CN114927096A - Gamma calibration method, device, computer equipment, storage medium - Google Patents

Abstract

The present disclosure relates to a Gamma calibration method, device, computer equipment, and storage medium. The method includes: acquiring historical calibration parameters; performing parameter data processing on the historical calibration parameters to determine initial distribution parameters, the parameter data processing at least including: clustering processing and mean value processing; using preset unpredicted parameters, all The historical calibration parameters and the initial distribution parameters are obtained, and prediction parameters are obtained according to the maximum likelihood estimation method; Gamma calibration is performed through the prediction parameters. By adopting this method, it is not necessary to repeatedly try the parameter values of the register, and the number of times of trying the parameters of the register is reduced, so as to perform Gamma calibration.

Description

Gamma calibration method, device, computer equipment, storage medium

technical field

The present disclosure relates to the field of display technology, and in particular, to a Gamma calibration method, device, computer equipment, and storage medium.

Background technique

In the OLED production line, Gamma modulation is an iterative optimization technology that makes the panel chromaticity and brightness approach the target value by changing the module register value. The purpose is to coordinate the real linear response of the module with the non-linear response perceived by the human eye, so as to achieve a natural transition and distinct luminous effect.

At present, the existing Gamma adjustment algorithm mainly uses the optical probe to obtain the color coordinate value and brightness value of the corresponding binding point by giving the initial R, G, B three sets of IC register values, and then driving the IC to light the module. Confirm whether it falls within the error range of the target value. If it is within the range, the debugging is over; if it is not within the range, change the register value and reconfirm the brightness and color coordinate values.

However, the current calibration algorithm of Gamma needs to repeatedly try the value of the register, resulting in a long adjustment time, and each module needs to repeat the above parameter trying steps, resulting in low efficiency.

SUMMARY OF THE INVENTION

Based on this, it is necessary to provide a Gamma calibration method, apparatus, computer equipment, and storage medium that do not need to repeatedly try the value of the register and reduce the number of times of trying the parameters of the register, aiming at the above technical problems.

In a first aspect, the present disclosure provides a gamma calibration method. The method includes:

Get historical calibration parameters;

Perform parameter data processing on the historical calibration parameters to determine initial distribution parameters, the parameter data processing at least includes: clustering processing and mean value processing;

Using the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters, and obtaining the predicted parameters according to the maximum likelihood estimation method;

Gamma calibration is performed with the predicted parameters.

In one embodiment, the use of the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters, and obtaining the predicted parameters according to the maximum likelihood estimation method, includes:

Determine the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter;

Adjust the initial distribution parameters to obtain the adjusted initial distribution parameters;

Re-determining the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the adjusted initial distribution parameter;

In the case where the probability value is the largest, the prediction parameter is determined according to the adjusted initial distribution parameter.

In one embodiment, the determining the prediction parameter according to the adjusted initial distribution parameter includes:

Calculate the deviation value of the initial distribution parameter and the adjusted initial distribution parameter;

In the case that the deviation value is smaller than a preset deviation threshold value, determining a prediction parameter according to the adjusted initial distribution parameter;

In the case that the deviation value is greater than or equal to a preset deviation threshold value, the initial distribution parameter is re-adjusted until the prediction parameter is determined.

In one embodiment, the use of the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters, and obtaining the predicted parameters according to the maximum likelihood estimation method, includes:

The following formula is used to calculate the probability value of the preset unpredicted parameter:

Q _i (z ⁽ⁱ⁾ )=P(z ⁽ⁱ⁾ |x ⁽ⁱ⁾ ; θ)

The following formula is used to calculate the prediction parameter that maximizes the probability value:

Among them, z is the unpredicted parameter, x is the historical calibration parameter, θ is the initial distribution parameter; p is the probability value of the unpredicted parameter.

In one of the embodiments, performing parameter data processing on the historical calibration parameters to determine initial distribution parameters includes at least one of the following:

Calculate the mean value of the historical calibration parameters, and determine the initial distribution parameter according to the mean value;

or,

The cluster center of the historical calibration parameter is determined by a clustering algorithm, and the initial distribution parameter is determined according to the cluster center. The clustering algorithm at least includes: K-means algorithm, density clustering algorithm and hierarchical clustering algorithm.

In one of the embodiments, the method further includes: acquiring the medium historical calibration parameters of the current luminance curve;

After switching the brightness curve of Gamma calibration, the prediction parameters of the current brightness curve are obtained by calculating according to the historical calibration parameters of the current brightness curve.

