CN114927096A - Gamma calibration method, device, computer equipment and storage medium - Google Patents
Gamma calibration method, device, computer equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
- G09G3/20—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters
- G09G3/22—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources
- G09G3/30—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources using electroluminescent panels
- G09G3/32—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources using electroluminescent panels semiconductive, e.g. using light-emitting diodes [LED]
- G09G3/3208—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources using electroluminescent panels semiconductive, e.g. using light-emitting diodes [LED] organic, e.g. using organic light-emitting diodes [OLED]
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- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G2320/00—Control of display operating conditions
- G09G2320/02—Improving the quality of display appearance
- G09G2320/0271—Adjustment of the gradation levels within the range of the gradation scale, e.g. by redistribution or clipping
- G09G2320/0276—Adjustment of the gradation levels within the range of the gradation scale, e.g. by redistribution or clipping for the purpose of adaptation to the characteristics of a display device, i.e. gamma correction
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Abstract
The disclosure relates to a Gamma calibration method, a Gamma calibration device, computer equipment and a storage medium. The method comprises the following steps: acquiring historical calibration parameters; performing parameter data processing on the historical calibration parameters to determine initial distribution parameters, wherein the parameter data processing at least comprises the following steps: clustering processing and mean processing; obtaining a prediction parameter by using a preset unpredicted parameter, the historical calibration parameter and the initial distribution parameter according to a maximum likelihood estimation method; and performing Gamma calibration through the prediction parameters. By adopting the method, the parameter value of the register does not need to be tried repeatedly, and the times of trying the parameter of the register are reduced, so that Gamma calibration is carried out.
Description
Technical Field
The present disclosure relates to the field of display technologies, and in particular, to a Gamma calibration method, apparatus, computer device, and storage medium.
Background
On the production line of the OLED, Gamma modulation is an iterative optimization technology which enables the chromaticity and the brightness of a panel to approach target values by changing module register values. The aim is to coordinate the real linear response of the module with the nonlinear response under the perception of human eyes, so as to achieve the luminous effect of natural transition and distinct hierarchy.
At present, the existing Gamma adjustment algorithm is mainly characterized in that three groups of IC register values of initial R, G, B are given, after an IC lighting module is driven, an optical probe is used for obtaining color coordinate values and brightness values of corresponding binding points, whether the color coordinate values and the brightness values fall within an error range of a target value or not is determined through comparison, and if the color coordinate values and the brightness values fall within the error range of the target value, the adjustment is finished; if not, the register value is changed again, and the brightness and color coordinate values are confirmed again.
However, the current Gamma calibration algorithm needs to repeatedly try the register value, resulting in long calibration time, and each die set needs to repeat the above parameter trying steps, resulting in low efficiency.
Disclosure of Invention
In view of the above, it is desirable to provide a Gamma calibration method, apparatus, computer device, and storage medium that can reduce the number of times a register parameter is tried without repeatedly trying a register value.
In a first aspect, the present disclosure provides a Gamma calibration method. The method comprises the following steps:
acquiring historical calibration parameters;
performing parameter data processing on the historical calibration parameters to determine initial distribution parameters, wherein the parameter data processing at least comprises the following steps: clustering processing and mean processing;
obtaining a prediction parameter by using a preset unpredicted parameter, the historical calibration parameter and the initial distribution parameter according to a maximum likelihood estimation method;
and performing Gamma calibration through the prediction parameters.
In one embodiment, the obtaining of the predicted parameter according to the maximum likelihood estimation method by using the preset unpredicted parameter, the historical calibration parameter, and the initial distribution parameter includes:
determining the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter;
adjusting the initial distribution parameters to obtain 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;
and under the condition that the probability value is maximum, determining a prediction parameter according to the adjusted initial distribution parameter.
In one embodiment, the determining the prediction parameter according to the adjusted initial distribution parameter includes:
calculating deviation values of the initial distribution parameters and the adjusted initial distribution parameters;
determining a prediction parameter according to the adjusted initial distribution parameter under the condition that the deviation value is smaller than a preset deviation threshold value;
and under the condition that the deviation value is greater than or equal to a preset deviation threshold value, readjusting the initial distribution parameters until determining the prediction parameters.
