Color correction method, device and related equipment

Abstract

The present disclosure provides a color correction method and device and related equipment, and relates to the technical field of image processing. The method comprises: obtaining color card actual imaging information and color card target imaging information; obtaining parameter requirements of a comparison parameter between color card calibration imaging information and color card target imaging information; generating an optimization function according to the color card actual imaging information, the color card target imaging information and the parameter requirements of the comparison parameter, taking a color correction matrix as an optimization variable; the optimization function takes the deviation between the actual value of the comparison parameter determined based on the color correction matrix and the parameter requirements of the comparison parameter as an optimization objective; determining the optimization result of the color correction matrix that meets the optimization objective according to the optimization function, and obtaining a color correction result. The present disclosure can make the actual value of the comparison parameter between the color card calibration imaging and the color card target imaging based on the color correction result meet the parameter requirements, without investing a large amount of labor cost, and also improves the speed of color correction.

Description

Technical Field

[0001] This disclosure relates to the field of image processing technology, and in particular to a color correction method, apparatus and related equipment. Background Technology

[0002] Currently, ISP (Image Signal Processing) is widely used in image acquisition devices to improve image quality. Physical imperfections in the lenses and image sensors of image acquisition devices (such as cameras and mobile phones) can cause problems that require compensation from the ISP module.

[0003] Specifically, the color error caused by color bleeding between color blocks at the sensor filter, meaning the image acquired by the sensor differs from the user's expected color, requires correction by the ISP module. Existing color correction schemes utilize image acquisition equipment to photograph a color chart (e.g., a 24-color chart) to obtain an actual image of the color chart, comparing it with a standard image of the color chart, and calibrating color accuracy based on the comparison results. A 3×3 Color Correction Matrix (CCM) is used to calibrate the actual image of the color chart. Through continuous manual adjustment of the CCM, the color deviation between the calibrated image of the color chart and the standard image, as well as the saturation of the calibrated image, are ensured to meet objective acceptance standards.

[0004] However, this color correction solution requires constant manual adjustment of the CCM, which makes the color correction process relatively slow and requires a large investment of manpower.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This disclosure provides a color correction method, apparatus, and related equipment, which at least to some extent overcomes the technical problem that color correction in related technologies is relatively slow and requires a large investment of manpower.

[0007] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0008] According to one aspect of this disclosure, a color correction method is provided, comprising:

[0009] Acquire the actual imaging information of the color chart and the target imaging information of the color chart;

[0010] The parameter requirements for the comparison parameters between the color chart calibration imaging information and the target imaging information of the color chart are as follows: wherein, the color chart calibration imaging information is obtained by calibrating the actual imaging information of the color chart using a color correction matrix;

[0011] Based on the actual imaging information of the color chart, the target imaging information of the color chart, and the parameter requirements of the comparison parameters, an optimization function is generated with the color correction matrix as the quantity to be optimized; wherein, the optimization function takes the deviation between the actual value of the comparison parameters determined based on the color correction matrix and the parameter requirements of the comparison parameters as the optimization objective;

[0012] Based on the optimization function, the optimization result of the color correction matrix that satisfies the optimization objective is determined, and the color correction result is obtained.

[0013] In some embodiments, the comparison parameters include at least:

[0014] The average value of the first color deviation between the color chart calibration imaging information and the color chart target imaging information, the maximum value of the first color deviation between the color chart calibration imaging information and the color chart target imaging information, the average value of the second color deviation between the color chart calibration imaging information and the color chart target imaging information, the maximum value of the second color deviation between the color chart calibration imaging information and the color chart target imaging information, and the saturation of the color chart calibration imaging.

[0015] In some embodiments, based on the actual imaging information of the color chart, the target imaging information of the color chart, and the parameter requirements of the comparison parameters, an optimization function is generated using the color correction matrix as the quantity to be optimized, including:

[0016] The actual imaging information of the color card is calibrated based on the color correction matrix to obtain the calibrated imaging information of the color card.

[0017] By comparing the color chart calibration imaging information and the color chart target imaging information, the actual values ​​of the comparison parameters between the color chart calibration imaging information and the color chart target imaging information are obtained;

[0018] Based on the actual value of the comparison parameter and the parameter requirements of the comparison parameter, determine the deviation between the actual value of the comparison parameter and the parameter requirements of the comparison parameter;

[0019] Using the weighted result of the deviation as the dependent variable and the color correction matrix as the independent variable, a mapping relationship between the dependent variable and the independent variable is constructed to obtain the optimization function;

[0020] The weighted result of the deviation is obtained by weighting the deviation.

[0021] Specifically, the deviation between the actual value of the comparison parameter and the parameter requirement of the comparison parameter includes:

[0022] The deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation, and the deviation of the saturation.

[0023] In some embodiments of this disclosure, after determining the deviation between the actual value of the comparison parameter and the parameter requirement of the comparison parameter, an optimization function is generated based on the actual imaging information of the color chart, the target imaging information of the color chart, and the parameter requirement of the comparison parameter, using the color correction matrix as the quantity to be optimized. The method further includes:

[0024] Based on the preset first weight parameter, second weight parameter, third weight parameter, fourth weight parameter and fifth weight parameter, the deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation and the deviation of the saturation are weighted to obtain the weighted result of the deviation.

