A method for correcting the color measurement results of a color difference meter

Abstract

The present application relates to the technical field of chromatic aberration correction, in particular to a color difference instrument color measurement result correction method, comprising the following steps: obtaining sample Lab value, calculating coordinate index correction matrix, obtaining original coefficient, inputting segmented regression, extracting features, calculating deviation, weighting to obtain regional offset, calling offset adaptive correction, neighborhood compensation channel offset, obtaining boundary previous value, boundary verification limiting amplitude, recalling processing to obtain output result, updating knowledge base, accumulating deviation, increment counting, and optimizing color measurement precision, in the present application, through regional analysis and segmented linear regression model, the accuracy and flexibility of chromatic aberration correction are improved, through weighting processing and neighborhood compensation technology, the inaccuracy caused by large error is effectively reduced, the corrected measurement value is ensured to be within a reasonable range, the unstable correction result of the traditional method is avoided, the adaptability is strong, the fault tolerance is strong, the color difference instrument measurement consistency and reliability are improved, and the present application has obvious advantages in complex chromatic aberration correction scene.

Description

Technical Field

[0001] This invention relates to the field of color difference correction technology, and in particular to a method for correcting color measurement results from a colorimeter. Background Technology

[0002] The field of color difference correction technology involves technologies related to detecting and adjusting the difference between the surface color of a measured object and a standard color. The core aspects include acquiring color measurement data through a colorimeter, expressing the measured values ​​using a specific color space, comparing the differences between the sample and the standard in multiple color channels, and analyzing and correcting the measured data based on the comparison results. This technical field systematically covers optical detection principles, the application of color space models, measurement data acquisition processes, and color difference data correction methods.

[0003] The traditional method for correcting colorimeter measurement results involves manually comparing or looking up values ​​in a preset table after the colorimeter has completed the color measurement. This method involves performing multiple repeated measurements under specific light conditions and manually adjusting the color channel readings based on a manually selected reference sample, or calculating the linear relationship between the sensor's three-channel output values ​​and the human eye's perceived values ​​using the least squares method to convert sensor readings into color space values. However, even after such conversion, there will still be a large nonlinear error term.

[0004] Existing technologies rely on manual comparison and preset numerical tables for correction, which are easily affected by human factors and have low processing efficiency. Traditional linear regression models fail to effectively handle nonlinear errors in color channels, resulting in limited correction effects. The lack of detailed analysis and dynamic adjustment for different measurement areas makes it difficult to flexibly correct for changes in measured values, thus affecting the accuracy of the correction results. In environments with high precision requirements, existing technologies cannot meet the diverse needs of color difference correction, leading to unstable correction effects and difficulty in achieving ideal results in complex environments. Summary of the Invention

[0005] To address the shortcomings of existing technologies that rely on manual comparison and preset numerical tables for correction, which are susceptible to human error and have low processing efficiency, and the limitations of traditional linear regression models in handling nonlinear errors in color channels, resulting in limited correction effectiveness, this invention provides a method for correcting colorimeter measurement results, comprising the following steps:

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for correcting colorimetric measurement results using a colorimeter, comprising the following steps:

[0007] S1: Obtain the Lab color space values ​​of the sample to be corrected using a colorimeter, calculate the grid's horizontal and vertical coordinates based on the values ​​of the a and b channels, analyze the original correction coefficients by indexing the pre-constructed L, a, and b correction matrices, and obtain the Lab measurement values ​​to be corrected.

[0008] S2: Based on the Lab measurement value to be corrected, perform regional analysis, input the Lab measurement value to be corrected into the piecewise linear regression model to extract regional features, calculate the deviation between the current measured Lab value and the original standard value in the corresponding grid, and perform weighted processing in combination with the measurement times of the grid in the counting matrix to obtain the regional correction offset.

[0009] S3: Call the regionalized correction offset to perform adaptive correction processing on the measurement results, perform nonlinear weight allocation according to the regionalized correction offset, and use neighborhood compensation technology to obtain correction parameters when the corresponding cell value of the counting matrix is ​​zero, perform channel offset correction, and obtain the correction value before boundary verification.

[0010] S4: Perform color gamut boundary constraint verification processing using the pre-verification correction value. When the corrected ab value exceeds the current grid boundary, limit the correction range to the grid boundary range and perform callback processing on the out-of-bounds value to obtain the output result after boundary verification.

[0011] As a further embodiment of the present invention, the Lab measurement value to be corrected includes a luminance component, a red-green luminance component, and a yellow-blue luminance component; the regionalized correction offset includes a local difference value, a regional weighted value, and a feature mapping value; the correction value before boundary verification includes a channel offset, a compensation coefficient, and an assigned weight; and the output result after boundary verification includes a boundary constraint value, an amplitude limit value, and a callback correction value.

[0012] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0013] S101: Obtain the Lab color space values ​​of the sample measured by the colorimeter, call the values ​​of channel a and channel b, calculate the horizontal and vertical coordinates of the two in the coordinate plane, and organize the horizontal and vertical coordinates with the L channel values ​​of the sample to generate coordinate positioning values.

[0014] S102: Based on the coordinate positioning value, call the pre-constructed L, a, b correction matrix, compare the data boundaries of the units in the matrix item by item according to the horizontal and vertical coordinate positions, filter the corresponding matrix unit data, and then compare the filtering results with the L channel values ​​item by item to unify the format and obtain the matrix index value.

[0015] S103: Based on the matrix index value, for the original correction coefficients in the correction matrix, call the Lab color space values ​​of the sample to perform weighted calculations item by item, combine the weighted correction coefficients with the original values ​​of the sample to generate the Lab measurement values ​​to be corrected.

[0016] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0017] S201: Based on the Lab measurement values ​​to be corrected, the measurement values ​​are sequentially input into the piecewise linear regression model. The values ​​are fitted according to the piecewise function coefficients within the interval. The corresponding parameters are extracted according to the slope and intercept of the fitted curve in the differential interval, and the regional piecewise feature coefficients are generated.

[0018] The regional segmentation characteristic coefficients refer to the slope and intercept obtained by piecewise linear regression fitting, which are used to characterize the correspondence between measured values ​​and standard values ​​within the differentiated intervals.

[0019] S202: Call the segmented feature coefficients of the region and compare them one by one with the original standard values ​​in the grid. Based on the difference between the current measured Lab value and the standard value, summarize the results using the difference accumulation method, and summarize the accumulated difference into a calculable index to obtain the grid difference quantity.

