Method for determining gamma data of OLED screen and display screen

Abstract

The present application provides a method for determining gamma data for an OLED screen and a display screen. The method includes: constructing a first gamma calculation model; obtaining target initial gamma data based on target reference gamma data and the first gamma calculation model; and controlling the OLED screen's luminescence based on the target initial gamma data. The target reference gamma data is a reference value for calculating the target initial gamma data, and the target initial gamma data includes the initial gamma values ​​of all registers controlling the OLED screen's luminescence. This method can directly input the gamma data of the band0 register as a reference value for each band's gamma value into the gamma calculation model, while simultaneously outputting the gamma values ​​of all band registers. This allows adjusting ten brightness levels in twice the time.

Description

Technical Field

[0001] The present application relates to the field of display technology, and in particular to a method for determining gamma data of an OLED screen and a display screen. Background Art

[0002] The OTP (One-Time-Programming) initial value algorithm in the OLED module is a one-time programming process that writes initial parameters or specific data into the OLED module. The current OTP initial value algorithm in the OLED module can only determine the gamma data in multiple band registers in sequence. This serial calculation method will cause the OTP adjustment time to be too long. In addition, the OTP adjustment time is greatly affected by the initial value of the gamma register. However, the current difference between the initial value of the gamma register and the actual value after the OTP adjustment is successful is large, resulting in a large number of OTP adjustments and a long process time. Therefore, the OTP initial value algorithm in the OLED module in the prior art has a large number of adjustments and an excessively long adjustment time. Summary of the Invention

[0003] The main purpose of this application is to provide a method for determining the gamma data of an OLED screen and a display screen, so as to at least solve the problem in the prior art that the OTP initial value algorithm in the OLED module has a large number of adjustments and a long adjustment time.

[0004] To achieve the above-mentioned purpose, according to one aspect of the present application, a method for determining the gamma data of an OLED screen is provided, including: constructing a first gamma calculation model, wherein the first gamma calculation model is trained using multiple sets of training data, and each set of training data in the multiple sets of training data includes: historical reference gamma data and historical actual gamma data corresponding to the historical reference gamma data, obtained within a historical time period, wherein the historical actual gamma data is the real gamma data used to control the luminescence of the OLED screen at a historical moment, and the historical reference gamma data is a reference value used to calculate the target gamma initial value data at a historical moment; according to the target reference gamma data and the first gamma calculation model, target gamma initial value data is obtained, and the OLED screen is controlled to emit light based on the target gamma initial value data, wherein the target reference gamma data is a reference value for calculating the target gamma initial value data, and the target gamma initial value data includes the gamma initial values ​​of all registers that control the luminescence of the OLED screen.

[0005] According to another aspect of the present application, a display screen is provided, the brightness of the display screen is determined based on the gamma data in each register, and the gamma data in each register is determined using any one of the methods for determining the gamma data of the OLED screen.

[0006] Applying the technical solution of the present application, the method for determining the gamma data of the above-mentioned OLED screen first uses the reference value used to calculate the target gamma initial value data at a historical moment and the real gamma data used to control the luminescence of the OLED screen at the corresponding historical moment to train to obtain a first gamma calculation model; then, based on the target reference gamma data and the first gamma calculation model, the target gamma initial value data is obtained, and the luminescence of the OLED screen is controlled based on the target gamma initial value data, wherein the target reference gamma data is a reference value for calculating the target gamma initial value data, and the target gamma initial value data includes the gamma initial values ​​of all registers that control the luminescence of the OLED screen. This method can directly input the gamma data of the band0 register as the reference value of the gamma value of each band into the gamma calculation model, and output the gamma values ​​of all band registers at the same time. Ten brightness levels can be adjusted in half the time, and the number of OTP adjustments is reduced, which greatly reduces the process time, solving the problem of the OTP initial value algorithm in the OLED module in the prior art having a large number of adjustments and a long adjustment time. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The drawings that constitute part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0008] Figure 1 A schematic flow chart of a method for determining gamma data of an OLED screen according to an embodiment of the present application is shown;

[0009] Figure 2 A schematic structural diagram of a grayscale reactor model provided according to an embodiment of the present application is shown;

[0010] Figure 3 A schematic structural diagram of the intermediate layer composition of a grayscale reactor model provided according to an embodiment of the present application is shown;

[0011] Figure 4 A schematic flow chart of another method for determining gamma data of an OLED screen provided in accordance with an embodiment of the present application is shown;

[0012] Figure 5A schematic diagram showing the effect of a method for determining gamma data of an OLED screen provided in an embodiment of the present application is shown;

[0013] Figure 6 A schematic diagram showing the effect of another method for determining gamma data of an OLED screen provided in an embodiment of the present application is shown;

[0014] Figure 7 A schematic diagram showing the effect of another method for determining gamma data of an OLED screen provided in an embodiment of the present application is shown;

[0015] Figure 8 A schematic diagram showing the effect of another method for determining gamma data of an OLED screen provided in an embodiment of the present application is shown;

[0016] Figure 9 A schematic diagram showing the effect of another method for determining gamma data of an OLED screen provided in an embodiment of the present application is shown;

[0017] Figure 10 A schematic diagram showing the effect of another method for determining gamma data of an OLED screen provided in an embodiment of the present application is shown;

[0018] Figure 11 A schematic structural diagram of a display screen provided according to an embodiment of the present application is shown.

