Correction method and device and display panel

Abstract

The invention provides a correction method and device and a display panel. The method comprises the following steps: acquiring a numerical value of an RGB register in a first display panel in a plurality of display panels; the first model is trained according to the numerical value of the RGB register in the first display panel, first training parameters are obtained, and the first training parameters comprise the weight obtained through gradient descent adjustment of the first model; acquiring the numerical value of the RGB register in the next display panel in the plurality of display panels; the first model is trained according to the numerical value of the RGB register in the next display panel, a next training parameter is obtained, and the initial value of the next training parameter is the previous training parameter. Through the method, the first model obtained through training has excellent compatibility, can adapt to display panels of different types and characteristics, and meets the correction requirements of different display panels.

Description

Technical Field

[0001] The present application relates to the field of display technology, and more specifically, to a correction method, device and display panel. Background Art

[0002] With the continuous development of display technology, users' requirements for display panel products are also increasing; users' expectations for display panel products are not limited to the improvement of image quality, but also include more sophisticated brightness adjustment and color adjustment functions. In this context, brightness adjustment and color adjustment have become one of the important indicators for measuring the quality of display panel products. Display panels with excellent brightness adjustment and color adjustment functions can provide a more comfortable visual experience according to different ambient light conditions and user needs. In the process of display panel brightness adjustment and color adjustment, the gamma correction process is crucial.

[0003] Currently, when a display panel is performing gamma correction, the gamma correction algorithm is encapsulated in a function by the manufacturer, and the specific details of the algorithm are invisible. For different types of display panels, the algorithm needs to be manually rewritten or adjusted, which increases the workload and cost of development and maintenance. Summary of the invention

[0004] The present application provides a correction method, device and display panel. Through this method, the first model obtained by training has excellent compatibility, can adapt to display panels of different types and characteristics, and meet the correction requirements of different display panels.

[0005] In a first aspect, a correction method is provided, the method comprising: obtaining values ​​of RGB registers in a first display panel among multiple display panels; training a first model according to the values ​​of the RGB registers in the first display panel to obtain first training parameters, the first training parameters comprising weights of the first model obtained by gradient descent adjustment; obtaining values ​​of RGB registers in a next display panel among the multiple display panels; training the first model according to the values ​​of the RGB registers in the next display panel to obtain next training parameters, the initial values ​​of the next training parameters being the previous training parameters.

[0006] In the embodiment of the present application, since the first model is trained based on the values ​​of the RGB registers of multiple display panels, the trained first model has excellent compatibility and can adapt to display panels of different types and characteristics and meet the correction requirements of different display panels. On the other hand, as the number of display panels increases during the training of the first model, the first model becomes more robust and the inference efficiency gradually improves.

[0007] In combination with the first aspect, in certain implementations of the first aspect, the training of the first model according to the numerical values ​​of the RGB registers in the first display panel to obtain first training parameters includes: inputting the numerical values ​​of the RGB registers in the first display panel into the first model to obtain first display parameters, the first display parameters including a first brightness level and a first color coordinate of the first display panel, and the first display parameters are used to indicate a gamma correction result of the first display panel; obtaining the first training parameters according to the first display parameters, preset display parameters and initialization parameters, the preset display parameters including a preset brightness level and a preset color coordinate, and the initialization parameters are initialization weights of the first model.

[0008] In an embodiment of the present application, the initialization parameters can be optimized based on the first display parameters and the preset display parameters to obtain the first training parameters. In this way, the convergence speed of the first model can be significantly accelerated, the reasoning accuracy and training stability of the first model can be improved, and at the same time, the training of the first model can be better adapted to the characteristics of the display panel and reduce the demand for computing resources.

[0009] In combination with the first aspect, in certain implementations of the first aspect, first training parameters are obtained based on the first display parameters, preset display parameters and initialization parameters, including: when the difference between the first brightness level and the preset brightness level is less than or equal to a first threshold, and the difference between the first color coordinate and the preset color coordinate is less than a second threshold, outputting the first training parameters.

