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

Gamma calibration method, device, computer equipment, storage medium Download PDF

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CN114927096A
CN114927096A CN202210664840.2A CN202210664840A CN114927096A CN 114927096 A CN114927096 A CN 114927096A CN 202210664840 A CN202210664840 A CN 202210664840A CN 114927096 A CN114927096 A CN 114927096A
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邱书云
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Suzhou HYC Technology Co Ltd
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    • GPHYSICS
    • G09EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09GARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G3/00Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
    • G09G3/20Control 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
    • G09G3/22Control 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
    • G09G3/30Control 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
    • G09G3/32Control 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]
    • G09G3/3208Control 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]
    • GPHYSICS
    • G09EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09GARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G2320/00Control of display operating conditions
    • G09G2320/02Improving the quality of display appearance
    • G09G2320/0271Adjustment of the gradation levels within the range of the gradation scale, e.g. by redistribution or clipping
    • G09G2320/0276Adjustment 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

本公开涉及一种Gamma校准方法、装置、计算机设备、存储介质。所述方法包括:获取历史校准参数;对所述历史校准参数进行参数数据处理,确定初始分布参数,所述参数数据处理至少包括:聚类处理和均值处理;利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数;通过所述预测参数进行Gamma校准。采用本方法无需反复尝试寄存器的参数值,且减少尝试寄存器的参数的次数,从而进行Gamma校准。

Figure 202210664840

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.

Figure 202210664840

Description

Gamma校准方法、装置、计算机设备、存储介质Gamma calibration method, device, computer equipment, storage medium

技术领域technical field

本公开涉及显示技术领域,特别是涉及一种Gamma校准方法、装置、计算机设备、存储介质。The present disclosure relates to the field of display technology, and in particular, to a Gamma calibration method, device, computer equipment, and storage medium.

背景技术Background technique

在OLED产线上,Gamma调制是一种通过改变模组寄存器值使面板色度、亮度趋近目标值的迭代优化技术。其目的是使模组真实的线性响应与人眼感知下的非线性响应相协调,达到自然过渡、层次分明的发光效果。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.

目前现有的Gamma调校算法,主要是通过给出初始R、G、B三组IC寄存器值,驱动IC点亮模组后,使用光学探头获取对应绑点的色坐标值、亮度值,对比确认是否落在目标值的误差范围内,如果在范围内即调试结束;如果不在范围内,再改变寄存器值,重新确认亮度及色坐标值。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.

然而,目前的Gamma的调校算法需要反复尝试寄存器的值,导致调整时间长,且每片模组都需要重复以上的参数尝试步骤,导致效率低。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.

发明内容SUMMARY OF THE INVENTION

基于此,有必要针对上述技术问题,提供一种无需反复尝试寄存器的值,且减少尝试寄存器的参数的次数的Gamma校准方法、装置、计算机设备、存储介质。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.

第一方面,本公开提供了一种Gamma校准方法。所述方法包括:In a first aspect, the present disclosure provides a gamma calibration method. The method includes:

获取历史校准参数;Get historical calibration parameters;

对所述历史校准参数进行参数数据处理,确定初始分布参数,所述参数数据处理至少包括:聚类处理和均值处理;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;

利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数;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;

通过所述预测参数进行Gamma校准。Gamma calibration is performed with the predicted parameters.

在其中一个实施例中,所述利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数,包括: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:

根据历史校准参数、未预测参数和初始分布参数,确定所述预设的未预测参数的概率值;Determine the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter;

调整所述初始分布参数,得到调整后初始分布参数;Adjust the initial distribution parameters to obtain the adjusted initial distribution parameters;

根据历史校准参数、未预测参数和调整后初始分布参数,重新确定所述预设的未预测参数的概率值;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;

在所述概率值最大的情况下,根据调整后初始分布参数确定预测参数。In the case where the probability value is the largest, the prediction parameter is determined according to the adjusted initial distribution parameter.

在其中一个实施例中,所述根据调整后初始分布参数确定预测参数,包括:In one embodiment, the determining the prediction parameter according to the adjusted initial distribution parameter includes:

计算所述初始分布参数和所述调整后初始分布参数的偏差值;Calculate the deviation value of the initial distribution parameter and the adjusted initial distribution parameter;

在所述偏差值小于预设的偏差阈值的情况下,根据所述调整后初始分布参数确定预测参数;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;

在所述偏差值大于等于预设的偏差阈值的情况下,重新调整所述初始分布参数,直至确定预测参数。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.

在其中一个实施例中,所述利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数,包括: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:

采用下述公式计算得到预设的未预测参数的概率值:The following formula is used to calculate the probability value of the preset unpredicted parameter:

Qi(z(i))=P(z(i)|x(i);θ)Q i (z (i) )=P(z (i) |x (i) ; θ)

采用下述公式计算得到使所述概率值最大的预测参数:The following formula is used to calculate the prediction parameter that maximizes the probability value:

Figure BDA0003692553100000021
Figure BDA0003692553100000021

其中,z为未预测参数,x为历史校准参数,θ为初始分布参数;p为未预测参数的概率值。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.