[0019] The above drawings include the following reference numerals:

[0020] 100. Display panel. DETAILED DESCRIPTION

[0021] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] For ease of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0025] One-time programmable time: One-time programmable time, referred to as OTP time;

[0026] Grayscale reactor model: Gray Response Amplifier, referred to as GRA model.

[0027] As described in the background, the OTP initialization algorithm used in existing OLED modules requires determining initial gamma values ​​one by one and adjusting the gamma values ​​in the band registers one by one. This means that if ten brightness levels need to be adjusted, the initial gamma values ​​of the band registers must be determined sequentially and then adjusted from the initial values ​​to the target values ​​to control the display's corresponding brightness. This requires ten times the time to adjust the ten brightness levels. This results in excessively long adjustment times. Furthermore, the initial gamma values ​​of the band registers determined by the OTP initialization algorithm used in existing OLED modules often differ significantly from the desired target values. This results in numerous OTP adjustments and a lengthy process.

[0028] In order to solve the problem in the prior art that the OTP initial value algorithm in the OLED module has a large number of adjustments and a long adjustment time, the embodiments of the present application provide a method for determining the gamma data of an OLED screen and a display screen.

[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0030] In this embodiment, a method for determining the gamma data of an OLED screen running on a mobile terminal, a computer terminal, or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] Figure 1 FIG. 1 is a flow chart of a method for determining gamma data of an OLED screen according to an embodiment of the present application. Figure 2 As shown, the method includes the following steps:

[0032] Step S101: construct a first gamma calculation model, wherein the first gamma calculation model is trained using multiple sets of training data, each set of training data including: historical reference gamma data and historical actual gamma data corresponding to the historical reference gamma data, acquired during a historical time period; the historical actual gamma data being actual gamma data used to control the luminescence of the OLED screen at a historical moment; and the historical reference gamma data being reference values ​​used to calculate target gamma initial value data at a historical moment.

[0033] Specifically, the band0 register in the OLED module is typically used to set the column address range of the OLED display. By setting the band0 register, you can specify the column address range of the OLED display, thereby controlling the position of the displayed content on the screen. This register is used to ensure that the display content is correctly displayed at a specific location on the OLED display.

[0034] The OLED module has multiple band registers, namely band0 through bandn. The historical reference gamma data is the initial gamma value of band0, used to calculate the gamma values ​​of other band registers at a given moment. The historical actual gamma data is the actual gamma data used by band1 through bandn registers to control the OLED screen's light emission. Training with both the historical reference gamma data and the actual gamma data yields a mapping between the initial gamma value of band0 and the actual gamma data of band1 through bandn registers.

[0035] Furthermore, the OTP initialization algorithm used in existing OLED modules requires determining the initial gamma values ​​of the band registers one by one and adjusting the gamma values ​​in the band registers one by one. This means that if ten brightness levels need to be adjusted, the initial gamma values ​​of the band registers must be determined sequentially and then adjusted from the initial values ​​to the target values ​​to control the display's corresponding brightness. This requires ten times the time to adjust the ten brightness levels. This results in excessively long adjustment times. Furthermore, the initial gamma values ​​of the band registers determined by the OTP initialization algorithm used in existing OLED modules often differ significantly from the desired target values. This results in numerous OTP adjustments and a lengthy process.

[0036] The first gamma calculation model can directly input the gamma data of the band0 register as the reference value for each band's gamma value into the gamma calculation model, while simultaneously outputting the gamma values ​​of all band registers. That is, when there are ten brightness levels that need to be adjusted, the first gamma calculation model inputs the gamma data of the ten band0 registers and simultaneously outputs the gamma values ​​of the ten band registers. This allows the adjustment of ten brightness levels in twice the time. Furthermore, because the first gamma calculation model is trained based on historical reference gamma data and historical actual gamma data, the gamma output value obtained using the first gamma calculation model is closer to the final target value to be adjusted. This reduces the number of OTP adjustments and significantly reduces process time.

[0037] Before constructing the first gamma calculation model, the method further includes the following steps:

[0038] Step S201, obtaining operating parameters of the target OLED screen, wherein the operating parameters include at least one of the following: size, contrast, resolution, and refresh rate of the OLED screen;

[0039] Step S202: Obtain a second mapping relationship, where the second mapping relationship is a mapping relationship between the operating parameters of the OLED screen and the reference gamma data.

[0040] Step S203 : determining the target reference gamma data corresponding to the operating parameters of the target OLED screen according to the operating parameters of the target OLED screen and the second mapping relationship.

[0041] Specifically, the reference gamma data of the band register can be accurately determined by the operating parameters of the OLED screen and the mapping relationship between the operating parameters of the OLED screen and the reference gamma data of the band register, wherein the reference gamma data is pre-set according to the operating parameters of the OLED screen.

[0042] In OLED modules, the gamma data in the band0 register controls the display's brightness and color performance. Gamma is a nonlinear factor used to adjust the display's brightness and contrast for a more realistic and natural look. Adjusting the gamma value improves the display's grayscale levels and color saturation, enhancing the display quality and viewing experience.