[0020] The grid difference refers to the cumulative sum of the differences between the measured value and the standard value, which is used to reflect the overall degree of deviation within a single grid.

[0021] S203: Based on the grid difference, weights are set according to the number of measurements corresponding to the grids in the counting matrix. The difference is weighted and calculated, and the weighted result is converted into an overall correction value to obtain the regional correction offset.

[0022] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0023] S301: Based on the regionalized correction offset, the measurement results are compared point by point. The difference between the grid point value of the measurement matrix and the regionalized correction offset is calculated. In the difference calculation, the values ​​are extracted according to the position coordinates and weighted and accumulated to generate the difference offset coefficient.

[0024] S302: Call the difference offset coefficient to perform nonlinear weight allocation. In the weight allocation, the cells with zero grid points in the counting matrix are compensated by the values ​​of the adjacent non-zero grid points. The compensation value is superimposed with the original allocation value and normalized to obtain the compensation weight value.

[0025] S303: Call the compensation weight value to perform channel offset correction. In the correction, the compensation weight and the channel offset parameter are superimposed one by one, and the channel offset parameter is redistributed in the superposition matrix to obtain the correction value before boundary verification.

[0026] As a further aspect of the present invention, the difference offset coefficient refers to the difference quantization coefficient obtained by comparing the grid point values ​​of the measurement matrix with the regionalized correction offset point by point and then weighting and accumulating them.

[0027] The normalization process adopts the proportional normalization method, which is to divide the compensation weight value by the sum of the compensation weight values.

[0028] The compensation weight value refers to the weight parameter obtained after performing neighbor value compensation and normalization on zero-value grid points in nonlinear weight allocation.

[0029] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0030] S401: Based on the boundary verification correction value and the current grid boundary range, determine whether the corrected ab value exceeds the boundary, compare the corrected ab value with the upper and lower limits of the grid boundary, mark the out-of-bounds data points, extract their corresponding coordinate information, and generate an out-of-bounds coordinate set;

[0031] S402: Call the grid position coordinates in the out-of-bounds coordinate set, re-limit the range of the ab values ​​that exceed the boundary according to the upper and lower limits of the boundary, adjust the difference of the out-of-bounds part to the critical position, recalculate the distribution range of the ab values ​​after the limit is set, and obtain the boundary interception value range.

[0032] S403: Based on the correction results in the boundary-truncation numerical range, integrate the ab values ​​within the boundary and the critical point, merge the adjusted data with the data that has not exceeded the boundary, and obtain the boundary verification output result.

[0033] As a further aspect of the present invention, the method further includes step S5:

[0034] S5: Based on the output results after the boundary verification, update and maintain the correction knowledge base, calculate the newly added Lab deviation and accumulate it to the Lab correction matrix of the corresponding grid coordinates, increment the value of the corresponding position of the counting matrix, and obtain the colorimeter color measurement accuracy optimization result.

[0035] The optimized results of the colorimeter colorimeter measurement accuracy include the cumulative deviation, the increment of the correction matrix, and the updated value of the counting matrix.

[0036] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0037] S501: Based on the boundary verification output result, obtain the value of the corresponding color block in the correction knowledge base, detect the Lab deviation of the newly added measurement point, calculate the difference between the newly added deviation value and the existing benchmark Lab value, record the deviation value according to the difference and update the correction knowledge base, and generate the newly added Lab deviation value.

[0038] S502: Call the newly added Lab deviation value, update the Lab correction matrix for the corresponding grid coordinates in the correction knowledge base, add the deviation value to the value at the corresponding position in the matrix, and generate the cumulative correction value of the grid coordinates;

[0039] S503: Based on the cumulative correction value of the grid coordinates, find the corresponding coordinate point in the counting matrix, increment the count value, update the counting matrix, and then store the correction matrix and the counting matrix together to generate the colorimeter color measurement accuracy optimization result.

[0040] As a further aspect of the present invention, the newly added Lab deviation value refers to the difference between the newly added measurement point and the benchmark Lab value in the correction knowledge base;

[0041] The cumulative correction value of the grid coordinates refers to the sum of the original Lab deviation values ​​of the corresponding grid coordinates in the correction matrix.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] This invention improves the accuracy and flexibility of color difference correction through regional analysis and piecewise linear regression models. Weighted processing and neighborhood compensation techniques effectively reduce inaccuracies caused by large errors and ensure that corrected measurements do not exceed reasonable ranges, avoiding the unstable correction results found in traditional methods. Furthermore, it exhibits strong adaptability to varying light sources and high tolerance to changes in the measurement environment, enhancing the measurement consistency and reliability of the colorimeter, with significant advantages, particularly in complex color difference correction scenarios. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the steps of the present invention;

[0046] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0047] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0048] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0049] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0050] Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0051] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0052] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0053] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0054] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0055] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0056] Please see Figure 1 This invention provides a method for correcting colorimetric measurement results using a colorimeter, comprising the following steps:

[0057] S1: Obtain the Lab color space values ​​of the sample to be corrected using a colorimeter, calculate the grid's horizontal and vertical coordinates based on the values ​​of the a and b channels, analyze the original correction coefficients by indexing the pre-constructed L, a, and b correction matrices, and obtain the Lab measurement values ​​to be corrected.

[0058] S2: Based on the Lab measurement value to be corrected, perform regional analysis, input the Lab measurement value to be corrected into the piecewise linear regression model to extract regional features, calculate the deviation between the current measured Lab value and the original standard value in the corresponding cell, and perform weighted processing in combination with the measurement times of the cells in the counting matrix to obtain the regional correction offset.

[0059] S3: Call the regionalized correction offset to perform adaptive correction processing on the measurement results, perform nonlinear weight allocation according to the regionalized correction offset, and use the neighborhood compensation technique to obtain correction parameters when the corresponding cell value of the counting matrix is ​​zero, perform channel offset correction, and obtain the correction value before boundary verification.

[0060] S4: Perform color gamut boundary constraint verification by correcting the value before boundary verification. When the corrected ab value exceeds the current grid boundary, the correction range is limited to the grid boundary range, and the out-of-bounds value is processed by callback to obtain the output result after boundary verification.

[0061] S5: Based on the output results after boundary verification, update and maintain the correction knowledge base, calculate the newly added Lab deviation and accumulate it to the Lab correction matrix of the corresponding grid coordinates, increment the value of the corresponding position of the counting matrix, and obtain the colorimeter color measurement accuracy optimization result.