在其中一个实施例中,所述对所述历史校准参数进行参数数据处理,确定初始分布参数,至少包括下述一种: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:

计算所述历史校准参数的均值,根据所述均值确定初始分布参数;Calculate the mean value of the historical calibration parameters, and determine the initial distribution parameter according to the mean value;

或,or,

通过聚类算法确定所述历史校准参数的聚类中心,根据所述聚类中心确定初始分布参数,所述聚类算法至少包括:K均值算法、密度聚类算法和层次聚类算法。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.

在其中一个实施例中,所述方法还包括:获取当前亮度曲线的中历史校准参数;In one of the embodiments, the method further includes: acquiring the medium historical calibration parameters of the current luminance curve;

在切换Gamma校准的亮度曲线后,根据当前亮度曲线的中历史校准参数计算得到当前亮度曲线的预测参数。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.

在其中一个实施例中,所述方法还包括:In one embodiment, the method further includes:

将所述预测参数存储至预先构建的预测参数集中;storing the prediction parameters into a pre-built prediction parameter set;

在所述预测参数集中预测参数的数量大于预设的参数阈值的情况下,根据所述预测参数的产生时间覆盖所述预测参数,使所述预测参数集中预测参数的数量等于所述预设的参数阈值。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.

第二方面,本公开还提供了一种Gamma校准装置。所述装置包括:In a second aspect, the present disclosure also provides a Gamma calibration device. The device includes:

数据获取模块,用于获取历史校准参数;Data acquisition module for acquiring historical calibration parameters;

数据处理模块,用于对所述历史校准参数进行参数数据处理,确定初始分布参数,所述参数数据处理至少包括:聚类处理和均值处理;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;

参数计算模块,用于利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数;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;

校准模块,用于通过所述预测参数进行Gamma校准。A calibration module for performing Gamma calibration through the predicted parameters.

第三方面,本公开还提供了一种计算机设备。所述计算机设备包括存储器和处理器,所述存储器存储有计算机程序,所述处理器执行所述计算机程序时实现上述任一方法实施例的步骤。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.

第四方面,本公开还提供了一种计算机可读存储介质。所述计算机可读存储介质,其上存储有计算机程序,所述计算机程序被处理器执行时实现上述任一方法实施例的步骤。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.

第五方面,本公开还提供了一种计算机程序产品。所述计算机程序产品,包括计算机程序,该计算机程序被处理器执行时实现上述任一方法实施例的步骤。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.

上述各实施例中,通过历史校准参数能够确定大部分的模组参数中相对稳定的区间范围,能够减少随机的尝试参数的次数。而在这个区间范围中对历史校准参数进行参数数据处理能够进一步的缩小预测参数的范围,减少了尝试参数的次数。而利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数时,能够借助算法确定预测参数,无需每个参数都需要进行调试,提高了效率。并且能够最大化的提高第一次点亮模组时的亮度符合目标值,减少了尝试寄存器的参数的次数,从而缩短单次Gamma校准的时间,提高单位时间内的产能。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.

附图说明Description of drawings

为了更清楚地说明本公开具体实施方式或现有技术中的技术方案,下面将对具体实施方式或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图是本公开的一些实施方式,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。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.

图1为一个实施例中Gamma校准方法的应用环境示意图;1 is a schematic diagram of an application environment of the Gamma calibration method in one embodiment;

图2为一个实施例中Gamma校准方法的流程示意图;2 is a schematic flowchart of a Gamma calibration method in one embodiment;

图3为一个实施例中历史校准参数确定过程的步骤的流程示意图;3 is a schematic flowchart of the steps of a historical calibration parameter determination process in one embodiment;

图4为一个实施例中Gamma调校步骤的流程示意图;Fig. 4 is the schematic flow chart of Gamma adjustment step in one embodiment;

图5为一个实施例中期望值与调试次数的分布示意图;FIG. 5 is a schematic diagram of the distribution of the expected value and the number of times of debugging in one embodiment;

图6为一个实施例中标准差与调试次数的分布示意图;6 is a schematic diagram of the distribution of standard deviation and debugging times in one embodiment;

图7为一个实施例中S206步骤的流程示意图;7 is a schematic flowchart of step S206 in one embodiment;

图8为一个实施例中确定预测参数的详细流程示意图;Fig. 8 is a detailed flowchart of determining prediction parameters in one embodiment;

图9为一个实施例中S310步骤的流程示意图;9 is a schematic flowchart of step S310 in one embodiment;

图10为一个实施例中另一种Gamma校准方法的流程示意图;10 is a schematic flowchart of another Gamma calibration method in one embodiment;

图11为另一个实施例中Gamma校准方法的流程示意图;11 is a schematic flowchart of a Gamma calibration method in another embodiment;

图12为一个实施例中Gamma校准装置的结构示意框图;12 is a schematic structural block diagram of a Gamma calibration device in one embodiment;

图13为一个实施例中计算机设备的内部结构示意图。FIG. 13 is a schematic diagram of the internal structure of a computer device in one embodiment.

具体实施方式Detailed ways

为了使本公开的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本公开进行进一步详细说明。应当理解,此处描述的具体实施例仅仅用以解释本公开,并不用于限定本公开。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.

需要说明的是,本文的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象,而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换,以便这里描述的本文的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外,术语“包括”和“具有”以及他们的任何变形,意图在于覆盖不排他的包含,例如,包含了一系列步骤或单元的过程、方法、装置、产品或设备不必限于清楚地列出的那些步骤或单元,而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。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.