In one embodiment, the obtaining of the predicted parameter according to the maximum likelihood estimation method by using the preset unpredicted parameter, the historical calibration parameter, and the initial distribution parameter includes:
calculating the probability value of the preset unpredicted parameter by adopting the following formula:
Q i (z (i) )＝P(z (i) |x (i) ；θ)
calculating a prediction parameter that maximizes the probability value using the following formula:
wherein z is an unpredicted parameter, x is a historical calibration parameter, and theta is an initial distribution parameter; p is the probability value of the unpredicted parameter.
In one embodiment, the performing parameter data processing on the historical calibration parameters to determine initial distribution parameters includes at least one of:
calculating the mean value of the historical calibration parameters, and determining initial distribution parameters according to the mean value;
or the like, or, alternatively,
determining a clustering center of the historical calibration parameters through a clustering algorithm, and determining initial distribution parameters according to the clustering center, wherein the clustering algorithm at least comprises the following steps: a K-means algorithm, a density clustering algorithm, and a hierarchical clustering algorithm.
In one embodiment, the method further comprises: acquiring a middle history calibration parameter of a current brightness curve;
and after the Gamma calibration brightness curve is switched, calculating to obtain the prediction parameters of the current brightness curve according to the historical calibration parameters in the current brightness curve.
In one embodiment, the method further comprises:
storing the prediction parameters into a pre-constructed prediction parameter set;
and covering the prediction parameters according to the generation time of the prediction parameters under the condition that the number of the prediction parameters in the prediction parameter set is larger than a preset parameter threshold value, so that the number of the prediction parameters in the prediction parameter set is equal to the preset parameter threshold value.
In a second aspect, the present disclosure also provides a Gamma calibration apparatus. The device comprises:
the data acquisition module is used for acquiring historical calibration parameters;
a data processing module, configured to perform parameter data processing on the historical calibration parameters, and determine initial distribution parameters, where the parameter data processing at least includes: clustering processing and mean processing;
the parameter calculation module is used for obtaining a prediction parameter according to a maximum likelihood estimation method by utilizing a preset unpredicted parameter, the historical calibration parameter and the initial distribution parameter;
and the calibration module is used for carrying out Gamma calibration according to the prediction parameters.
In a third aspect, the present disclosure also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of any of the above method embodiments when executing the computer program.
In a fourth aspect, the present disclosure also provides a computer-readable storage medium. The computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of any of the above-mentioned method embodiments.
In a fifth aspect, the present disclosure also provides a computer program product. The computer program product comprising a computer program that when executed by a processor performs the steps of any of the above-described method embodiments.
In the embodiments, the relatively stable interval range in most of the module parameters can be determined through the historical calibration parameters, so that the number of times of randomly trying the parameters can be reduced. And parameter data processing is carried out on the historical calibration parameters in the interval range, so that the range of the predicted parameters can be further narrowed, and the number of parameter trying is reduced. When 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, the predicted parameters can be determined by means of an algorithm, debugging of each parameter is not needed, and efficiency is improved. And the brightness of the first time of lighting the module can be improved to be in accordance with the target value, and the times of trying the parameters of the register are reduced, so that the time of single Gamma calibration is shortened, and the productivity in unit time is improved.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of an exemplary Gamma calibration method;
FIG. 2 is a schematic flow chart of a Gamma calibration method according to an embodiment;
FIG. 3 is a flowchart illustrating the steps of a historical calibration parameter determination process in one embodiment;
fig. 4 is a schematic flow chart illustrating a Gamma calibration step according to an embodiment;
FIG. 5 is a diagram illustrating distribution of expected values and debug times in one embodiment;
FIG. 6 is a diagram illustrating a distribution of standard deviation and debug times in one embodiment;
FIG. 7 is a flowchart illustrating the step S206 according to an embodiment;
FIG. 8 is a detailed flow diagram illustrating the determination of prediction parameters in one embodiment;
FIG. 9 is a flowchart illustrating the step S310 according to an embodiment;
FIG. 10 is a schematic flow chart of another Gamma calibration method in one embodiment;
FIG. 11 is a schematic flowchart of a Gamma calibration method in another embodiment;
FIG. 12 is a block diagram showing a schematic structure of a Gamma calibration device according to an embodiment;
fig. 13 is a schematic diagram of an internal configuration of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the disclosure and are not intended to limit the disclosure.