[0062] The Lab measurements to be corrected include luminance components, red-green hue components, and yellow-blue hue components. The regionalized correction offset includes local difference values, regional weighting values, and feature mapping values. The correction values ​​before boundary verification include channel offset, compensation coefficient, and assigned weights. The output results after boundary verification include boundary constraint values, amplitude limit values, and callback correction values. The colorimeter color measurement accuracy optimization results include cumulative deviation, correction matrix increment, and count matrix update values.

[0063] Please see Figure 2 The specific steps of S1 are as follows:

[0064] S101: Obtain the Lab color space values ​​of the sample measured by the colorimeter, call the values ​​of channel a and channel b, calculate the horizontal and vertical coordinates of the two in the coordinate plane, and organize the horizontal and vertical coordinates with the L channel values ​​of the sample to generate coordinate positioning values.

[0065] When obtaining the Lab color space values ​​of a sample from a colorimeter, the colorimeter first reads the surface reflectance spectrum data of the sample and converts it into tristimulus values. Then, the colorimeter's internal calculation logic obtains the corresponding L channel (brightness value), a channel (color axis component values ​​from green to red), and b channel (color axis component values ​​from blue to yellow). In this example, the Lab values ​​of a certain fabric sample are measured as L=62.38, a=-3.52, and b=18.77. Subsequently, the values ​​of the a and b channels are retrieved, with the a channel used as the x-axis value Xa and the b channel as the y-axis value Yb. During coordinate transformation, Xa=-3.52 and Yb=18.77 are mapped to a predetermined unit scale. Using a coordinate grid with units of 0.1, the horizontal coordinate is determined by the negative value of channel a, which places it in the left half-plane, and the vertical coordinate is determined by the positive value of channel b, which places it in the upper half-plane. In this mapping, the two-dimensional coordinates of the sample are located as (-3.5, 18.8). These two-dimensional coordinates are then mapped one-to-one with the sample's L-channel value L = 62.38, forming a set of triplet data: {L, Xa, Yb} = {62.38, -3.5, 18.8}. To ensure subsequent positioning accuracy, the same sample needs to be tested three times for averaging. For example, if the three measured L values ​​are 62.38, 62.42, and 62.36, then the mean L is Lmean = (62.38, -3.5, 18.8). (8 + 62.42 + 62.36) / 3 = 62.387. The mean values ​​of Xa and Yb are calculated using the same method, thus obtaining the mean coordinate positioning values ​​{62.387, -3.51, 18.78}. In this sorting operation, the specific steps for calculating the average value are as follows: first, call the corresponding channel values ​​one by one, perform floating-point addition on the corresponding channel values ​​measured each time and record the sum; then, call the value of the number of measurements n and perform a division operation with the sum to obtain the final mean value. For example, in the example above, the mean value of channel a = (-3.52) + (-3.50) + (-3.51) / 3 = -3.51, and the mean value of channel b = (18.77 + 18.80 + 18.78) / 3 = 18. 0.783. The judgment steps involved in the above calculation are as follows: If a channel value exceeds the preset physical reasonable range (L value 0~100, a value -128~127, b value -128~127), the measurement data is directly discarded, and the measurement is re-measured to supplement the number of valid data sets. For the judgment of "higher" and "lower", for example, for the L channel value, 0≤L<33 can be set as the low brightness range, 33≤L<66 as the medium brightness range, and 66≤L≤100 as the high brightness range. In this example, L=62.387 belongs to the medium brightness range. Finally, when generating the coordinate positioning value, the sorted triplet mean data is called to output the positioning value table entries in a unified format for subsequent matrix comparison.

[0066] Table 1: Original Measurements and Mean Calculations for Sample Lab

[0067]

[0068] As shown in Table 1, by performing floating-point addition and division on the three measurement data, the mean values ​​of the three channels L, a, and b are obtained. The mean value is the stable positioning value of the sample in the Lab color space. Combined with the two-dimensional coordinates (a, b) and the corresponding L value, the final coordinate positioning value is formed.

[0069] S102: Based on the coordinate positioning value, call the pre-constructed L, a, b correction matrix, compare the data boundaries of the cells in the matrix item by item according to the horizontal and vertical coordinate positions, filter the corresponding matrix cell data, and then compare the filtering results with the L channel values ​​item by item to unify the format and obtain the matrix index value.

[0070] Based on the coordinate positioning values ​​{62.387, -3.51, 18.783}, the system first calls three pre-constructed sets of data: the L-channel correction matrix, the a-channel correction matrix, and the b-channel correction matrix. These three matrices are stored in partitions according to the measurement range of the L, a, and b values, respectively. For example, the L-channel matrix is ​​indexed in rows of 5 units, the a-channel matrix in columns of 2 units, and the b-channel matrix in columns of 2 units. The first step in calling the correction matrix data is to directly read the horizontal coordinate Xa = -3.51 and the vertical coordinate Yb = 18.783 from the positioning values, perform interval position determination on Xa, and compare it with the matrix column indices. The boundary values ​​are compared item by item. The column index boundaries of matrix a channel are from -128 to 127, arranged in order with a step size of 2. -4 to -2 correspond to one column. Therefore, -3.51 is greater than -4 and less than -2, so it is determined to fall within the range of that column. At the same time, the same action is performed on Yb, comparing 18.783 with the boundary values ​​of the row indices of matrix b channel. For example, if row indices 18 to 20 form an interval, and 18.783 is within that interval, then it is determined to fall within the range of that row. This determines the specific unit coordinates of the matrix in the a and b dimensions. Then, the same judgment is performed using the L value = 62.387. For example, if the L channel matrix has rows from 60 to 65, then 65... The next row is 70. Within this value range, 62.387 falls within the interval of 60 to 65. This determines the corresponding row index in matrix L. When filtering corresponding matrix cell data, the system retrieves cells from the three-dimensional matrix structure that satisfy the conditions of intervals a, b, and L. The original correction vector in these cells is temporarily stored for subsequent calculations. During the filtering process, the comparison operation is specifically performed as follows: the currently measured value is compared with the lower boundary of each interval in the matrix for a greater than or equal to condition, and then with the upper boundary for a less than condition. Only cells that simultaneously satisfy both conditions are selected. If the value is exactly equal to the upper boundary of an interval, then... When making a judgment, the data is assigned to the next interval. After the horizontal and vertical coordinates are judged, the selected matrix cell data is compared with the current L value with the same data precision. If the matrix cell contains multiple correction coefficient groups, the data needs to be formatted into a unified array representation, such as arranging it as a ternary array [Lc, ac, bc]. At this time, the matrix index value generation action is to call the determined row index and column index and combine them into a unique index identifier, such as (L row index = 13, a column index = 64, b column index = 69). This identifier is stored as the matrix index value, which serves as the direct call entry point for subsequent weighted correction operations.