[0025] Specifically, according to the following formula, based on preset first weighting parameters, second weighting parameters, third weighting parameters, fourth weighting parameters, and fifth weighting parameters, the deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation, and the deviation of the saturation are weighted and processed to obtain the weighted result of the deviation:

[0026] K = k1X1 + k2X2 + k3X3 + k4X4 + k5X5

[0027] Wherein, K represents the weighted result of the deviation; k1 represents the first weight parameter; X1 represents the deviation of the average value of the first color deviation; k2 represents the second weight parameter; X2 represents the deviation of the maximum value of the first color deviation; k3 represents the third weight parameter; X3 represents the deviation of the average value of the second color deviation; k4 represents the fourth weight parameter; X4 represents the deviation of the maximum value of the second color deviation; k5 represents the fifth weight parameter; and X5 represents the deviation of the saturation.

[0028] In some embodiments of this disclosure, the optimization result of the color correction matrix that satisfies the optimization objective is determined according to the optimization function, and the color correction result is obtained, including:

[0029] By using a random segmentation estimation method, the minimum value of the weighted result of the deviation is determined, and the color correction matrix corresponding to the minimum value of the weighted result of the deviation is obtained.

[0030] The color correction matrix corresponding to the minimum value of the weighted result of the deviation is determined as the color correction result.

[0031] In some embodiments of this disclosure, a random segmentation estimation method is used to determine the minimum value of the weighted result of the bias, including:

[0032] Based on the dimension of the color correction matrix, a multidimensional space is established with multiple vectors in the color correction matrix as independent variables and the weighted result of the deviation as the dependent variable.

[0033] The multidimensional space is randomly divided into a predetermined number of subspaces;

[0034] Repeat the following steps until the filtered data points are unique data points, and determine the unique data points as the minimum points of the weighted results of the deviation:

[0035] Randomly select a data point in any of the subspaces, calculate the weighted result of the deviation of the data point, and obtain the weighted result of the deviation of multiple data points;

[0036] By comparing the weighted results of the deviations between any two adjacent data points, the data points with larger weighted results of the deviations are filtered out to obtain the filtered data points; wherein, the number of filtered data points is half the number of data points before filtering.

[0037] The subspace is regenerated using the filtered data points as the center.

[0038] Specifically, after determining the unique data point as the minimum point of the weighted result of the deviation, the method of determining the minimum value of the weighted result of the deviation using random segmentation estimation further includes:

[0039] Perform a minimum value check on the unique data point;

[0040] If the verification fails, the process is repeated to randomly select a data point in any of the subspaces, calculate the weighted result of the deviation of the data point, and obtain the weighted result of the deviation of multiple data points to redetermine the unique data point.

[0041] According to another aspect of this disclosure, a color correction device is also provided, comprising:

[0042] The imaging information acquisition module is used to acquire the actual imaging information of the color chart and the target imaging information of the color chart;

[0043] The parameter requirement acquisition module is used to acquire the parameter requirements of the comparison parameters between the color card calibration imaging information and the color card target imaging information; wherein, the color card calibration imaging information is obtained by calibrating the actual imaging information of the color card using a color correction matrix;

[0044] The optimization function generation module is used to generate an optimization function based on the actual imaging information of the color chart, the target imaging information of the color chart, and the parameter requirements of the comparison parameters, using the color correction matrix as the quantity to be optimized; the optimization function takes the deviation between the actual value of the comparison parameter determined based on the color correction matrix and the parameter requirements of the comparison parameter as the optimization objective; and

[0045] The optimization result determination module is used to determine the optimization result of the color correction matrix that satisfies the optimization objective based on the optimization function, and obtain the color correction result.

[0046] According to another aspect of this disclosure, an electronic device is also provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the color correction method described in any of the preceding claims by executing the executable instructions.

[0047] According to another aspect of this disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the color correction method described in any one of the preceding claims.

[0048] According to another aspect of this disclosure, a computer program product is also provided, including a computer program that, when executed by a processor, implements the color correction method of any of the above.

[0049] The color correction method provided in the embodiments of this disclosure uses the color correction matrix as the quantity to be optimized. Combined with the parameter requirements of the comparison parameters between the color card calibration imaging information and the color card target imaging information obtained after calibration based on the color correction matrix, an optimization function is generated with the deviation between the actual value of the comparison parameter and the parameter requirements as the optimization objective. The optimization is performed to determine the color correction matrix with the smallest deviation as the color correction result. This allows the actual value of the comparison parameter between the color card calibration imaging and the color card target imaging obtained based on the color correction result to meet the parameter requirements without multiple manual adjustments. This reduces the need for a large investment of manpower and also improves the speed of color correction.

[0050] Furthermore, compared to the existing technology that involves manually adjusting the color correction matrix multiple times to determine whether the color card calibration image matches the target image of the color card, the optimal solution of the color correction matrix is ​​determined by solving the optimization function. Under the premise of meeting the parameter requirements of the comparison parameters, the obtained color correction result is more accurate.