[0043] The first gamma calculation model is constructed, including the following steps:

[0044] Step S301, obtaining an initial gamma calculation model, wherein the initial gamma calculation model is a model based on a grayscale reactor model;

[0045] The input data dimension of the initial gamma calculation model is the same as the output data dimension.

[0046] Specifically, the input data dimension and output data dimension of the initial gamma calculation model are the same, which can greatly reduce the process time of determining the gamma data, so that multiple brightness levels can be adjusted simultaneously in a very short time.

[0047] Step S302, obtaining historical reference gamma data and historical actual gamma data;

[0048] Step S303: Input the historical reference gamma data and the historical actual gamma data into the initial gamma calculation model for iterative training, and determine the trained model as the first gamma calculation model.

[0049] Specifically, the Gray Response Amplifier (GRA) model is a grayscale processing technology widely used in OLED (Organic Light Emitting Diode) displays. This model adjusts the grayscale levels of OLED display pixels to achieve more accurate and smooth grayscale representation, improving display quality and image quality. The GRA model can enhance the color accuracy and brightness uniformity of OLED displays, making the display more realistic and detailed.

[0050] The historical reference gamma data and the historical actual gamma data are input into the initial gamma calculation model for iterative training, including the following steps:

[0051] Step S401: Input the historical reference gamma data into the initial gamma calculation model to perform forward propagation calculation to obtain predicted initial gamma data;

[0052] Each set of training data includes a plurality of the above-mentioned historical reference gamma data, a plurality of the above-mentioned historical actual gamma data, and a plurality of the above-mentioned predicted initial gamma data. The above-mentioned historical reference gamma data, the above-mentioned historical actual gamma data, the above-mentioned predicted initial gamma data, and the above-mentioned registers correspond one-to-one. The above-mentioned historical reference gamma data and the above-mentioned historical actual gamma data are input into the above-mentioned initial gamma calculation model for iterative training, and the trained model is determined as the above-mentioned first gamma calculation model, including:

[0053] Step S4011, performing error function calculation on each of the above-mentioned predicted initial gamma data and the corresponding above-mentioned historical actual gamma data to obtain multiple sub-calculation errors;

[0054] Step S4012: If all of the sub-calculation errors are less than a preset error value, it is determined that the iterative training of the initial gamma calculation model is completed, and the initial gamma calculation model at this time is determined as the first gamma calculation model;

[0055] Step S4013: When at least one of the above sub-calculation errors is greater than or equal to the above-mentioned preset error value, back propagation calculation is performed on the above-mentioned predicted initial gamma data to continue iterative training of the above-mentioned initial gamma calculation model until all of the above-mentioned sub-calculation errors are less than the above-mentioned preset error value.

[0056] Specifically, the error function can be used to accurately judge the accuracy of the model, and the model can be continuously trained based on the accuracy of the model (i.e., continuous forward propagation and backward propagation). By continuously adjusting the parameters and hyperparameters of the model during the training process, the performance of the model can be improved.

[0057] Algorithm parameters and hyperparameters are designed to help the algorithm learn and adjust model performance. Parameters refer to model parameters such as weights and biases learned by the algorithm, which directly affect model output. Hyperparameters, on the other hand, are parameters that need to be set before model training, such as the learning rate and regularization parameter. The choice of hyperparameters directly affects model performance and training results. By adjusting parameters and hyperparameters, the model can better fit the data, improve its generalization ability, and thus achieve better prediction results. Therefore, the selection of parameters and hyperparameters requires careful debugging and optimization.

[0058] Step S402: performing loss function calculation on the predicted initial gamma data and the corresponding historical actual gamma data to obtain a calculation error, wherein the predicted initial gamma data and the historical actual gamma data correspond one to one;

[0059] Step S403: Determine whether the iterative training of the initial gamma calculation model is completed based on the size of the calculation error.

[0060] Specifically, the error function can be used to accurately predict the accuracy of the model. The smaller the error, the higher the accuracy of the model.

[0061] Among them, according to the size of the above-mentioned calculation error, determining whether the iterative training of the above-mentioned initial gamma calculation model is completed includes: when the above-mentioned calculation error is less than the preset error value, determining that the iterative training of the above-mentioned initial gamma calculation model is completed, and determining the above-mentioned initial gamma calculation model at this time as the above-mentioned first gamma calculation model.

[0062] Specifically, since the purpose of the model is to make the predicted gamma data output by the model as close as possible to the actual gamma data, the actual gamma data is used as the standard. When the calculation error is less than the preset error value, it is proved that the accuracy of the initial gamma calculation model meets the standard requirements. At this time, the initial gamma calculation model is the first gamma calculation model.

[0063] The initial gamma calculation model includes an input layer, a hidden layer, and an output layer. Determining whether the iterative training of the initial gamma calculation model is completed based on the magnitude of the calculation error further includes the following steps:

[0064] Step S4031: When the calculation error is greater than or equal to a preset error value, perform back propagation calculation on the predicted initial gamma data;

[0065] Step S4032: obtain an optimizer, and adjust the weights of each layer in the initial gamma calculation model based on the calculation error and the optimizer to continue iterative training of the initial gamma calculation model until the calculation error is less than the preset error value.