[0071] S103: Based on the matrix index value, for the original correction coefficients in the correction matrix, call the Lab color space values ​​of the sample to perform weighted calculations item by item, combine the weighted correction coefficients with the original values ​​of the sample to generate the Lab measurement values ​​to be corrected.

[0072] Based on the matrix index value, the original correction coefficient set corresponding to that position in the correction matrix is ​​first retrieved. This coefficient set consists of three correction factors for the L, a, and b channels respectively. The original correction coefficient set retrieved from the matrix is ​​{ΔL=0.85, Δa=-0.12, Δb=0.34}. Then, the Lab color space values ​​of the sample {62.387, -3.51, 18.783} are sequentially taken for calculation. The specific process of weighted calculation is to first assign a pre-set weight value to each channel. This weight value is set according to the proportion of color difference to visual perception sensitivity. For example, the weight of the L channel is 0.6, the weight of the a channel is 0.25, and the weight of the b channel is 0.15. In this example, the weight allocation is based on the statistical results of the sample's sensitivity in the differentiated channels, and the total weight is determined to be 1 within the range of 0 to 1 through a judgment condition. Specifically, the weighted correction amount for the L channel is... The weighted correction amount for channel a is... The weighted correction for channel b is... The weighted correction values ​​all maintain the same decimal precision as the original channel values. Then, the original measured values ​​of the channels are directly added to the corresponding weighted correction values. Therefore, the correction result for channel L is... The correction result for channel a is The correction result for channel b is In this process, the numerical range of each addition operation must be judged. For example, the L value must be kept in the range of 0 to 100. If the correction exceeds the upper limit of the range, it must be truncated to the maximum value of 100. If it is lower than the lower limit, it must be truncated to 0. For the a and b values, they must be kept in the range of -128 to 127. In this example, the correction results all fall within the valid range, so no truncation operation is required. Finally, the three corrected channel values ​​are combined into a new Lab data set {62.897, -3.54, 18.834}. This data set is the Lab measurement value to be corrected after the weighted correction operation. This set of values ​​will enter the subsequent stage for further data verification and comparison in order to determine the stability and consistency of the correction.

[0073] Please see Figure 3 The specific steps of S2 are as follows:

[0074] S201: Based on the Lab measurement values ​​to be corrected, the measurement values ​​are sequentially input into the piecewise linear regression model. The values ​​are fitted according to the piecewise function coefficients within the interval. The corresponding parameters are extracted according to the slope and intercept of the fitted curve in the differential interval, and the regional piecewise feature coefficients are generated.

[0075] Regional segmentation characteristic coefficients refer to the slope and intercept obtained by piecewise linear regression fitting, which are used to characterize the correspondence between measured values ​​and standard values ​​within the differentiated intervals.

[0076] Based on the Lab measurement values ​​to be corrected, L=62.897, a=-3.54, and b=18.834 were first input into the piecewise linear regression calculation process. The piecewise linear regression structure here is based on continuous intervals divided by the channel value range. For example, for the L channel, 0 to 33 is set as the low brightness segment, 33 to 66 as the medium brightness segment, and 66 to 100 as the high brightness segment. For the a channel, -128 to -64 is set as the strong greenish segment, -64 to 0 as the greenish segment, and 0 to 64 as the reddish segment. The range of 64 to 127 is designated as a strong reddish band. The b channel is further divided into a strong blue band (-128 to -64), a blue band (-64 to 0), a yellow band (0 to 64), and a strong yellow band (64 to 127). Each channel value to be corrected is positioned within its designated interval based on its range. For example, L=62.897 falls in the medium brightness band, a=-3.54 in the greenish band, and b=18.834 in the yellowish band. Then, the piecewise function coefficients for each corresponding interval are called. For instance, the coefficients for the middle segment of L are set to the slope. ,intercept The coefficient group of the segment containing the value 'a' is ,intercept The coefficient group of the segment containing the b value is ,intercept During the fitting process, each channel value is processed. Linear operations, where For the corresponding measurement channel values, , The stored coefficient set from the segment containing that channel, such as the fitted value of the L channel. a-channel fitted value b-channel fitted value To ensure accurate interval selection, boundary comparisons are performed before calling each coefficient group. The input value is checked against both the upper and lower limits of the interval for both greater than / equal to and less than. For example, for a = -3.54, it is checked whether it is ≥ -64 and < 0. If the conditions are met, the green segment coefficient group is selected. After calculating the fitted values ​​for the three channels, the channel features are directly extracted based on the slope and intercept of the fitted curve. For example, the feature coefficients of segment L correspond to the slope of that segment. =0.045, intercept =60.1, the characteristic coefficients of segment a are 0.011 and -4.0, and the characteristic coefficients of segment b are 0.024 and 16.5. Combining these three pairs of coefficients, the final set of regional segment characteristic coefficients is generated as {(0.045, 60.1), (0.011, -4.0), (0.024, 16.5)}.

[0077] S202: Call the regional segmentation characteristic coefficients and compare them one by one with the original standard values ​​in the grid. Based on the difference between the current measured Lab value and the standard value, summarize the results using the difference accumulation method, and summarize the accumulated differences into a calculable index to obtain the grid difference quantity.

[0078] Grid variability refers to the cumulative sum of the differences between measured values ​​and standard values, used to reflect the overall degree of deviation within a single grid cell;

[0079] The region segmentation feature coefficient set {(0.045, 60.1), (0.011, -4.0), (0.024, 16.5)} is called. The current Lab measurement value {62.897, -3.54, 18.834} is then compared with the original standard value stored in the cell. Assuming the standard Lab value for a certain region is {63.120, -3.60, 18.920}, the difference in the L channels is first calculated. Channel A difference b channel difference Each difference is calculated by first taking the current measured value as the minuend, then taking the standard value as the subtrahend, performing the subtraction to obtain the result accurate to three decimal places, and then proceeding to the difference accumulation step, where the differences of the three channels are added to the corresponding accumulation variables. For example, if the initial accumulation values ​​are all 0, the following steps are performed. , , If multiple sets of standard values ​​exist in the current cell, it is necessary to iterate through each set of values ​​and calculate the difference with the measured value, and then accumulate them sequentially. For example, when comparing with another set of standard values ​​{63.050, -3.50, 18.850}, the difference obtained is... , , Continue execution , , The judgment condition in the entire accumulation process is: if the absolute value of the difference is less than 0.005, it is considered negligible and does not participate in the accumulation, so as to avoid interference caused by small measurement fluctuations. After all comparisons are completed, the accumulated difference of the three channels is organized into a unified format index vector {-0.376, 0.020, -0.102}. This vector is the grid difference between the grid and the current measured Lab value in all comparisons.