[0010] In an embodiment of the present application, whether to output the first training parameters is determined by the difference between the first brightness level and the preset brightness level, and the difference between the first color coordinate and the preset color coordinate, which can further accelerate the convergence speed of the first model and further improve the reasoning accuracy and training stability of the first model.

[0011] In combination with the first aspect, in some implementations of the first aspect, the method further includes: deploying the trained first model on a correction device.

[0012] In a possible implementation manner, the correction device may be located in the display panel, or may be a device independent of the display panel.

[0013] In a second aspect, a correction method is provided, the method comprising: obtaining a value of an RGB register of a first display panel; inputting the value of the RGB register of the first display panel into a first model to obtain a second display parameter, the second display parameter comprising a second brightness level and a second color coordinate of the first display panel, the second display parameter being used to indicate a gamma correction result of the first display panel; wherein the first model is trained based on the values ​​of the RGB registers in the multiple display panels, preset display parameters and initialization parameters, the multiple display panels include the first display panel, the preset display parameters comprise a preset brightness level and a preset color coordinate, and the initialization parameter is an initialization weight of the first model.

[0014] In the embodiment of the present application, by using the first model, a good gamma correction effect can be achieved for the first display panel. Moreover, since the first model is trained based on the values ​​of the RGB registers of multiple display panels, the trained first model has excellent compatibility and can adapt to display panels of different types and characteristics, meeting the correction requirements of different display panels.

[0015] In a third aspect, a correction device is provided, comprising: an acquisition unit and a processing unit; the acquisition unit is used to acquire the value of an RGB register in a first display panel among multiple display panels; the processing unit is used to train a first model according to the value of the RGB register in the first display panel to obtain first training parameters, wherein the first training parameters include weights of the first model obtained by gradient descent adjustment; the acquisition unit is also used to acquire the value of an RGB register in a next display panel among the multiple display panels; the processing unit is also used to train the first model according to the value of the RGB register in the next display panel to obtain next training parameters, wherein the initial value of the next training parameter is the previous training parameter.

[0016] In combination with the third aspect, in certain implementations of the third aspect, the processing unit is specifically used to: input the numerical value of the RGB register in the first display panel into the first model to obtain a first display parameter, the first display parameter including a first brightness level and a first color coordinate of the first display panel, and the first display parameter is used to indicate a gamma correction result of the first display panel; obtain the first training parameter according to the first display parameter, a preset display parameter and an initialization parameter, the preset display parameter including a preset brightness level and a preset color coordinate, and the initialization parameter is an initialization weight of the first model.

[0017] In combination with the third aspect, in certain implementations of the third aspect, the processing unit is specifically used to output the first training parameters when the difference between the first brightness level and the preset brightness level is less than or equal to a first threshold, and the difference between the first color coordinate and the preset color coordinate is less than a second threshold.

[0018] In combination with the third aspect, in certain implementations of the third aspect, the processing unit is further used to deploy the trained first model on the correction device.

[0019] In a fourth aspect, a correction device is provided, the device comprising: an acquisition unit and a processing unit; the acquisition unit is used to acquire the value of the RGB register of the first display panel; the processing unit is used to input the value of the RGB register of the first display panel into the first model to obtain a second display parameter, the second display parameter comprising a second brightness level and a second color coordinate of the first display panel, the second display parameter being used to indicate a gamma correction result of the first display panel; wherein the first model is trained based on the values ​​of the RGB registers in the multiple display panels, preset display parameters and initialization parameters, the multiple display panels include the first display panel, the preset display parameters include a preset brightness level and a preset color coordinate, and the initialization parameter is an initialization weight of the first model.

[0020] In a fifth aspect, a correction device is provided, comprising: at least one processor and a memory, wherein the at least one processor is coupled to the memory and is used to read and execute instructions in the memory, so that the device implements the method in any one of the implementation modes of the first aspect or the second aspect mentioned above.