It should be noted that the terms "first," "second," and the like in the description and claims herein and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments herein described are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or device.
In this document, the term "and/or" is only one kind of association relationship describing the associated object, meaning that three kinds of relationships may exist. For example, a and/or B, may represent: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter associated objects are in an "or" relationship.
The embodiment of the disclosure provides a Gamma calibration method, which can be applied to an application environment as shown in fig. 1. Wherein the upper computer 102 is in communication 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 upper computer 102, reads and writes the IC register of the module product, and transmits the IC register to the upper computer 102. The upper computer 102 controls the optical probe 106 to collect the brightness and color coordinate values. The upper computer 102 records historical calibration parameters after Gamma calibration is performed on the module product every time. When Gamma calibration is performed 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 initial distribution parameters. The parameter data processing performed by the upper computer 102 may at least include: and clustering the parameter data or performing mean processing on the parameter data. The upper computer 102 obtains the prediction parameters according to a maximum likelihood estimation method by using preset unpredicted parameters, historical calibration parameters and initial distribution parameters. And the upper computer 102 performs Gamma calibration through the prediction parameters. The upper computer 102 may be, but is 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, and is used for providing electric signals and driving display signals for the module. The individual modules (for example, the modules before being assembled into a finished mobile phone) are not provided with an external power supply, and external display signals can be provided through the signal generator to provide voltage and current for lighting (for example, different brightness requires different current magnitudes), display signals (for example, color pictures and gray scale pictures) and the like. The module product may include various types of finished screens (e.g., a mobile phone screen, a computer screen, a display screen, a television screen, etc.).
In one embodiment, as shown in fig. 2, a Gamma calibration method is provided, which is described by taking the method as an example applied to the upper computer 102 in fig. 1, and includes the following steps:
s202, obtaining historical calibration parameters.
The historical calibration parameters may be calibration parameters obtained after a new module product is calibrated by Gamma. The calibration parameters can typically be determined by a certain number of adjustments.
Specifically, when a new module product needs Gamma calibration, the historical calibration parameters of the module product corresponding to the new module product type can be obtained.
S204, performing parameter data processing on the historical calibration parameters to determine initial distribution parameters, wherein the parameter data processing at least comprises the following steps: clustering processing and mean processing.
The parameter data processing may be a processing manner of the historical calibration parameters in the present embodiment, so as to make the initial distribution parameters closer to the predicted parameters. The clustering process may be a process of processing the historical calibration parameters by a clustering algorithm. The averaging process may be a way of calculating an average of the historical calibration parameters.
Specifically, the historical calibration parameters may be processed in a clustering processing manner or an averaging processing manner, and the initial distribution parameters may be determined after the processing.
And S206, obtaining a prediction parameter according to a maximum likelihood estimation method by using a preset unpredicted parameter, the historical calibration parameter and the initial distribution parameter.
Wherein the maximum likelihood estimation method may be an EM algorithm. The EM algorithm may be generally referred to as expectation maximization (lmax) which is a maximum likelihood estimation method for solving probabilistic model parameters from incomplete data or data sets with data loss (hidden variables present).
Specifically, according to a preset unpredicted parameter, the historical calibration parameter and the initial distribution parameter, an expected estimation of the preset unpredicted parameter, namely a probability value of the unpredicted parameter is given, and according to the probability value of the unpredicted parameter, a maximum likelihood estimation of the initial distribution parameter is given. The maximum likelihood estimate may be the prediction parameter in this embodiment. The pre-set unpredicted parameters may be hidden variables in a maximum likelihood estimation method.
And S208, performing Gamma calibration according to the prediction parameters.
Specifically, after the predicted parameters are determined, the predicted parameters can be regarded as target values, and the register parameters are set R, G, B through the target values, so that the module product is lighted.