[0051] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0053] Figure 1 This diagram illustrates a color correction method according to an embodiment of the present disclosure.

[0054] Figure 2 A flowchart illustrating the implementation process of S106 in some embodiments of this disclosure is shown.

[0055] Figure 3 This diagram illustrates another implementation process of S106 in some embodiments of the present disclosure.

[0056] Figure 4 A flowchart illustrating the implementation process of S108 in some embodiments of this disclosure is shown.

[0057] Figure 5 This diagram illustrates a flowchart of the implementation process of S402 in some embodiments of this disclosure;

[0058] Figure 6 This diagram illustrates another implementation process of S402 in some embodiments of the present disclosure.

[0059] Figure 7 A flowchart illustrating the process of determining the minimum value of K in a specific example of this disclosure is shown;

[0060] Figure 8 A schematic diagram of a color correction device according to an embodiment of the present disclosure is shown; and

[0061] Figure 9 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0062] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0063] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0064] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0065] This disclosure provides a color correction method that can be executed by any electronic device with computing power.

[0066] Figure 1 A flowchart of a color correction method according to an embodiment of this disclosure is shown, such as Figure 1 As shown, the color correction method provided in this embodiment includes the following steps:

[0067] S102, acquire the actual imaging information of the color chart and the target imaging information of the color chart;

[0068] It should be noted that a color chart is a tool used in color calibration to achieve a unified standard within a certain range. In practice, a color chart can be a 24-color chart. The actual imaging information of the color chart refers to the image information of the actual image captured by the image acquisition device, including the color information of the color blocks. The target imaging information of the color chart is the pre-set standard image information used as a reference for color correction; that is, the calibrated image of the color chart, after correction of the actual image, is intended to become the target image of the color chart.

[0069] S104, obtain the parameter requirements for the comparison parameters between the color card calibration imaging information and the color card target imaging information; wherein, the color card calibration imaging information is obtained by calibrating the actual imaging information of the color card using a color correction matrix;

[0070] In some embodiments of this disclosure, the comparison parameters include at least: the average value of the first color deviation between the color chart calibration imaging information and the color chart target imaging information, the maximum value of the first color deviation between the color chart calibration imaging information and the color chart target imaging information, the average value of the second color deviation between the color chart calibration imaging information and the color chart target imaging information, the maximum value of the second color deviation between the color chart calibration imaging information and the color chart target imaging information, and the saturation of the color chart calibration image. Specifically, based on the color chart calibration imaging information and the color chart target imaging information, the average and maximum values ​​of the color deviation Δc (excluding brightness), the average and maximum values ​​of the color deviation ΔE (including brightness), and the saturation can be calculated. Specifically, the comparison parameters include at least: the average and maximum values ​​of Δc between the color chart calibration imaging information and the color chart target imaging information, the average and maximum values ​​of ΔE between the color chart calibration imaging information and the color chart target imaging information, and the saturation of the color chart calibration image.

[0071] It should be noted that the parameter requirements for the comparison parameters are pre-set requirements for the above five specific parameters. For example, the average and maximum values ​​of Δc between the color chart calibration imaging information and the color chart target imaging information should be as close to 0 as possible; the average and maximum values ​​of ΔE between the color chart calibration imaging information and the color chart target imaging information should be as close to 0 as possible; and the saturation value of the color chart calibration imaging needs to be within a preset range, for example, it can be less than or equal to 125. Those skilled in the art will understand that the above parameter requirements are merely examples and are not intended to limit the scope of this disclosure. Specific parameter requirements should be set according to actual color correction requirements.

[0072] S106. Based on the actual imaging information of the color card, the target imaging information of the color card, and the parameter requirements of the comparison parameters, an optimization function is generated with the color correction matrix as the quantity to be optimized. The optimization function takes the deviation between the actual value of the comparison parameter determined based on the color correction matrix and the parameter requirements of the comparison parameter as the optimization objective.

[0073] S108. Based on the optimization function, determine the optimization result of the color correction matrix that satisfies the optimization objective, and obtain the color correction result.

[0074] As can be seen from the above steps, the color correction method provided in this embodiment uses the color correction matrix as the quantity to be optimized. Combined with the parameter requirements of the comparison parameters between the color card calibration imaging information and the color card target imaging information obtained after calibration based on the color correction matrix, an optimization function is generated with the deviation between the actual value of the comparison parameter and the parameter requirements as the optimization objective. The optimization is performed to determine the color correction matrix with the smallest deviation as the color correction result. This allows the actual value of the comparison parameter between the color card calibration imaging and the color card target imaging obtained based on the color correction result to meet the parameter requirements without multiple manual adjustments. This eliminates the need for a large investment of manpower and also improves the speed of color correction.

[0075] Furthermore, compared to the existing technology that involves manually adjusting the color correction matrix multiple times to determine whether the color card calibration image matches the target image of the color card, the optimal solution of the color correction matrix is ​​determined by solving the optimization function. Under the premise of meeting the parameter requirements of the comparison parameters, the obtained color correction result is more accurate.