[0080] S203: Based on the grid difference, weights are set according to the number of measurements corresponding to the grids in the counting matrix. The difference is weighted and calculated, and the weighted result is converted into an overall correction value to obtain the regional correction offset.

[0081] After obtaining the cell difference vector {-0.376, 0.020, -0.102}, it is necessary to set the channel weights based on the number of times each cell was measured during the measurement process in the counting matrix. Assuming channel L has a cumulative measurement count of 5, channel a has 3, and channel b has 2, first calculate the weight coefficient for each channel. The specific method for setting the weights is as follows: divide the number of measurements for each channel by the sum of the number of measurements for all three channels, for example, the total number of measurements. Then the L channel weight Channel weights of channel a b channel weight In the weighted calculation, the cell difference of the corresponding channel is directly multiplied by its weight to obtain the weighted difference value: , , After calculating the weighted difference of the three channels, they are summed to obtain the overall correction value. This value represents the net offset of the weighted difference of this cell to the overall color measurement. It is then defined as the regional correction offset. If the value is negative, it indicates that the overall correction should be reduced in the corresponding direction. If it is positive, it indicates that the value should be increased. In this example, the result is -0.2024, which indicates that the corresponding color parameter should be negatively adjusted in subsequent correction steps.

[0082] Table 2: Weighted Calculation Table of Lattice Difference

[0083]

[0084] As shown in Table 2, by converting the difference of each channel into a weight according to the number of measurements of that channel and summing the products, the final regional correction offset of -0.2024 can be obtained, which is used for numerical adjustment in subsequent correction steps.

[0085] Please see Figure 4 The specific steps of S3 are as follows:

[0086] S301: The measurement results are compared point by point based on the regionalized correction offset. The difference between the grid point value of the measurement matrix and the regionalized correction offset is calculated. In the difference calculation, the values ​​are extracted according to the position coordinates and weighted and accumulated to generate the difference offset coefficient.

[0087] Based on regionalized offset correction To perform point-by-point comparison of measurement results, the position coordinates of each grid point in the measurement matrix are retrieved sequentially. and the corresponding actual measurement Lab data For each grid point, its three-channel values ​​are first extracted from the measurement matrix. For example, at a certain grid point... The corresponding L, a, and b values ​​are measured as follows: Then, the regionalized offset is corrected. The specific procedure in the difference calculation step applied to this grid point is: perform difference value calculation. , , The difference calculation involves subtracting the original value of the current grid point from its offset to obtain the new value. If the matrix grid point is located at the intersection of multiple regions, the offset of the region to be prioritized must be determined based on the coordinate segment to ensure that only a single region parameter is involved in the calculation. Then, numerical extraction and weighted accumulation based on position coordinates are performed. For example, for... The weight of a grid point is determined by the proportion of the cumulative number of measurements taken in its row and column within the entire matrix. Assuming the statistical weight of this grid point is... Then, when accumulating, multiply by the weight first to obtain... , , This process iterates through the matrix grid points, summing the weighted difference value of each grid point in the L, a, and b channels respectively. For example, assuming the measurement matrix has 100 grid points, the accumulated result after the iteration is... , , Finally, the weighted sums of these three channels are combined into a set of three-dimensional vectors. This set of vectors is the difference offset coefficient formed by position-weighted accumulation, which is used for the next step of nonlinear weight allocation calculation.

[0088] S302: Call the difference offset coefficient to perform non-linear weight allocation. In the weight allocation, the cells with zero grid points in the counting matrix are compensated by the values ​​of the adjacent non-zero grid points. The compensation value is superimposed with the original allocation value and normalized to obtain the compensation weight value.

[0089] Call the difference offset coefficient vector The nonlinear weighting step begins by scanning the measurement matrix cell by cell to detect the number of measurements at each cell in the counting matrix. ,when When a grid point is determined to be an empty measurement cell, neighbor compensation processing is required. This determination process involves reading the measurement count for each grid point and performing an equality comparison with zero. If the values ​​are equal, the coordinates are recorded and added to the compensation queue. In the compensation calculation, for each empty grid point, the non-zero grid point values ​​within its Manhattan distance set (i.e., the upper, lower, left, and right adjacent grid points) are extracted.

[0090] ;

[0091] The average difference offset coefficient of the non-zero grid points is used as the compensation value. For example, if there is no measurement value at coordinate (5, 6), the weighted difference offset values ​​of the non-zero grid points found in (4, 6), (6, 6), (5, 5), and (5, 7) are 2.53, 2.49, 2.60, and 2.58, respectively. The compensation value is... This compensation value will be added to the original allocated difference offset weight, that is, for the original weight The grid points are adjusted to After completing the compensation of the zero-measurement grid points, a set of compensation weight matrices is obtained. To perform proportional normalization, it is necessary to calculate The sum For example, the sum calculation result is Then, for each compensation weight value in the matrix, the following steps are performed. The division operation ensures that the compensation weights of the grid points are between 0 and 1, and that their sum is exactly 1. For example... The grid point normalization result is The result after full matrix normalization The set represents the compensation weight values, which are used in the next step of channel offset correction calculation.

[0092] S303: Call the compensation weight value to perform channel offset correction. In the correction, the compensation weight is superimposed one by one with the channel offset parameter, and the channel offset parameter is redistributed in the superposition matrix to obtain the correction value before boundary verification.