In one embodiment, the method further includes:

storing the prediction parameters into a pre-built prediction parameter set;

When the number of prediction parameters in the prediction parameter set is greater than a preset parameter threshold, the prediction parameters are overwritten according to the generation time of the prediction parameters, so that the number of prediction parameters in the prediction parameter set is equal to the preset parameter threshold parameter threshold.

In a second aspect, the present disclosure also provides a Gamma calibration device. The device includes:

Data acquisition module for acquiring historical calibration parameters;

a data processing module for performing parameter data processing on the historical calibration parameters to determine initial distribution parameters, where the parameter data processing at least includes: clustering processing and mean value processing;

a parameter calculation module, configured to obtain the predicted parameters according to the maximum likelihood estimation method by using the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters;

A calibration module for performing Gamma calibration through the predicted parameters.

In a third aspect, the present disclosure also provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of any of the above method embodiments when the processor executes the computer program.

In a fourth aspect, the present disclosure also provides a computer-readable storage medium. The computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by a processor, implements the steps of any of the foregoing method embodiments.

In a fifth aspect, the present disclosure also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of any of the above method embodiments.

In the above embodiments, relatively stable intervals of most module parameters can be determined through historical calibration parameters, which can reduce the number of random parameter attempts. In this interval, the parameter data processing of the historical calibration parameters can further narrow the range of the prediction parameters and reduce the number of parameter attempts. However, when the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters are used, and the predicted parameters are obtained according to the maximum likelihood estimation method, the predicted parameters can be determined with the help of an algorithm, and each parameter does not need to be debugged. Increased efficiency. And it can maximize the brightness when the module is turned on for the first time to meet the target value, reduce the number of attempts to register parameters, thereby shortening the time for a single Gamma calibration and improving the productivity per unit time.

Description of drawings

In order to illustrate the specific embodiments of the present disclosure or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description The drawings are some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

1 is a schematic diagram of an application environment of the Gamma calibration method in one embodiment;

2 is a schematic flowchart of a Gamma calibration method in one embodiment;

3 is a schematic flowchart of the steps of a historical calibration parameter determination process in one embodiment;

Fig. 4 is the schematic flow chart of Gamma adjustment step in one embodiment;

FIG. 5 is a schematic diagram of the distribution of the expected value and the number of times of debugging in one embodiment;

6 is a schematic diagram of the distribution of standard deviation and debugging times in one embodiment;

7 is a schematic flowchart of step S206 in one embodiment;

Fig. 8 is a detailed flowchart of determining prediction parameters in one embodiment;

9 is a schematic flowchart of step S310 in one embodiment;

10 is a schematic flowchart of another Gamma calibration method in one embodiment;

11 is a schematic flowchart of a Gamma calibration method in another embodiment;

12 is a schematic structural block diagram of a Gamma calibration device in one embodiment;

FIG. 13 is a schematic diagram of the internal structure of a computer device in one embodiment.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the present disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure, but not to limit the present disclosure.

It should be noted that the terms "first", "second" and the like in the description and claims herein and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It is to be understood that data so used may be interchanged under appropriate circumstances such that the embodiments herein described can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product or device comprising a series of steps or units is not necessarily limited to those expressly listed Rather, those steps or units may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.

In this document, the term "and/or" is merely an association relationship for describing associated objects, indicating that three kinds of relationships can exist. For example, A and/or B can mean that A exists alone, A and B exist at the same time, and B exists alone. In addition, the character "/" in this document generally indicates that the related objects are an "or" relationship.

The embodiment of the present disclosure provides a Gamma calibration method, which can be applied to the application environment shown in FIG. 1 . The upper computer 102 communicates with the optical probe 106 and the signal generator 104 . The signal generator 104 drives the module product according to the signal transmitted by the host computer 102 , reads and writes the IC register of the module product, and transmits the IC register to the host computer 102 . The host computer 102 controls the optical probe 106 to collect luminance and color coordinate values. The upper computer 102 records historical calibration parameters after each time Gamma calibration is performed on the module product. When performing Gamma calibration on the next module product, the upper computer 102 acquires historical calibration parameters. The upper computer 102 performs parameter data processing on the historical calibration parameters to determine the initial distribution parameters. The parameter data processing performed by the host computer 102 may at least include: performing clustering processing on the parameter data or performing mean value processing on the parameter data. The host computer 102 obtains the predicted parameters according to the maximum likelihood estimation method by using the preset unpredicted parameters, historical calibration parameters and initial distribution parameters. The upper computer 102 performs Gamma calibration through the predicted parameters. The upper computer 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and the like. The signal generator can be a Pattern Generator signal generator, which is used to provide the module with electrical signals and drive the display signals. A separate module (such as a module that is not assembled into a finished mobile phone) has no external power supply, and the external display signal can be lit by providing voltage and current through a signal generator (for example, different brightness requires different current levels), display Signals (such as color pictures, grayscale pictures), etc. Modular products can include various types of finished screens (such as mobile phone screens, computer screens, monitor screens, TV screens, etc.).