[0021] In a sixth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a program code, and when the computer program code runs on a computer, the computer executes a method in any one of the implementations of the first aspect or the second aspect.

[0022] In a seventh aspect, a chip is provided, the chip comprising a circuit, the circuit being used to execute the method in any one of the implementations of the first aspect or the second aspect.

[0023] In an eighth aspect, a computer program product is provided, which includes a computer program. When the computer program is executed by a processor, the method in any one of the implementations of the first aspect or the second aspect is executed.

[0024] In a ninth aspect, a display panel is provided, comprising the device according to any one of the implementations of the third aspect or the fourth aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a schematic flow chart of a correction method provided in an embodiment of the present application;

[0026] Figure 2 is a schematic flow chart of the model training provided in the embodiment of the present application;

[0027] Figure 3 is a graph showing the relationship between the model reasoning time and the number of items provided in the embodiment of the present application;

[0028] Figure 4 is a schematic flow chart of another calibration method provided in an embodiment of the present application;

[0029] Figure 5 is a schematic diagram of a correction device provided in an embodiment of the present application;

[0030] Figure 6 It is a schematic diagram of another correction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0032] The prefixes such as "first" and "second" used in the embodiments of the present application are only used to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of prefixes such as ordinal numbers used to distinguish description objects in the embodiments of the present application does not constitute a limitation on the described objects. For the statement of the described objects, please refer to the description in the context of the claims or embodiments, and the use of such prefixes should not constitute an unnecessary limitation.

[0033] In order to better understand the present application, the terms designed in the present application are first explained and illustrated.

[0034] (1) Gamma correction: This is a technique used to adjust the brightness and contrast of an image or display system. The gamma function is used to make the image display consistent on different devices. This correction ensures that the color and brightness of the image conform to the perception characteristics of the human eye and the display characteristics of the device, thereby improving the accuracy and consistency of the visual effect.

[0035] (2) Meta-learning: Unlike machine learning, which aims to enable machines to learn, meta-learning aims to enable machines to learn how to learn. The core of meta-learning is to enable the model to quickly adapt to or solve new tasks by using the knowledge learned from other tasks within a small number of training samples and a limited time.

[0036] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0037] With the continuous development of display technology, users' requirements for display panel products are also increasing; users' expectations for display panel products are not limited to the improvement of image quality, but also include more sophisticated brightness adjustment and color adjustment functions. In this context, brightness adjustment and color adjustment have become one of the important indicators for measuring the quality of display panel products. Display panels with excellent brightness adjustment and color adjustment functions can provide a more comfortable visual experience according to different ambient light conditions and user needs. In the process of display panel brightness adjustment and color adjustment, the gamma correction process is crucial.

[0038] Currently, when a display panel is performing gamma correction, the gamma correction algorithm is encapsulated in a function by the manufacturer, and the specific details of the algorithm are invisible. For different types of display panels, the algorithm needs to be manually rewritten or adjusted, which increases the workload and cost of development and maintenance.

[0039] For example, since the details of the algorithm are not visible after packaging, the gamma correction algorithm cannot be directly customized for different types of display panels (such as LCD, OLED, etc.). Every time the display panel type is changed or upgraded, manufacturers need to manually rewrite or adjust the algorithm, which increases the development workload and cost.

[0040] For example, since the algorithm is encapsulated in a function, the manufacturer needs to redesign and repackage the algorithm when updating or adjusting it, which makes it difficult to quickly update and respond to new algorithms. In addition, manufacturers need to maintain different versions of algorithms to support multiple display panel models, which also increases maintenance costs.

[0041] The present application provides a correction method, device and display panel. Through this method, the first model obtained by training has excellent compatibility, can adapt to display panels of different types and characteristics, and meet the correction requirements of different display panels.