[0093] The compensation weight value is called to perform the channel offset correction operation. First, each grid point in the matrix is ​​traversed one by one, and the compensation weight value is multiplied one-to-one with the corresponding channel offset parameter to obtain the weighted offset. For example, in the L, a, b three-channel offset parameter matrix, a certain grid point The offset parameters are respectively , , Its normalized compensation weight is The weighted result for this grid point is (L-channel) (Channel a) (Channel b) This operation is performed sequentially for each grid point in the matrix, and the weighted offset of each grid point is added together in the corresponding channel. For example, after traversing all the grid points, the weighted sum of the L channels can be obtained. Weighted sum of channel a weighted sum of channel b The weighted sum is defined as the overall compensation value of the channel. Then, this overall compensation value is added to the original total offset parameter of the channel. For example, if the original total offset parameter is... , , The corrected channel value is , , Finally, the corrected values ​​of the three channels are redistributed back to the corresponding grid positions in the matrix, so that each grid point is updated with the corrected set of channel values ​​in the superposition matrix. For example, the original value of grid point (5, 6) {62.91, -3.55, 18.84} is updated to {63.422, -3.714, 19.078} after being adjusted by the corresponding weighted compensation superposition and overall compensation correction rules. After the grid points are updated, the resulting matrix is ​​the corrected value matrix before entering the boundary verification stage.

[0094] Table 3: Example Table of Channel Offset Correction Calculation

[0095]

[0096] As shown in Table 3, by multiplying the normalized compensation weights one by one with the channel offset parameters, superimposing the results onto the original channel values, and then redistributing the entire matrix, a corrected result matrix can be obtained for boundary verification.

[0097] Please see Figure 5 The specific steps of S4 are as follows:

[0098] S401: Based on the correction value before boundary verification and the current grid boundary range, determine whether the corrected ab value exceeds the boundary. Compare the corrected ab value with the upper and lower limits of the grid boundary, mark the out-of-bounds data points, extract their corresponding coordinate information, and generate an out-of-bounds coordinate set.

[0099] When judging based on the pre-corrected value before boundary verification and the current cell's ab boundary range, the corrected a and b channel values ​​of each cell in the matrix are read one by one, and the preset upper and lower boundary limits of the corresponding cell are called. For example, if the allowed range of the a channel of the current cell is [-5.00, 4.00] and the allowed range of the b channel is [15.00, 19.00], then a comparison action is performed for each cell in turn. The comparison action consists of two steps: the first step is to perform a greater than judgment, comparing the a value with the upper limit of a. The upper bound is then determined to be out of bounds; the second step is to perform a less than check, comparing the value of 'a' with the lower bound of 'a'. If the value exceeds the lower bound, it is considered an out-of-bounds error. Similarly, a greater than or less than comparison is performed on the b value to detect cases exceeding the boundary of the b channel. For example, at grid point (5, 6), the a value -3.714 is greater than the lower limit -5.00 and less than the upper limit 4.00, so it is not in the a channel out-of-bounds set. However, the b value 19.078 is greater than the b upper limit 19.00 and the difference 19.078 - 19.00 = 0.078 > 0, so it is determined to be an out-of-bounds error of the b channel upper bound. The coordinates of this grid point (5, 6) are recorded in the out-of-bounds coordinate set and the channel type is marked as b. If the upper bound is exceeded, coordinate information is extracted for each grid point that exceeds the bound during the entire scan. The (i, j) two-dimensional index and the out-of-bounds channel identifier are grouped and stored, for example [(5, 6, b+), (8, 9, a−), (3, 4, a+), (3, 4, b−)]. In this process, if both a and b values ​​of a grid point exceed the bound, they are recorded once in the out-of-bounds coordinate set so that the amplitude can be limited by channel in the subsequent processing stage. After the matrix grid point scan is completed, the out-of-bounds judgment and coordinate extraction are completed, and the out-of-bounds coordinate set is generated.

[0100] S402: Call the grid position coordinates in the out-of-bounds coordinate set, re-limit the range of the ab values ​​that exceed the boundary according to the upper and lower limits of the boundary, adjust the difference of the out-of-bounds part to the critical position, recalculate the distribution range of the ab values ​​after the limit is set, and obtain the boundary intercepted value range.

[0101] The out-of-bounds coordinate set [(5, 6, b+), (8, 9, a−), (3, 4, a+), (3, 4, b−)] is invoked sequentially. The corresponding grid point positions are then called, and the a and b values ​​exceeding the boundary are subject to amplitude limiting. This limiting process is performed channel by channel. First, the original corrected value of the grid point and its corresponding upper and lower boundary limits are retrieved. For example, for the grid point (5, 6, b+), its b value is 19.078, and the upper limit of the b channel is 19.000. Then, the difference is calculated. If the result is greater than 0, it indicates that the upper bound has been exceeded, so the value of b is adjusted to the upper limit. For the grid point (8, 9, a−), its a value is -5.346, and the lower limit of the a channel is -5.000. Perform the difference calculation. Since the result is less than 0 and the absolute value is greater than 0, it is determined to be out of bounds, and the value of 'a' is adjusted to the lower limit. For the grid point (3, 4, a+), the value of a is 4.128, and the upper limit of the a channel is 4.000. Execute... If the result is greater than 0, the value of 'a' will be adjusted to 4.000; for the grid point (3, 4, b−), the value of 'b' is 14.968, and the lower limit of the 'b' channel is 15.000. Then execute... If the result is less than 0, the value of b is increased to 15.000 to meet the boundary condition. Each adjustment only applies to the corresponding channel value, while the values ​​of other channels remain unchanged. After adjusting the magnitude of the out-of-bounds grid points, the numerical range of the two channels a and b in the entire matrix is ​​re-statistically calculated. The statistical method is to traverse the grid points and record the minimum and maximum values ​​of the two channels respectively. For example, after adjustment, the minimum value of channel a is -5.000 and the maximum value is 4.000, and the minimum value of channel b is 15.000 and the maximum value is 19.000. Finally, these two ranges constitute a new boundary truncation numerical interval for subsequent data integration processing within the boundary.

[0102] S403: Based on the correction results in the boundary-truncation numerical range, integrate the ab values ​​within the boundary and the critical point, merge the adjusted data with the data that has not exceeded the boundary, and output the boundary verification results.

