actual brightness data respectively to obtain normalized first corrected brightness data and normalized actual brightness data.

[0159] Optionally, prior to the processing unit 502, the color shift correction device further includes:

[0160] The second acquisition unit 510 is used to acquire actual brightness data of several regions of the image through a colorimeter.

[0161] For detailed implementation methods, please refer to... Figures 1 to 4 Examples are not detailed here.

[0162] This application provides a computer-readable storage medium on which a program is stored, and when the program is executed on a computer, it performs the following... Figure 1 , Figure 2 , Figure 3 and Figure 4 The method in the middle.

[0163] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0165] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0167] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A color cast correction method of white picture luminance compensation, characterized by, The method comprises the following steps: Collecting the luminance data of a plurality of region images in a target screen under different exposure times, and calculating the average luminance data of the plurality of region images respectively; Establishing a mapping relationship according to the average luminance data and the actual luminance data of the region images; Calculating the Euclidean distance from the center of each of the plurality of region images to the center of the target screen respectively; Constructing a luminance-exposure response function according to the mapping relationship and the Euclidean distance, and obtaining a luminance-exposure characteristic matrix; First correcting the average luminance data by using the luminance-exposure characteristic matrix, and obtaining first corrected luminance data; Calculating an amplitude gain according to the first corrected luminance data and the actual luminance data; Constructing a chroma-luminance correction function of each pixel point in the target screen according to the amplitude gain; Second correcting the first corrected luminance data by using the chroma-luminance correction function, and obtaining second corrected luminance data; The step of constructing a luminance-exposure response function according to the mapping relationship and the Euclidean distance, and obtaining a luminance-exposure characteristic matrix specifically comprises: Calculating an image center standard coefficient according to the mapping relationship of the four region images of the center of the target screen; Calculating a trend parameter according to the image center standard coefficient and the Euclidean distance; Constructing a luminance-exposure response function according to the trend parameter and the center coordinates of the target screen, and obtaining a luminance-exposure characteristic matrix.

2. The color cast correction method according to claim 1, characterized by, The step of constructing a chroma-luminance correction function of each pixel point in the target screen according to the amplitude gain specifically comprises: Constructing a two-dimensional surface model of the target screen according to the amplitude gain; Constructing a chroma-luminance correction function of each pixel point in the target screen according to the two-dimensional surface model.

3. The color cast correction method of claim 1, wherein, Before the step of calculating an amplitude gain according to the first corrected luminance data and the actual luminance data, the color cast correction method further comprises: Normalizing the first corrected luminance data and the actual luminance data respectively to obtain normalized first corrected luminance data and normalized actual luminance data.

4. The color cast correction method according to claim 3, characterized by, The step of calculating an amplitude gain according to the first corrected luminance data and the actual luminance data specifically comprises: Calculating an amplitude gain according to the normalized first corrected luminance data and the normalized actual luminance data.

5. The color cast correction method according to any one of claims 1 to 4, characterized in that, Before the step of establishing a mapping relationship according to the average luminance data and the actual luminance data of the region images, the color cast correction method further comprises: Collecting the actual luminance data of the plurality of region images by using a colorimeter.

6. The color cast correction method of claim 1, wherein, The step of constructing a luminance-exposure response function according to the trend parameter and the center coordinates of the target screen, and obtaining a luminance-exposure characteristic matrix specifically comprises: Constructing a luminance-exposure response function suitable for each pixel point by using the trend parameter and the center coordinates of the target screen according to the following formula 4, and obtaining a RGB corresponding luminance-exposure characteristic matrix: , Formula 4; wherein, is a trend parameter, (Row_C, Col_C) is a target screen center coordinate, (r, c) is a coordinate position of a pixel point, R , G , and B are RGB corresponding luminance-exposure characteristic matrices; is an exponential function; is a parameter value of the kth exposure time, k is a positive integer, used to identify different exposure sequences; n is a positive integer, used to represent the number of groups of images collected.

7. The color cast correction method of claim 2, wherein, The step of constructing a two-dimensional surface model of the target screen according to the amplitude gain specifically comprises: According to the following formula 6, assuming that the amplitude gain changes smoothly with the spatial position, a two-dimensional curved surface model of the target screen is constructed: , Equation 6; wherein, is the amplitude gain, (Row_C, Col_C) is the target screen center coordinate, (RowM*N, ColM*N) is the center coordinate of each region image, is the error term, is the coefficient to be solved, M represents the number of pixel points of the image in the horizontal direction, and N represents the number of pixel points of the image in the vertical direction.

8. A color cast correction device for white picture luminance compensation, characterized by, The application relates to a color cast correction method and device. The acquisition unit is used for acquiring the luminance data of a plurality of area images in the target screen under different exposure times, and calculating the luminance data mean value of the plurality of area images respectively; The processing unit is used for establishing a mapping relationship according to the luminance data mean value and the actual luminance data of the area images; The first calculation unit is used for calculating the Euclidean distance from the center of the plurality of area images to the center of the target screen respectively; The first construction unit is used for constructing a luminance-exposure response function according to the mapping relationship and the Euclidean distance, and obtaining a luminance-exposure characteristic matrix; The correction unit is used for performing first correction on the luminance data mean value by using the luminance-exposure characteristic matrix, and obtaining first corrected luminance data; The second calculation unit is used for calculating the amplitude gain according to the first corrected luminance data and the actual luminance data; The second construction unit is used for constructing a chroma-luminance correction function of each pixel point in the target screen according to the amplitude gain; The correction unit is used for performing second correction on the first corrected luminance data by using the chroma-luminance correction function, and obtaining second corrected luminance data.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium has a program saved thereon, and the program performs the color cast correction method when executed on the computer.

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