In one embodiment, as shown in FIG. 2 , a Gamma calibration method is provided, and the method is applied to the host computer 102 in FIG. 1 as an example for description, including the following steps:

S202, obtaining historical calibration parameters.

The historical calibration parameters can usually be calibration parameters obtained after a new module product is calibrated by Gamma. Calibration parameters can usually be determined by a certain number of adjustments.

Specifically, when Gamma calibration is required for a new module product, historical calibration parameters of the module product corresponding to the new module product type can be acquired.

S204: Perform parameter data processing on the historical calibration parameters to determine initial distribution parameters, where the parameter data processing at least includes: clustering processing and mean value processing.

Wherein, the parameter data processing can generally be the method of processing the historical calibration parameters in this embodiment, in order to make the initial distribution parameters closer to the predicted parameters. The clustering process may be a way of processing historical calibration parameters through a clustering algorithm. Averaging can be a way of computing the average of historical calibration parameters.

Specifically, the historical calibration parameters can be processed through a cluster processing method or an average processing method, and the initial distribution parameters can be determined after the processing.

S206, using the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters, and obtaining the predicted parameters according to the maximum likelihood estimation method.

The maximum likelihood estimation method may be the EM algorithm. The EM algorithm can generally be called an expectation-maximization algorithm, which is a maximum-likelihood estimation method for solving probabilistic model parameters from incomplete data or data sets with missing data (with hidden variables).

Specifically, according to the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters give the expected estimates of the preset unpredicted parameters, that is, the probability values of the unpredicted parameters, according to the probability values of the unpredicted parameters, gives maximum likelihood estimates of the parameters of the initial distribution. The maximum likelihood estimation may be the prediction parameter in this embodiment. The preset unpredicted parameters can be latent variables in the maximum likelihood estimation method.

S208, Gamma calibration is performed by using the predicted parameters.

Specifically, after the prediction parameter is determined above, the prediction parameter can be regarded as a target value, and the R, G, and B register parameters are set through the target value to light up the module product.

In the above-mentioned Gamma calibration method, a relatively stable interval range of most of the module parameters can be determined through historical calibration parameters, which can reduce the number of random parameter attempts. In this range, the parameter data processing of the historical calibration parameters can further narrow the range of the prediction parameters and further reduce the number of parameter attempts. However, when the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters are used, and the predicted parameters are obtained according to the maximum likelihood estimation method, the predicted parameters can be determined with the help of an algorithm, and each parameter does not need to be debugged. Increased efficiency. And it can maximize the brightness when the module is turned on for the first time to meet the target value, reduce the number of attempts to register parameters, thereby shortening the time for a single Gamma calibration and improving the productivity per unit time.

In one embodiment, as shown in FIG. 3 , the process of determining the historical calibration parameters includes:

Determine whether the current module has a predicted value.

If there is a predicted value in the currently tested module, read the current predicted parameter value, use the predicted value to set the parameters of the register, perform Gamma adjustment, and record the historical calibration parameters.

If there is no predicted value for the currently tested module, determine whether there is pre-stored parameter data set during the last adjustment (stored parameter data), if so, read the last parameter data (stored parameter data) ), set the parameters of the register according to the last parameter data, perform Gamma adjustment, and record the historical calibration parameters.

If not, set the initial value, set the parameters of the register according to the initial value, perform Gamma adjustment, and record the historical calibration parameters.

It can be understood that, in some embodiments of the present disclosure, the predicted value, the initial value and the predicted parameter may all be parameters of the register. Registers usually refer to R, G, B registers.

As shown in Figure 4, the Gamma adjustment steps include: lighting the module through the parameters of the register, and collecting the brightness and color coordinate values on the module after optical detection. Judging whether the brightness and color coordinate values are within the range, if so, end, if not, adjust the parameters of the register. When the number of times of adjusting the parameters of the register is small, the predicted parameters obtained by using the obtained historical calibration parameters vary greatly. When the number of times of adjusting the parameters of the register reaches a certain number, the accuracy of the predicted parameters is improved, and the purpose of optimization can be achieved. As shown in Table 1, the debugging relationship table.