[0103] The numerical range is extracted based on the boundary (channel a [-5.000, 4.000], channel b [15.000, 19.000]). The correction results for the grid points are then integrated based on this range. First, each grid point in the matrix is ​​traversed to determine if its a and b values ​​belong to the adjusted result set. If they belong to the boundary data, the original correction value is directly retained. If they belong to the critical point (i.e., grid points where a value equals -5.000 or 4.000, and b value equals 15.000 or 19.000), the already limited critical value is retained and merged with the data that did not exceed the boundary in the new matrix. For example, the adjusted value of grid point (5, 6) is {L=63.422, a=-3.714, b=19.000}. Because the b value... The value of grid point (8, 9) is equal to the upper bound and becomes a critical point. This value is retained and entered into the output matrix. The adjusted value of grid point (8, 9) is {L=61.235, a=-5.000, b=16.742}. Since the value of a is equal to the lower bound, it becomes a critical point and is retained and entered into the output matrix. Grid point (3, 4) is adjusted to the critical value within the boundary in both channels a and b {L=64.017, a=4.000, b=15.000}. It is also directly incorporated into the final matrix. Grid points that do not appear in the out-of-bounds coordinate set (such as (6, 5)={L=62.881, a=-1.352, b=17.641}) are retained in full to ensure that the matrix after boundary verification consists of the adjusted critical point data and the data that do not exceed the boundary, forming a complete output matrix.

[0104] Table 4: Example of partial grid point output after boundary verification

[0105]

[0106] As shown in Table 4, after the unified merging of the critical point and boundary data, the resulting matrix is ​​the final output matrix after boundary verification, which can be directly called by subsequent steps.

[0107] Please see Figure 6 The specific steps of S5 are as follows:

[0108] S501: Based on the output results after boundary verification, obtain the value of the corresponding color block in the correction knowledge base, detect the Lab deviation of the newly added measurement point, calculate the difference between the newly added deviation value and the existing benchmark Lab value, record the deviation value according to the difference and update the correction knowledge base, and generate the newly added Lab deviation value.

[0109] Based on the output results after boundary verification, first, according to the coordinates of each grid point... The corrected Lab values ​​are read sequentially, and then the current baseline Lab value of the corresponding color block in the correction knowledge base is queried. This query process locates the corresponding record entry in the knowledge base index table based on the grid coordinates. For example, for coordinates (5, 6), the baseline Lab value of the corrected knowledge base record is... The Lab value of the newly added measurement point Deviation detection is performed on this record, and the deviation is calculated in the order of processing the three channels one by one, starting with the L channel deviation. Then execute channel a deviation Then execute the b channel offset. The channel deviation is retained to three decimal places to ensure consistent accuracy during subsequent accumulation. Next, the difference between the newly added deviation and the existing baseline value is compared. This "comparison" involves performing absolute value calculations and a zero threshold check. Record the deviation value if it is greater than the threshold of 0, otherwise ignore it. In this example, all three channels are greater than the threshold of 0, so they are all recorded. The new deviation value is then displayed in the structure. The data is temporarily stored, and grid point coordinate labels (5, 6) are attached to each channel. When adding new measurement points to the entire matrix, the {deviation vector, grid point coordinates} of multiple points are continuously written to the cache queue, and finally summarized to form a set of new Lab deviation values. For example, a portion of the data is shown in Table 5 below.

[0110] Table 5: Example Table of Newly Added Lab Deviation Values

[0111]

[0112] As shown in Table 5, the newly added Lab deviation values ​​are obtained by comparing the corrected output matrix with the knowledge base benchmark value cell by cell, recording each deviation that exceeds the zero threshold, and forming a deviation set for subsequent correction matrix updates.

[0113] S502: Call the newly added Lab deviation value, update the Lab correction matrix for the corresponding grid coordinates in the correction knowledge base, add the deviation value to the value at the corresponding position in the matrix, and generate the cumulative correction value of the grid coordinates;

[0114] Call the newly added Lab deviation value set The grid coordinates recorded for each element in the set are called sequentially. The corresponding Lab correction matrix position is located in the correction knowledge base, and an accumulation update operation is performed. This process is performed separately for the three channels L, a, and b. For example, for the deviation recorded in the set (5, 6), First, read the currently stored cumulative correction value at position (5, 6) in the correction matrix. Assuming its current value is Then the addition operations are performed sequentially: , , After completing the three-channel numerical update of the coordinate, the data is directly written back to the storage unit corresponding to the correction matrix. For the deviation corresponding to (8, 9) recorded in the set... Similarly, read the original value. And perform the summation separately, for example, the original value Updated to This process will be applied one by one. If a channel in the set of coordinate points has no stored value during the update process (when recording for the first time), the deviation value is automatically written directly into the matrix cell as the initial value without accumulation. Finally, after traversing the entire set of newly added Lab deviation values, the resulting correction matrix is ​​the cumulative correction value matrix of the grid coordinates after accumulating and integrating the deviation records of this batch.

[0115] S503: Based on the cumulative correction value of the grid coordinates, find the corresponding coordinate point in the counting matrix, increment the count value, update the counting matrix, and then store the correction matrix and the counting matrix together to generate the colorimeter color measurement accuracy optimization result.

[0116] Based on the cumulative correction value of the grid coordinates, the counting matrix is ​​updated synchronously according to the coordinate values. First, the non-zero coordinate positions in the cumulative correction matrix are read one by one. Then, locate the same coordinate cell in the counting matrix and perform a count increment operation. This operation increments the current count value by 1 to record that the corresponding grid point has undergone a new deviation accumulation correction. For example, when the original count value of position (5, 6) in the counting matrix is... When, execute The update is performed if the original count value at position (8, 9) is... Then update to This point-by-point incrementing method cycles through the list of coordinates for newly added deviation records. When a coordinate that has never appeared in the counting matrix is ​​processed, its count value is initialized to 1, until all coordinates involved in the cumulative correction matrix have been updated. Finally, the updated correction matrix and the counting matrix are stored together in the system's persistent storage area to form a complete data snapshot file. The correction matrix reflects the cumulative numerical offset of the grid points in the L, a, and b channels, while the counting matrix reflects the number of times the grid points have been corrected. This storage result is the final result of the colorimeter's colorimeter measurement accuracy optimization.