在本文中,术语“和/或”仅仅是一种描述关联对象的关联关系,表示可以存在三种关系。例如,A和/或B,可以表示:单独存在A,同时存在A和B,单独存在B这三种情况。另外,本文中字符“/”,一般表示前后关联对象是一种“或”的关系。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.

本公开实施例提供了一种Gamma校准方法,可以应用于如图1所示的应用环境中。其中,上位机102与光学探头106和信号发生器104进行通信。信号发生器104根据上位机102传输的信号驱动模组产品,并读写模组产品的IC寄存器并将IC寄存器传输至上位机102。上位机102控制光学探头106采集亮度、色坐标值。上位机102每次对模组产品进行Gamma校准后,记录历史校准参数。在对下一个模组产品进行Gamma校准时,上位机102获取历史校准参数。上位机102对历史校准参数进行参数数据处理,确定初始分布参数。上位机102进行参数数据处理至少可以包括:对参数数据进行聚类处理或者对参数数据进行均值处理。上位机102利用预设的未预测参数、历史校准参数和初始分布参数,根据极大似然估计法得到预测参数。上位机102通过预测参数进行Gamma校准。其中,上位机102可以但不限于是各种个人计算机、笔记本电脑、智能手机、平板电脑等。信号发生器可以为Pattern Generator信号发生器,用来给模组提供电信号、显示信号的驱动。单独的模组(例如未组装成成品手机前的模组)是无外部电源,外部显示信号的,可以通过信号发生器来提供电压电流来点亮(例如不同的亮度需要不同电流大小),显示信号(例如彩色图片,灰阶图片)等。模组产品可以包含各种类型的成品屏幕(如手机屏幕、电脑屏幕、显示器屏幕、电视机屏幕等)。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.).

在一个实施例中,如图2所示,提供了一种Gamma校准方法,以该方法应用于图1中的上位机102为例进行说明,包括以下步骤: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,获取历史校准参数。S202, obtaining historical calibration parameters.

其中,历史校准参数通常可以是在一个新的模组产品通过Gamma校准后,得到的校准参数。校准参数通常情况下可以通过一定数量的调整次数进行确定。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.

具体地,在一个新的模组产品需要进行Gamma校准时,可以获取与该新的模组产品类型对应的模组产品的历史校准参数。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,对所述历史校准参数进行参数数据处理,确定初始分布参数,所述参数数据处理至少包括:聚类处理和均值处理。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.

其中,参数数据处理通常可以是在本实施例中对历史校准参数进行处理的方式,目的是使初始分布参数更加接近预测参数。聚类处理可以是通过聚类算法对历史校准参数进行处理的方式。均值处理可以是计算历史校准参数的平均值的方式。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.

具体地,可以通过聚类处理方式或均值处理方式对历史校准参数进行处理,处理后可以确定初始分布参数。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,利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数。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.

其中,极大似然估计法可以是EM算法。EM算法通常可以称为期望最大算法,其是一种从不完全数据或有数据丢失的数据集(存在隐含变量)中求解概率模型参数的最大似然估计方法。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).

具体地,根据预设的未预测参数,所述历史校准参数和所述初始分布参数给出预设的未预测参数的期望估计,即未预测参数的概率值,根据未预测参数的概率值,给出初始分布参数的极大似然估计。极大似然估计即可以是本实施例中的预测参数。预设的未预测参数可以是极大似然估计法中的隐变量。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,通过所述预测参数进行Gamma校准。S208, Gamma calibration is performed by using the predicted parameters.

具体地,上述确定预测参数之后,可以将该预测参数看为目标值,通过该目标值设置R、G、B寄存器参数,点亮模组产品。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.

上述Gamma校准方法中,通过历史校准参数能够确定大部分的模组参数中相对稳定的区间范围,能够减少随机的尝试参数的次数。而在这个区间范围中对历史校准参数进行参数数据处理能够进一步的缩小预测参数的范围,进一步减少了尝试参数的次数。而利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数时,能够借助算法确定预测参数,无需每个参数都需要进行调试,提高了效率。并且能够最大化的提高第一次点亮模组时的亮度符合目标值,减少了尝试寄存器的参数的次数,从而缩短单次Gamma校准的时间,提高单位时间内的产能。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.

在一个实施例中,如图3所示,历史校准参数的确定过程包括:In one embodiment, as shown in FIG. 3 , the process of determining the historical calibration parameters includes:

判断当前模组是否有预测值。Determine whether the current module has a predicted value.

若当前测试的模组中有预测值,则读取当前预测参数值,使用预测值设置寄存器的参数,进行Gamma调校,记录历史校准参数。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.

若当前测试的模组未存在预测值,则确定是否有预先存储的上一次调校时设置的参数数据(有已存储参数数据),若有,则读取上一次参数数据(已存储参数数据),根据上一次参数数据设置寄存器的参数,进行Gamma调校,记录历史校准参数。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.

若没有,则设置初始值,根据初始值设置寄存器的参数,进行Gamma调校,记录历史校准参数。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.

可以理解的是,在本公开的一些实施例中,预测值、初始值和预测参数都可以为寄存器的参数。寄存器通常指的是R、G、B寄存器。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.

