Brightness correction method and device, electronic equipment and computer readable storage medium

Abstract

Embodiments of the present application provide a brightness correction method and device, electronic equipment and computer readable storage medium, and relate to the technical field of computer. The method comprises: determining a second brightness correction parameter of a prediction area of a display interface through a first brightness correction parameter and a preset parameter prediction model; and then performing brightness correction processing on the prediction area based on the second brightness correction parameter. Since the parameter prediction model is obtained based on model training on sample data, the sample data comprises a first sample correction parameter of a sample reference area and a second sample correction parameter of a sample prediction area; that is, the parameter prediction model obtains the relationship between the first sample correction parameter and the second sample correction parameter in the training process, so that the accuracy of the predicted second brightness correction parameter is high, and when the prediction area is corrected based on the second brightness correction parameter, the correction time can be shortened and the efficiency of the correction processing can be improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a brightness correction method, apparatus, electronic device, and computer-readable storage medium. Background Technology

[0002] In image display scenarios, the human eye's perception of the brightness of a display interface is non-linear. For example, this non-linearity can be manifested in the fact that the human eye is more sensitive to brightness in a dark environment than in a bright environment. To adapt to the non-linear characteristics of the human eye's brightness perception and ensure that the human eye perceives the correct brightness, it is usually necessary to calibrate the brightness of the electronic device's display interface. For example, brightness calibration may include adjusting the relationship curve between the grayscale and brightness of the display interface to be non-linear.

[0003] Typically, when performing brightness calibration on a display interface, an initial brightness calibration parameter is used as a reference. Therefore, the accuracy of the initial brightness parameter affects the final brightness calibration result. However, in related technologies, the accuracy of the obtained initial brightness calibration parameter is low, resulting in a longer brightness calibration time and lower brightness calibration efficiency. Summary of the Invention

[0004] The purpose of this application is to at least solve one of the above-mentioned technical defects, in particular the technical defect that the accuracy of the initial brightness correction parameters is low, resulting in a long brightness correction time and low brightness correction efficiency.

[0005] According to one aspect of this application, a brightness correction method is provided, the method comprising:

[0006] Obtain the first brightness correction parameters for the reference area of ​​the display interface of the electronic device;

[0007] Based on the first brightness correction parameter and the preset parameter prediction model, the second brightness correction parameter of the prediction area of ​​the display interface is determined.

[0008] Wherein, the prediction area is the display area corresponding to the under-display camera; the reference area is the main display area, which is the display area in the display interface other than the prediction area;

[0009] The parameter prediction model is obtained by training the model on sample data; the sample data includes the first sample correction parameters of the sample reference region and the second sample correction parameters of the sample prediction region.

[0010] Based on the second brightness correction parameter, brightness correction processing is performed on the predicted region.

[0011] Optionally, obtaining the first brightness correction parameters of the reference area of ​​the display interface of the electronic device includes:

[0012] Obtain the third brightness correction parameters of the reference area of ​​the display interface under a preset brightness level and / or a preset grayscale level;

[0013] The first brightness correction parameter is obtained by preprocessing the third brightness correction parameter.

[0014] Optionally, obtaining the third brightness correction parameters of the reference area of ​​the display interface at a preset brightness level and / or a preset grayscale level includes:

[0015] Under a preset brightness level and / or a preset grayscale level, the brightness correction parameters of the target color channel of the reference area of ​​the display interface are obtained as the third brightness correction parameters.

[0016] Optionally, when the third brightness correction parameter includes the brightness correction parameter of the target color channel of the reference area of ​​the display interface under a preset brightness level and a preset grayscale level,

[0017] The process of preprocessing the third brightness correction parameter to obtain the first brightness correction parameter includes:

[0018] The third brightness correction parameter is normalized and graphically processed to obtain the brightness image parameter corresponding to the third brightness correction parameter, wherein the first brightness correction parameter includes the brightness image parameter.

[0019] Optionally, before obtaining the first brightness correction parameters of the reference area of ​​the display interface of the electronic device, the method further includes:

[0020] Obtain training samples;

[0021] The first sample brightness correction parameter of the training sample is input into the initial model to obtain the prediction result corresponding to each training sample; the prediction result includes the prediction brightness correction parameter of the sample prediction region;

[0022] The training loss value is determined based on the predicted brightness correction parameters and the second sample correction parameters;

[0023] Based on the training loss value, the initial model is repeatedly trained until the parameter prediction model that meets the training termination condition is obtained.

[0024] Optionally, the model structure of the parameter prediction model includes at least one of the following:

[0025] At least two convolutional layers;

[0026] Pooling layer;

[0027] Array flattening layer;

[0028] Fully connected layer.

[0029] Optionally, the step of performing brightness correction processing on the predicted region based on the second brightness correction parameter includes:

[0030] The second brightness correction parameter is determined as the initial correction parameter for performing brightness correction processing on the predicted region;

[0031] The predicted region is subjected to brightness correction processing based on the initial correction parameters to determine the target correction parameters for the predicted region.