[0117] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for correcting colorimetric measurements using a colorimeter, characterized in that, Includes the following steps: S1: Obtain the Lab color space values ​​of the sample to be corrected using a colorimeter, calculate the grid's horizontal and vertical coordinates based on the values ​​of the a and b channels, analyze the original correction coefficients by indexing the pre-constructed L, a, and b correction matrices, and obtain the Lab measurement values ​​to be corrected. S2: Based on the Lab measurement value to be corrected, perform regional analysis, input the Lab measurement value to be corrected into the piecewise linear regression model to extract regional features, calculate the deviation between the current measured Lab value and the original standard value in the corresponding grid, and perform weighted processing in combination with the measurement times of the grid in the counting matrix to obtain the regional correction offset. S3: Call the regionalized correction offset to perform adaptive correction processing on the measurement results, perform nonlinear weight allocation according to the regionalized correction offset, and use neighborhood compensation technology to obtain correction parameters when the corresponding cell value of the counting matrix is ​​zero, perform channel offset correction, and obtain the correction value before boundary verification. S4: Perform color gamut boundary constraint verification processing using the correction value before boundary verification. When the corrected ab value exceeds the current grid boundary, limit the correction range to the grid boundary range and perform callback processing on the out-of-bounds value to obtain the output result after boundary verification. The specific steps of S1 are as follows: S101: Obtain the Lab color space values ​​of the sample measured by the colorimeter, call the values ​​of channel a and channel b, calculate the horizontal and vertical coordinates of the two in the coordinate plane, and organize the horizontal and vertical coordinates with the L channel values ​​of the sample to generate coordinate positioning values. S102: Based on the coordinate positioning value, call the pre-constructed L, a, b correction matrix, compare the data boundaries of the units in the matrix item by item according to the horizontal and vertical coordinate positions, filter the corresponding matrix unit data, and then compare the filtering results with the L channel values ​​item by item to unify the format and obtain the matrix index value. S103: Based on the matrix index value, for the original correction coefficients in the correction matrix, call the Lab color space values ​​of the sample to perform weighted calculations item by item, combine the weighted correction coefficients with the original values ​​of the sample to generate the Lab measurement values ​​to be corrected. The specific steps of S2 are as follows: S201: Based on the Lab measurement values ​​to be corrected, the measurement values ​​are sequentially input into the piecewise linear regression model. The values ​​are fitted according to the piecewise function coefficients within the interval. The corresponding parameters are extracted according to the slope and intercept of the fitted curve in the differential interval, and the regional piecewise feature coefficients are generated. S202: Call the segmented feature coefficients of the region and compare them one by one with the original standard values ​​in the grid. Based on the difference between the current measured Lab value and the standard value, summarize the results using the difference accumulation method, and summarize the accumulated difference into a calculable index to obtain the grid difference quantity. S203: Based on the grid difference, weights are set according to the number of measurements corresponding to the grids in the counting matrix. The difference is weighted and calculated, and the weighted result is converted into an overall correction value to obtain the regional correction offset. The specific steps for S3 are as follows: S301: Based on the regionalized correction offset, the measurement results are compared point by point. The difference between the grid point value of the measurement matrix and the regionalized correction offset is calculated. In the difference calculation, the values ​​are extracted according to the position coordinates and weighted and accumulated to generate the difference offset coefficient. S302: Call the difference offset coefficient to perform nonlinear weight allocation. In the weight allocation, the cells with zero grid points in the counting matrix are compensated by the values ​​of the adjacent non-zero grid points. The compensation value is superimposed with the original allocation value and normalized to obtain the compensation weight value. S303: Call the compensation weight value to perform channel offset correction. In the correction, the compensation weight is superimposed one by one with the channel offset parameter, and the channel offset parameter is redistributed in the superposition matrix to obtain the correction value before boundary verification. The specific steps of S4 are as follows: S401: Based on the correction value before boundary verification and the current grid boundary range, determine whether the corrected ab value exceeds the boundary, compare the corrected ab value with the upper and lower limits of the grid boundary, mark the out-of-bounds data points, extract their corresponding coordinate information, and generate an out-of-bounds coordinate set; S402: Call the grid position coordinates in the out-of-bounds coordinate set, redefine the range of the ab values ​​that exceed the boundary according to the upper and lower limits of the boundary, adjust the difference of the out-of-bounds part to the critical position, recalculate the distribution range of the ab values ​​after the limit is set, and obtain the boundary interception value range. S403: Based on the correction results in the boundary-truncation numerical range, integrate the ab values ​​within the boundary and the critical point, merge the adjusted data with the data that has not exceeded the boundary, and obtain the boundary verification output result.

2. The method for correcting colorimetric measurement results according to claim 1, characterized in that, The Lab measurement values ​​to be corrected include luminance components, red-green components, and yellow-blue components. The regionalized correction offset includes local difference values, regional weighted values, and feature mapping values. The correction values ​​before boundary verification include channel offset, compensation coefficient, and assigned weights. The output results after boundary verification include boundary constraint values, amplitude limit values, and callback correction values.

3. The method for correcting colorimetric measurement results according to claim 2, characterized in that, The difference offset coefficient refers to the difference quantification coefficient obtained by comparing the grid point values ​​of the measurement matrix with the regionalized correction offset point by point and then weighting and accumulating them. The normalization process uses a proportional normalization method, which involves dividing the compensation weight value by the sum of the compensation weight values. The compensation weight value refers to the weight parameter obtained after performing neighbor value compensation and normalization on zero-value grid points in nonlinear weight allocation.

4. The method for correcting colorimetric measurements using a colorimeter according to claim 1, characterized in that, The method also includes step S5: S5: Based on the output results after the boundary verification, update and maintain the correction knowledge base, calculate the newly added Lab deviation and accumulate it to the Lab correction matrix of the corresponding grid coordinates, increment the value of the corresponding position of the counting matrix, and obtain the colorimeter color measurement accuracy optimization result. The optimized results of the colorimeter colorimeter measurement accuracy include the cumulative deviation, the increment of the correction matrix, and the updated value of the counting matrix.

5. The method for correcting colorimetric measurements using a colorimeter according to claim 4, characterized in that, The specific steps of S5 are as follows: S501: Based on the boundary verification output result, obtain the value of the corresponding color block in the correction knowledge base, detect the Lab deviation of the newly added measurement point, calculate the difference between the newly added deviation value and the existing benchmark Lab value, record the deviation value according to the difference and update the correction knowledge base, and generate the newly added Lab deviation value. S502: Call the newly added Lab deviation value, update the Lab correction matrix for the corresponding grid coordinates in the correction knowledge base, add the deviation value to the value at the corresponding position in the matrix, and generate the cumulative correction value of the grid coordinates; S503: Based on the cumulative correction value of the grid coordinates, find the corresponding coordinate point in the counting matrix, increment the count value, update the counting matrix, and then store the correction matrix and the counting matrix together to generate the colorimeter color measurement accuracy optimization result.

6. The method for correcting colorimetric measurements using a colorimeter according to claim 5, characterized in that, The newly added Lab deviation value refers to the difference between the newly added measurement point and the benchmark Lab value in the corrected knowledge base; The cumulative correction value of the grid coordinates refers to the sum of the original Lab deviation values ​​of the corresponding grid coordinates in the correction matrix.

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