如图4所示,Gamma调校步骤包括:通过寄存器的参数点亮模组,光学探后采集模组上的亮度,色坐标值。判断亮度,色坐标值是否在范围内,若在,则结束,若不在则调整寄存器的参数。当调整寄存器的参数次数较少时,利用得到的历史校准参数得到预测参数变化较大。当调整寄存器的参数次数达到一定数量时,预测的参数准确性提高,可以达到优化的目的。如表1调试关系表所示。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.

表1 调试关系表Table 1 Debug relationship table

调试次数Debug times 期望值expected value 方差variance 标准差standard deviation 耗时(s)Time (s) 1010 26152615 25806.425806.4 160.6437160.6437 0.0940.094 5050 2664.022664.02 33207.733207.7 182.2298182.2298 0.0940.094 200200 2638.682638.68 31609.131609.1 177.7895177.7895 0.2970.297 500500 2648.952648.95 31511.431511.4 177.5145177.5145 0.7810.781 20002000 2641.482641.48 31220.531220.5 176.6932176.6932 2.9072.907 50005000 2638.122638.12 31243.831243.8 176.7592176.7592 6.4536.453 ...... ...... ...... ...... ......

根据表1调试关系表可以得到如图5和图6的关系示意图。如图5和图6所示,当调试次数达到500次左右时,可以期望值和标准差趋于稳定,因此在本实施例中,一定数量可以为500次,相应的得到的历史校准参数比较符合预测参数。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.

在一个实施例中,如图7所示,所述利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数,包括: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,根据历史校准参数、未预测参数和初始分布参数,确定所述预设的未预测参数的概率值。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,调整所述初始分布参数,得到调整后初始分布参数。S304: Adjust the initial distribution parameters to obtain the adjusted initial distribution parameters.

S306,根据历史校准参数、未预测参数和调整后初始分布参数,重新确定所述预设的未预测参数的概率值。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,判断概率值是否最大。S308, determine whether the probability value is the largest.

S310,在所述概率值最大的情况下,根据调整后初始分布参数确定预测参数。S310, in the case where the probability value is the largest, determine a prediction parameter according to the adjusted initial distribution parameter.

具体地,如图8所示,极大似然估计法得到预测参数可分为Expectation步和Maximization步。Expectation步可以包括:确定初始化分布参数和未预测参数。其中未预测参数可以看做极大似然估计法中隐变量,其可以为未观测到的参数,在本实施例中可以为未预测参数。Maximization步:根据历史校准参数、未预测参数和初始分布参数,确定所述预设的未预测参数的概率值。不断调整初始分布参数,得到调整后初始分布参数。不断重复S304步骤。并重新计算预设的未预测参数的概率值。在所述概率值最大的情况下,证明调整后初始分布参数达到要求,则根据调整后初始分布参数确定预测参数。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.

上述表述为原理上的描述,下面以具体的方式进行说明。在本实施例的另一种实施方式中,Expectation步:可以采用下述公式计算得到预设的未预测参数的概率值: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:

Qi(z(i))=P(z(i)|x(i);6)Q i (z (i) )=P(z (i) |x (i) ; 6)

Maximization步:采用下述公式计算得到使所述概率值最大的预测参数:Maximization step: Use the following formula to calculate the prediction parameters that maximize the probability value:

Figure BDA0003692553100000091
Figure BDA0003692553100000091

其中,z为未预测参数,x为历史校准参数,θ为初始分布参数;p为未预测参数的概率值,p(z(i)|x(i);θ)代表预测参数的后验分布。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 .

具体地,对于每一个i,计算根据上一次迭代的模型参数或者初始分布参数来计算出预测参数的后验分布(可以看做预测参数的期望),来作为未预测参数的现估计值。求使Qi(z(i))获得极大时的初始分布参数。将似然函数最大化以获得新的初始分布参数,该初始分布参数可以是预测参数。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.

Qi(z(i))求出来代入到Maximization步的θ式中,θ式求出来新的θ,相当于调整后初始分布参数。将调整后初始分布参数又反代回Qi(z(i))中,如此不断的迭代,就可以得到使似然函数最大化的初始化分布参数θ了。该使似然函数最大化的初始化分布参数θ可以为最终的预测参数。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.

本实施例中,通过使用极大似然估计法,能够非常可靠地找到最优的收敛值,进而确定预测值,而初始分布参数也是之前通过参数数据处理进行得到的,因此能够进一步提高极大似然估计法收敛的效率。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.

在一个实施例中,如图9所示,所述根据调整后初始分布参数确定预测参数,包括:In one embodiment, as shown in FIG. 9 , the determining of the prediction parameters according to the adjusted initial distribution parameters includes:

S402,计算所述初始分布参数和所述调整后初始分布参数的偏差值;S402, calculating the deviation value of the initial distribution parameter and the adjusted initial distribution parameter;

S404,判断所述偏差值是否小于预设的偏差阈值;S404, determine whether the deviation value is less than a preset deviation threshold;

S406,在所述偏差值小于预设的偏差阈值的情况下,根据所述调整后初始分布参数确定预测参数;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,在所述偏差值大于等于预设的偏差阈值的情况下,重新调整所述初始分布参数,直至确定预测参数。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.

其中,在本实施例中的初始分布参数可以为调整后初始分布参数,前一个初始分布参数。例如,初始分布参数为A,调整一次得到初始分布参数为A1、调整一次得到初始分布参数为A2,该A2为调整后初始分布参数,则本实施例中提到的初始分布参数为A1。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.