[0066] Specifically, if the calculation error is greater than or equal to the preset error value, it proves that the accuracy of the initial gamma calculation model at the current moment does not meet the required accuracy. Therefore, the initial gamma calculation model needs to be further trained to ensure that the accuracy of the initial gamma calculation model meets the required standard. During the training process of the initial gamma calculation model, the weights of each layer in the initial gamma calculation model are adjusted through backpropagation calculations, and the optimizer is used to adjust the model parameters and hyperparameters to optimize the model performance.

[0067] Among them, such as Figure 2 As shown, the input X of the neural network model includes m data, and the output value Ypre is obtained by forward propagation through multiple n layers of processing, where Ypre has the same dimension as X and also includes m data. The real value Yreal is used to compare the model output value Ypre with the loss function, and backpropagation is performed based on the comparison result to train the neural network model, where Ypre is the predicted initial gamma data, Yreal is the actual gamma data, and layer1...layern are the first layer...nth layer of the neural network model respectively.

[0068] The hidden layer of the initial gamma calculation model includes a batch normalization layer, a linear layer, and an activation layer. The historical reference gamma data and the historical actual gamma data are input into the initial gamma calculation model for iterative training, including the following steps:

[0069] Step S501, normalizing the historical reference gamma data based on the batch normalization layer to obtain normalized gamma data;

[0070] Step S502, performing a nonlinear transformation on the normalized gamma data based on the linear layer to obtain nonlinearly transformed gamma data;

[0071] Step S503: Processing the gamma data after the nonlinear change based on the activation layer to perform iterative training.

[0072] Specifically, in neural networks, batch normalization layers can accelerate the training process of neural networks, reduce the problem of vanishing or exploding gradients, reduce the neural network's dependence on initial weights, improve model stability, reduce the risk of overfitting, improve the model's generalization ability, and make it easier for the neural network to converge to the optimal solution. The linear layer is a basic component in a neural network, used to perform linear transformations and feature extraction on input data. It can achieve weighted summation of input data to obtain new feature representations and learn relationships and patterns between input data, thereby improving the model's expressive power. The activation function in the activation layer can introduce nonlinear factors, thereby making the neural network more expressive, enabling the neural network to learn nonlinear relationships and patterns, improving the model's fitting ability, and solving the problem of vanishing or exploding gradients, making neural network training more stable and efficient.

[0073] Among them, such as Figure 3 As shown in the figure, the i-th layer of the neural network model consists of a normalization layer, a linear layer and an activation layer, and the neural network model has a total of n layers.

[0074] Step S102, obtaining target gamma initial value data according to the target reference gamma data and the above-mentioned first gamma calculation model, and controlling the luminescence of the above-mentioned OLED screen based on the above-mentioned target gamma initial value data, wherein the above-mentioned target reference gamma data is a reference value for calculating the target gamma initial value data, and the above-mentioned target gamma initial value data includes the gamma initial values ​​of all registers that control the luminescence of the above-mentioned OLED screen.

[0075] Specifically, the target reference gamma data is input into the first gamma calculation model, and the output target gamma initial value data is very close to the actual gamma value that ultimately controls the luminescence of the OLED screen, which greatly reduces the number of OTP adjustments and the adjustment time.

[0076] The process of obtaining target gamma initial value data based on the target reference gamma data and the first gamma calculation model includes the following steps:

[0077] Step S601, extracting the model parameters of the first gamma calculation model;

[0078] Step S602: Importing the model parameters of the first gamma calculation model into the OTP algorithm to obtain a second gamma calculation model, wherein the second gamma calculation model is a calculation model in the OTP algorithm having the same structure and parameters as the first gamma calculation model;

[0079] The step of importing the model parameters of the first gamma calculation model into the OTP algorithm to obtain the second gamma calculation model includes: importing the forward propagation calculation parameters of the first gamma calculation model into the OTP algorithm to obtain the second gamma calculation model.

[0080] Specifically, the forward propagation parameters of a neural network are those used during the neural network's forward propagation process, which is used to pass input data through the neurons in each layer to calculate the output. These parameters, including the weights and biases of each layer, are used to calculate the output of each layer. Backward propagation parameters are those used during the backward propagation process of a neural network, which are used to calculate the gradient of the loss function and update the parameters of each layer to minimize the loss function. These parameters, including the gradient value and learning rate of each layer, are used to calculate the updated values ​​of the parameters of each layer.

[0081] Therefore, the forward propagation parameters are used to calculate the output of the neural network, while the back propagation parameters are used to update the parameters of the neural network to improve the performance of the model. The two play different but complementary roles in the training process of the neural network.

[0082] Since the first gamma calculation model is already a trained model, it is only necessary to input the forward propagation calculation parameters into the OTP algorithm to obtain a runnable second gamma calculation model, without the need to transmit the back propagation calculation parameters and waste computing and storage resources.

[0083] Step S603: input the target reference gamma data as an input value into the second gamma calculation model, and obtain the output of the second gamma calculation model as the target gamma initial value data.