Table 1 Debug relationship table

Debug times expected value variance standard deviation Time (s) 10 2615 25806.4 160.6437 0.094 50 2664.02 33207.7 182.2298 0.094 200 2638.68 31609.1 177.7895 0.297 500 2648.95 31511.4 177.5145 0.781 2000 2641.48 31220.5 176.6932 2.907 5000 2638.12 31243.8 176.7592 6.453 ... ... ... ... ...

According to the debugging relationship table in Table 1, the relationship diagrams as shown in FIG. 5 and FIG. 6 can be obtained. As shown in Fig. 5 and Fig. 6, when the debugging times reach about 500 times, the expected value and the standard deviation can be stabilized. Therefore, in this embodiment, the certain number can be 500 times, and the corresponding historical calibration parameters obtained are relatively consistent prediction parameters.

In one embodiment, as shown in FIG. 7 , using the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters to obtain the predicted parameters according to the maximum likelihood estimation method, including:

S302: Determine the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter.

S304: Adjust the initial distribution parameters to obtain the adjusted initial distribution parameters.

S306: Re-determine the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter, and the adjusted initial distribution parameter.

S308, determine whether the probability value is the largest.

S310, in the case where the probability value is the largest, determine a prediction parameter according to the adjusted initial distribution parameter.

Specifically, as shown in Figure 8, the prediction parameters obtained by the maximum likelihood estimation method can be divided into an Expectation step and a Maximization step. The Expectation step may include: determining initial distribution parameters and unpredicted parameters. The unpredicted parameter may be regarded as a latent variable in the maximum likelihood estimation method, which may be an unobserved parameter, and may be an unpredicted parameter in this embodiment. Maximization step: Determine the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter. The initial distribution parameters are continuously adjusted to obtain the adjusted initial distribution parameters. Step S304 is repeated continuously. And recalculate the probability values of the preset unpredicted parameters. In the case where the probability value is the largest, it is proved that the adjusted initial distribution parameters meet the requirements, and the prediction parameters are determined according to the adjusted initial distribution parameters.

The above expressions are descriptions in principle, and the following descriptions are given in a specific manner. In another implementation of this embodiment, the Expectation step: the following formula can be used to calculate the probability value of the preset unpredicted parameter:

Q _i (z ⁽ⁱ⁾ )=P(z ⁽ⁱ⁾ |x ⁽ⁱ⁾ ; 6)

Maximization step: Use the following formula to calculate the prediction parameters that maximize the probability value:

Among them, z is the unpredicted parameter, x is the historical calibration parameter, θ is the initial distribution parameter; p is the probability value of the unpredicted parameter, p(z ⁽ⁱ⁾ |x ⁽ⁱ⁾ ; θ) represents the posterior distribution of the predicted parameter .

Specifically, for each i, the posterior distribution of the predicted parameters (which can be regarded as the expectation of the predicted parameters) is calculated according to the model parameters or initial distribution parameters of the previous iteration, as the current estimated value of the unpredicted parameters. Find the initial distribution parameters that maximize Q _i (z ⁽ⁱ⁾ ). The likelihood function is maximized to obtain a new initial distribution parameter, which can be a prediction parameter.

Q _i (z ⁽ⁱ⁾ ) is obtained and substituted into the θ formula of the Maximization step, and the θ formula obtains a new θ, which is equivalent to the adjusted initial distribution parameters. The adjusted initial distribution parameters are reversely substituted into Q _i (z ⁽ⁱ⁾ ), and through continuous iteration, the initial distribution parameters θ that maximize the likelihood function can be obtained. The initial distribution parameter θ that maximizes the likelihood function can be the final prediction parameter.

In this embodiment, by using the maximum likelihood estimation method, the optimal convergence value can be found very reliably, and then the predicted value can be determined, and the initial distribution parameters are also obtained through parameter data processing before, so it can be further improved. Efficiency of Likelihood Estimation Convergence.

In one embodiment, as shown in FIG. 9 , the determining of the prediction parameters according to the adjusted initial distribution parameters includes:

S402, calculating the deviation value of the initial distribution parameter and the adjusted initial distribution parameter;

S404, determine whether the deviation value is less than a preset deviation threshold;

S406, when the deviation value is smaller than a preset deviation threshold value, determine a prediction parameter according to the adjusted initial distribution parameter;

S408, when the deviation value is greater than or equal to a preset deviation threshold value, readjust the initial distribution parameter until a prediction parameter is determined.

