Gamma adjustment method, computer readable storage medium, and computer program product

Abstract

The disclosure provides a gamma adjustment method, a computer readable storage medium and a computer program product. The gamma adjustment method comprises the following steps: obtaining a model of a to-be-tested module, determining a target model corresponding to the model and an error probability density distribution function according to the model, the target model being used to represent a mapping relationship between a gamma register value and a target influence factor, and the error probability density distribution function being used to represent an error distribution between a predicted gamma register value output by the target model and an actual gamma register value; obtaining a target influence factor value when the to-be-tested module displays a target gray scale picture, and inputting the target influence factor value into the target model to obtain a first gamma register value; detecting whether a display picture of the to-be-tested module meets a requirement when the to-be-tested module performs gamma adjustment by using the first gamma register value; and when the display picture does not meet the requirement, adjusting the first gamma register value by using the error probability density distribution function until the display picture meets the requirement.

Description

Technical Field

[0001] This disclosure relates to the field of display technology. More specifically, it relates to a gamma modulation method, a computer-readable storage medium, and a computer program product. Background Technology

[0002] OLED (organic light emitting diode) displays are characterized by vibrant colors, high contrast, faster response times, and energy efficiency, and are widely used in an increasing number of electronic products. However, after prolonged use, OLED displays may exhibit uneven brightness or color inconsistencies in certain areas. Gamma correction is an important solution for optimizing the display's performance.

[0003] In existing gamma correction schemes, sample displays need to be debugged first to obtain gamma data, which is then written into the gamma register of each display. However, due to manufacturing errors, different displays inevitably exhibit some degree of deviation. Consequently, the required gamma data for each display will also differ. Furthermore, existing gamma register value debugging schemes cannot meet the varying needs of different displays and are inefficient. Summary of the Invention

[0004] The purpose of this disclosure is to provide a gamma adjustment method, a computer-readable storage medium, and a computer program product to solve the problem of differences in the required gamma register values ​​for various displays in the related art, and to improve the accuracy of gamma register values ​​and the efficiency of gamma register value adjustment.

[0005] To achieve the above objectives, the present disclosure adopts the following technical solution:

[0006] The first aspect of this disclosure provides a gamma modulation method, comprising the following steps:

[0007] Obtain the model number of the module to be tested, and determine the target model and error probability density distribution function corresponding to the model number. The target model is used to represent the mapping relationship between the gamma register value and the target influence factor, and the error probability density distribution function is used to represent the error distribution between the predicted gamma register value output by the target model and the actual gamma register value.

[0008] Obtain the target influence factor value when the module under test displays the target grayscale image, and input the target influence factor value into the target model to obtain the first gamma register value;

[0009] When the module under test uses the first gamma register value to adjust the gamma, it is determined whether the display screen of the module under test meets the requirements.

[0010] When the displayed image does not meet the requirements, the first gamma register value is adjusted using the error probability density distribution function. Using the adjusted first gamma register value, the step of detecting whether the display image of the module under test meets the requirements when the gamma is adjusted using the first gamma register value is repeated until the displayed image meets the requirements.

[0011] Optionally, the step of adjusting the value of the first gamma register using the error probability density distribution function includes:

[0012] Obtain a first probability value, and obtain a first error value interval corresponding to the first probability value from the error probability density distribution function;

[0013] A first error value is determined from the first error value range according to the first method, and the first gamma register value is adjusted with the first error value as the step size.

[0014] Optionally, after the step of adjusting the first gamma register value with the first error value as the step size, the method further includes:

[0015] When the module under test performs gamma adjustment using the adjusted first gamma register value, it is determined whether the display screen of the module under test meets the requirements.

[0016] If the requirements are not met, repeat the step of adjusting the value of the first gamma register using the error probability density distribution function until the display screen of the module under test meets the requirements.

[0017] Optionally, the gamma adjustment method further includes:

[0018] Obtain sample modules of different models;

[0019] For any given model, based on multiple sample modules of that model, determine the target model and error probability density distribution function corresponding to that model.

[0020] Optionally, for any given model, the step of determining the target model corresponding to that model based on multiple sample modules includes:

[0021] The influence factors when the sample module displays the target grayscale image are obtained, and the influence factors include three spectral stimulus values;

[0022] A correlation analysis is performed on the three spectral stimulus values, and a target influencing factor is determined based on the correlation analysis results. The target influencing factor includes at least one of the three spectral stimulus values.

[0023] A target model is established based on the actual gamma register values ​​and target impact factor values ​​of the sample modules to represent the mapping relationship between gamma register values ​​and target impact factors.

[0024] Optionally, the step of performing correlation analysis on the spectral stimulus values ​​and determining the target influencing factor based on the correlation analysis results includes:

[0025] To determine whether the three spectral stimulus values ​​are suitable for correlation analysis;

[0026] When the three spectral stimulus values ​​are suitable for correlation analysis, a correlation matrix is ​​calculated, which includes a correlation coefficient representing the correlation between any two spectral stimulus values.

[0027] Calculate the eigenvalues ​​of the correlation matrix, and determine the number of spectral stimulus values ​​in the target influencing factor based on the eigenvalues;

[0028] The target influence factor is determined based on the number of spectral stimulus values ​​in the target influence factor and the correlation matrix.

[0029] Optionally, the step of determining the target impact factor based on the number of spectral stimulus values ​​in the target impact factor and the correlation matrix includes:

[0030] When the number of spectral stimulus values ​​in the target influence factor is 1, any one of the three spectral stimulus values ​​is randomly selected as the target influence factor.

[0031] When the number of spectral stimulus values ​​in the target influence factor is 2, the two spectral stimulus values ​​corresponding to the maximum correlation coefficient in the correlation matrix are selected as the target influence factor, and the maximum correlation coefficient is less than 1.

[0032] Optionally, the step of determining the number of spectral stimulus values ​​in the target influencing factor based on the eigenvalues ​​includes:

[0033] A first curve is visualized, which represents the mapping relationship between the feature values ​​and the number of spectral stimulus values.

[0034] The number of spectral stimulus values ​​corresponding to the inflection point in the first curve is taken as the number of spectral stimulus values ​​in the target influence factor.

[0035] Optionally, the step of establishing a target model representing the mapping relationship between the gamma register values ​​and the target influence factor based on the true gamma register values ​​and the target influence factor values ​​of the sample module includes:

[0036] Based on the actual gamma register values ​​and target impact factor values ​​of the sample modules, at least two prediction models are established to represent the mapping relationship between gamma register values ​​and target impact factors.

[0037] For any prediction model, obtain the predicted gamma register value of the prediction model, and calculate the error between the predicted gamma register value and the actual gamma register value;

[0038] The prediction model with the smallest error among all prediction models is taken as the target model for the corresponding model of the sample module.

[0039] Optionally, for any prediction model, the step of obtaining the predicted gamma register value of the prediction model and calculating the error between the predicted gamma register value and the true gamma register value includes:

[0040] For any prediction model, obtain the prediction gamma register value of the prediction model at different gray levels;

[0041] Calculate the error between the predicted gamma register value and the actual gamma register value at different gray levels;

[0042] The sum of error values ​​is obtained by summing the error values ​​at different gray levels;

[0043] The prediction model with the smallest error among all prediction models is selected as the target model for the corresponding model of the sample module.

[0044] Optionally, the color channels of the module under test include a first channel, a second channel, and a third channel, and the error values ​​include the error values ​​of the first channel, the second channel, and the third channel; the step of summing the error values ​​at different gray levels to obtain the sum of error values ​​includes: summing the error values ​​of the first channel, the second channel, and the third channel at different gray levels to obtain the sum of error values;

[0045] The gamma modulation method further includes:

[0046] Determine whether the minimum error value of the target model is greater than or equal to a first error threshold, wherein the minimum error value is the minimum value among the first channel error value, the second channel error value, and the third channel error value;

[0047] If the judgment result is yes, then adjust the model parameters of the target model until the minimum error value is less than the first error threshold.