[0084] Specifically, by importing the model parameters of the first gamma calculation model into the OTP algorithm, a second gamma calculation model identical to the first gamma calculation model can be directly obtained. Importing the model parameters of the first gamma calculation model into the OTP algorithm to obtain the second gamma calculation model eliminates the need to separately execute the OTP algorithm and the first gamma calculation model in subsequent applications. Instead, the gamma data of band 0 can be directly input into the OTP algorithm to obtain the initial gamma values ​​for bands 1 to 2.

[0085] The initial values ​​of the OTP algorithm in the OLED module are used to initialize and calibrate the OLED module. These initial values ​​include the OLED's brightness, contrast, color, and other parameters. By adjusting these initial values, the OLED display becomes clearer, more accurate, and more stable. These initial values ​​are designed to ensure the consistency and stability of the OLED display, enabling it to achieve optimal display effects in different environments.

[0086] The OTP (One-Time-Programming) algorithm in OLED modules is a one-time programming process used to write initial parameters or specific data into the OLED module. This algorithm is typically designed and implemented by the OLED module manufacturer, and its specific implementation method may vary depending on the OLED module model and manufacturer. Generally speaking, this algorithm writes the initial parameters or specific data into the OLED module's OTP memory to ensure that the OLED module is functioning properly and has the preset characteristics when it leaves the factory. This process is usually completed during the manufacturing process. Once completed, the data cannot be modified or erased, hence the name one-time programming.

[0087] There's a certain relationship between the OTP adjustment time and the initial gamma register value. This is because both are used to adjust display parameters, thus affecting the display quality. Generally speaking, the initial gamma register value affects the display's brightness and contrast, while the OTP adjustment time affects the display's response speed and stability. Therefore, there's a certain correlation between the two. Adjusting the initial gamma register value can affect the display quality, indirectly affecting the required OTP adjustment time. Therefore, the relationship between the adjustment time and the initial gamma register value needs to be analyzed and adjusted based on the specific display and requirements.

[0088] Among them, there are multiple target reference gamma data, there are multiple target gamma initial value data, the target reference gamma data correspond to the target gamma initial value data one by one, each target gamma initial value data is the gamma initial value of the corresponding register, the target reference gamma data is input as the input value into the second gamma calculation model, and the output of the second gamma calculation model is the target gamma initial value data, including: inputting all the target gamma reference data into the second gamma calculation model at the same time, so that the second gamma calculation model simultaneously outputs all the target gamma initial value data.

[0089] Specifically, this can take twice the time to obtain all target gamma initial value data at the same time, greatly saving process time.

[0090] The OLED screen is controlled to emit light based on the target gamma initial value data, including the following steps:

[0091] Step S701, input the above target gamma initial value data into the OTP algorithm;

[0092] Among them, the target gamma initial value data of different registers are different.

[0093] Specifically, different band registers may have different uses or functions, and therefore their initial gamma values ​​may differ. Gamma values ​​are typically used to adjust the brightness and contrast of a display to ensure accurate and clear image display. Different band registers may require different initial gamma values ​​to meet specific display requirements or application scenarios. Therefore, even adjacent band registers may have different initial gamma values.

[0094] Wherein, the absolute value of the difference between the target gamma initial value data of one of the registers and the corresponding target gamma data is less than or equal to a preset absolute value.

[0095] Specifically, since the second gamma calculation model is the same model as the first gamma calculation model, and the first gamma calculation model is trained using reference gamma data and actual gamma data, the target gamma initial value data of the register obtained using the second gamma calculation model is very close to the target gamma data ultimately used to control the luminescence of the OLED screen, which greatly reduces the number of OTP adjustments and the adjustment time.

[0096] Step S702, using the above-mentioned OTP algorithm and based on the above-mentioned target gamma initial value data corresponding to each of the above-mentioned registers, adjust the gamma data of each of the above-mentioned registers to obtain the target gamma data corresponding to the above-mentioned registers, and the above-mentioned target gamma data is used to control the above-mentioned OLED screen to display the target brightness.

[0097] Specifically, the second gamma calculation model generates only the initial gamma value for the register. This initial gamma value must then be adjusted using the OTP algorithm to obtain the target gamma value that ultimately controls the OLED screen's display brightness. Because the initial target gamma value closely matches the target gamma value ultimately used to control the OLED screen's luminescence, the number of OTP adjustments and the time required are significantly reduced.

[0098] The OTP algorithm is used to adjust the gamma data of each register based on the target gamma initial value data corresponding to each register to obtain the target gamma data corresponding to the register, including the following steps:

[0099] Step S7021, obtaining the target display brightness of the OLED screen;

[0100] Step S7022: Acquire a first mapping relationship, where the first mapping relationship is a correspondence between the display brightness of the OLED screen and the gamma data of each of the registers.

[0101] Step S7023: Determine target gamma data corresponding to each of the registers according to the target display brightness of the OLED screen and the first mapping relationship.

[0102] Specifically, the gamma data in the band0 register controls the display's brightness and color performance, adjusting the display's brightness and contrast. Adjusting the gamma value improves the display's grayscale levels and color saturation, enhancing the display quality and viewing experience. Therefore, it's necessary to first determine the target display brightness for the OLED screen before determining the final gamma data values ​​required for each band register. This gamma value is then used as a guideline for adjusting the gamma data in the band registers.