In the Gamma calibration method, the relatively stable interval range in most module parameters can be determined through historical calibration parameters, and the number of times of randomly trying the parameters can be reduced. And parameter data processing is carried out on the historical calibration parameters in the interval range, so that the range of the predicted parameters can be further narrowed, and the number of parameter trying is further reduced. When 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, the predicted parameters can be determined by means of an algorithm, debugging of each parameter is not needed, and efficiency is improved. And the brightness of the first lighting module can be improved to be in accordance with the target value, and the times of trying the parameters of the register are reduced, so that the time of single Gamma calibration is shortened, and the productivity in unit time is improved.
In one embodiment, as shown in FIG. 3, the determination of the historical calibration parameters includes:
and judging whether the current module has a predicted value.
If the module under test has a predicted value, reading the current predicted parameter value, setting the parameter of the register by using the predicted value, performing Gamma adjustment, and recording the historical calibration parameter.
If the module tested at present has no predicted value, determining whether parameter data (stored parameter data) set in the last calibration process is stored in advance, if so, reading the parameter data (stored parameter data) at the last time, setting the parameter of the register according to the parameter data at the last time, performing Gamma calibration, and recording historical calibration parameters.
If not, setting an initial value, setting parameters of a register according to the initial value, carrying out Gamma adjustment, and recording historical calibration parameters.
It will be appreciated that in some embodiments of the present disclosure, the predictor, initial value, and prediction parameter may all be parameters of a register. The registers are commonly referred to as R, G, B registers.
As shown in fig. 4, the Gamma calibration step includes: the module is lightened through the parameters of the register, and the brightness and color coordinate values on the module are acquired after optical detection. And judging whether the brightness and the color coordinate value are in the range, if so, ending, and otherwise, adjusting the parameters of the register. When the number of times of adjusting the parameters of the register is less, the obtained historical calibration parameters are used for obtaining larger predicted parameter change. When the parameter times of the register are adjusted to reach a certain number, the accuracy of the predicted parameters is improved, and the purpose of optimization can be achieved. As shown in table 1 debug relationship table.
TABLE 1 debug relationship Table
| Number of times of debugging | Expected value | Variance (variance) | Standard deviation of | Time consuming(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 |
| ... | ... | ... | ... | ... |
The relationship diagram of fig. 5 and fig. 6 can be obtained by debugging the relationship table according to table 1. As shown in fig. 5 and fig. 6, when the number of times of debugging reaches about 500, the expected value and the standard deviation tend to be stable, so in this embodiment, a certain number may be 500, and the corresponding obtained historical calibration parameters are relatively in accordance with the predicted parameters.
In one embodiment, as shown in fig. 7, the obtaining a predicted parameter according to a maximum likelihood estimation method by using a preset unpredicted parameter, the historical calibration parameter, and the initial distribution parameter includes:
s302, determining the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter.
S304, adjusting the initial distribution parameters to obtain adjusted initial distribution parameters.
S306, 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.
S308, judging whether the probability value is maximum or not.
And S310, under the condition that the probability value is maximum, determining a prediction parameter according to the adjusted initial distribution parameter.
Specifically, as shown in fig. 8, the prediction parameters obtained by the maximum likelihood estimation method can be divided into an expecteration step and a Maximization step. The expecteration step may include: an initialized distribution parameter and an unpredicted parameter are determined. The non-predicted parameters may be regarded as hidden variables in the maximum likelihood estimation method, which may be non-observed parameters, and may be non-predicted parameters in this embodiment. A Maximization step: and determining the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter. And continuously adjusting the initial distribution parameters to obtain the adjusted initial distribution parameters. The step S304 is repeated. And recalculate the probability value of the pre-set unpredicted parameter. And under the condition that the probability value is maximum, proving that the adjusted initial distribution parameters meet the requirements, and determining the prediction parameters according to the adjusted initial distribution parameters.
The above description is made in a schematic manner, and is explained in a concrete manner. In another embodiment of this embodiment, the expanding step: the probability value of the preset unpredicted parameter can be calculated by adopting the following formula:
Q i (z (i) )＝P(z (i) |x (i) ；6)
a Maximization step: calculating a prediction parameter that maximizes the probability value using the following formula:
wherein z is an unpredicted parameter, x is a historical calibration parameter, and theta is an initial distribution parameter; p is the probability value of the unpredicted parameter, p (z) (i) |x (i) (ii) a θ) represents the posterior distribution of the predicted parameter.