[0048] Optionally, the number of prediction models is three, referred to as a first prediction model, a second prediction model, and a third prediction model, respectively. The first prediction model is a linear model, the second prediction model is a nonlinear model, and the third prediction model is a piecewise nonlinear model. The third prediction model includes a first sub-model and a second sub-model. The gray level of the first sub-model is less than a first gray level threshold, and the gray level of the second sub-model is greater than or equal to the first gray level threshold.

[0049] Optionally, the first probability value is randomly generated, and the value of the first probability value is greater than 0 and less than or equal to 1;

[0050] The step of determining the first error value from the first error value interval according to the first method includes:

[0051] The mean, edge value, or fixed position value of the first error value interval is determined as the first error value.

[0052] A second aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the gamma modulation method as described above.

[0053] A third aspect of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the gamma modulation method as described above.

[0054] The beneficial effects of this disclosure are as follows:

[0055] The gamma adjustment method of this disclosure has two advantages. First, different models of display modules correspond to different target models. Thus, when predicting gamma register values ​​for different models of the module under test using their respective target models, the deviation between gamma register values ​​between different models of display modules can be overcome, improving the accuracy of the predicted gamma register values. Second, there are deviations between the gamma register values ​​of display modules of the same model. By adjusting the predicted first gamma register value through the error probability density distribution function, the deviation between the gamma register values ​​of display modules of the same model can be overcome, further improving the accuracy of the predicted gamma register values. Furthermore, since the error between the predicted value and the true value of the same model of display module is basically similar under a fixed grayscale, adjusting the first gamma register value through the error probability density distribution function can ensure the accuracy of the adjusted first gamma register value and shorten the debugging time of the first gamma register value, thus improving efficiency. Attached Figure Description

[0056] The specific embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0057] Figure 1A flowchart of the gamma modulation method provided in this embodiment of the disclosure;

[0058] Figure 2 This is a flowchart for determining the target model corresponding to any given model.

[0059] Figure 3 The heatmap for the correlation coefficients corresponding to Table 1;

[0060] Figure 4 This is a schematic diagram illustrating the mapping relationship between the number of eigenvalues ​​and the number of spectral stimulus values.

[0061] Figure 5 This is a schematic diagram comparing the error between the predicted gamma register value and the actual gamma register value of the first prediction model.

[0062] Figure 6 for Figure 5 Data on the predicted gamma register values ​​and the actual gamma register values ​​corresponding to each target grayscale level;

[0063] Figure 7 This is a schematic diagram comparing the error between the predicted gamma register value and the actual gamma register value of the second prediction model.

[0064] Figure 8 for Figure 7 Data on the predicted gamma register values ​​and the actual gamma register values ​​corresponding to each target grayscale level;

[0065] Figure 9 This is a schematic diagram comparing the error between the predicted gamma register value and the actual gamma register value of the third prediction model.

[0066] Figure 10 for Figure 9 Data on the predicted gamma register values ​​and the actual gamma register values ​​corresponding to each target grayscale level;

[0067] Figure 11 This is a flowchart for determining the error probability density distribution function corresponding to any given model.

[0068] Figure 12 This is a schematic diagram of the error probability density distribution histograms for different models of sample modules. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0070] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “including,” “comprising,” or “containing,” and similar terms mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. The terms “connected,” “linked,” or similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms “upper,” “lower,” “left,” and “right,” etc., are used only to indicate relative positional relationships, and these relative positional relationships may change accordingly when the absolute position of the described objects changes.

[0071] In related technologies, in order to optimize the display effect of the display module, it is necessary to perform gamma debugging on the display module to obtain the correspondence between the grayscale of each binding point and the gamma register value. After gamma debugging, the gamma register value corresponding to the grayscale of each binding point needs to be burned into the gamma register.

[0072] The main methods for determining the gamma register value of the display module include the following two:

[0073] (1) Select several modules from the batch display modules as sample modules, obtain the gamma register value of the sample modules through debugging, and use the obtained gamma register value as the gamma register value of the batch display modules.

[0074] (2) Select several modules from the batch display modules as sample modules, obtain the gamma register value of the sample modules through debugging, fit the gamma register value of the sample modules to obtain the fitting curve, and then use the fitting curve to determine the gamma register value of the batch display modules.

[0075] However, due to manufacturing errors, different display modules inevitably exhibit some degree of deviation. Consequently, the gamma register values ​​of different display modules may also vary. On one hand, there will be deviations between different models of display modules; on the other hand, there will be deviations among individual display modules of the same model. Neither of the above two solutions can resolve the deviation problem of gamma register values ​​between different display modules. Furthermore, to improve the accuracy of gamma register values, each display module can be debugged to obtain its gamma register value, but this approach has low overall efficiency.

[0076] To address the aforementioned technical problems, this disclosure provides a gamma modulation method, a computer-readable storage medium, and a computer program product, which are described below in conjunction with various specific embodiments.

[0077] Please refer to Figure 1 , Figure 1 A flowchart of the gamma modulation method provided in the embodiments of this disclosure is shown below. Figure 1 As shown, it includes the following steps:

[0078] Step S101: Obtain the model number of the module to be tested, and determine the target model and error probability density distribution function corresponding to the model number. The target model is used to represent the mapping relationship between the gamma register value and the target influence factor, and the error probability density distribution function is used to represent the error distribution between the predicted gamma register value output by the target model and the actual gamma register value.

[0079] In this embodiment of the disclosure, the module under test refers to the display module under test, that is, the display module whose gamma register value needs to be determined. Since different models of display modules have certain deviations, it is necessary to predetermine the corresponding target model and error probability density distribution function for each model of display module.

[0080] The target influence factor refers to the primary influence factor among all influence factors. Specifically, an influence factor is a factor or parameter that affects the gamma register value. Influence factors include three spectral stimulus values, also known as tristimulus values, and the target influence factor includes at least one spectral stimulus value. Tristimulus values ​​are numerical values ​​used to approximately describe the intensity of the three stimuli of a color. They are based on the "three primary colors" theory, which states that any color is the result of the interaction of the three basic colors: red, green, and blue. In color matching experiments, the number of the three primary colors required to achieve color matching with the test color is called the tristimulus value. These values ​​reflect the relative intensity of the color in the red, green, and blue color channels, and are usually represented as X, Y, Z or R, G, B in color science, depending on the color space used. In display modules, tristimulus values ​​control the intensity of the red, green, and blue color channels to achieve precise control of color and brightness, further affecting the accuracy and consistency of the color.

[0081] In one possible implementation, before step S101, the gamma adjustment method further includes the following steps: acquiring sample modules of different models; for any model, determining the target model and error probability density distribution corresponding to that model based on multiple sample modules of that model. Then, when it is necessary to determine the gamma register value of the module under test, the model of the module under test is first determined, and then the target model and error probability density distribution function matching that model are obtained, thereby avoiding deviations in gamma register values ​​caused by different models of display modules. Furthermore, by establishing a dedicated prediction model, i.e., a target model, for each model, it can be ensured that the gamma adjustment method can seamlessly support multiple different models of display modules, improving the compatibility of the gamma adjustment method with different models of display modules.

[0082] Step S102: Obtain the target influence factor value when the module under test displays the target grayscale image, and input the target influence factor value into the target model to obtain the first gamma register value.

[0083] In this embodiment, the target grayscale image refers to an image with a target grayscale level, and the target grayscale level corresponds one-to-one with the binding point grayscale level that needs to be gamma-tuned. For example, if the binding point grayscale level of the display module includes grayscale levels 1, 2, 32, 64, 128, 164, 192, 254, and 255, then the target grayscale level also includes grayscale levels 1, 2, 32, 64, 128, 164, 192, 254, and 255. In specific implementation, it is necessary to determine the first gamma register value corresponding to each target grayscale level.