[0042] Figure 1It is a schematic flow chart of a correction method provided in an embodiment of the present application. The execution subject of method 100 may be a model training device, and method 100 may include steps S101 to S104.

[0043] S101, obtaining values ​​of RGB registers in a first display panel among a plurality of display panels.

[0044] The first display panel may be any one of a plurality of display panels. For example, the first display panel is the first display panel of the plurality of display panels.

[0045] S102, training a first model according to the values ​​of the RGB registers in the first display panel to obtain first training parameters.

[0046] The first training parameters include the weights of the first model obtained by gradient descent adjustment;

[0047] In a possible implementation, step S102 includes: inputting the value of the RGB register in the first display panel into the first model to obtain a first display parameter, the first display parameter including a first brightness level and a first color coordinate of the first display panel, the first display parameter being used to indicate a gamma correction result of the first display panel; obtaining a first training parameter according to the first display parameter, a preset display parameter and an initialization parameter. The preset display parameter includes a preset brightness level and a preset color coordinate, and the initialization parameter is an initialization weight of the first model. In this way, the convergence speed of the first model can be significantly accelerated, the reasoning accuracy and training stability of the first model can be improved, and at the same time, the training of the first model can be better adapted to the characteristics of the display panel and reduce the demand for computing resources.

[0048] Optionally, the first display parameter may specifically be a corrected RGB value, which may correspond to the first brightness level and the first color coordinate of the first display panel, that is, in the above process, the gamma correction result of the first display panel may be reflected by the first brightness level and the first color coordinate.

[0049] Optionally, the first brightness level and the first color coordinates can be measured by an optical measuring device to obtain specific values.

[0050] In one possible implementation, a first training parameter is obtained based on a first display parameter, a preset display parameter and an initialization parameter, including: when a difference between a first brightness level and a preset brightness level is less than or equal to a first threshold, and a difference between a first color coordinate and a preset color coordinate is less than a second threshold, outputting the first training parameter.

[0051] Optionally, the first threshold value and the second threshold value may be the same or different. For example, the first threshold value is equal to the second threshold value, which is equal to 0.001.

[0052] S103, obtaining a value of an RGB register in a next display panel among the multiple display panels.

[0053] S104, training the first model according to the values ​​of the RGB registers in the next display panel to obtain the next training parameters.

[0054] Among them, the initial value of the next training parameter is the previous training parameter.

[0055] In a possible implementation, after step S104, method 100 further includes: deploying the trained first model on a calibration device.

[0056] Optionally, the correction device may be located in the display panel, or the correction device may be a device independent of the display panel.

[0057] In the embodiment of the present application, since the first model is trained based on the values ​​of the RGB registers of multiple display panels, the trained first model has excellent compatibility and can adapt to display panels of different types and characteristics and meet the correction requirements of different display panels. On the other hand, as the number of display panels increases during the training of the first model, the first model becomes more robust and the inference efficiency gradually improves.

[0058] Combine the following Figure 2 and Figure 3 The training process and training effect of the first model are described in detail. Figure 2 In the equation, ω represents the parameters of the first model; ω init represents the initial parameters of the first model; ω1 represents the parameters of the first model after the first project training; loss represents the difference between the predicted value and the true value of the first model; lv and X / Y represent the display panel brightness and color coordinates predicted by the first model, respectively; and They respectively represent the real values ​​of brightness and color coordinates (which may correspond to the brightness level and color coordinates preset in method 100); f represents the model training function; and different items may represent different types of display panels.

[0059] like Figure 2 As shown, when the first project (corresponding to the first display panel in method 100) is trained, the grayscale and brightness level are specified to obtain the initialization parameter ω init ; Input the value of the RGB register of the first display panel into the first model for model training, use the optical measurement device to measure the lv and X / Y parameters, and compare the lv and X / Y parameters with the specified grayscale and brightness level and If the three indicators are within the preset specifications (for example, lv and The difference between X and Y and The difference between the first and second thresholds is less than or equal to the second threshold), the first training is completed, and the first training parameter ω1 is obtained. If one of the three indicators is not within the preset specifications, the initialization parameter ω is readjusted by gradient descent. init , until all three indicators meet the preset specifications, the first training process ends. After the first training is completed, F(item 1) = f1.