[0032] Optionally, the target color channel includes a first target color channel, a second target color channel, and a third target color channel;

[0033] The brightness correction process includes gamma correction.

[0034] According to another aspect of this application, a brightness correction device is provided, the device comprising:

[0035] The acquisition module is used to acquire the first brightness correction parameters of the reference area of ​​the display interface of the electronic device;

[0036] The prediction module is used to determine the second brightness correction parameter of the prediction area of ​​the display interface based on the first brightness correction parameter and the preset parameter prediction model.

[0037] Wherein, the prediction area is the display area corresponding to the under-display camera; the reference area is the main display area, which is the display area in the display interface other than the prediction area;

[0038] The parameter prediction model is obtained by training the model on sample data; the sample data includes the first sample correction parameters of the sample reference region and the second sample correction parameters of the sample prediction region.

[0039] The correction module is used to perform brightness correction processing on the predicted area based on the second brightness correction parameters.

[0040] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0041] One or more processors;

[0042] Memory;

[0043] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: perform the brightness correction method according to any one of the first aspects of this application.

[0044] For example, in a third aspect of this application, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0045] The memory is used to store at least one executable instruction that causes the processor to perform an operation corresponding to the brightness correction method shown in the first aspect of this application.

[0046] According to another aspect of this application, a computer-readable storage medium is provided, wherein when the computer program is executed by a processor, it implements the brightness correction method according to any one of the first aspects of this application.

[0047] For example, in a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the brightness correction method shown in the first aspect of the present application.

[0048] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various alternative implementations of the first aspect described above.

[0049] The beneficial effects of the technical solution provided in this application are:

[0050] In this embodiment, a second brightness correction parameter for the predicted area of ​​the display interface is determined using the first brightness correction parameter and a preset parameter prediction model. Then, based on the second brightness correction parameter, brightness correction processing is performed on the predicted area. The predicted area is the display area corresponding to the under-display camera; the reference area is the main display area; the sample data used to train the parameter prediction model includes correction parameters for the sample reference area and correction parameters for the sample predicted area. Both the correction parameters for the sample reference area and the correction parameters for the sample predicted area are parameters from the actual correction process. By training the parameter prediction model with a large number of parameters from the actual correction process, the parameter prediction model possesses the data characteristics of the actual prediction process. Thus, the accuracy of the second brightness correction parameter predicted using the first brightness correction parameter of the main display area is high. Subsequently, when performing brightness correction processing on the display area corresponding to the under-display camera based on the second brightness correction parameter (e.g., using the second brightness correction parameter as the initial brightness correction parameter), the correction time can be shortened, and the efficiency of the correction processing can be improved. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0052] Figure 1 This is a schematic diagram of the system architecture of a brightness correction method provided in an embodiment of this application;

[0053] Figure 2 This is one of the flowcharts illustrating a brightness correction method provided in an embodiment of this application;

[0054] Figure 3 This is one of the application scenario diagrams of a brightness correction method provided in the embodiments of this application;

[0055] Figure 4 This is a second schematic diagram illustrating an application scenario of a brightness correction method provided in an embodiment of this application.

[0056] Figure 5 A schematic diagram of the model structure of a brightness correction method provided in an embodiment of this application;

[0057] Figure 6 This is a second schematic flowchart illustrating a brightness correction method provided in an embodiment of this application.

[0058] Figure 7a This is one of the data schematic diagrams of a brightness correction method provided in an embodiment of this application;

[0059] Figure 7b This is a second schematic diagram of a brightness correction method provided in an embodiment of this application;

[0060] Figure 8a This is the third schematic diagram of a brightness correction method provided in an embodiment of this application;

[0061] Figure 8b This is the fourth schematic diagram of a brightness correction method provided in the embodiments of this application;

[0062] Figure 9 This is a schematic diagram of the structure of a brightness correction device provided in an embodiment of this application;

[0063] Figure 10 This is a schematic diagram of the structure of a brightness correction electronic device provided in an embodiment of this application. Detailed Implementation

[0064] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0065] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.”

[0066] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0067] At least some of the brightness correction methods provided in this application involve fields such as machine learning in the field of artificial intelligence, as well as various fields of cloud technology, such as cloud computing, cloud services, and related data computing and processing in the field of big data.

[0068] Artificial intelligence (AI) is the theory, methods, technology, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology within computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a way similar to human intelligence. AI studies the design principles and implementation methods of various intelligent machines, enabling them to possess the functions of perception, reasoning, and decision-making.

[0069] Artificial intelligence (AI) is a comprehensive discipline encompassing a wide range of fields, including both hardware and software technologies. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies primarily include computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0070] Machine learning (ML) is a multidisciplinary field involving probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers can simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to endow computers with intelligence; its applications span all areas of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and instructional learning.