[0084] In practice, the module under test is controlled to display the target grayscale image, and the tristimulus values ​​of the module under test when displaying the target grayscale image are obtained using a color analyzer. Since the target influence factor includes at least one of the tristimulus values, the target influence factor value can be obtained based on the tristimulus values ​​obtained by the color analyzer. Then, the target influence factor value is input into the target model to obtain the gamma register value corresponding to the target influence factor value. For distinction, this gamma register value is recorded as the first gamma register value. After obtaining the first gamma register value, the first gamma register value is written into the gamma register of the module under test, and step S103 is executed.

[0085] Step S103: Detect whether the display screen of the module under test meets the requirements when the module under test uses the first gamma register value for gamma adjustment.

[0086] Optionally, after determining the first gamma register value corresponding to each target grayscale of the module under test through the target model and writing the first gamma register value into the gamma register of the module under test, it is necessary to further check whether the display screen of the module under test meets the requirements when the module under test uses the first gamma register value for gamma adjustment. For example, the color brightness of the display screen can be checked by a color analyzer. If the color brightness of the display screen meets the requirements, it means that the predicted first gamma register value is relatively accurate and can be used directly. If the color brightness of the display screen does not meet the requirements, it means that the predicted first gamma register value deviates significantly from the actual gamma register value required by the module under test, and the first gamma register value needs to be adjusted to meet the requirements, i.e., step S104 is executed.

[0087] Step S104: When the displayed image does not meet the requirements, the first gamma register value is adjusted using the error probability density distribution function. Using the adjusted first gamma register value, the step of detecting whether the displayed image of the module under test meets the requirements when the gamma is adjusted using the first gamma register value is repeated until the displayed image meets the requirements. It is understood that the adjustment process of the first gamma register value may need to be repeated multiple times until the adjusted first gamma register value makes the displayed image of the module under test meet the requirements.

[0088] In this embodiment of the disclosure, since the error probability density distribution function corresponds one-to-one with the model of the display module, that is, the error probability density distribution function is for the same model and is used to represent the difference distribution between the predicted gamma register value and the actual gamma register value of the display module under the same model, the error probability density distribution function can be used to roughly predict the error between the predicted value and the actual value at any gray level of the module under test, and the first gamma register value can be adjusted using the predicted error value, so as to ensure the reliability of the adjusted first gamma register value and make it meet the requirements.

[0089] Compared with related technologies, the gamma adjustment method of this disclosure has the following advantages: First, different models of display modules correspond to different target models. Thus, when predicting the gamma register value for different models of the module under test using their respective target models, the deviation between gamma register values ​​of different models of display modules can be overcome, improving the accuracy of the predicted gamma register value. Second, there is a deviation between the gamma register values ​​of display modules of the same model. By adjusting the predicted first gamma register value through the error probability density distribution function, the deviation between the gamma register values ​​of display modules of the same model can be overcome, further improving the accuracy of the predicted gamma register value. Moreover, since the error between the predicted value and the true value of the same model of display module is basically similar under a fixed grayscale, adjusting the first gamma register value through the error probability density distribution function can ensure the accuracy of the adjusted first gamma register value and shorten the debugging time of the first gamma register value, thus improving efficiency.

[0090] Before performing steps S101 to S104, the gamma adjustment method of this disclosure requires pre-determining the corresponding target model and error probability density distribution function for each model.

[0091] In one possible implementation, such as Figure 2 As shown, for any given model, the steps for determining the target model corresponding to that model based on multiple sample modules include steps S201 to S203. Specifically:

[0092] Step S201: Obtain the influencing factors when the sample module displays the target grayscale image. These influencing factors include three spectral stimulus values. In practice, the sample module is controlled to display the target grayscale image, and then the tristimulus values ​​are measured using a color analyzer. For example, for each target grayscale, the tristimulus values ​​when the sample module displays each target grayscale image are measured.

[0093] Step S202: Perform correlation analysis on the three spectral stimulus values, and determine the target influencing factor based on the correlation analysis results. The target influencing factor includes at least one of the three spectral stimulus values.

[0094] Optionally, the step of performing correlation analysis on the three spectral stimulus values ​​and determining the target influencing factor based on the correlation analysis results includes:

[0095] Step S2021: Detect whether the three spectral stimulus values ​​are suitable for correlation analysis;

[0096] Step S2022: When the three spectral stimulus values ​​are suitable for correlation analysis, calculate the correlation matrix, which includes the correlation coefficient representing the correlation between any two spectral stimulus values.

[0097] Step S2023: Calculate the eigenvalues ​​of the correlation matrix, and determine the number of spectral stimulus values ​​in the target influencing factor based on the eigenvalues;

[0098] Step S2024: Determine the target influence factor based on the number of spectral stimulus values ​​in the target influence factor and the correlation matrix.

[0099] In this embodiment of the disclosure, before performing correlation analysis on the three spectral stimulus values, a pre-analysis is first performed to detect whether they are suitable for correlation analysis. Only when the three spectral stimulus values ​​are suitable for correlation analysis will the process proceed to step S2022; otherwise, step S2022 will not be performed. This avoids wasting time and resources on unsuitable data to perform invalid correlation analysis, and helps to ensure the accuracy and effectiveness of the correlation analysis results.

[0100] In one possible implementation, the suitability of the tristimulus values ​​for correlation analysis is determined by the KMO (Kaiser-Meyer-Olkin) test and / or the Bartlett test of sphericity.

[0101] Specifically, the KMO test compares the relative magnitudes of the simple correlation coefficients and partial correlation coefficients among the original variables. When the sum of squares of the simple correlation coefficients among all variables is much greater than the sum of squares of the partial correlation coefficients, the KMO value is close to 1. The closer the KMO value is to 1, the stronger the correlation between the variables, and the more suitable the original variables are for factor analysis. When the sum of squares of the simple correlation coefficients among all variables is close to 0, the KMO value is close to 0. The closer the KMO value is to 0, the weaker the correlation between the variables, and the less suitable the original variables are for factor analysis. Generally, a KMO value greater than or equal to 0.9 is very suitable for factor analysis, a KMO value greater than 0.6 and less than 0.9 is also suitable, and a KMO value less than 0.5 is not suitable for factor analysis.

[0102] Bartlett's Test of Sphericity is primarily used to test whether data conforms to the sphericity assumption, i.e., to examine whether there is a high degree of correlation between variables in a dataset. The starting point for Bartlett's Test of Sphericity is the correlation coefficient matrix of the variables. Its null hypothesis is that the correlation coefficient matrix is ​​an identity matrix, meaning that all elements on the diagonal of the correlation coefficient matrix are 1 (indicating a correlation coefficient of 1 between the variable and itself), and all elements off the diagonal are zero (indicating no correlation between the variables). The test statistic is derived from the determinant of the correlation coefficient matrix. If this statistic is large, and its corresponding p-value is less than the user-defined significance level (usually 0.05), then the null hypothesis should be rejected, indicating that the correlation coefficient matrix cannot be an identity matrix, meaning there is a correlation between the original variables, making it suitable for correlation analysis.

[0103] Optionally, using the Bartlett's test of sphericity and the KMO test together can more comprehensively assess whether the tristimulus values ​​are suitable for factor analysis. Only when both the KMO test and the Bartlett's test of sphericity pass can the tristimulus values ​​be considered suitable for factor analysis.

[0104] In practice, the KMO test method includes the following steps:

[0105] (1) Calculate the simple correlation coefficient matrix between the three spectral stimulus values. The simple correlation coefficient is an indicator that measures the linear relationship between two variables. For example, the simple correlation coefficient is the Pearson correlation coefficient.