[0060] When training the second project, the grayscale and brightness levels are specified, the values ​​of the RGB registers of the second display panel and ω1 are input into the first model for training, and the above model training process is repeated to obtain the second training parameter ω2. After the second training, F(project 1, project 2) = f2, and the time for the second model training is shortened compared to the first model training time.

[0061] Similarly, when training the Nth project, specify the grayscale and brightness level, and compare the value of the RGB register of the Nth display panel with ω n-1 Input the first model for training, repeat the above model training process, and get the parameter ω n , after the nth model training, F(item 1, item 2…, item n) = fn, where n is a positive integer greater than 2.

[0062] like Figure 3 As shown, during the training process of the first model, as the number of items gradually increases, the reasoning time of the first model gradually decreases, that is, as the number of display panels increases during the training process of the first model, the robustness of the first model becomes stronger and the reasoning efficiency gradually improves.

[0063] Figure 4 It is a schematic flow chart of another correction method provided in an embodiment of the present application. Method 400 is a process of reasoning with a trained first model. Method 400 can be executed by a correction device or a display panel. Method 400 can include steps S401 and S402.

[0064] S401, obtaining the value of the RGB register of the first display panel.

[0065] S402, inputting the value of the RGB register of the first display panel into the first model to obtain a second display parameter, wherein the second display parameter includes a second brightness level and a second color coordinate of the first display panel, and the second display parameter is used to indicate a gamma correction result of the first display panel.

[0066] Among them, the first model is trained based on the values ​​of RGB registers in multiple display panels, preset display parameters and initialization parameters, the multiple display panels include the first display panel, the preset display parameters include preset brightness levels and preset color coordinates, and the initialization parameters are the initialization weights of the first model.

[0067] Specifically, the training process of the first model can be Figure 3 Describe the process.

[0068] In the embodiment of the present application, by using the first model, a good gamma correction effect can be achieved for the first display panel. Moreover, since the first model is trained based on the values ​​of the RGB registers of multiple display panels, the trained first model has excellent compatibility and can adapt to display panels of different types and characteristics, meeting the correction requirements of different display panels.

[0069] It should be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between the various embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0070] The embodiments of the present application also provide a device for implementing any of the above methods, wherein the device includes a unit for implementing each step performed in any of the above methods.

[0071] Figure 5 is a schematic diagram of a correction device provided by an embodiment of the present application. The device 500 may include an acquisition unit 510, a storage unit 520 and a processing unit 530. The acquisition unit 510 is used to acquire instructions and / or data. The acquisition unit 510 may also be called a communication interface or a communication unit. The storage unit 520 is used to implement a corresponding storage function and store corresponding instructions and / or data. The processing unit 530 is used to perform data processing so that the device 500 implements the aforementioned correction method.

[0072] In one embodiment, the device 500 includes: an acquisition unit 510 and a processing unit 530; the acquisition unit 510 is used to acquire the value of the RGB register in the first display panel among multiple display panels; the processing unit 530 is used to train the first model according to the value of the RGB register in the first display panel to obtain the first training parameters, and the first training parameters include the weights of the first model obtained by gradient descent adjustment; the acquisition unit 510 is also used to acquire the value of the RGB register in the next display panel among the multiple display panels; the processing unit 530 is also used to train the first model according to the value of the RGB register in the next display panel to obtain the next training parameters, and the initial value of the next training parameters is the previous training parameter.