[0103] Among them, the above method also includes: obtaining multiple data adjustment times, the above data adjustment times correspond one-to-one to the above registers, the above data adjustment times are the adjustment times for adjusting the gamma data of the above registers using the above OTP algorithm and based on the corresponding above target gamma initial value data, and the above data adjustment time is less than or equal to the preset time.

[0104] Specifically, since the target gamma initial value data is very close to the target gamma data ultimately used to control the luminescence of the OLED screen, the number of OTP adjustments and the adjustment time will be greatly reduced, that is, the OTP adjustment time using the above embodiment is much shorter than the OTP adjustment time in the prior art.

[0105] The method for determining the gamma data of the above-mentioned OLED screen of the present application first uses the reference value used to calculate the target gamma initial value data at a historical moment and the real gamma data used to control the OLED screen light emission at the corresponding historical moment to train to obtain a first gamma calculation model; then, based on the target reference gamma data and the first gamma calculation model, the target gamma initial value data is obtained, and the OLED screen light emission is controlled based on the target gamma initial value data, wherein the target reference gamma data is a reference value for calculating the target gamma initial value data, and the target gamma initial value data includes the gamma initial values ​​of all registers that control the OLED screen light emission. This method can directly input the gamma data of the band0 register as the reference value of the gamma value of each band into the gamma calculation model, and output the gamma values ​​of all band registers at the same time. Ten brightness levels can be adjusted in half the time, and the number of OTP adjustments is reduced, which greatly reduces the process time, solving the problem of the OTP initial value algorithm in the OLED module in the prior art having a large number of adjustments and a long adjustment time.

[0106] Figure 4 This is a flow chart of another method for determining the gamma data of an OLED screen. Figure 4 As shown, the above method first uses the OTP algorithm to parse data to obtain historical reference gamma data and its corresponding historical actual gamma data. Then, the historical reference gamma data and its corresponding historical actual gamma data are batch-imported into the MySQL lightweight database system to obtain a gamma data table. Then, the data in the gamma data table is read and a Ctorch framework algorithm model with different structures is established for each project to train the GRA algorithm model. Then, the GRA algorithm model is iteratively trained to obtain the trained GRA algorithm model as the first gamma calculation model. The model parameters of the first gamma calculation model are exported to the OTP program, and the GRA algorithm model with the same structure in the OTP is obtained as the second gamma calculation model. Then, the gamma reference data of band0 is input into the second gamma calculation model, and the model output is the gamma initial value data of band1 to bandn.

[0107] Figures 5 to 10 Each of them is a schematic diagram of the effect of a method for determining the gamma data of an OLED screen, and a large amount of data is used to verify the effect of the above method for determining the gamma data of the OLED screen.

[0108] like Figure 5As shown, when the grayscale value is 0, the data in which the error between the target gamma initial value data and the target gamma data of the band1 register is within the range of <5 accounts for 87% of the overall test data, the data in which the error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 5 and less than 10 accounts for 12.86% of the overall test data, the data in which the error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 10 and less than 15 accounts for 0.15% of the overall test data, and the data in which the error between the target gamma initial value data and the target gamma data of the band1 register is greater than 15 accounts for 0% of the overall test data.

[0109] like Figure 6 As shown, when the grayscale value is 1, the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of <5 accounts for 99.25% of the entire test data, the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 5 and less than 10 accounts for 0.75% of the entire test data, and the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 10 accounts for 0% of the entire test data.

[0110] like Figure 7 As shown, when the grayscale value is 2, the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of <5 accounts for 98.95% of the entire test data, the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 5 and less than 10 accounts for 1.05% of the entire test data, and the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 10 accounts for 0% of the entire test data.

[0111] like Figure 8 As shown, when the grayscale value is 3, the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of <5 accounts for 95.37% of the entire test data, the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 5 and less than 10 accounts for 4.63% of the entire test data, and the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 10 accounts for 0% of the entire test data.

[0112] like Figure 9As shown, when the grayscale value is 4, the data in which the error between the target gamma initial value data and the target gamma data of the band1 register is within the range of <5 accounts for 99.25% of the entire test data, the data in which the error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 5 and less than 10 accounts for 0.75% of the entire test data, and the data in which the error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 10 accounts for 0% of the entire test data.

[0113] like Figure 10 As shown, when the grayscale value is 5, the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of <5 accounts for 98.8% of the entire test data, the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 5 and less than 10 accounts for 1.2% of the entire test data, and the data whose error between the target gamma initial value data and the target gamma data of the band1 register is within the range of greater than 10 accounts for 0% of the entire test data.

[0114] It can be seen from the above test data that the error between the target gamma initial value data of the band register obtained by the above-mentioned method for determining the gamma data of the OLED screen and the target gamma data is generally controlled within the range of less than 5, and the error of a small amount of data is within the range of greater than 5 and less than 10. It can be determined that the gamma initial value obtained by the determination method of the present application will be closer to the target value to be finally adjusted, which will reduce the number of OTP adjustments and the adjustment time, and greatly reduce the process time.