[0076] In a specific embodiment of this disclosure, the implementation process of S106 is as follows: Figure 2 As shown, it includes the following steps:

[0077] S202, calibrate the actual imaging information of the color card based on the color correction matrix to obtain the color card calibration imaging information;

[0078] S204, compare the color card calibration imaging information and the color card target imaging information to obtain the actual value of the comparison parameter between the color card calibration imaging information and the color card target imaging information;

[0079] S206, Based on the actual value of the comparison parameter and the parameter requirements of the comparison parameter, determine the deviation between the actual value of the comparison parameter and the parameter requirements of the comparison parameter;

[0080] It should be noted that the deviation between the actual value of the comparison parameter and the parameter requirement of the comparison parameter includes: the deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation, and the deviation of saturation.

[0081] S208 uses the weighted result of the deviation as the dependent variable and the color correction matrix as the independent variable to construct the mapping relationship between the dependent and independent variables, thus obtaining the optimization function; where the weighted result of the deviation is obtained after weighting the deviation.

[0082] It should be noted that the color correction matrix contains 9 vectors in a 3×3 matrix. Using these 9 vectors as independent variables and the weighted result of the deviation as the dependent variable, a mapping relationship can be established between the dependent variable and the nine independent variables, which can be used as an optimization function.

[0083] In some embodiments of this disclosure, the implementation process of S106 is as follows: Figure 3 As shown, in Figure 2 In addition to the above, the following steps are also included:

[0084] S302, based on the preset first weight parameter, second weight parameter, third weight parameter, fourth weight parameter and fifth weight parameter, the deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation and the saturation deviation are weighted and processed to obtain the weighted result of the deviation.

[0085] Specifically, according to the following formula, based on the preset first weight parameter, second weight parameter, third weight parameter, fourth weight parameter, and fifth weight parameter, the deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation, and the saturation deviation are weighted and processed to obtain the weighted result of the deviation:

[0086] K = k1X1 + k2X2 + k3X3 + k4X4 + k5X5

[0087] Where K represents the weighted result of the deviation; k1 represents the first weight parameter; X1 represents the deviation of the average value of the first color deviation; k2 represents the second weight parameter; X2 represents the deviation of the maximum value of the first color deviation; k3 represents the third weight parameter; X3 represents the deviation of the average value of the second color deviation; k4 represents the fourth weight parameter; X4 represents the deviation of the maximum value of the second color deviation; k5 represents the fifth weight parameter; and X5 represents the saturation deviation.

[0088] It should be noted that k1+k2+k3+k4+k5=1, the first weight parameter, the second weight parameter, the third weight parameter, the fourth weight parameter, and the fifth weight parameter are preset. The value of each weight parameter can be set according to the importance of the parameter corresponding to each weight parameter. For example, if the saturation deviation is considered to be more important to the weighted result of the deviation, the value of k5 can be set to be larger.

[0089] In some embodiments of this disclosure, the implementation process of S108 is as follows: Figure 4 As shown, it includes the following steps:

[0090] S402, using the random segmentation estimation method, determine the minimum value of the weighted result of the deviation, and obtain the color correction matrix corresponding to the minimum value of the weighted result of the deviation;

[0091] S404 determines the color correction matrix corresponding to the minimum value of the weighted result of the deviation as the color correction result.

[0092] Since the deviation between the actual value of the comparison parameter and the parameter requirement is weighted and used as the dependent variable of the optimization function, it is desirable to minimize the weighted result of the deviation. Therefore, optimizing the optimization function can be transformed into finding the minimum value of the weighted result of the deviation, and the color correction matrix at this point is the optimal result of the optimization.

[0093] In practice, the optimization function contains nine independent variables. To determine the minimum point of the dependent variable, an exhaustive search method or the stochastic partitioning estimation method provided in this disclosure can be used. The principle of the stochastic partitioning estimation method is to work backwards, similar to a talent show, through multiple rounds of comparison, to find the optimal value. Using the partitioning estimation method, a rough estimate of the value of K representing a given set is first obtained. Then, this value is compared with the results of neighboring data in the nine-dimensional space containing that given set, and the optimal value is retained. This process is repeated, comparing with surrounding representative locations until only one given set remains in the entire range; this is the optimal solution to the optimization function.

[0094] Accordingly, in some embodiments of this disclosure, the implementation process of S402 is as follows: Figure 5 As shown, it includes the following steps:

[0095] S502, based on the dimension of the color correction matrix, establish a multidimensional space with multiple vectors in the color correction matrix as independent variables and the weighted result of the deviation as the dependent variable;

[0096] It should be noted that in the embodiments of this disclosure, the dimension of the color correction matrix can be 3×3, establishing a 9-dimensional space with the 9 vectors in the color correction matrix as independent variables and the weighted result of the deviation as the dependent variable.

[0097] S504, randomly divides the multidimensional space into a preset number of subspaces;

[0098] It should be noted that the multidimensional space is randomly and evenly divided into a preset number of subspaces, and this preset number can be any value, such as 100, 254, or 1024. A larger preset number indicates a more refined spatial partition, resulting in a more accurate optimal solution for the optimization function. Therefore, computational complexity should be considered, and the preset number should be set as large as possible, provided that computational resources can support it.