具体地,上述确定了预测参数时,如何证明该预测参数有效可以最终的值来使用,可以通过本实施例来对预测参数进行验证。即极大似然估计法是否收敛。计算初始分布参数和调整后初始分布参数的偏差值,如果偏差值很小或者偏差值为0,偏差值小于预设的偏差阈值,则确定极大似然估计法收敛,则可以将调整后初始分布参数确定为预测参数。若偏差值很大,偏差值大于预设的偏差阈值,则确定极大似然估计法收敛,需要再次调整初始分布参数,即回到S304步骤,直至确定预测参数。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.

在本实施例中,通过偏差值来确定极大似然估计法是否收敛,能够准确的确定预测参数,使预测参数符合目标值,提高Gamma校准的准确性。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.

在一个实施例中,所述对所述历史校准参数进行参数数据处理,确定初始分布参数,至少包括下述一种:In one embodiment, performing parameter data processing on the historical calibration parameters to determine initial distribution parameters includes at least one of the following:

计算所述历史校准参数的均值,根据所述均值确定初始分布参数;Calculate the mean value of the historical calibration parameters, and determine the initial distribution parameter according to the mean value;

或,or,

通过聚类算法确定所述历史校准参数的聚类中心,根据所述聚类中心确定初始分布参数,所述聚类算法至少包括:K均值算法、密度聚类算法和层次聚类算法。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.

具体地,可以计算历史校准参数的均值,将该均值设置为初始分布参数,此种方式折中了所有的历史校准参数,不会产生较大的偏差,相较于随机选择初始分布参数能够缩短调整时间。或者通过聚类算法确定所述历史校准参数的聚类中心,该聚类中心通常情况下表示历史校准参数中较为接近实际的预测参数,相较于随机选择也可以缩短调整时间。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.

在本实施例中,通过两种方式确定初始分布参数,相较于随机选择初始分布参数能够提高确定预测参数的速度,可以缩短Gamma校准的时间。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.

在一个实施例中,所述方法还包括:获取当前亮度曲线的中历史校准参数;In one embodiment, the method further includes: acquiring a medium history calibration parameter of the current luminance curve;

在切换Gamma校准的亮度曲线后,根据当前亮度曲线的中历史校准参数计算得到当前亮度曲线的预测参数。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.

通常情况下,一个亮度值下有不同灰阶的Gamma值,组成一条Gamma曲线,通常称为一个亮度曲线。Usually, there are different grayscale Gamma values under a luminance value, forming a Gamma curve, which is usually called a luminance curve.

具体地,在进行Gamma校准时,通常需要对一个模组进行不同亮度曲线下的测试。每次进行Gamma校准都需要获取当前亮度曲线中的历史校准参数。在切换Gamma校准的亮度曲线时,可以根据之前获取的当前亮度曲线中的历史校准参数计算得到当前亮度曲线的预测参数。可以采用单条亮度曲线下所有灰阶同时计算的模式。其中,灰阶指的是最亮和最暗之间的亮度变化,一般通过8bit来表示,及256个亮度阶梯。同时计算指的是一个亮度曲线下的多个灰阶(不同产品会从256个灰阶下挑选具有代表性的几个灰阶,例如1,3,5,9,13,17,22,29,35,41,51,65,81,93,104,114,134,159,177,198,225,247,255)下的参数同时通过极大似然估计法预测每个灰阶的R、G、B寄存器参数。(关于如何预测可以参见上述实施例,在此不进行重复赘述)。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).

因此,不会对切换亮度曲线产生影响。而在下一个新的模组需要进行预测参数时,且该模组的切换为当前亮度曲线,则可以将当前亮度曲线的预测参数进行使用,从而进行Gamma校准,而无需等待。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.

在一个实施例中,所述方法还包括:In one embodiment, the method further includes:

将所述预测参数存储至预先构建的预测参数集中;storing the prediction parameters into a pre-built prediction parameter set;

在所述预测参数集中预测参数的数量大于预设的参数阈值的情况下,根据所述预测参数的产生时间覆盖所述预测参数,使所述预测参数集中预测参数的数量等于所述预设的参数阈值。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.

具体地,随着生产累积的测试的模组数量越来越多,可以将得到的预测参数存储至预先构建的预测参数集中。通常情况下,预设数据集中存储预测参数是有限的,不会占用太多的存储空间,因此在所述预测参数集中预测参数的数量大于预设的参数阈值的情况下,且在校准时产生了新的预测参数,则可以根据预测参数的产生时间对预测参数进行覆盖。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.

在一些示例性的实施例中,例如,预测参数集中按照产生时间存储的预测参数为B、B1、B2。其中,B为产生时间最早的预测参数。在预测参数集中预测参数的数量大于预设的参数阈值的情况下,而在校准时产生了新的预测参数C,则可以将C覆盖B,则预测参数集中的预测参数可以为:B1、B2、C。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.

在本实施例中,因为预测参数是一直生产累积的过程,如果预测参数集存储满就停止更新数据的话,可能因为产品不同批次的差异可能会导致预测参数不准确,因此本实施例根据所述预测参数的产生时间覆盖所述预测参数,能够避免上述问题。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.