[0106] (2) Calculate the partial correlation coefficient, which is the correlation between two variables after considering the influence of other variables. For three spectral stimulus values, it is necessary to calculate the partial correlation coefficient of each pair of spectral stimulus values ​​under the control of the influence of the third spectral stimulus value.

[0107] (3) Calculate the KMO value. The formula for calculating the KMO value is as follows:

[0108]

[0109] Where, r i,j a represents the simple correlation coefficient between variables i and j. i,j Let represent the partial correlation coefficient between variables i and j, where i and j represent any two of the tristimulus values.

[0110] (4) Based on the calculated KMO values, determine whether the three spectral stimulus values ​​are suitable for correlation analysis. In practice, the KMO value between any two spectral stimulus values ​​can be calculated, and the suitability of the three spectral stimulus values ​​for factor analysis can be determined based on each KMO value.

[0111] For example, suppose that for any two spectral stimuli, such as X and Y, the calculated KMO value is 0.7429, indicating a strong correlation between the two spectral stimuli X and Y, making them suitable for correlation analysis. Similarly, the KMO values ​​between X and Z, and Y and Z can be obtained. When the KMO values ​​between X and Z, and Y and Z are also greater than a certain value, such as greater than 0.6, the KMO test is considered passed, meaning that the three spectral stimuli are suitable for factor analysis. The next step can proceed directly to step S2022 or continue with the Bartlett's test of sphericity, proceeding to step S2022 if the Bartlett's test of sphericity is passed.

[0112] In step S2022, a correlation matrix is ​​calculated, which includes correlation coefficients representing the correlation between any two spectral stimulus values.

[0113] Optionally, the correlation coefficient can be of the Pearson correlation coefficient, Kendall's rank correlation coefficient, Spearman's rank correlation coefficient, or other types. In this embodiment, the Pearson correlation coefficient is used as an example for description. Specifically, the formula for calculating the Pearson correlation coefficient is as follows:

[0114]

[0115]

[0116] Where X and Y are any two spectral stimulus values, cov represents the covariance, σ represents the standard deviation, and X i Represents the i-th X, Y i This represents the i-th Y. This represents the average value of X. Let represent the average value of Y, and n represent the sample size. For example, for sample module A, the correlation matrix of the calculated tristimulus values ​​is shown in Table 1, and the corresponding heatmap of the correlation coefficients in Table 1 is shown below. Figure 3 As shown.

[0117] Table 1. Relevant matrix of Model A sample module

[0118]

[0119]

[0120] In Table 1, X, Y, and Z represent three spectral stimulus values. Figure 3 The shade of gray represents the magnitude of the correlation coefficient; the darker the gray, the closer the correlation coefficient is to 1. Figure 3The diagonal portion of the coordinate axis represents the correlation between any spectral stimulus value and itself, and its value is always 1. For example, the data in the first row and first column represents the correlation coefficient between the spectral stimulus value X and itself (i.e., the spectral stimulus value X). From Table 1 and... Figure 3 It can be seen that the correlation coefficients of the three spectral stimulus values ​​are all close to 1, indicating that the three spectral stimulus values ​​are almost perfectly correlated.

[0121] After obtaining the correlation matrix, the eigenvalues ​​of the correlation matrix are further calculated, and the number of spectral stimulus values ​​in the target influencing factor is determined based on the eigenvalues. The eigenvalues ​​of the correlation matrix are key parameters for measuring the strength of the linear relationship between multiple variables, and their calculation mainly relies on general methods for solving matrix eigenvalues. Various methods exist for calculating eigenvalues, such as the definition method, the characteristic polynomial method, and numerical methods. This embodiment utilizes mature eigenvalue calculation methods from related technologies to calculate the eigenvalues ​​of the correlation matrix.

[0122] For example, the formula for the eigenvalues ​​of a matrix is: λE-A=0, where A represents a z-order matrix, λ represents the eigenvalues ​​of matrix A, and E represents the identity matrix. For the correlation matrix shown in Table 1, z=3, substituting it into the above formula, three eigenvalues ​​can be obtained after calculation. To facilitate the determination of the relationship between the number of eigenvalues ​​and the number of spectral stimulus values, the relationship between the number of eigenvalues ​​and the number of spectral stimulus values ​​is visualized.

[0123] Optionally, the step of determining the number of spectral stimulus values ​​in the target impact factor based on the feature value includes: visually displaying a first curve, the first curve being used to represent the mapping relationship between the feature value and the number of spectral stimulus values; and taking the number of spectral stimulus values ​​corresponding to the inflection point in the first curve as the number of spectral stimulus values ​​in the target impact factor.

[0124] Please refer to Figure 4 , Figure 4 This diagram illustrates the mapping relationship between the number of eigenvalues ​​and the number of spectral stimulus values. The horizontal axis represents the number of spectral stimulus values, and the vertical axis represents the value of the eigenvalues. The number of spectral stimulus values ​​corresponding to the inflection point in the first curve is then used as the number of spectral stimulus values ​​in the target influencing factor. The inflection point is defined as the point where the first curve shows a broken line and the value of the eigenvalue drops sharply. Before the inflection point, increasing the number of spectral stimulus values ​​leads to a significant decrease in the value of the eigenvalue; after the inflection point, increasing the number of spectral stimulus values ​​results in a smaller change in the value of the eigenvalue, and the eigenvalue corresponding to the inflection point is greater than 0. For example... Figure 4In the illustrated embodiment, the x-coordinate of the inflection point is 1, meaning the number of spectral stimulus values ​​is 1. This implies that when the tristimulus values ​​are almost perfectly correlated, a single spectral stimulus value can be randomly selected from the tristimulus values ​​as the target influence factor. It is understood that for different modules, the number of spectral stimulus values ​​corresponding to the inflection point of the calculated first curve may be different, possibly one, two, or three.

[0125] (2) Determine the target influence factor based on the number of spectral stimulus values ​​in the target influence factor and the correlation matrix.

[0126] The process of determining the target impact factor differs depending on the number of spectral stimulus values ​​in the target impact factor. Optionally, the steps for determining the target impact factor based on the number of spectral stimulus values ​​in the target impact factor and the correlation matrix include:

[0127] (21) When the number of spectral stimulus values ​​in the target influence factor is 1, any one of the three spectral stimulus values ​​is randomly selected as the target influence factor, such as randomly selecting one of the spectral stimulus values ​​X, Y, and Z as the target influence factor.

[0128] (22) When the number of spectral stimulus values ​​in the target impact factor is 2, the two spectral stimulus values ​​corresponding to the maximum correlation coefficient in the correlation matrix are selected as the target impact factor, wherein the maximum correlation coefficient is less than 1. For example, when the number of spectral stimulus values ​​is 2, assuming that the maximum correlation coefficient less than 1 in the correlation matrix is ​​0.997, and the spectral stimulus values ​​corresponding to the maximum correlation coefficient are X and Y, then X and Y are selected as the target impact factor.

[0129] (23) When the number of spectral stimulus values ​​in the target impact factor is 3, the three spectral stimulus values ​​are directly used as the target impact factor.

[0130] In this embodiment of the disclosure, since the tristimulus values ​​are highly correlated, in order to reduce the number of influencing factors when building the target model and make the current model more accurate and simple, a correlation analysis is first performed on the tristimulus values. Then, based on the correlation analysis, at least one spectral stimulus value is selected from the tristimulus values ​​as the target influencing factor. When the number of determined spectral stimulus values ​​is less than 3, the effect of representing all influencing factors with the main influencing factor is achieved, which can realize the dimensionality reduction of the data, that is, reduce the number of variables in the target model, while retaining the information of the original data as much as possible.

[0131] Step S203: Establish a target model to represent the mapping relationship between the gamma register value and the target influence factor based on the actual gamma register value and the target influence factor value of the sample module.

[0132] In this embodiment of the disclosure, the gamma register of the sample module stores the gamma register value obtained during debugging, which is the actual gamma register value. After determining the target influence factor, it is necessary to establish a mapping relationship between the gamma register value and the target influence factor, which is the target model.