Wherein, the initial distribution parameter in this embodiment may be the adjusted initial distribution parameter and the previous initial distribution parameter. For example, if the initial distribution parameter is A, the initial distribution parameter obtained by one adjustment is A1, and the initial distribution parameter obtained by one adjustment is A2, where A2 is the initial distribution parameter after adjustment, then the initial distribution parameter mentioned in this embodiment is A1.

Specifically, when the prediction parameter is determined above, how to prove that the prediction parameter is valid and can be used with the final value can be verified by this embodiment. That is, whether the maximum likelihood estimation method converges. Calculate the deviation value of the initial distribution parameter and the adjusted initial distribution parameter. If the deviation value is small or the deviation value is 0, and the deviation value is smaller than the preset deviation threshold, it is determined that the maximum likelihood estimation method has converged, and the adjusted initial The distribution parameters are determined as prediction parameters. If the deviation value is large and the deviation value is greater than the preset deviation threshold, it is determined that the maximum likelihood estimation method has converged, and the initial distribution parameters need to be adjusted again, that is, returning to step S304 until the prediction parameters are determined.

In this embodiment, the deviation value is used to determine whether the maximum likelihood estimation method converges, so that the prediction parameters can be accurately determined, so that the prediction parameters conform to the target value, and the accuracy of Gamma calibration is improved.

In one embodiment, performing parameter data processing on the historical calibration parameters to determine initial distribution parameters includes at least one of the following:

Calculate the mean value of the historical calibration parameters, and determine the initial distribution parameter according to the mean value;

or,

The cluster center of the historical calibration parameter is determined by a clustering algorithm, and the initial distribution parameter is determined according to the cluster center. The clustering algorithm at least includes: K-means algorithm, density clustering algorithm and hierarchical clustering algorithm.

Specifically, the mean value of the historical calibration parameters can be calculated, and the mean value can be set as the initial distribution parameter. This method compromises all the historical calibration parameters and will not generate large deviations. Compared with randomly selecting the initial distribution parameters, it can shorten the time Adjust the time. Alternatively, the cluster center of the historical calibration parameter is determined by a clustering algorithm, and the cluster center usually represents a prediction parameter in the historical calibration parameter that is closer to the actual, and the adjustment time can be shortened compared with random selection.

In this embodiment, the initial distribution parameters are determined in two ways. Compared with randomly selecting the initial distribution parameters, the speed of determining the prediction parameters can be improved, and the time for Gamma calibration can be shortened.

In one embodiment, the method further includes: acquiring a medium history calibration parameter of the current luminance curve;

After switching the brightness curve of Gamma calibration, the prediction parameters of the current brightness curve are obtained by calculating according to the historical calibration parameters of the current brightness curve.

Usually, there are different grayscale Gamma values under a luminance value, forming a Gamma curve, which is usually called a luminance curve.

Specifically, when performing Gamma calibration, it is usually necessary to test a module under different luminance curves. Every time you perform Gamma calibration, you need to obtain the historical calibration parameters in the current luminance curve. When switching the brightness curve of Gamma calibration, the prediction parameter of the current brightness curve can be obtained by calculating according to the historical calibration parameters in the current brightness curve obtained before. A mode in which all grayscales under a single luminance curve are calculated at the same time can be used. Among them, the gray scale refers to the brightness change between the brightest and the darkest, which is generally represented by 8 bits and has 256 brightness steps. Simultaneous calculation refers to multiple grayscales under a luminance curve (different products will select representative grayscales from 256 grayscales, such as 1, 3, 5, 9, 13, 17, 22, 29 , 35, 41, 51, 65, 81, 93, 104, 114, 134, 159, 177, 198, 225, 247, 255) parameters simultaneously predict the R, G, B register parameters. (For how to predict, reference may be made to the above-mentioned embodiment, which will not be repeated here).

Therefore, there is no effect on switching the brightness curve. When the next new module needs to predict parameters, and the module is switched to the current brightness curve, the predicted parameters of the current brightness curve can be used to perform Gamma calibration without waiting.

In one embodiment, the method further includes:

storing the prediction parameters into a pre-built prediction parameter set;

When the number of prediction parameters in the prediction parameter set is greater than a preset parameter threshold, the prediction parameters are overwritten according to the generation time of the prediction parameters, so that the number of prediction parameters in the prediction parameter set is equal to the preset parameter threshold parameter threshold.