在一个实施例中,如图10所示,本公开实施例还提供了另一种Gamma校准方法,方法包括以下步骤: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:

在更换模组的情况下,确定该模组产品的预测值,该预测值可以是之前存储的也可以是本领域技术人员设置的。将预测值写入R、G、B寄存器中,然后点亮模组。光学探头采集模组产品的色坐标值、亮度等数据。判断该色坐标值、亮度等数据是否在目标范围内。若在,则可以根据预测值确定历史校准参数。根据历史校准参数确定下个该类型模组的预测值。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.

若不在,则可以微调预测值,然后根据微调后的预测值调整寄存器参数,直至点亮模组后,色坐标值、亮度等数据在目标范围内。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.

需要说明的是,此处微调预测值可以利用同一款模组之间的误差符合正态分布的特性进行微调,具体地,正态分布是分析现有一定数量的模组的实际调试完成的参数得到的,基于这个特性,大部分的模组的参数都会在一个相对稳定的区间范围,可以在这个区间范围对预测值进行微调,可以减少微调的次数。本领域技术人员也可以以其他方式进行微调预测值。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.

在另一个实施例中,如图11所示,本公开实施例还提供了另一种Gamma校准方法,所述方法包括以下步骤: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,若当前测试的模组中有预测值,使用预测值设置寄存器的参数,进行Gamma调校,记录历史校准参数。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,若当前测试的模组未存在预测值,则确定是否有预先存储的上一次调校时设置的参数数据。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,若有,则读取上一次参数数据,根据上一次参数数据设置寄存器的参数,进行Gamma调校,记录历史校准参数。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,若没有,则设置初始值,根据初始值设置寄存器的参数,进行Gamma调校,记录历史校准参数。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,获取历史校准参数。S510, acquiring historical calibration parameters.

S512,计算所述历史校准参数的均值,根据所述均值确定初始分布参数。S512: Calculate the mean value of the historical calibration parameters, and determine the initial distribution parameter according to the mean value.

或,S514,通过聚类算法确定所述历史校准参数的聚类中心,根据所述聚类中心确定初始分布参数。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,根据历史校准参数、未预测参数和初始分布参数,确定所述预设的未预测参数的概率值。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,调整所述初始分布参数,得到调整后初始分布参数。S518: Adjust the initial distribution parameters to obtain the adjusted initial distribution parameters.

S520,根据历史校准参数、未预测参数和调整后初始分布参数,重新确定所述预设的未预测参数的概率值。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,判断概率值是否最大。S522, determine whether the probability value is the largest.

S524,在所述概率值最大的情况下,计算所述初始分布参数和所述调整后初始分布参数的偏差值。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,判断所述偏差值是否小于预设的偏差阈值。S526: Determine whether the deviation value is smaller than a preset deviation threshold value.

S528,在所述偏差值小于预设的偏差阈值的情况下,根据所述调整后初始分布参数确定预测参数。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.

在所述偏差值大于等于预设的偏差阈值的情况下,重新调整所述初始分布参数,直至确定预测参数。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.

关于本实施例中的具体实施方式和限定可参见上述实施例,在本实施例中不进行重复赘述。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.

应该理解的是,虽然如上所述的各实施例所涉及的流程图中的各个步骤按照箭头的指示依次显示,但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明,这些步骤的执行并没有严格的顺序限制,这些步骤可以以其它的顺序执行。而且,如上所述的各实施例所涉及的流程图中的至少一部分步骤可以包括多个步骤或者多个阶段,这些步骤或者阶段并不必然是在同一时刻执行完成,而是可以在不同的时刻执行,这些步骤或者阶段的执行顺序也不必然是依次进行,而是可以与其它步骤或者其它步骤中的步骤或者阶段的至少一部分轮流或者交替地执行。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.

基于同样的发明构思,本公开实施例还提供了一种用于实现上述所涉及的Gamma校准方法的Gamma校准装置。该装置所提供的解决问题的实现方案与上述方法中所记载的实现方案相似,故下面所提供的一个或多个Gamma校准装置实施例中的具体限定可以参见上文中对于Gamma校准方法的限定,在此不再赘述。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.

在一个实施例中,如图12所示,提供了一种Gamma校准装置1100,包括:数据获取模块1102、数据处理模块1104、参数计算模块1106和校准模块1108,其中: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:

数据获取模块1102,用于获取历史校准参数;a data acquisition module 1102, configured to acquire historical calibration parameters;

数据处理模块1104,用于对所述历史校准参数进行参数数据处理,确定初始分布参数,所述参数数据处理至少包括:聚类处理和均值处理;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;

参数计算模块1106,用于利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数;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;

校准模块1108,用于通过所述预测参数进行Gamma校准。The calibration module 1108 is configured to perform Gamma calibration by using the predicted parameters.

在所述装置的一个实施例中,所述参数计算模块1106,包括:In an embodiment of the apparatus, the parameter calculation module 1106 includes:

调整模块,调整所述初始分布参数,得到调整后初始分布参数;an adjustment module to adjust the initial distribution parameters to obtain the adjusted initial distribution parameters;

概率值计算模块,用于根据历史校准参数、未预测参数和初始分布参数,确定所述预设的未预测参数的概率值,以及,根据历史校准参数、未预测参数和调整后初始分布参数,重新确定所述预设的未预测参数的概率值。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.