[0099] S506, repeat S508 to S512 until the filtered data point is a unique data point. This unique data point is then identified as the minimum point of the weighted result of the bias.

[0100] S508: Randomly select a data point in any subspace, calculate the weighted result of the deviation of the data point, and obtain the weighted result of the deviation of multiple data points.

[0101] By randomly selecting a data point as the representative of the subspace, the weighted result representing the deviation of the subspace is obtained, eliminating the need to calculate for all data points in the subspace, thus greatly reducing the amount of computation and improving computational efficiency.

[0102] S510, compare the weighted results of the deviations between any two adjacent data points, and filter out the data points with larger weighted deviation results to obtain the filtered data points; wherein, the number of filtered data points is half the number of data points before filtering;

[0103] S512 regenerates the subspace centered on the filtered data points.

[0104] The above steps demonstrate that the data from the nine dimensions is divided into a predetermined number of nine-dimensional solids. A random point within each nine-dimensional solid represents the weighted level of the deviation of that solid. The K-value represented by each random point is compared (PK) with the K-value of any random point in a nearby nine-dimensional solid; the random point with the smaller K-value is retained. Each random point is only compared once. In each round of comparison, only 50% of the data points remain. Using these retained data points as the center, the data is re-divided into nine-dimensional solids of the same size as the initial nine-dimensional solid. Random points are then randomly selected from the newly generated nine-dimensional solids to represent each solid, and the comparison is repeated until only one random data point remains.

[0105] Since the above process utilizes a random point representing a subspace, this random point may be far from the average level of the subspace and thus cannot actually represent it. Consequently, the obtained unique data point may not be the optimal solution of the optimization function, requiring verification of this unique data point. In some embodiments of this disclosure, the implementation process of S402 is as follows: Figure 6 As shown, in Figure 5 In addition to the above, the following steps are also included:

[0106] S602, perform minimum value verification on unique data points;

[0107] S604 If the verification fails, a new unique data point will be determined.

[0108] That is, a minimum value check is performed on the unique data point. If the check is successful, the unique data point is determined as the minimum value of the weighted result of the deviation. If the check fails, S506 to S512 are executed again to obtain a new unique data point. The minimum value check is then performed on the newly determined unique data point until the check is successful.

[0109] In some embodiments of this disclosure, during the screening process, the top few data points, including the unique data point, with the smallest weighted deviation are determined, for example, the top 4 data points. Two more data points are randomly selected from the vicinity of these 4 data points, for a total of 8 data points. The K value of these 8 data points is calculated and compared with the K value of the unique data point. If the K value of the unique data point is the smallest, the verification is successful; otherwise, the verification fails.

[0110] The above verification process ensures the accuracy of the minimum value determined by random segmentation estimation, thereby reducing the amount of computation while guaranteeing the accuracy of the optimal solution.

[0111] To better illustrate the color correction method provided in this disclosure, a specific example is provided for further explanation. In this specific example, the color correction matrix is ​​as follows:

[0112]

[0113] Among them, A1+A2+A3=1; B1+B2+B3=1; C1+C2+C3=1.

[0114] And X1 = Δc mean -0, X2 = Δc max -0, X3 = ΔE mean -0, X4 = ΔE max -0, X5 = (125-S) / 100.

[0115] Where, Δc mean The average value of Δc represents the difference between the color chart calibration imaging information and the color chart target imaging information; Δc max The maximum value of Δc represents the relationship between the color chart calibration imaging information and the color chart target imaging information; ΔE mean The average value of ΔE between the color chart calibration imaging information and the color chart target imaging information; ΔE max The maximum value of ΔE represents the relationship between the color chart calibration imaging information and the color chart target imaging information; S represents the saturation of the color chart calibration imaging.

[0116] And Δc mean Δc max ΔE mean ΔE max Each parameter value of S is related to the values ​​of nine parameters: A1, A2, A3, B1, B2, B3, C1, C2, and C3. In this specific example, k1 = k2 = k3 = k4 = k5 = 0.2, which means that the value of K is determined by the values ​​of these nine parameters: A1, A2, A3, B1, B2, B3, C1, C2, and C3.

[0117] The specific process for determining the minimum value of K is as follows: Figure 7 As shown, it includes:

[0118] Establish a 9-dimensional space;

[0119] Divide the 9-dimensional space into 100 9 Subspace;

[0120] Randomly select a data point in each subspace to represent that subspace, and calculate the K value for multiple data points;

[0121] PK selects the 50% of data points with smaller K values;

[0122] Determine if the selected data points are unique:

[0123] If not unique, regenerate a subspace of the same size as the initial segmentation, centered on the selected data point; then randomly select a data point in each subspace to represent that subspace, calculate the K value of multiple data points, and proceed with the following steps.

[0124] If unique, randomly select 8 data points from the vicinity of the first four points in the sort with the smallest K value, calculate the K value of the 8 data points, and compare it with the K value of the unique data point:

[0125] If the K value of the unique data point is smaller, output the values ​​of the nine parameters A1, A2, A3, B1, B2, B3, C1, C2, and C3 corresponding to the unique data point;

[0126] If the K value of the unique data point is not the smallest, repeat the above steps until the values ​​of the nine parameters that meet the judgment conditions are output.