Specifically, as the number of accumulated test modules in production increases, the obtained prediction parameters may be stored in a pre-built prediction parameter set. Usually, the prediction parameters stored in the preset data set are limited and will not occupy too much storage space. Therefore, when the number of prediction parameters in the prediction parameter set is greater than the preset parameter threshold, and the If a new prediction parameter is selected, the prediction parameter can be covered according to the generation time of the prediction parameter.

In some exemplary embodiments, for example, the prediction parameters stored in the prediction parameter set according to the generation time are B, B1, and B2. Among them, B is the prediction parameter with the earliest generation time. When the number of prediction parameters in the prediction parameter set is greater than the preset parameter threshold, and a new prediction parameter C is generated during calibration, C can be covered by B, and the prediction parameters in the prediction parameter set can be: B1, B2 , C.

In this embodiment, because the prediction parameters are always in the process of production and accumulation, if the prediction parameter set is full and stops updating the data, the prediction parameters may be inaccurate due to the difference between different batches of products. The generation time of the prediction parameter covers the prediction parameter, which can avoid the above problem.

In one embodiment, as shown in FIG. 10 , an embodiment of the present disclosure further provides another Gamma calibration method, which includes the following steps:

In the case of replacing the module, the predicted value of the product of the module is determined, and the predicted value may be previously stored or set by those skilled in the art. Write the predicted value into the R, G, B registers, then light the module. The optical probe collects the color coordinate value, brightness and other data of the module product. Determine whether the color coordinate value, brightness and other data are within the target range. If so, historical calibration parameters can be determined from the predicted values. Determine the next predicted value for this type of module based on historical calibration parameters.

If not, you can fine-tune the predicted value, and then adjust the register parameters according to the fine-tuned predicted value, until the module is turned on, the color coordinate value, brightness and other data are within the target range.

It should be noted that the fine-tuning prediction value here can be fine-tuned by using the characteristic that the error between the same modules conforms to the normal distribution. Specifically, the normal distribution is a parameter that analyzes the actual debugging of a certain number of existing modules. It is obtained that based on this characteristic, the parameters of most modules will be in a relatively stable range, and the predicted value can be fine-tuned within this range, which can reduce the number of fine-tuning. Those skilled in the art can also fine-tune the predicted value in other ways.

In another embodiment, as shown in FIG. 11 , an embodiment of the present disclosure further provides another Gamma calibration method, which includes the following steps:

S502, if there is a predicted value in the currently tested module, use the predicted value to set the parameters of the register, perform Gamma adjustment, and record the historical calibration parameters.

S504, if the currently tested module does not have a predicted value, determine whether there is pre-stored parameter data set during the last adjustment.

S506, if yes, read the last parameter data, set the parameters of the register according to the last parameter data, perform Gamma adjustment, and record the historical calibration parameters.

S508, if not, set the initial value, set the parameters of the register according to the initial value, perform Gamma adjustment, and record the historical calibration parameters.

S510, acquiring historical calibration parameters.

S512: Calculate the mean value of the historical calibration parameters, and determine the initial distribution parameter according to the mean value.

Or, S514, determine the cluster center of the historical calibration parameter by using a clustering algorithm, and determine the initial distribution parameter according to the cluster center.

S516: Determine the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter, and the initial distribution parameter.

S518: Adjust the initial distribution parameters to obtain the adjusted initial distribution parameters.

S520: Re-determine the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter, and the adjusted initial distribution parameter.

S522, determine whether the probability value is the largest.

S524, in the case that the probability value is the largest, calculate the deviation value between the initial distribution parameter and the adjusted initial distribution parameter.

S526: Determine whether the deviation value is smaller than a preset deviation threshold value.

S528, in the case that the deviation value is smaller than a preset deviation threshold value, determine a prediction parameter according to the adjusted initial distribution parameter.

In the case that the deviation value is greater than or equal to a preset deviation threshold value, the initial distribution parameter is re-adjusted until the prediction parameter is determined.

For the specific implementation manners and limitations in this embodiment, reference may be made to the foregoing embodiment, which will not be repeated in this embodiment.

It should be understood that, although the steps in the flowcharts involved in the above embodiments are sequentially displayed according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited to the order, and these steps may be performed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but may be performed at different times The execution order of these steps or phases is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or phases in the other steps.