预测参数确定模块,用于在所述概率值最大的情况下,根据调整后初始分布参数确定预测参数。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.

在所述装置的一个实施例中,所述参数计算模块1106,还包括:偏差值计算模块,用于计算所述初始分布参数和所述调整后初始分布参数的偏差值。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.

所述预测参数确定模块,还用于在所述偏差值小于预设的偏差阈值的情况下,根据所述调整后初始分布参数确定预测参数。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.

所述调整模块,还用于在所述偏差值大于等于预设的偏差阈值的情况下,重新调整所述初始分布参数,直至确定预测参数。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.

在所述装置的一个实施例中,所述参数计算模块1106,还采用下述公式计算得到预设的未预测参数的概率值: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:

Qi(z(i))=P(z(i)|x(i);θ)Q i (z (i) )=P(z (i) |x (i) ; θ)

采用下述公式计算得到使所述概率值最大的预测参数:The following formula is used to calculate the prediction parameter that maximizes the probability value:

Figure BDA0003692553100000151
Figure BDA0003692553100000151

其中,z为未预测参数,x为历史校准参数,θ为初始分布参数;p为未预测参数的概率值。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.

在所述装置的一个实施例中,所述数据处理模块1104包括:均值处理模块,用于计算所述历史校准参数的均值,根据所述均值确定初始分布参数。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.

聚类处理模块,用于通过聚类算法确定所述历史校准参数的聚类中心,根据所述聚类中心确定初始分布参数,所述聚类算法至少包括:K均值算法、密度聚类算法和层次聚类算法。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.

在所述装置的一个实施例中,所述装置还包括:切换模块,用于获取当前亮度曲线的中历史校准参数,在切换Gamma校准的亮度曲线后,根据当前亮度曲线的中历史校准参数计算得到当前亮度曲线的预测参数。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.

在所述装置的一个实施例中,所述装置还包括:参数集存储模块,用于将所述预测参数存储至预先构建的预测参数集中。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.

预测参数覆盖模块,用于在所述预测参数集中预测参数的数量大于预设的参数阈值的情况下,根据所述预测参数的产生时间覆盖所述预测参数,使所述预测参数集中预测参数的数量等于所述预设的参数阈值。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.

上述Gamma校准装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中,也可以以软件形式存储于计算机设备中的存储器中,以便于处理器调用执行以上各个模块对应的操作。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.

在一个实施例中,提供了一种计算机设备,该计算机设备可以是服务器,其内部结构图可以如图13所示。该计算机设备包括通过系统总线连接的处理器、存储器和网络接口。其中,该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质和内存储器。该非易失性存储介质存储有操作系统、计算机程序和数据库。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该计算机设备的数据库用于存储预测参数数据和历史校准参数数据。该计算机设备的网络接口用于与外部的终端通过网络连接通信。该计算机程序被处理器执行时以实现一种Gamma校准方法。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.

本领域技术人员可以理解,图13中示出的结构,仅仅是与本公开方案相关的部分结构的框图,并不构成对本公开方案所应用于其上的计算机设备的限定,具体的计算机设备可以包括比图中所示更多或更少的部件,或者组合某些部件,或者具有不同的部件布置。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.

在一个实施例中,提供了一种计算机设备,包括存储器和处理器,存储器中存储有计算机程序,该处理器执行计算机程序时实现上述各方法实施例中的步骤。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.

在一个实施例中,提供了一种计算机可读存储介质,其上存储有计算机程序,计算机程序被处理器执行时实现上述各方法实施例中的步骤。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.

在一个实施例中,提供了一种计算机程序产品,包括计算机程序,该计算机程序被处理器执行时实现上述各方法实施例中的步骤。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.

本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程,是可以通过计算机程序来指令相关的硬件来完成,所述的计算机程序可存储于一非易失性计算机可读取存储介质中,该计算机程序在执行时,可包括如上述各方法的实施例的流程。其中,本公开所提供的各实施例中所使用的对存储器、数据库或其它介质的任何引用,均可包括非易失性和易失性存储器中的至少一种。非易失性存储器可包括只读存储器(Read-OnlyMemory,ROM)、磁带、软盘、闪存、光存储器、高密度嵌入式非易失性存储器、阻变存储器(ReRAM)、磁变存储器(Magnetoresistive Random Access Memory,MRAM)、铁电存储器(Ferroelectric Random Access Memory,FRAM)、相变存储器(Phase Change Memory,PCM)、石墨烯存储器等。易失性存储器可包括随机存取存储器(Random Access Memory,RAM)或外部高速缓冲存储器等。作为说明而非局限,RAM可以是多种形式,比如静态随机存取存储器(Static Random Access Memory,SRAM)或动态随机存取存储器(Dynamic RandomAccess Memory,DRAM)等。本公开所提供的各实施例中所涉及的数据库可包括关系型数据库和非关系型数据库中至少一种。非关系型数据库可包括基于区块链的分布式数据库等,不限于此。本公开所提供的各实施例中所涉及的处理器可为通用处理器、中央处理器、图形处理器、数字信号处理器、可编程逻辑器、基于量子计算的数据处理逻辑器等,不限于此。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.

以上实施例的各技术特征可以进行任意的组合,为使描述简洁,未对上述实施例中的各个技术特征所有可能的组合都进行描述,然而,只要这些技术特征的组合不存在矛盾,都应当认为是本说明书记载的范围。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.