[0127] As can be seen from the above process, this unique data point can approximately represent the optimal solution of the optimization function, thus obtaining the optimal value of the color matrix during color correction.

[0128] Based on the same inventive concept, this disclosure also provides a color correction device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the method embodiments described above, the implementation of this device embodiment can refer to the implementation of the method embodiments described above, and repeated details will not be repeated.

[0129] Figure 8 This diagram illustrates a color correction device according to an embodiment of the present disclosure, such as... Figure 8 As shown, the device includes:

[0130] The imaging information acquisition module 801 is used to acquire the actual imaging information of the color chart and the target imaging information of the color chart.

[0131] The parameter requirement acquisition module 802 is used to acquire the parameter requirements of the comparison parameters between the color card calibration imaging information and the color card target imaging information; wherein, the color card calibration imaging information is obtained by calibrating the actual imaging information of the color card using a color correction matrix;

[0132] The optimization function generation module 803 is used to generate an optimization function based on the actual imaging information of the color card, the target imaging information of the color card, and the parameter requirements of the comparison parameters, using the color correction matrix as the quantity to be optimized; the optimization function takes the deviation between the actual value of the comparison parameter determined based on the color correction matrix and the parameter requirements of the comparison parameter as the optimization objective; and

[0133] The optimization result determination module 804 is used to determine the optimization result of the color correction matrix that satisfies the optimization objective based on the optimization function, and thus obtain the color correction result.

[0134] It should be noted that the imaging information acquisition module 801, parameter requirement acquisition module 802, optimization function generation module 803, and optimization result determination module 804 mentioned above correspond to S102 to S108 in the method embodiment. The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above method embodiment. It should be noted that the above modules, as part of the device, can be executed in a computer system such as a set of computer-executable instructions.

[0135] In some embodiments of this disclosure, the comparison parameters include at least: the average value of the first color deviation between the color chart calibration imaging information and the color chart target imaging information, the maximum value of the first color deviation between the color chart calibration imaging information and the color chart target imaging information, the average value of the second color deviation between the color chart calibration imaging information and the color chart target imaging information, the maximum value of the second color deviation between the color chart calibration imaging information and the color chart target imaging information, and the saturation of the color chart calibration image.

[0136] Accordingly, the optimization function generation module 803 is specifically used to: calibrate the actual imaging information of the color card based on the color correction matrix to obtain the color card calibration imaging information;

[0137] By comparing the color chart calibration imaging information and the color chart target imaging information, the actual values ​​of the comparison parameters between the color chart calibration imaging information and the color chart target imaging information are obtained.

[0138] Based on the actual values ​​of the comparison parameters and the parameter requirements of the comparison parameters, determine the deviation between the actual values ​​of the comparison parameters and the parameter requirements of the comparison parameters;

[0139] Using the weighted result of the deviation as the dependent variable and the color correction matrix as the independent variable, a mapping relationship between the dependent and independent variables is constructed to obtain the optimization function; where the weighted result of the deviation is obtained after weighting the deviation.

[0140] In some embodiments of this disclosure, the deviation between the actual value of the comparison parameter and the parameter requirement of the comparison parameter includes: the deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation, and the deviation of saturation.

[0141] Accordingly, the optimization function generation module 803 is also specifically used to: perform weighted processing on the deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation, and the deviation of saturation based on the preset first weight parameter, second weight parameter, third weight parameter, fourth weight parameter, and fifth weight parameter, to obtain the weighted result of the deviation.

[0142] The optimization function generation module 803 is specifically used to: according to the following formula, based on the preset first weight parameter, second weight parameter, third weight parameter, fourth weight parameter, and fifth weight parameter, to perform weighted processing on the deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation, and the saturation deviation, to obtain the weighted result of the deviation:

[0143] K = k1X1 + k2X2 + k3X3 + k4X4 + k5X5

[0144] Where K represents the weighted result of the deviation; k1 represents the first weight parameter; X1 represents the deviation of the average value of the first color deviation; k2 represents the second weight parameter; X2 represents the deviation of the maximum value of the first color deviation; k3 represents the third weight parameter; X3 represents the deviation of the average value of the second color deviation; k4 represents the fourth weight parameter; X4 represents the deviation of the maximum value of the second color deviation; k5 represents the fifth weight parameter; and X5 represents the saturation deviation.

[0145] In some embodiments of this disclosure, the optimization result determination module 804 is specifically used to: determine the minimum value of the weighted result of the deviation using a random segmentation estimation method, and obtain the color correction matrix corresponding to the minimum value of the weighted result of the deviation; and determine the color correction matrix corresponding to the minimum value of the weighted result of the deviation as the color correction result.