Specifically, for each i, a posterior distribution of the predicted parameters (which can be considered as the expectation of the predicted parameters) calculated from the model parameters or initial distribution parameters of the last iteration is calculated as the current estimated values of the unpredicted parameters. Make Q i (z (i) ) The initial distribution parameter at maximum is obtained. The likelihood function is maximized to obtain new initial distribution parameters, which may be prediction parameters.
Q i (z (i) ) The new theta is obtained by the theta equation obtained and substituted into the theta equation in the Maximization step, and is equivalent to the adjusted initial distribution parameter. The adjusted initial distribution parameters are substituted back to Q i (z (i) ) Such successive iterations may result in an initialized distribution parameter θ that maximizes the likelihood function. The initialized distribution parameter θ that maximizes the likelihood function may 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 the predicted value is further determined, and the initial distribution parameters are obtained by parameter data processing, so that the efficiency of the maximum likelihood estimation method convergence can be further improved.
In one embodiment, as shown in fig. 9, the determining the prediction parameter according to the adjusted initial distribution parameter includes:
s402, calculating deviation values of the initial distribution parameters and the adjusted initial distribution parameters;
s404, judging whether the deviation value is smaller than a preset deviation threshold value;
s406, determining a prediction parameter according to the adjusted initial distribution parameter under the condition that the deviation value is smaller than a preset deviation threshold value;
s408, readjusting the initial distribution parameters under the condition that the deviation value is greater than or equal to a preset deviation threshold value until the prediction parameters are determined.
The initial distribution parameter in this embodiment may be an adjusted initial distribution parameter, and a previous initial distribution parameter. For example, if the initial distribution parameter is a, the initial distribution parameter obtained by adjusting once is a1, the initial distribution parameter obtained by adjusting once is a2, and a2 is the adjusted initial distribution parameter, the initial distribution parameter mentioned in this embodiment is a 1.
Specifically, when the prediction parameter is determined, how to prove that the prediction parameter is valid can be used by a final value can be verified by the present embodiment. I.e. whether the maximum likelihood estimation method converges. Calculating deviation values of the initial distribution parameters and the adjusted initial distribution parameters, if the deviation values are very small or the deviation values are 0 and the deviation values are smaller than a preset deviation threshold value, determining that the maximum likelihood estimation method is converged, and determining the adjusted initial distribution parameters as prediction parameters. If the deviation value is large and is larger than the preset deviation threshold value, determining that the maximum likelihood estimation method is converged, and adjusting the initial distribution parameters again, namely returning to the step S304 until the prediction parameters are determined.
In the embodiment, whether the maximum likelihood estimation method is converged or not is determined through the deviation value, so that the prediction parameters can be accurately determined, the prediction parameters are made to accord with the target values, and the accuracy of Gamma calibration is improved.
In one embodiment, the performing parameter data processing on the historical calibration parameters to determine initial distribution parameters includes at least one of:
calculating the mean value of the historical calibration parameters, and determining initial distribution parameters according to the mean value;
or the like, or a combination thereof,
determining a clustering center of the historical calibration parameters through a clustering algorithm, and determining initial distribution parameters according to the clustering center, wherein the clustering algorithm at least comprises the following steps: a K-means algorithm, a density clustering algorithm, and a hierarchical clustering algorithm.
Specifically, the average value of the historical calibration parameters can be calculated, and the average value is set as the initial distribution parameter, so that all the historical calibration parameters are compromised, larger deviation is avoided, and the adjustment time can be shortened compared with the random selection of the initial distribution parameters. Or determining the clustering center of the historical calibration parameters through a clustering algorithm, wherein the clustering center generally represents a prediction parameter which is closer to the actual historical calibration parameters, and compared with random selection, the adjustment time can be shortened.
In this embodiment, the initial distribution parameters are determined in two ways, so that the speed of determining the prediction parameters can be increased compared with the speed of randomly selecting the initial distribution parameters, and the time of Gamma calibration can be shortened.
In one embodiment, the method further comprises: acquiring a middle history calibration parameter of a current brightness curve;
and after the brightness curve of Gamma calibration is switched, calculating to obtain the prediction parameters of the current brightness curve according to the historical calibration parameters in the current brightness curve.