[0133] The number of spectral stimulus values ​​in the target influence factor affects the amount of sample data required to build the target model. The fewer the number of spectral stimulus values ​​in the target influence factor, the smaller the amount of sample data required to build the target model. For any target grayscale, the sample data consists of the target influence factor value measured by a color analyzer when the sample module displays the target grayscale image, i.e., the spectral stimulus value. For example, when the target influence factor includes one spectral stimulus value, for any target grayscale, each sample module can be controlled to display the target grayscale image 3-4 times and measure the corresponding spectral stimulus value; when there are two target influence factors, for any target grayscale, it may be necessary to control each sample module to display the target grayscale image 10 times or more and measure the corresponding spectral stimulus value.

[0134] In one possible implementation, the steps of establishing a target model representing the mapping relationship between the gamma register values ​​and the target impact factor based on the true gamma register values ​​and the target impact factor values ​​of the sample module include:

[0135] Step S2031: Based on the actual gamma register value and the target influence factor value of the sample module, establish at least two prediction models to represent the mapping relationship between the gamma register value and the target influence factor.

[0136] Step S2032: For any prediction model, obtain the predicted gamma register value of the prediction model, and calculate the error between the predicted gamma register value and the actual gamma register value.

[0137] Step S2033: Select the prediction model with the smallest error among all prediction models as the target model corresponding to the sample module model.

[0138] In this embodiment of the disclosure, in order to improve the prediction accuracy of the target model, at least two prediction models are established, and then the prediction model with the smallest error is selected as the target model from the at least two prediction models. Optionally, the number of prediction models is three, which are respectively represented as the first prediction model, the second prediction model, and the third prediction model.

[0139] In one possible implementation, the first prediction model, the second prediction model, and the third prediction model are of different types. Optionally, the first prediction model is a linear model, the second prediction model is a nonlinear model, and the third prediction model is a piecewise nonlinear model. The third prediction model includes a first sub-model and a second sub-model, wherein the gray level of the first sub-model is less than a first gray level threshold, and the gray level of the second sub-model is greater than or equal to the first gray level threshold.

[0140] This setup offers several advantages. First, different types of prediction models may learn different feature representations and patterns from the data. By establishing multiple different types of prediction models and selecting the best-performing one, higher prediction accuracy can be achieved compared to a single prediction model. Second, different types of prediction models have different assumptions and preferences regarding the data, thus they may have different adaptability to different types of data. By selecting the prediction model that best suits the sample module, the generalization ability of the prediction model can be enhanced. Furthermore, establishing multiple prediction models and selecting the best-performing one as the final target model can reduce the risk of failure for a single type of prediction model. In some cases, the performance of a single type of prediction model may fluctuate significantly due to changes in data or the influence of noise. By combining multiple different types of prediction models, such fluctuations can be reduced and stability improved.

[0141] Optionally, the first prediction model is a linear model, which can be expressed as:

[0142] When S = 1, R = a1*X1 + b1, G = a2*X1 + b2, B = a3*X1 + b3;

[0143] When S=2, R=a1*X1+b1*X2+c1, G=a2*X1+b2*X2+c2, B=a3*X1+b3*X2+c3;

[0144] When S=3, R=a1*X1+b1*X2+c1*X3+d1, G=a2*X1+b2*X2+c2*X3+d2, B=a3*X1+b3*X2+c3*X3+d3;

[0145] Where R, G, and B represent the predicted gamma register values ​​corresponding to the red, green, and blue color channels, respectively; X1, X2, and X3 represent the target influence factors; S represents the number of spectral stimulus values ​​in the target influence factors; and a1, b1, c1, d1, a2, b2, c2, d2, a3, b3, c3, and d3 represent the fitted weight coefficients. When S = 1, X1 is one of the spectral stimulus values ​​X, Y, and Z; when S = 2, X1 and X2 are any one of X, Y, and Z, and X1 and X2 are different; when S = 3, X1, X2, and X3 are one of X, Y, and Z, and X1, X2, and X3 are all different.

[0146] In practice, the first prediction model shown above is obtained by fitting the true gamma register value of the sample module with the measured target influence factor value (i.e., spectral stimulus value) of the sample module.

[0147] The fitting process is described using the first prediction model for a certain color channel (e.g., the red channel) when S=1 as an example. For instance, the display module has m bound-point gray levels, corresponding to m target gray levels, denoted as G1, G2, ..., Gm. The true gamma register values ​​of the red channel in the sample module are denoted as GAM11, GAM12, ..., GAM1m. The target influence factor values ​​(such as spectral stimulus values ​​X, Y, or Z) at the target gray levels G1, G2, ..., Gm, measured by a color analyzer, are denoted as X11, X12, ..., X1m. During fitting, multiple coordinate points (X11, GAM11), (X12, GAM12), ..., (X1m, GAM1m) are used to fit the model, resulting in the first prediction model for the red channel: R = a1*X1 + b1. Similarly, the first prediction models for the green and blue channels can be fitted. When S is greater than 1, the principle for fitting the first prediction model is the same, and will not be elaborated here.

[0148] For example, please refer to Figure 5 and Figure 6 , Figure 5 This is a diagram illustrating the error comparison between the predicted gamma register value and the actual gamma register value of the first prediction model. Figure 6 for Figure 5 The data of predicted gamma register values ​​and actual gamma register values ​​corresponding to each target grayscale level. Figure 5 The horizontal axis represents grayscale, and the vertical axis represents gamma register value. The blue curve represents the prediction curve formed by the predicted gamma register values ​​at different grayscale levels obtained through the first prediction model, and the orange curve represents the true gamma register value of the sample module. It can be understood that the true gamma register value of the sample module is discrete data, and each true gamma register value corresponds to a target grayscale or a bound-point grayscale. However, for ease of description... Figure 5 The values ​​of multiple real gamma registers are represented by continuous orange curves. Figure 6 The first column represents the target grayscale. The R column represents the true gamma register value of the red channel corresponding to each target grayscale. The R_pre column represents the predicted gamma register value of the red channel corresponding to each target grayscale obtained through the first prediction model. The R_diff column represents the difference between the R_pre and R column data. Similarly, the G column represents the true gamma register value of the green channel corresponding to each target grayscale, the G_pre column represents the predicted gamma register value of the green channel corresponding to each target grayscale obtained through the first prediction model, and the G_diff column represents the difference between the G_pre and G column data; the B column represents the true gamma register value of the blue channel corresponding to each target grayscale, the B_pre column represents the predicted gamma register value of the blue channel corresponding to each target grayscale obtained through the first prediction model, and the B_diff column represents the difference between the B_pre and B column data. The process of obtaining the R_pre column data is as follows: The target influence factor values ​​of the sample module at each target grayscale are measured using a color analyzer. These target influence factor values ​​are then substituted into the first prediction model for the red channel to obtain the predicted gamma register value for the red channel corresponding to the target grayscale. Similarly, the G_pre and B_pre column data can be obtained.

[0149] refer to Figure 5 and Figure 6 The smaller the difference between the predicted gamma register value and the actual gamma register value, that is, the smaller the error, the higher the prediction accuracy of the first prediction model. Conversely, the larger the difference between the predicted gamma register value and the actual gamma register value, the lower the prediction accuracy of the first prediction model. Figure 5 and Figure 6 This represents the first prediction model obtained based on a sample module of a certain model. Figure 5 and Figure 6 It can be seen that the difference between the predicted gamma register value and the actual gamma register value is relatively large for this model of sample module. To ensure the prediction accuracy of the final target model, if the error of the first prediction model exceeds the first error threshold, it indicates that the prediction accuracy is very low, and therefore this first prediction model cannot be used as the target model. Understandably, for other models of sample modules, the prediction accuracy of the obtained first prediction model can also be relatively high. Figure 5 and Figure 6 It only provides a set of sample data.