Based on the same inventive concept, an embodiment of the present disclosure also provides a Gamma calibration device for implementing the above-mentioned Gamma calibration method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more embodiments of the Gamma calibration device provided below can refer to the above limitations on the Gamma calibration method, It is not repeated here.

In one embodiment, as shown in FIG. 12, a Gamma calibration device 1100 is provided, including: a data acquisition module 1102, a data processing module 1104, a parameter calculation module 1106 and a calibration module 1108, wherein:

a data acquisition module 1102, configured to acquire historical calibration parameters;

The data processing module 1104 is configured to perform parameter data processing on the historical calibration parameters to determine initial distribution parameters, the parameter data processing at least includes: clustering processing and mean value processing;

A parameter calculation module 1106, configured to obtain the predicted parameters according to the maximum likelihood estimation method by using the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters;

The calibration module 1108 is configured to perform Gamma calibration by using the predicted parameters.

In an embodiment of the apparatus, the parameter calculation module 1106 includes:

an adjustment module to adjust the initial distribution parameters to obtain the adjusted initial distribution parameters;

a probability value calculation module, configured to determine the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter, and, according to the historical calibration parameter, the unpredicted parameter and the adjusted initial distribution parameter, The probability value of the preset unpredicted parameter is re-determined.

The prediction parameter determination module is configured to determine the prediction parameter according to the adjusted initial distribution parameter when the probability value is the largest.

In an embodiment of the apparatus, the parameter calculation module 1106 further includes: a deviation value calculation module, configured to calculate the deviation value of the initial distribution parameter and the adjusted initial distribution parameter.

The prediction parameter determination module is further configured to determine a prediction parameter according to the adjusted initial distribution parameter when the deviation value is smaller than a preset deviation threshold.

The adjustment module is further configured to readjust the initial distribution parameter until the prediction parameter is determined when the deviation value is greater than or equal to a preset deviation threshold.

In an embodiment of the device, the parameter calculation module 1106 also uses the following formula to calculate the probability value of the preset unpredicted parameter:

Q _i (z ⁽ⁱ⁾ )=P(z ⁽ⁱ⁾ |x ⁽ⁱ⁾ ; θ)

The following formula is used to calculate the prediction parameter that maximizes the probability value:

Among them, z is the unpredicted parameter, x is the historical calibration parameter, θ is the initial distribution parameter; p is the probability value of the unpredicted parameter.

In an embodiment of the apparatus, the data processing module 1104 includes: a mean value processing module, configured to calculate the mean value of the historical calibration parameters, and determine the initial distribution parameter according to the mean value.

The clustering processing module is used to determine the cluster center of the historical calibration parameter through a clustering algorithm, and determine the initial distribution parameter according to the cluster center, and the clustering algorithm at least includes: K-means algorithm, density clustering algorithm and Hierarchical clustering algorithm.

In an embodiment of the apparatus, the apparatus further includes: a switching module, configured to acquire the mid-history calibration parameters of the current luminance curve, and after switching the luminance curves for Gamma calibration, calculate the mid-history calibration parameters according to the mid-history calibration parameters of the current luminance curve Get the prediction parameters of the current brightness curve.

In an embodiment of the apparatus, the apparatus further comprises: a parameter set storage module, configured to store the prediction parameters into a pre-built prediction parameter set.

A prediction parameter covering module is configured to cover the prediction parameters according to the generation time of the prediction parameters when the number of prediction parameters in the prediction parameter set is greater than a preset parameter threshold, so that the prediction parameters in the prediction parameter set are The number is equal to the preset parameter threshold.

Each module in the above-mentioned Gamma calibration device can be implemented in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided, and the computer device may be a server, and its internal structure diagram may be as shown in FIG. 13 . The computer device includes a processor, memory, and a network interface connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The nonvolatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium. The computer facility's database is used to store predicted parameter data and historical calibration parameter data. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program when executed by the processor implements a gamma calibration method.

Those skilled in the art can understand that the structure shown in FIG. 13 is only a block diagram of a partial structure related to the solution of the present disclosure, and does not constitute a limitation on the computer equipment to which the solution of the present disclosure is applied. The specific computer device may be Include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, including a memory and a processor, where a computer program is stored in the memory, and the processor implements the steps in the foregoing method embodiments when the processor executes the computer program.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

In one embodiment, a computer program product is provided, including a computer program, which implements the steps in each of the foregoing method embodiments when the computer program is executed by a processor.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage In the medium, when the computer program is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to memory, database or other media used in the various embodiments provided by the present disclosure may include at least one of non-volatile and volatile memory. Non-volatile memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive Random Memory) Access Memory, MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration and not limitation, the RAM may be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM). The database involved in the various embodiments provided by the present disclosure may include at least one of a relational database and a non-relational database. The non-relational database may include a blockchain-based distributed database, etc., but is not limited thereto. The processors involved in the various embodiments provided by the present disclosure may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., and are not limited to this.