Generally, Gamma values with different gray levels at a luminance value form a Gamma curve, which is generally called a luminance curve.
In particular, when Gamma calibration is performed, it is usually necessary to perform tests under different brightness curves for one module. Each Gamma calibration requires obtaining historical calibration parameters in the current luminance curve. When the brightness curve of Gamma calibration is switched, the prediction parameters of the current brightness curve can be obtained by calculation according to the historical calibration parameters in the current brightness curve obtained before. A mode in which all gray levels under a single luminance curve are calculated at the same time may be employed. Where gray scale refers to the change in brightness between the brightest and darkest, typically represented by 8 bits, and 256 brightness steps. The simultaneous calculation refers to predicting R, G, B register parameters for each gray scale by maximum likelihood estimation method at the same time for a plurality of gray scales (different products can select representative ones from 256 gray scales, 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) under a brightness curve. (for how to predict, see the above embodiments, and repeated description is omitted here).
Therefore, the switching luminance curve is not affected. When the next new module needs to predict the parameters and the module is switched to the current brightness curve, the prediction parameters of the current brightness curve can be used, so that Gamma calibration is performed without waiting.
In one embodiment, the method further comprises:
storing the prediction parameters into a pre-constructed prediction parameter set;
and covering the prediction parameters according to the generation time of the prediction parameters under the condition that the number of the prediction parameters in the prediction parameter set is larger than a preset parameter threshold value, so that the number of the prediction parameters in the prediction parameter set is equal to the preset parameter threshold value.
Specifically, as the number of modules of the accumulated tests is increased, the obtained prediction parameters can be stored into a pre-constructed prediction parameter set. In general, the storage of the prediction parameters in the preset data set is limited, and does not occupy too much storage space, so that in the case that the number of the prediction parameters in the prediction parameter set is greater than the preset parameter threshold, and a new prediction parameter is generated during calibration, the prediction parameters can be overwritten according to the generation time of the prediction parameters.
In some exemplary embodiments, for example, the prediction parameters stored by generation time in the prediction parameter set are B, B1, B2. Wherein, B is the prediction parameter with the earliest generation time. In the case that 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 may be covered by B, and the prediction parameters in the prediction parameter set may be: b1, B2 and C.
In this embodiment, since the prediction parameters are accumulated in the production process, if the data updating is stopped when the prediction parameter set is fully stored, the prediction parameters may be inaccurate due to the difference between different batches of the product, so the embodiment overrides the prediction parameters according to the generation time of the prediction parameters, and the above problem can be avoided.
In one embodiment, as shown in fig. 10, another Gamma calibration method is further provided in an embodiment of the present disclosure, and the method includes the following steps:
in the case of a replacement module, a prediction value for the module product is determined, which may be previously stored or set by one skilled in the art. The prediction is written R, G, B into the register and the module is then lit. The optical probe acquires data such as color coordinate values, brightness and the like of the module product. And judging whether the data such as the color coordinate value, the brightness and the like are in the target range. If so, historical calibration parameters may be determined from the predicted values. And determining the predicted value of the next module of the type according to the historical calibration parameters.
If not, the predicted value can be finely adjusted, and then the register parameters are adjusted according to the finely adjusted predicted value until the data such as color coordinate values, brightness and the like are in the target range after the module is lightened.
It should be noted that, the fine tuning of the predicted value may be performed by using a characteristic that an error between the same type of modules conforms to normal distribution, specifically, the normal distribution is obtained by analyzing parameters of actual debugging of a certain number of modules. The fine tuning of the predicted values may also be performed in other ways by those skilled in the art.
In another embodiment, as shown in fig. 11, another Gamma calibration method is further provided in an embodiment of the present disclosure, where the method includes the following steps:
and S502, if the module under current test has a predicted value, setting parameters of a register by using the predicted value, performing Gamma adjustment, and recording historical calibration parameters.
S504, if the module tested at present has no predicted value, determining whether the parameter data set during the last time of calibration is prestored.
And S506, if yes, reading the last parameter data, setting the parameters of the register according to the last parameter data, performing Gamma adjustment, and recording historical calibration parameters.