[0150] Optionally, the second prediction model is a nonlinear model, up to cubic, and by default, the target influence factor includes only one spectral stimulus value, such as any one of X, Y, and Z. As the previous analysis shows, the three spectral stimulus values ​​usually have a high correlation; therefore, it is acceptable to default to the target influence factor including only one spectral stimulus value. For example, the second prediction model can be expressed as:

[0151] R = a1 * X1 3 +b1*X1 2 +c1*X1+d1;

[0152] G = a² * X¹ 3 +b2*X1 2 +c2*X1+d2;

[0153] B = a³ * X¹ 3 +b3*X1 2 +c3*X1+d3;

[0154] R, G, and B represent the predicted gamma register values ​​corresponding to the red, green, and blue color channels, respectively. X1 represents the target influence factor, which can be any one of X, Y, and Z. a1, b1, c1, d1, a2, b2, c2, d2, a3, b3, c3, and d3 represent the fitted weight coefficients.

[0155] In practical implementation, the weighting coefficients shown above are obtained by fitting the actual gamma register of the sample module with the measured target influence factor value (i.e., spectral stimulus value) of the sample module. The specific fitting process can be found in the specific example of the first prediction model, and will not be described in detail here. For example, in one embodiment, the weighting coefficients obtained through polynomial fitting are as follows:

[0156] a1=0.00000887, b1=-0.01484406, c1=7.90668233, d1=1555.03343137;

[0157] a2=0.00001253, b2=-0.02174869, c2=11.60321901, d2=1131.31947988;

[0158] a3=0.00000581, b3=-0.01088559, c3=6.54118545, d3=1874.70832545;

[0159] Please refer to Figures 7 to 8 , Figure 7 This diagram illustrates the error comparison between the predicted gamma register value and the actual gamma register value of the second prediction model. Figure 8 for Figure 7 The data of predicted gamma register values ​​and actual gamma register values ​​corresponding to each target grayscale level. Figure 7 The horizontal axis represents grayscale, the vertical axis represents gamma register value, the continuous curve represents the prediction curve formed by the predicted gamma register values ​​at different grayscale levels obtained by the second prediction model, the blue scatter values ​​represent the true gamma register values ​​of the sample module, and the red curve on the left represents the prediction curve corresponding to the red channel, the green curve in the middle represents the prediction curve corresponding to the green channel, and the blue curve on the right represents the prediction curve corresponding to the blue channel. Figure 8 The data definitions for each column can be found in the reference. Figure 6 The difference lies in the description. Figure 8 The R_pre column represents the predicted gamma register value of the red channel corresponding to each target grayscale obtained through the second prediction model; the G_pre column represents the predicted gamma register value of the green channel corresponding to each target grayscale obtained through the second prediction model; and the B_pre column represents the predicted gamma register value of the blue channel corresponding to each target grayscale obtained through the second prediction model. Figure 8 It can be seen that in the second prediction model, the error levels of low grayscale and high grayscale are different. At low grayscale, the difference between the predicted gamma register value and the actual gamma register value is small, while at high grayscale, the difference between the predicted gamma register value and the actual gamma register value is large.

[0160] To improve prediction accuracy, the third prediction model in this embodiment employs a piecewise nonlinear model, using different sub-models for different grayscale intervals. Optionally, using a first grayscale threshold as the dividing point, the grayscale interval of the display module is divided into a first grayscale interval and a second grayscale interval. The first grayscale interval is a low grayscale interval where the grayscale is less than the first grayscale threshold, and the second grayscale interval is a high grayscale interval where the grayscale is greater than or equal to the first grayscale threshold. Correspondingly, the third prediction model includes a first sub-model and a second sub-model. For each target grayscale in the first grayscale interval, the first sub-model is used to predict its gamma register value; for each target grayscale in the second grayscale interval, the second sub-model is used to predict its gamma register value.

[0161] Optionally, the third prediction model is represented as:

[0162]

[0163] Where y represents the predicted gamma register value, X1 represents the target influence factor, Gray represents the target gray level, G0 represents the first gray level threshold, and a1, b1, c1, d1, a2, b2, c2, and d2 represent the fitted weight coefficients. For example:

[0164] For the red channel, the weighting coefficients of the third prediction model are:

[0165] a1=0.00000211, b1=-0.00420886, c1=3.39095058, d1=1978.0759;

[0166] a2=0.08608976, b2=-4.04854718, c2=69.20743046, d2=1453.7703.

[0167] For the green channel, the weighting coefficients of the third prediction model are:

[0168] a1=0.00000224, b1=-0.00472095, c1=4.01479007, d1=1869.6743;

[0169] a2=0.75912124, b2=-34.10615732, c2=448.48592615, d2=73.7117.

[0170] For the blue channel, the weighting coefficients of the third prediction model are:

[0171] a1=0.00000162, b1=-0.00333009, c1=2.87800404, d1=2264.0376;

[0172] a2=0.05921602, b2=-3.13091602, c2=57.32745871, d2=1828.841.

[0173] Please refer to Figures 9 to 10 , Figure 9 This diagram illustrates the error comparison between the predicted gamma register value and the actual gamma register value from the third prediction model. Figure 10 for Figure 9 The data of predicted gamma register values ​​and actual gamma register values ​​corresponding to each target grayscale level. Figure 9 The horizontal axis represents grayscale, the vertical axis represents gamma register value, the continuous curve represents the prediction curve formed by the predicted gamma register values ​​at different grayscale levels obtained through the third prediction model, and the scatter plot values ​​represent the actual gamma register values ​​of the sample module. Specifically, Figure 9 The three continuous curves in the first column on the left correspond to the prediction curves of the red, green, and blue channels, respectively. The three continuous curves in the second column in the middle correspond to the prediction curves formed by the second sub-models of the red, green, and blue channels, respectively. The three curves in the third column on the right correspond to the prediction curves formed by the first sub-models of the red, green, and blue channels, respectively. Figure 10 The definitions of each column of data can be found in [reference]. Figure 6 The difference lies in the description. Figure 10 The R_pre column represents the predicted gamma register value of the red channel corresponding to each target grayscale obtained through the third prediction model; the G_pre column represents the predicted gamma register value of the green channel corresponding to each target grayscale obtained through the third prediction model; and the B_pre column represents the predicted gamma register value of the blue channel corresponding to each target grayscale obtained through the third prediction model. Figure 10 and Figure 8 A comparison shows that Figure 10 Compared with the error of each target gray level Figure 8 It has decreased.

[0174] In this embodiment of the disclosure, Figures 5 to 10 The first, second, and third prediction models shown are all obtained from the same sample module. It can be seen that different prediction models correspond to different predicted gamma register values, and the differences are significant. To improve the accuracy of the predicted gamma register values, this disclosure proposes a scheme to select the prediction model with the smallest error among all prediction models as the target model for the corresponding model of the sample module; that is, selecting the model with the best prediction accuracy from the first, second, and third prediction models as the final target model.

[0175] In one possible implementation, for any prediction model, the steps of obtaining the predicted gamma register value of the prediction model and calculating the error between the predicted gamma register value and the true gamma register value include:

[0176] (1) For any prediction model, obtain the predicted gamma register value of the prediction model under different gray levels; (2) Calculate the error value between the predicted gamma register value and the actual gamma register value under different gray levels, and sum the error values ​​under different gray levels to obtain the sum of error values. The sum of error values ​​is used to represent the error between the predicted gamma register value and the actual gamma register value of the prediction model.

[0177] The different gray levels in steps (1) and (2) above are the target gray levels, that is, the binding point gray levels. For any gray level of any prediction model, the target influence factor value measured by the color analyzer at that gray level is input into the prediction model to obtain the prediction gamma register value of the prediction model at that gray level.