[0073] In a possible implementation, the processing unit 530 is specifically used to: input the numerical value of the RGB register in the first display panel into the first model to obtain a first display parameter, wherein the first display parameter includes a first brightness level and a first color coordinate of the first display panel, and the first display parameter is used to indicate a gamma correction result of the first display panel; obtain the first training parameter according to the first display parameter, a preset display parameter and an initialization parameter, wherein the preset display parameter includes a preset brightness level and a preset color coordinate, and the initialization parameter is an initialization weight of the first model.

[0074] In a possible implementation, the processing unit 530 is specifically used to output the first training parameter when the difference between the first brightness level and the preset brightness level is less than or equal to a first threshold, and the difference between the first color coordinate and the preset color coordinate is less than a second threshold.

[0075] In a possible implementation, the processing unit 530 is further configured to deploy the trained first model on a calibration device.

[0076] In one embodiment, the device 500 includes: an acquisition unit 510 and a processing unit 530; the acquisition unit 510 is used to acquire the value of the RGB register of the first display panel; the processing unit 530 is used to input the value of the RGB register of the first display panel into the first model to obtain a second display parameter, the second display parameter includes a second brightness level and a second color coordinate of the first display panel, and the second display parameter is used to indicate the gamma correction result of the first display panel; wherein the first model is trained based on the values ​​of the RGB registers in multiple display panels, preset display parameters and initialization parameters, the multiple display panels include the first display panel, the preset display parameters include a preset brightness level and a preset color coordinate, and the initialization parameter is an initialization weight of the first model.

[0077] Figure 6 6 is a schematic diagram of another correction device provided in an embodiment of the present application, wherein the device 600 includes: a memory 610, a processor 620, and a communication interface 630. The memory 610, the processor 620, and the communication interface 630 are connected through an internal connection path, the memory 610 is used to store instructions, and the processor 620 is used to execute the instructions stored in the memory 610 to control the communication interface 630 to obtain information, or to enable the device 600 to implement the correction method of the display panel. Optionally, the memory 610 can be coupled to the processor 620 through an interface, or can be integrated with the processor 620.

[0078] It should be noted that the communication interface 630 uses a transceiver device such as, but not limited to, a transceiver. The communication interface 630 may also include an input / output interface.

[0079] The processor 620 stores one or more computer programs, and the one or more computer programs include instructions. When the instructions are executed by the processor 620, the device 600 executes the display panel correction method in the above-mentioned embodiments.

[0080] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 620 or an instruction in the form of software. The method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in the processor for execution. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 610, and the processor 620 reads the information in the memory 610 and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0081] Optionally, Figure 6 The communication interface 630 in the embodiment can be implemented Figure 5 The acquisition unit 510 in Figure 6 The memory 610 in the embodiment can realize Figure 5 The storage unit 520, Figure 6 The processor 620 in the embodiment can implement Figure 5 The processing unit 530 in.

[0082] The embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program code, and when the computer program code is executed on a computer, the computer executes the above Figure 1 or Figure 4Any of the methods in .

[0083] The present application also provides a computer program product, which includes a computer program. When the computer program is executed, the computer executes the above Figure 1 or Figure 4 Any of the methods in .

[0084] The embodiment of the present application further provides a display panel, the display panel comprising: Figure 5 or Figure 6 The device described in any one of the above.

[0085] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0086] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0087] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0088] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0090] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0091] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A calibration method, characterized in that: The method comprises: Acquire a value of an RGB register in a first display panel among the multiple display panels; Training a first model according to the values ​​of the RGB registers in the first display panel to obtain first training parameters, wherein the first training parameters include weights of the first model obtained by gradient descent adjustment; Obtaining a value of an RGB register in a next display panel among the multiple display panels; The first model is trained according to the values ​​of the RGB registers in the next display panel to obtain the next training parameters, and the initial values ​​of the next training parameters are the previous training parameters.