[0115] In this embodiment, a display screen is also provided. Figure 11 A schematic diagram of a display screen provided by an embodiment of the present invention, such as Figure 11 As shown, the display screen further includes a display panel 100. The brightness of the display screen is determined according to the gamma data in each register, and the gamma data in each of the above registers is determined by using any of the above methods for determining the gamma data of the OLED screen.

[0116] In addition to the OLED main screen, other bands in the OLED module may include touch screens, fingerprint readers, cameras, speakers, microphones, and other components. These components can be integrated with the OLED main screen to form a complete OLED module for use in mobile phones, tablets, smart watches, and other devices.

[0117] The brightness of the display screen described in this application is determined based on the gamma data in each register. The gamma data in each register is determined using any of the aforementioned methods for determining gamma data for OLED screens. This display screen can directly input the gamma data in the band0 register as a reference value for each band's gamma value into the gamma calculation model, while simultaneously outputting the gamma values ​​of all band registers. This allows for ten brightness adjustments in twice the time, reduces the number of OTP adjustments, and significantly shortens the process time. This addresses the issue of the OTP initialization algorithm in existing OLED modules requiring numerous adjustments and taking excessively long adjustment times.

[0118] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0119] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0120] 1) The method for determining the gamma data of the above-mentioned OLED screen of the present application first uses the reference value for calculating the target gamma initial value data at a historical moment and the real gamma data for controlling the luminescence of the OLED screen at the corresponding historical moment to train to obtain a first gamma calculation model; then, according to the target reference gamma data and the first gamma calculation model, the target gamma initial value data is obtained, and the luminescence of the OLED screen is controlled based on the target gamma initial value data, wherein the target reference gamma data is a reference value for calculating the target gamma initial value data, and the target gamma initial value data includes the gamma initial values ​​of all registers that control the luminescence of the OLED screen. This method can directly input the gamma data of the band0 register as the reference value of the gamma value of each band into the gamma calculation model, and output the gamma values ​​of all band registers at the same time. Ten brightness levels can be adjusted in twice the time, and the number of OTP adjustments is reduced, which greatly reduces the process time, and solves the problem of the OTP initial value algorithm in the OLED module in the prior art having a large number of adjustments and a long adjustment time.

[0121] 2) The brightness of the display screen of the present application is determined based on the gamma data in each register. The gamma data in each register is determined using any of the above-mentioned methods for determining gamma data for OLED screens. This display screen can directly input the gamma data of the band0 register as a reference value for the gamma value of each band into the gamma calculation model, and simultaneously output the gamma values ​​of all band registers. This allows ten brightness levels to be adjusted in half the time, and reduces the number of OTP adjustments, significantly reducing process time. This solves the problem of the OTP initial value algorithm in the prior art OLED module requiring a large number of adjustments and taking too long to adjust.

[0122] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for determining gamma data of an OLED screen, characterized in that: include: Constructing a first gamma calculation model, wherein the first gamma calculation model is trained using multiple sets of training data, each set of training data including: historical reference gamma data and historical actual gamma data corresponding to the historical reference gamma data, acquired during a historical time period, wherein the historical actual gamma data is real gamma data used to control the luminescence of the OLED screen at a historical moment, and the historical reference gamma data is a reference value used to calculate target gamma initial value data at a historical moment; Obtaining target gamma initial value data according to target reference gamma data and the first gamma calculation model, and controlling the OLED screen to emit light based on the target gamma initial value data, wherein the target reference gamma data is a reference value for calculating the target gamma initial value data, and the target gamma initial value data includes gamma initial values ​​of all registers that control the OLED screen to emit light; Obtaining target gamma initial value data according to target reference gamma data and the first gamma calculation model, including: extracting model parameters of the first gamma calculation model; Importing the model parameters of the first gamma calculation model into the OTP algorithm to obtain a second gamma calculation model, where the second gamma calculation model is a calculation model in the OTP algorithm with the same structure and parameters as the first gamma calculation model; The target reference gamma data is input as an input value into the second gamma calculation model, and the output of the second gamma calculation model is the target gamma initial value data.

2. The determination method according to claim 1, characterized in that Construct the first gamma calculation model, including: Obtaining an initial gamma calculation model, wherein the initial gamma calculation model is a model based on a grayscale reactor model; Obtain historical reference gamma data and historical actual gamma data; The historical reference gamma data and the historical actual gamma data are input into the initial gamma calculation model for iterative training, and the trained model is determined as the first gamma calculation model.

3. The determination method according to claim 2, characterized in that: Inputting the historical reference gamma data and the historical actual gamma data into the initial gamma calculation model for iterative training includes: Inputting the historical reference gamma data into the initial gamma calculation model to perform forward propagation calculation to obtain predicted initial gamma data; Performing loss function calculation on the predicted initial gamma data and the corresponding historical actual gamma data to obtain a calculation error, wherein the predicted initial gamma data and the historical actual gamma data correspond one to one; According to the size of the calculation error, it is determined whether the iterative training of the initial gamma calculation model is completed.

4. The determination method according to claim 3, characterized in that: Determining whether the iterative training of the initial gamma calculation model is completed according to the size of the calculation error includes: When the calculation error is less than the preset error value, it is determined that the iterative training of the initial gamma calculation model is completed, and the initial gamma calculation model at this time is determined as the first gamma calculation model.