[0178] Assuming the red, green, and blue channels of the display module are designated as the first, second, and third channels respectively, the corresponding color channels of the module under test and the sample module include the first, second, and third channels. In this case, the error value calculated in step (2) includes the error value of the first channel, the error value of the second channel, and the error value of the third channel. The step of summing the error values ​​at different gray levels to obtain the sum of the error values ​​includes: summing the error values ​​of the first, second, and third channels at different gray levels to obtain the sum of the error values. The specific calculation formula is as follows:

[0179]

[0180] Where Diff represents the sum of error values, R_diff i G_diff represents the error value of the red channel in the i-th grayscale. i B_diff represents the green channel error value of the i-th grayscale level. i n1 represents the blue channel error value of the i-th gray level, and n1 represents the number of gray levels. For example, if the display module includes 27 target gray levels, then n1 equals 27.

[0181] Correspondingly, the step of selecting the prediction model with the smallest error among all prediction models as the target model of the corresponding model of the sample module is as follows: the prediction model with the smallest sum of error values ​​among all prediction models is selected as the target model of the corresponding model of the sample module.

[0182] In one possible implementation, the gamma modulation method further includes the following steps:

[0183] (1) Determine whether the minimum error value of the target model is greater than or equal to a first error threshold, wherein the minimum error value is the minimum of the first channel error value, the second channel error value, and the third channel error value; (2) If the determination result is yes, adjust the model parameters of the target model until the minimum error value is less than the first error threshold. For example, the first channel error value is represented as R_diff. i The error value of the second channel is represented as G_diff. i The error value of the third channel is represented as B_diff. i The minimum error value is R_diff i G_diff i B_diff i The minimum value in.

[0184] Adjusting the model parameters of the target model is essentially the process of adjusting the weight coefficients within it. This adjustment requires acquiring more sample data for refitting. For example, if the minimum error value of the target model is greater than or equal to the first error threshold, the sample module needs to display the target grayscale image multiple times. A color analyzer is then used to obtain the target influence factor values ​​for each display of the target grayscale image. More sample data is then used for fitting to obtain new weight coefficients, thus creating a new target model. This process is repeated until the minimum error value of the target model is less than the first error threshold.

[0185] In one possible implementation, please refer to Figure 11 For any given model, the steps for determining the error probability density distribution function corresponding to that model based on multiple sample modules include:

[0186] Step S301: Based on the target model of this model, obtain the predicted gamma register values ​​of the target model under different gray levels, and calculate the error value between the predicted gamma register values ​​and the actual gamma register values ​​under different gray levels.

[0187] Step S302: Group the obtained error values ​​according to their size, calculate the frequency or relative frequency of each group of error values, and then plot an error probability density distribution histogram with the error value on the x-axis and the frequency or relative frequency on the y-axis. The histogram can intuitively show the error distribution.

[0188] Step S303 involves fitting the error probability density distribution histogram to obtain the error probability density distribution function. In practice, a suitable probability density distribution function can be selected to fit the distribution of error values ​​based on the shape and characteristics of the histogram.

[0189] The error probability density distribution function can be used to calculate the probability that the error value falls within a certain interval. For example, the calculation formula is as follows:

[0190]

[0191] Here, X represents the error value, and P(a≤X≤b) represents the probability that the error value is greater than or equal to a and less than or equal to b. Similarly, based on the error probability density distribution function, when a certain probability value is known, the corresponding error value interval can be obtained. Optionally, the error value interval corresponding to a certain probability value can be determined by solving equations or looking up tables.

[0192] For example, please refer to Figure 12 , Figure 12This is a histogram of the error probability density distribution for different sample modules. The horizontal axis represents the error value, and the vertical axis represents the frequency. The positive and negative values ​​represent the direction of deviation of the error value, that is, the direction of deviation of the predicted value from the true value. A positive sign indicates that the predicted value is greater than the true value, and a negative sign indicates that the predicted value is less than the true value. Figure 12 In the error probability density distribution histogram on the left, the error values ​​mainly range from -150 to 150, and are mainly distributed between -50 and 40. In the error probability density distribution histogram on the right, the error values ​​also mainly range from -150 to 150, but are mainly distributed between -50 and 70. It can be seen that the error probability density distributions of different sample modules are different, and the error probability density distribution functions obtained by subsequent fitting are also different.

[0193] For each model of sample module, after determining the target model and error probability density distribution function corresponding to each model through the above description, steps S101 to S104 can be executed.

[0194] In one possible implementation, the step of adjusting the first gamma register value using an error probability density distribution function includes:

[0195] Step S401: Obtain a first probability value and obtain the first error value interval corresponding to the first probability value from the error probability density distribution function.

[0196] Optionally, the first probability value is randomly generated, and the value of the first probability value is greater than 0 and less than or equal to 1. For example, in the error probability density distribution, the error value has an 80% probability of falling within the error value range of 0-10. If the randomly generated first probability value α is between 0 and 0.8, then the first error value range is determined to be 0-10.

[0197] Step S402: Determine a first error value from the first error value range according to the first method, and adjust the first gamma register value with the first error value as the step size.

[0198] The first error value range may include a set of error values. When adjusting the first gamma register value, a specific error value needs to be selected from the first error value range as the step size for adjustment. Optionally, the method of selecting this specific error value is denoted as the first method.

[0199] For example, the step of determining the first error value from the first error value interval according to the first method includes: determining the mean, edge value, or fixed position value of the first error value interval as the first error value. Alternatively, the first method can be expressed as selecting the mean, edge value (which may be the maximum value, minimum value, or one of both), or 3 / 4 value (i.e., fixed position value) of the first error value interval as the first error value. Then, the first gamma register value is adjusted with the first error value as a step size. Specifically, the adjustment process involves: using the sum of the first error value and the first gamma register value as the adjusted first gamma register value, and writing the adjusted first gamma register value into the gamma register of the module under test.

[0200] Step S403: When the module under test performs gamma adjustment using the adjusted first gamma register value, it is detected whether the display screen of the module under test meets the requirements. If it meets the requirements, the adjustment ends. If it does not meet the requirements, the step of adjusting the first gamma register value using the error probability density distribution function is repeated until the display screen of the module under test meets the requirements. Specifically, if it does not meet the requirements, the process returns to step S401, that is, a probability value is randomly generated again as the first probability value, and steps S402 to S403 are repeated.

[0201] It is understandable that the first probability value generated randomly again can be the same as or different from the first random probability value generated. Furthermore, in other embodiments, the first probability value may not be randomly generated, but rather adjusted in a certain order, such as obtaining first probability values ​​of 0.8, 0.5, 0.4, 0.2, etc.

[0202] In this embodiment, for display modules of the same model, the errors between the predicted and actual values ​​of each display module are basically similar under a fixed grayscale. Therefore, when adjusting the first gamma register value of the module under test, a probabilistic value can be selected from the error probability density distribution of the fixed grayscale. For example, based on the error probability density distribution function, a first error value can be randomly determined and the first error value can be used as the step size to adjust the first gamma register value. This adjustment process can take into account the randomness and uncertainty of the error, thereby more comprehensively reflecting the actual situation. At the same time, since the error probability density distribution function is derived from the sample data of multiple sample modules, this scheme also has a certain statistical reliability and scientific validity. For any module under test, the accuracy of its adjusted first gamma register value can be guaranteed, and the debugging time of the first gamma register value can be shortened. Furthermore, the error probability density distribution function in this embodiment includes not only the magnitude of the error but also the direction of the error (represented by the positive or negative of the error value). By quantifying the magnitude and direction of the error, the deviation between the predicted value and the true value can be represented more accurately. When the first error value is subsequently determined using the error probability density distribution function, the obtained first error value also includes both magnitude and direction. When the first error value is used to adjust the first gamma register value, the reliability of the adjusted first gamma register value can be improved, and its stability can be increased.