The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features It is considered to be the range described in this specification.

The above-mentioned embodiments only represent several embodiments of the present disclosure, and the descriptions thereof are relatively specific and detailed, but should not be construed as limiting the scope of the present disclosure. It should be noted that, for those skilled in the art, without departing from the concept of the present disclosure, several modifications and improvements can be made, which all belong to the protection scope of the present disclosure. Accordingly, the scope of protection of the present disclosure should be determined by the appended claims.

Claims (10)

1. a Gamma calibration method, is characterized in that, described method comprises: Get historical calibration parameters; Perform parameter data processing on the historical calibration parameters to determine initial distribution parameters, the parameter data processing at least includes: clustering processing and mean value processing; Using the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters, and obtaining the predicted parameters according to the maximum likelihood estimation method; Gamma calibration is performed with the predicted parameters. 2. The method according to claim 1, characterized in that, said using preset unpredicted parameters, said historical calibration parameters and said initial distribution parameters, and obtaining predicted parameters according to a maximum likelihood estimation method, comprising: : Determine the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter; Adjust the initial distribution parameters to obtain the adjusted initial distribution parameters; Re-determining the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the adjusted initial distribution parameter; In the case where the probability value is the largest, the prediction parameter is determined according to the adjusted initial distribution parameter. 3. The method according to claim 2, wherein the determining the prediction parameter according to the adjusted initial distribution parameter comprises: Calculate the deviation value of the initial distribution parameter and the adjusted initial distribution parameter; In the case that the deviation value is smaller than a preset deviation threshold value, determining a prediction parameter according to the adjusted initial distribution parameter; In the case that the deviation value is greater than or equal to a preset deviation threshold value, the initial distribution parameter is re-adjusted until the prediction parameter is determined. 4. The method according to claim 1 or 2, wherein the predicted parameters are obtained according to the maximum likelihood estimation method by using the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters. ,include: The following formula is used to calculate the probability value of the preset unpredicted parameter: Q _i (z ⁽ⁱ⁾ )=p(z ⁽ⁱ⁾ |x ⁽ⁱ⁾ ; θ) The following formula is used to calculate the prediction parameter that maximizes the probability value:

Among them, z is the unpredicted parameter, x is the historical calibration parameter, θ is the initial distribution parameter; p is the probability value of the unpredicted parameter. 5. The method according to claim 1, characterized in that, performing parameter data processing on the historical calibration parameters to determine initial distribution parameters, comprising at least one of the following: Calculate the mean value of the historical calibration parameters, and determine the initial distribution parameter according to the mean value; or, The cluster center of the historical calibration parameter is determined by a clustering algorithm, and the initial distribution parameter is determined according to the cluster center. The clustering algorithm at least includes: K-means algorithm, density clustering algorithm and hierarchical clustering algorithm. 6. The method according to any one of claims 1-5, wherein the method further comprises: Obtain the mid-history calibration parameters of the current brightness curve; After switching the brightness curve of Gamma calibration, the prediction parameters of the current brightness curve are obtained by calculating according to the historical calibration parameters of the current brightness curve. 7. The method of claim 1, wherein the method further comprises: storing the prediction parameters into a pre-built prediction parameter set; When the number of prediction parameters in the prediction parameter set is greater than a preset parameter threshold, the prediction parameters are overwritten according to the generation time of the prediction parameters, so that the number of prediction parameters in the prediction parameter set is equal to the preset parameter threshold parameter threshold. 8. A Gamma calibration device, wherein the device comprises: Data acquisition module for acquiring historical calibration parameters; a data processing module for performing parameter data processing on the historical calibration parameters to determine initial distribution parameters, where the parameter data processing at least includes: clustering processing and mean value processing; a parameter calculation module, configured to obtain the predicted parameters according to the maximum likelihood estimation method by using the preset unpredicted parameters, the historical calibration parameters and the initial distribution parameters; A calibration module for performing Gamma calibration through the predicted parameters. 9. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the method according to any one of claims 1 to 7 when the processor executes the computer program. step. 10. A computer-readable storage medium on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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