5. The determination method according to claim 3, characterized in that: The initial gamma calculation model includes an input layer, a hidden layer, and an output layer. Determining whether iterative training of the initial gamma calculation model is completed according to the magnitude of the calculation error includes: When the calculation error is greater than or equal to a preset error value, performing back propagation calculation on the predicted initial gamma data; Obtain an optimizer, and adjust the weights of each layer in the initial gamma calculation model based on the calculation error and the optimizer to continue iterative training of the initial gamma calculation model until the calculation error is less than the preset error value.

6. The determination method according to claim 3, characterized in that: Each set of training data includes a plurality of the historical reference gamma data, a plurality of the historical actual gamma data, and a plurality of the predicted initial gamma data. The historical reference gamma data, the historical actual gamma data, the predicted initial gamma data, and the registers correspond one to one. The historical reference gamma data and the historical actual gamma data are input into the initial gamma calculation model for iterative training, and the trained model is determined as the first gamma calculation model, including: Perform error function calculation on each of the predicted initial gamma data and the corresponding historical actual gamma data to obtain multiple sub-calculation errors; When all the sub-calculation errors are less than a preset error value, determining that the iterative training of the initial gamma calculation model is completed, and determining the initial gamma calculation model at this time as the first gamma calculation model; When at least one of the sub-calculation errors is greater than or equal to the preset error value, back propagation calculation is performed on the predicted initial gamma data to continue iterative training of the initial gamma calculation model until all of the sub-calculation errors are less than the preset error value.

7. The determination method according to claim 2, characterized in that: The input data dimension and output data dimension of the initial gamma calculation model are the same.

8. The determination method according to claim 2, characterized in that: The hidden layer of the initial gamma calculation model includes a batch normalization layer, a linear layer, and an activation layer. The historical reference gamma data and the historical actual gamma data are input into the initial gamma calculation model for iterative training, including: Normalizing the historical reference gamma data based on the batch normalization layer to obtain normalized gamma data; Performing a nonlinear change on the normalized gamma data based on the linear layer to obtain nonlinearly changed gamma data; The nonlinearly changed gamma data is processed based on the activation layer to perform iterative training.

9. The determination method according to claim 1, characterized in that: Importing the model parameters of the first gamma calculation model into the OTP algorithm to obtain a second gamma calculation model, including: The forward propagation calculation parameters of the first gamma calculation model are imported into the OTP algorithm to obtain the second gamma calculation model.

10. The determination method according to claim 1, characterized in that: There are a plurality of target reference gamma data and a plurality of target initial gamma value data, the target reference gamma data and the target initial gamma value data correspond one-to-one, each target initial gamma value data is the initial gamma value of the corresponding register, the target reference gamma data is input as an input value into the second gamma calculation model, and the output of the second gamma calculation model is the target initial gamma value data, including: All of the target gamma reference data are simultaneously input into the second gamma calculation model, so that the second gamma calculation model simultaneously outputs all of the target gamma initial value data.

11. The determination method according to claim 1, characterized in that: Controlling the OLED screen to emit light based on the target gamma initial value data includes: Inputting the target gamma initial value data into the OTP algorithm; The OTP algorithm is used and based on the target gamma initial value data corresponding to each register, the gamma data of each register is adjusted to obtain the target gamma data corresponding to the register, and the target gamma data is used to control the target brightness displayed by the OLED screen.

12. The determination method according to claim 11, characterized in that: Adopting the OTP algorithm and based on the target gamma initial value data corresponding to each register, adjusting the gamma data of each register to obtain the target gamma data corresponding to the register, including: Obtaining the target display brightness of the OLED screen; Obtaining a first mapping relationship, where the first mapping relationship is a correspondence between the display brightness of the OLED screen and the gamma data of each of the registers; The target gamma data corresponding to each of the registers is determined according to the target display brightness of the OLED screen and the first mapping relationship.

13. The determination method according to claim 11, characterized in that: The target gamma initial value data of different registers are different.

14. The determination method according to claim 11, characterized in that: An absolute value of a difference between the target gamma initial value data of one of the registers and the corresponding target gamma data is less than or equal to a preset absolute value.

15. The determination method according to claim 11, characterized in that: The method further comprises: Acquire multiple data adjustment times, where the data adjustment times correspond one-to-one to the registers, and the data adjustment times are the adjustment times for adjusting the gamma data of the registers using the OTP algorithm and based on the corresponding target gamma initial value data. The data adjustment times are less than or equal to the preset time.

16. The determination method according to claim 1, characterized in that: Before constructing the first gamma calculation model, the method further includes: Obtaining operating parameters of the target OLED screen, wherein the operating parameters include at least one of the following: size, contrast, resolution, and refresh rate of the OLED screen; Obtain a second mapping relationship, where the second mapping relationship is a mapping relationship between the operating parameters of the OLED screen and the reference gamma data, The target reference gamma data corresponding to the operating parameters of the target OLED screen is determined according to the operating parameters of the target OLED screen and the second mapping relationship.

17. A display screen, characterized in that: The brightness of the display screen is determined based on the gamma data in each register, and the gamma data in each register is determined using the method for determining the gamma data of the OLED screen according to any one of claims 1 to 16.

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