[0146] In some embodiments of this disclosure, the optimization result determination module 804 is specifically used for: establishing a multidimensional space based on the dimension of the color correction matrix, with multiple vectors in the color correction matrix as independent variables and the weighted result of the deviation as the dependent variable; randomly dividing the multidimensional space into a preset number of subspaces; repeating the following steps until the filtered data points are unique data points, and determining the unique data points as the minimum point of the weighted result of the deviation: randomly selecting a data point in any subspace, calculating the weighted result of the deviation of the data point, and obtaining the weighted result of the deviation of multiple data points; comparing the weighted results of the deviation of any two adjacent data points, filtering out the data points with larger weighted results of the deviation, and obtaining the filtered data points; wherein the number of filtered data points is half the number of data points before filtering; and regenerating the subspace with the filtered data points as the center.

[0147] In some embodiments of this disclosure, the optimization result determination module 804 is further specifically used for: performing a minimum value verification on the unique data point; if the verification fails, re-determining the unique data point.

[0148] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0149] The following reference Figure 9 To describe an electronic device 900 according to such an embodiment of the present disclosure. Figure 9 The electronic device 900 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0150] like Figure 9 As shown, the electronic device 900 is manifested in the form of a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: at least one processing unit 910, at least one storage unit 920, and a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910).

[0151] The storage unit stores program code that can be executed by the processing unit 910, causing the processing unit 910 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 910 can perform the following steps of the above method embodiments:

[0152] Acquire the actual imaging information of the color chart and the target imaging information of the color chart;

[0153] The parameter requirements for the comparison parameters between the color chart calibration imaging information and the color chart target imaging information are as follows: The color chart calibration imaging information is obtained by calibrating the actual imaging information of the color chart using a color correction matrix.

[0154] Based on the actual imaging information of the color chart, the target imaging information of the color chart, and the parameter requirements of the comparison parameters, an optimization function is generated with the color correction matrix as the quantity to be optimized. The optimization function takes the deviation between the actual value of the comparison parameter determined based on the color correction matrix and the parameter requirements of the comparison parameter as the optimization objective.

[0155] Based on the optimization function, the optimization result of the color correction matrix that satisfies the optimization objective is determined, and the color correction result is obtained.

[0156] Storage unit 920 may include readable media in the form of volatile storage units, such as random access memory (RAM) 9201 and / or cache memory 9202, and may further include read-only memory (ROM) 9203.

[0157] Storage unit 920 may also include a program / utility 9204 having a set (at least one) program module 9205, such program module 9205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0158] Bus 930 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0159] Electronic device 900 can also communicate with one or more external devices 940 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 900, and / or with any device that enables electronic device 900 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 950. Furthermore, electronic device 900 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 960. As shown, network adapter 960 communicates with other modules of electronic device 900 via bus 930. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0160] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0161] In particular, according to embodiments of this disclosure, the process described above with reference to the flowchart can be implemented as a computer program product, which includes a computer program that, when executed by a processor, implements the above-described color correction method.

[0162] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the methods described above is stored thereon. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code, which, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0163] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0164] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0165] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0166] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0167] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0168] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0169] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0170] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A color correction method, characterized in that, include: Acquire the actual imaging information of the color chart and the target imaging information of the color chart; The parameter requirements for the comparison parameters between the color chart calibration imaging information and the color chart target imaging information are as follows: wherein, the color chart calibration imaging information is obtained by calibrating the actual imaging information of the color chart using a color correction matrix; the comparison parameters include at least: the average value of the first color deviation between the color chart calibration imaging information and the color chart target imaging information, the maximum value of the first color deviation between the color chart calibration imaging information and the color chart target imaging information, the average value of the second color deviation between the color chart calibration imaging information and the color chart target imaging information, the maximum value of the second color deviation between the color chart calibration imaging information and the color chart target imaging information, and the saturation of the color chart calibration imaging; Based on the actual imaging information of the color chart, the target imaging information of the color chart, and the parameter requirements of the comparison parameters, an optimization function is generated with the color correction matrix as the quantity to be optimized; wherein, the optimization function takes the deviation between the actual value of the comparison parameters determined based on the color correction matrix and the parameter requirements of the comparison parameters as the optimization objective; Specifically, based on the actual imaging information of the color chart, the target imaging information of the color chart, and the parameter requirements of the comparison parameters, an optimization function is generated using the color correction matrix as the quantity to be optimized, including: The actual imaging information of the color card is calibrated based on the color correction matrix to obtain the calibrated imaging information of the color card. By comparing the color chart calibration imaging information and the color chart target imaging information, the actual values ​​of the comparison parameters between the color chart calibration imaging information and the color chart target imaging information are obtained; Based on the actual value of the comparison parameter and the parameter requirements of the comparison parameter, determine the deviation between the actual value of the comparison parameter and the parameter requirements of the comparison parameter; Using the weighted result of the deviation as the dependent variable and the color correction matrix as the independent variable, a mapping relationship between the dependent variable and the independent variable is constructed to obtain the optimization function; The weighted result of the deviation is obtained by weighting the deviation. Based on the optimization function, the optimization result of the color correction matrix that satisfies the optimization objective is determined, and the color correction result is obtained.

2. The color correction method according to claim 1, characterized in that, The deviation between the actual value of the comparison parameter and the parameter requirement of the comparison parameter includes: The deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation, and the deviation of the saturation.