And S508, if not, setting an initial value, setting parameters of a register according to the initial value, performing Gamma adjustment, and recording historical calibration parameters.
And S510, acquiring historical calibration parameters.
S512, calculating the average value of the historical calibration parameters, and determining initial distribution parameters according to the average value.
Or, S514, determining the clustering center of the historical calibration parameter through a clustering algorithm, and determining an initial distribution parameter according to the clustering center.
And S516, determining the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter.
S518, adjusting the initial distribution parameters to obtain adjusted initial distribution parameters.
And S520, 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.
S522, determining whether the probability value is maximum.
And S524, calculating deviation values of the initial distribution parameters and the adjusted initial distribution parameters under the condition that the probability value is maximum.
And S526, judging whether the deviation value is smaller than a preset deviation threshold value.
S528, determining a prediction parameter according to the adjusted initial distribution parameter under the condition that the deviation value is smaller than a preset deviation threshold value.
And under the condition that the deviation value is greater than or equal to a preset deviation threshold value, readjusting the initial distribution parameters until determining the prediction parameters.
For specific implementation and limitation in this embodiment, reference may be made to the above-described embodiment, and repeated descriptions in this embodiment are omitted.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the present disclosure further provides a Gamma calibration apparatus for implementing the above 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 that specific limitations in one or more embodiments of the Gamma calibration device provided below can be referred to the limitations of the Gamma calibration method in the above description, and details are not repeated here.
In one embodiment, as shown in fig. 12, there is provided a Gamma calibration apparatus 1100, comprising: a data acquisition module 1102, a data processing module 1104, a parameter calculation module 1106, and a calibration module 1108, wherein:
a data obtaining module 1102, configured to obtain historical calibration parameters;
a data processing module 1104, configured to 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 processing;
a parameter calculation module 1106, configured to obtain a prediction parameter according to a maximum likelihood estimation method by using a preset unpredicted parameter, the historical calibration parameter, and the initial distribution parameter;
a calibration module 1108, configured to perform Gamma calibration according to the prediction parameter.
In one embodiment of the apparatus, the parameter calculating module 1106 includes:
the adjusting module is used for adjusting the initial distribution parameters to obtain adjusted initial distribution parameters;
and the probability value calculation module is used for determining the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter, and 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.
And the prediction parameter determining module is used for determining a prediction parameter according to the adjusted initial distribution parameter under the condition that the probability value is maximum.
In an embodiment of the apparatus, the parameter calculating module 1106 further includes: and the deviation value calculating module is used for calculating the deviation values of the initial distribution parameters and the adjusted initial distribution parameters.
And the prediction parameter determining 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 adjusting 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 apparatus, the parameter calculating module 1106 further calculates a probability value of the preset unpredicted parameter by using the following formula:
Q i (z (i) )＝P(z (i) |x (i) ；θ)
calculating a prediction parameter that maximizes the probability value using the following formula:
wherein z is an unpredicted parameter, x is a historical calibration parameter, and theta is an initial distribution parameter; p is the probability value of the unpredicted parameter.
In one embodiment of the apparatus, the data processing module 1104 comprises: and the mean value processing module is used for calculating the mean value of the historical calibration parameters and determining initial distribution parameters according to the mean value.
A clustering processing module, configured to determine a clustering center of the historical calibration parameter through a clustering algorithm, and determine an initial distribution parameter according to the clustering center, where the clustering algorithm at least includes: a K-means algorithm, a density clustering algorithm, and a hierarchical clustering algorithm.
In one embodiment of the apparatus, the apparatus further comprises: and the switching module is used for acquiring the middle history calibration parameters of the current brightness curve, and calculating to obtain the prediction parameters of the current brightness curve according to the middle history calibration parameters of the current brightness curve after the Gamma calibration brightness curve is switched.
In one embodiment of the apparatus, the apparatus further comprises: and the parameter set storage module is used for storing the prediction parameters into a pre-constructed prediction parameter set.
And the prediction parameter covering module is used for covering the prediction parameters according to the generation time of the prediction parameters under the condition that the number of the prediction parameters in the prediction parameter set is greater than a preset parameter threshold value, so that the number of the prediction parameters in the prediction parameter set is equal to the preset parameter threshold value.