[0203] Optionally, the gamma adjustment method in this embodiment can be applied to the driving unit of a display module. The driving unit includes a gamma register for storing gamma register values. The gamma adjustment method in this embodiment is used to generate a first gamma register value that meets the requirements and store it in the gamma register.

[0204] Based on the same inventive concept, a second aspect of this disclosure provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the gamma modulation method described above. In specific implementations, the computer storage medium may include various storage media capable of storing program code, such as a Universal Serial Bus Flash Drive (USB), a portable hard drive, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

[0205] Based on the same inventive concept, a third aspect of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the gamma adjustment method as described above. Since the principle by which the computer program solves the problem is similar to the principle of the display driving method, the implementation of the computer program can be found in the implementation of the display driving method, and repeated details will not be elaborated further.

[0206] Computer program products may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable 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 thereof.

[0207] In this embodiment, the display module can be an organic light-emitting diode (OLED) display module. It is understood that the display module can also be of other types depending on actual needs; for example, it can be a quantum dot light-emitting diode (QLED) display module or a micro light-emitting diode (MicroLED) display module, etc.

[0208] Obviously, the above embodiments of this disclosure are merely examples for clearly illustrating this disclosure, and are not intended to limit the implementation of this disclosure. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all implementation methods here. Any obvious variations or modifications derived from the technical solutions of this disclosure are still within the protection scope of this disclosure.

Claims

1. A gamma adjustment method, characterized by, The method comprises the following steps: acquiring a model of a to-be-tested module, determining a target model corresponding to the model and an error probability density distribution function according to the model, the target model being used to represent a mapping relationship between a gamma register value and a target influence factor, and the error probability density distribution function being used to represent an error distribution between a predicted gamma register value output by the target model and a real gamma register value; acquiring a target influence factor value when the to-be-tested module displays a target gray scale picture, and inputting the target influence factor value into the target model to obtain a first gamma register value; detecting whether a display picture of the to-be-tested module meets a requirement when the to-be-tested module performs gamma adjustment by using the first gamma register value; when the display picture does not meet the requirement, adjusting the first gamma register value by using the error probability density distribution function, and repeatedly performing the step of detecting whether the display picture of the to-be-tested module meets the requirement when the to-be-tested module performs gamma adjustment by using the first gamma register value until the display picture meets the requirement.

2. The gamma adjustment method of claim 1, wherein, The step of adjusting the first gamma register value by using the error probability density distribution function comprises: acquiring a first probability value, and acquiring a first error value interval corresponding to the first probability value from the error probability density distribution function; determining a first error value from the first error value interval according to a first method, and adjusting the first gamma register value by using the first error value as a step.

3. The gamma adjustment method of claim 2, wherein, After the step of adjusting the first gamma register value by using the first error value as a step, the method further comprises: detecting whether the display picture of the to-be-tested module meets the requirement when the to-be-tested module performs gamma adjustment by using the adjusted first gamma register value; if the requirement is not met, repeatedly performing the step of adjusting the first gamma register value by using the error probability density distribution function until the display picture of the to-be-tested module meets the requirement.

4. The gamma adjustment method of claim 1, wherein, The method further comprises: acquiring sample modules of different models; for any model, determining a target model corresponding to the model and an error probability density distribution function according to a plurality of sample modules of the model.

5. The gamma adjustment method of claim 4, wherein, For any model, the step of determining a target model corresponding to the model according to a plurality of sample modules of the model comprises: acquiring an influence factor when a sample module displays a target gray scale picture, the influence factor comprising three spectral stimulus values; performing correlation analysis on the three spectral stimulus values, determining a target influence factor according to a correlation analysis result, the target influence factor comprising at least one of the three spectral stimulus values; establishing a target model used to represent a mapping relationship between a gamma register value and a target influence factor according to a real gamma register value of the sample module and a target influence factor value.

6. The gamma adjustment method of claim 5, wherein, The step of performing correlation analysis on the spectral stimulus values and determining a target influence factor according to a correlation analysis result comprises: detecting whether the three spectral stimulus values are suitable for correlation analysis; calculating a correlation matrix including correlation coefficients representing correlations between any two of the three spectral stimulus values when the three spectral stimulus values are suitable for correlation analysis; calculating eigenvalues of the correlation matrix, and determining the number of spectral stimulus values in the target influence factor according to the eigenvalues; determining the target influence factor according to the number of spectral stimulus values in the target influence factor and the correlation matrix.

7. The gamma adjustment method of claim 6, wherein, The step of determining the target influence factor according to the number of spectral stimulus values in the target influence factor and the correlation matrix comprises: when the number of spectral stimulus values in the target influence factor is 1, randomly selecting any one of the three spectral stimulus values as the target influence factor; when the number of spectral stimulus values in the target influence factor is 2, selecting two spectral stimulus values corresponding to the maximum correlation coefficient from the correlation matrix as the target influence factor, the maximum correlation coefficient being less than 1.

8. The gamma adjustment method of claim 6, wherein, The step of determining the number of spectral stimulus values in the target influence factor according to the eigenvalues comprises: visually displaying a first curve for representing a mapping relationship between the eigenvalues and the number of spectral stimulus values; taking the number of spectral stimulus values corresponding to an inflection point in the first curve as the number of spectral stimulus values in the target influence factor.

9. The gamma adjustment method of claim 5, wherein, The step of establishing the target model for representing the mapping relationship between the gamma register value and the target influence factor according to the real gamma register value and the target influence factor value of the sample module comprises: establishing at least two prediction models for representing the mapping relationship between the gamma register value and the target influence factor according to the real gamma register value and the target influence factor value of the sample module; for any prediction model, obtaining a predicted gamma register value of the prediction model, and calculating an error between the predicted gamma register value and the real gamma register value; taking the prediction model with the minimum error in each prediction model as the target model of the corresponding type of the sample module.

10. The gamma adjustment method of claim 9, wherein, The step of, for any prediction model, obtaining a predicted gamma register value of the prediction model, and calculating an error between the predicted gamma register value and the real gamma register value comprises: for any prediction model, obtaining predicted gamma register values of the prediction model at different gray scales; calculating error values between the predicted gamma register values at different gray scales and the real gamma register value; summing the error values at different gray scales to obtain a sum of error values; taking the prediction model with the minimum error in each prediction model as the target model of the corresponding type of the sample module.

11. The gamma adjustment method of claim 10, wherein, The color channels of the to-be-tested module comprise a first channel, a second channel and a third channel, and the error values comprise a first channel error value, a second channel error value and a third channel error value; The step of summing the error values at different gray scales to obtain a sum of error values comprises: summing the first channel error value, the second channel error value and the third channel error value at different gray scales to obtain a sum of error values. The gamma adjustment method further comprises: determining whether the minimum error value of the target model is greater than or equal to a first error threshold, the minimum error value being the minimum value among the first channel error value, the second channel error value and the third channel error value; if the determination result is yes, adjusting the model parameter of the target model until the minimum error value is less than the first error threshold.

12. The gamma adjustment method of claim 9, wherein, The number of the prediction models is three, which are respectively denoted as a first prediction model, a second prediction model and a third prediction model, the first prediction model is a linear model, the second prediction model is a nonlinear model, and the third prediction model is a piecewise nonlinear model, the third prediction model comprises a first sub-model and a second sub-model, the gray scale of the first sub-model is less than a first gray scale threshold, and the gray scale of the second sub-model is greater than or equal to the first gray scale threshold.

13. The gamma adjustment method of claim 3, wherein, The first probability value is randomly generated, and the first probability value is greater than 0 and less than or equal to 1. The step of determining the first error value from the first error value interval according to the first method comprises: determining a mean value, an edge value or a fixed position value of the first error value interval as the first error value.

14. A computer readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by a processor, implements the steps of the gamma adjustment method according to any one of claims 1 to 13.

15. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the gamma adjustment method according to any one of claims 1 to 13.

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