2. The method according to claim 1, characterized in that The step of training the first model according to the values ​​of the RGB registers in the first display panel to obtain the first training parameters includes: Inputting the value of the RGB register in the first display panel into the first model to obtain a first display parameter, wherein the first display parameter includes a first brightness level and a first color coordinate of the first display panel, and the first display parameter is used to indicate a gamma correction result of the first display panel; The first training parameters are obtained according to the first display parameters, preset display parameters and initialization parameters, wherein the preset display parameters include preset brightness levels and preset color coordinates, and the initialization parameters are initialization weights of the first model.

3. The method according to claim 2, characterized in that Obtaining first training parameters according to the first display parameters, preset display parameters and initialization parameters, including: When the difference between the first brightness level and the preset brightness level is less than or equal to a first threshold, and the difference between the first color coordinate and the preset color coordinate is less than a second threshold, the first training parameter is output.

4. The method according to any one of claims 1 to 3, characterized in that The method also includes: deploying the trained first model on a correction device.

5. A calibration method, characterized in that: The method comprises: Get the value of the RGB register of the first display panel; Inputting the value of the RGB register of the first display panel into the first model to obtain a second display parameter, wherein the second display parameter includes a second brightness level and a second color coordinate of the first display panel, and the second display parameter is used to indicate a gamma correction result of the first display panel; Among them, the first model is trained based on the values ​​of the RGB registers in the multiple display panels, preset display parameters and initialization parameters, the multiple display panels include the first display panel, the preset display parameters include preset brightness levels and preset color coordinates, and the initialization parameters are the initialization weights of the first model.

6. A calibration device, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire the value of the RGB register in the first display panel among the multiple display panels; The processing unit is used to train a first model according to the values ​​of the RGB registers in the first display panel to obtain first training parameters, wherein the first training parameters include weights of the first model obtained by gradient descent adjustment; The acquisition unit is further used to acquire the value of the RGB register in the next display panel among the multiple display panels; The processing unit is further used to train the first model according to the numerical value of the RGB register in the next display panel to obtain the next training parameter, and the initial value of the next training parameter is the previous training parameter.

7. The calibration device according to claim 6, characterized in that The processing unit is specifically used for: Inputting the value of the RGB register in the first display panel into the first model to obtain a first display parameter, wherein the first display parameter includes a first brightness level and a first color coordinate of the first display panel, and the first display parameter is used to indicate a gamma correction result of the first display panel; The first training parameters are obtained according to the first display parameters, preset display parameters and initialization parameters, wherein the preset display parameters include preset brightness levels and preset color coordinates, and the initialization parameters are initialization weights of the first model.

8. The calibration device according to claim 7, characterized in that: The processing unit is specifically configured to output the first training parameter when the difference between the first brightness level and the preset brightness level is less than or equal to a first threshold, and the difference between the first color coordinate and the preset color coordinate is less than a second threshold.

9. The calibration device according to any one of claims 6 to 8, characterized in that: The processing unit is also used to deploy the trained first model on the correction device.

10. A calibration device, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire the value of the RGB register of the first display panel; The processing unit is used to input the value of the RGB register of the first display panel into the first model to obtain a second display parameter, wherein the second display parameter includes a second brightness level and a second color coordinate of the first display panel, and the second display parameter is used to indicate a gamma correction result of the first display panel; Among them, the first model is trained based on the values ​​of the RGB registers in the multiple display panels, preset display parameters and initialization parameters, the multiple display panels include the first display panel, the preset display parameters include preset brightness levels and preset color coordinates, and the initialization parameters are the initialization weights of the first model.

11. A calibration device, characterized in that: The method comprises a processor and a memory, wherein the processor is coupled to the memory, the memory is used to store computer programs or instructions, and the processor is used to execute the computer program or instructions in the memory, so that the method according to any one of claims 1 to 5 is executed.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program code, and when the computer program code is executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 5.

13. A display panel, characterized in that: The display panel comprises a device as claimed in any one of claims 6 to 11.

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