All or part of the modules in the Gamma calibration device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 13. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is for storing predicted parameter data and historical calibration parameter data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a Gamma calibration method.
Those skilled in the art will appreciate that the architecture shown in fig. 13 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above-described method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
In an embodiment, a computer program product is provided, comprising a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided by the present disclosure may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in embodiments provided by the present disclosure may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the various embodiments provided in this disclosure may be, without limitation, general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, or the like.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several implementation modes of the present disclosure, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the present disclosure. It should be noted that various changes and modifications can be made by one skilled in the art without departing from the spirit of the disclosure, and these changes and modifications are all within the scope of the disclosure. Therefore, the protection scope of the present disclosure should be subject to the appended claims.
Claims (10)
1. A Gamma calibration method, the method comprising:
acquiring historical calibration parameters;
performing parameter data processing on the historical calibration parameters to determine initial distribution parameters, wherein the parameter data processing at least comprises the following steps: clustering processing and mean processing;
obtaining a prediction parameter by using a preset unpredicted parameter, the historical calibration parameter and the initial distribution parameter according to a maximum likelihood estimation method;
and performing Gamma calibration through the prediction parameters.
2. The method of claim 1, wherein the obtaining of the predicted parameters according to the maximum likelihood estimation method by using the pre-set unpredicted parameters, the historical calibration parameters and the initial distribution parameters comprises:
determining a probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter;
adjusting the initial distribution parameters to obtain 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;
and under the condition that the probability value is maximum, determining a prediction parameter according to the adjusted initial distribution parameter.
3. The method of claim 2, wherein determining the prediction parameters according to the adjusted initial distribution parameters comprises:
calculating deviation values of the initial distribution parameters and the adjusted initial distribution parameters;
determining a prediction parameter according to the adjusted initial distribution parameter under the condition that the deviation value is smaller than a preset deviation threshold value;
and under the condition that the deviation value is greater than or equal to a preset deviation threshold value, readjusting the initial distribution parameters until determining the prediction parameters.
4. The method according to claim 1 or 2, wherein the obtaining of 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 comprises:
calculating the probability value of the preset unpredicted parameter by adopting the following formula:
Q i (z (i) )＝p(z (i) |x (i) ；θ)
calculating a prediction parameter that maximizes the probability value using the following formula:
wherein z is an unpredicted parameter, x is a historical calibration parameter, and theta is an initial distribution parameter; p is the probability value of the unpredicted parameter.
5. The method of claim 1, wherein the performing parameter data processing on the historical calibration parameters to determine initial distribution parameters comprises at least one of:
calculating the average value of the historical calibration parameters, and determining initial distribution parameters according to the average value;
or the like, or, alternatively,
determining a clustering center of the historical calibration parameters through a clustering algorithm, and determining initial distribution parameters according to the clustering center, wherein the clustering algorithm at least comprises the following steps: a K-means algorithm, a density clustering algorithm, and a hierarchical clustering algorithm.
6. The method according to any one of claims 1-5, further comprising:
acquiring a middle history calibration parameter of a current brightness curve;
and after the brightness curve of Gamma calibration is switched, calculating to obtain the prediction parameters of the current brightness curve according to the historical calibration parameters in the current brightness curve.
7. The method of claim 1, further comprising:
storing the prediction parameters into a pre-constructed prediction parameter set;
and covering the prediction parameters according to the generation time of the prediction parameters under the condition that the number of the prediction parameters in the prediction parameter set is greater than a preset parameter threshold value, so that the number of the prediction parameters in the prediction parameter set is equal to the preset parameter threshold value.
8. A Gamma calibration apparatus, the apparatus comprising:
the data acquisition module is used for acquiring historical calibration parameters;
a data processing module, configured to perform parameter data processing on the historical calibration parameters, and determine initial distribution parameters, where the parameter data processing at least includes: clustering processing and mean processing;
the parameter calculation module is used for obtaining a prediction parameter according to a maximum likelihood estimation method by utilizing a preset unpredicted parameter, the historical calibration parameter and the initial distribution parameter;
and the calibration module is used for carrying out Gamma calibration according to the prediction parameters.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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