3. The color correction method according to claim 2, characterized in that, After determining the deviation between the actual value of the comparison parameter and the parameter requirement of the comparison parameter, based on the actual imaging information of the color card, the target imaging information of the color card, and the parameter requirement of the comparison parameter, an optimization function is generated using the color correction matrix as the quantity to be optimized, and the function further includes: Based on the preset first weight parameter, second weight parameter, third weight parameter, fourth weight parameter and fifth weight parameter, the deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation and the deviation of the saturation are weighted to obtain the weighted result of the deviation.

4. The color correction method according to claim 3, characterized in that, According to the following formula, based on preset first weight parameters, second weight parameters, third weight parameters, fourth weight parameters, and fifth weight parameters, the deviation of the average value of the first color deviation, the deviation of the maximum value of the first color deviation, the deviation of the average value of the second color deviation, the deviation of the maximum value of the second color deviation, and the deviation of the saturation are weighted and processed to obtain the weighted result of the deviation: in, This represents the weighted result of the aforementioned deviation; Indicates the first weight parameter; This represents the deviation of the average value of the first color deviation; This represents the second weighting parameter; The deviation represents the maximum value of the first color deviation; This represents the third weighting parameter; This represents the deviation of the average value of the second color deviation; This represents the fourth weighting parameter; The deviation representing the maximum value of the second color deviation; This represents the fifth weighting parameter; This indicates the deviation in saturation.

5. The color correction method according to claim 1, characterized in that, Based on the optimization function, the optimization result of the color correction matrix that satisfies the optimization objective is determined, and the color correction result is obtained, including: By using a random segmentation estimation method, the minimum value of the weighted result of the deviation is determined, and the color correction matrix corresponding to the minimum value of the weighted result of the deviation is obtained. The color correction matrix corresponding to the minimum value of the weighted result of the deviation is determined as the color correction result.

6. The color correction method according to claim 5, characterized in that, Using a random segmentation estimation method, the minimum value of the weighted result of the bias is determined, including: Based on the dimension of the color correction matrix, a multidimensional space is established with multiple vectors in the color correction matrix as independent variables and the weighted result of the deviation as the dependent variable. The multidimensional space is randomly divided into a predetermined number of subspaces; Repeat the following steps until the filtered data points are unique data points, and determine the unique data points as the minimum points of the weighted results of the deviation: Randomly select a data point in any of the subspaces, calculate the weighted result of the deviation of the data point, and obtain the weighted result of the deviation of multiple data points; By comparing the weighted results of the deviations between any two adjacent data points, the data points with larger weighted results of the deviations are filtered out to obtain the filtered data points; wherein, the number of filtered data points is half the number of data points before filtering. The subspace is regenerated using the filtered data points as the center.

7. The color correction method according to claim 6, characterized in that, After determining the unique data point as the minimum point of the weighted result of the deviation, the method of determining the minimum value of the weighted result of the deviation using random segmentation estimation further includes: Perform a minimum value check on the unique data point; If the verification fails, the unique data point will be re-determined.

8. A color correction device, characterized in that, include: The imaging information acquisition module is used to acquire the actual imaging information of the color chart and the target imaging information of the color chart; The parameter requirement acquisition module is used to acquire the parameter requirements for the comparison parameters between the color chart calibration imaging information and the color chart target imaging information; wherein, the color chart calibration imaging information is obtained by calibrating the actual imaging information of the color chart using a color correction matrix; the comparison parameters include at least: the average value of the first color deviation between the color chart calibration imaging information and the color chart target imaging information, the maximum value of the first color deviation between the color chart calibration imaging information and the color chart target imaging information, the average value of the second color deviation between the color chart calibration imaging information and the color chart target imaging information, the maximum value of the second color deviation between the color chart calibration imaging information and the color chart target imaging information, and the saturation of the color chart calibration imaging; The optimization function generation module is used to generate an optimization function based on the actual imaging information of the color chart, the target imaging information of the color chart, and the parameter requirements of the comparison parameters, using the color correction matrix as the quantity to be optimized; the optimization function takes the deviation between the actual value of the comparison parameter determined based on the color correction matrix and the parameter requirements of the comparison parameter as the optimization objective; and The optimization result determination module is used to determine the optimization result of the color correction matrix that satisfies the optimization objective based on the optimization function, and to obtain the color correction result; The optimization function generation module is specifically used to: calibrate the actual imaging information of the color card based on the color correction matrix to obtain the color card calibration imaging information; By comparing the color chart calibration imaging information and the color chart target imaging information, the actual values ​​of the comparison parameters between the color chart calibration imaging information and the color chart target imaging information are obtained; Based on the actual value of the comparison parameter and the parameter requirements of the comparison parameter, determine the deviation between the actual value of the comparison parameter and the parameter requirements of the comparison parameter; Using the weighted result of the deviation as the dependent variable and the color correction matrix as the independent variable, a mapping relationship between the dependent variable and the independent variable is constructed to obtain the optimization function; The weighted result of the deviation is obtained by weighting the deviation.

9. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the color correction method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the color correction method according to any one of claims 1 to 7.

Images