以上所述实施例仅表达了本公开的几种实施方式,其描述较为具体和详细,但并不能因此而理解为对本公开专利范围的限制。应当指出的是,对于本领域的普通技术人员来说,在不脱离本公开构思的前提下,还可以做出若干变形和改进,这些都属于本公开的保护范围。因此,本公开的保护范围应以所附权利要求为准。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)

1.一种Gamma校准方法,其特征在于,所述方法包括:1. a Gamma calibration method, is characterized in that, described method comprises: 获取历史校准参数;Get historical calibration parameters; 对所述历史校准参数进行参数数据处理,确定初始分布参数,所述参数数据处理至少包括:聚类处理和均值处理;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; 利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数;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; 通过所述预测参数进行Gamma校准。Gamma calibration is performed with the predicted parameters. 2.根据权利要求1所述的方法,其特征在于,所述利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数,包括: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: : 根据历史校准参数、未预测参数和初始分布参数,确定所述预设的未预测参数的概率值;Determine the probability value of the preset unpredicted parameter according to the historical calibration parameter, the unpredicted parameter and the initial distribution parameter; 调整所述初始分布参数,得到调整后初始分布参数;Adjust the initial distribution parameters to obtain the adjusted initial distribution parameters; 根据历史校准参数、未预测参数和调整后初始分布参数,重新确定所述预设的未预测参数的概率值;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; 在所述概率值最大的情况下,根据调整后初始分布参数确定预测参数。In the case where the probability value is the largest, the prediction parameter is determined according to the adjusted initial distribution parameter. 3.根据权利要求2所述的方法,其特征在于,所述根据调整后初始分布参数确定预测参数,包括:3. The method according to claim 2, wherein the determining the prediction parameter according to the adjusted initial distribution parameter comprises: 计算所述初始分布参数和所述调整后初始分布参数的偏差值;Calculate the deviation value of the initial distribution parameter and the adjusted initial distribution parameter; 在所述偏差值小于预设的偏差阈值的情况下,根据所述调整后初始分布参数确定预测参数;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; 在所述偏差值大于等于预设的偏差阈值的情况下,重新调整所述初始分布参数,直至确定预测参数。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.根据权利要求1或2所述的方法,其特征在于,所述利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数,包括: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: 采用下述公式计算得到预设的未预测参数的概率值:The following formula is used to calculate the probability value of the preset unpredicted parameter: Qi(z(i))=p(z(i)|x(i);θ)Q i (z (i) )=p(z (i) |x (i) ; θ) 采用下述公式计算得到使所述概率值最大的预测参数:The following formula is used to calculate the prediction parameter that maximizes the probability value:
Figure FDA0003692553090000021
Figure FDA0003692553090000021
其中,z为未预测参数,x为历史校准参数,θ为初始分布参数;p为未预测参数的概率值。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.根据权利要求1所述的方法,其特征在于,所述对所述历史校准参数进行参数数据处理,确定初始分布参数,至少包括下述一种: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: 计算所述历史校准参数的均值,根据所述均值确定初始分布参数;Calculate the mean value of the historical calibration parameters, and determine the initial distribution parameter according to the mean value; 或,or, 通过聚类算法确定所述历史校准参数的聚类中心,根据所述聚类中心确定初始分布参数,所述聚类算法至少包括:K均值算法、密度聚类算法和层次聚类算法。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.根据权利要求1-5任一项所述的方法,其特征在于,所述方法还包括:6. The method according to any one of claims 1-5, wherein the method further comprises: 获取当前亮度曲线的中历史校准参数;Obtain the mid-history calibration parameters of the current brightness curve; 在切换Gamma校准的亮度曲线后,根据当前亮度曲线的中历史校准参数计算得到当前亮度曲线的预测参数。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.根据权利要求1所述的方法,其特征在于,所述方法还包括:7. The method of claim 1, wherein the method further comprises: 将所述预测参数存储至预先构建的预测参数集中;storing the prediction parameters into a pre-built prediction parameter set; 在所述预测参数集中预测参数的数量大于预设的参数阈值的情况下,根据所述预测参数的产生时间覆盖所述预测参数,使所述预测参数集中预测参数的数量等于所述预设的参数阈值。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. 8.一种Gamma校准装置,其特征在于,所述装置包括:8. A Gamma calibration device, wherein the device comprises: 数据获取模块,用于获取历史校准参数;Data acquisition module for acquiring historical calibration parameters; 数据处理模块,用于对所述历史校准参数进行参数数据处理,确定初始分布参数,所述参数数据处理至少包括:聚类处理和均值处理;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; 参数计算模块,用于利用预设的未预测参数、所述历史校准参数和所述初始分布参数,并根据极大似然估计法得到预测参数;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; 校准模块,用于通过所述预测参数进行Gamma校准。A calibration module for performing Gamma calibration through the predicted parameters. 9.一种计算机设备,包括存储器和处理器,所述存储器存储有计算机程序,其特征在于,所述处理器执行所述计算机程序时实现权利要求1至7中任一项所述的方法的步骤。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. 10.一种计算机可读存储介质,其上存储有计算机程序,其特征在于,所述计算机程序被处理器执行时实现权利要求1至7中任一项所述的方法的步骤。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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