[0071] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation steps as shown in the following embodiments or drawings, the method may include more or fewer operation steps based on conventional or non-inventive methods. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application.

[0072] First, combine Figure 1 This is a system architecture diagram of the brightness correction method provided in the embodiments of this application. The system may include a server 101 and a terminal cluster, wherein the server 101 can be considered as a backend server for performing brightness correction.

[0073] The terminal cluster may include: terminal 102, terminal 103, terminal 104, ..., wherein each terminal has a client installed that supports brightness correction. Communication connections may exist between the terminals; for example, there is a communication connection between terminal 102 and terminal 103, and a communication connection between terminal 103 and terminal 104.

[0074] Meanwhile, server 101 can provide services to the terminal cluster through communication connection function. Any terminal in the terminal cluster can have a communication connection with server 101. For example, terminal 102 has a communication connection with server 101, and terminal 103 has a communication connection with server 101. The above-mentioned communication connection is not limited to the connection method. It can be directly or indirectly connected through wired communication, or directly or indirectly connected through wireless communication, or through other methods.

[0075] The network for the aforementioned communication connection can be a wide area network (WAN), a local area network (LAN), or a combination of both. This application does not impose any restrictions on this.

[0076] The brightness correction method of this application embodiment can be executed on the server side or the terminal side, and the execution subject is not limited in this application embodiment. The method provided in this application embodiment can be executed by a computer device, which includes, but is not limited to, a terminal (including the aforementioned user terminal) or a server (including the aforementioned server 101). The aforementioned server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The aforementioned terminal can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and the server can be directly or indirectly connected through wired or wireless communication, and this application does not impose any restrictions.

[0077] Of course, the methods provided in the embodiments of this application are not limited to... Figure 1 The application scenarios shown can also be used in other possible scenarios, and this application embodiment does not impose any limitations. Figure 1 The functions that each device in the application scenario shown can achieve will be described in subsequent method embodiments, and will not be elaborated on here.

[0078] This application provides a possible implementation, which can be executed by any electronic device. Optionally, any electronic device can be a server device with brightness correction capabilities, or a device or chip integrated into these devices. Figure 2 As shown, this is one of the flowcharts of a brightness correction method provided in an embodiment of this application. The method includes the following steps:

[0079] Step S201: Obtain the first brightness correction parameters of the reference area of ​​the display interface of the electronic device.

[0080] Optionally, the embodiments of this application can be applied to the field of computer technology, specifically to the application scenario of brightness correction of the display interface of electronic devices.

[0081] The human eye's perception of brightness is non-linear. For example, this non-linearity can be manifested in the fact that the human eye is more sensitive to brightness in a dark environment than in a bright environment.

[0082] To adapt to the non-linear characteristics of human eye's brightness perception and ensure accurate brightness perception, thereby guaranteeing the perceived depth of an image, it is typically necessary to correct the brightness of the display interface. For example, brightness correction may include adjusting the grayscale-brightness relationship curve of the display interface to be non-linear. In this embodiment, brightness correction can be a process of finding the optimal grayscale-brightness relationship curve, which can be called gamma tuning. The optimal grayscale-brightness relationship curve can be understood as the curve that best matches the brightness perception characteristics of the human eye.

[0083] Specifically, in this embodiment, the brightness correction of the display interface can be divided into brightness correction of a reference area in the display interface and brightness correction of a predicted area in the display interface. Among these, combined with... Figure 3 As shown, the reference area in the display interface can be Figure 3 The main display area shown is... Figure 3 The normal region in the display interface; the prediction region in the display interface can be... Figure 3 The area shown corresponds to the under-display camera (FDC). In other words, the reference area is the area outside the FDC area in the display interface.

[0084] In practical implementations, the brightness of the FDC area is weaker due to the influence of the camera, resulting in a longer gamma correction time for the FDC area compared to the normal area. In this embodiment, the brightness correction parameters of the FDC area can be predicted based on the brightness correction parameters of the normal area of ​​the display interface; that is, the normal area is used as a reference area to predict the brightness correction parameters of the FDC area.

[0085] To facilitate the differentiation of brightness correction parameters for different areas, the brightness correction parameters for the reference area can be referred to as the first brightness correction parameter. The first brightness correction parameter characterizes the brightness-related parameters of the reference area during the brightness correction process. In the gamma correction of this embodiment, the first brightness correction parameter can be the gamma value of the reference area, which is the voltage value controlling the brightness of the reference area. Optionally, in practical implementation scenarios, the gamma value is the register value of the three color channels of the reference area of ​​the display interface, namely the register values ​​of the red (R), green (G), and blue (B) color channels, where the register value size is 12 bits, i.e., the register value range is 0-4095.

[0086] It should be noted that the gamma value of the reference area is the target gamma value of the reference area obtained after gamma correction. The brightness of the reference area can be controlled by this target gamma value.

[0087] Step S202: Based on the first brightness correction parameter and the preset parameter prediction model, determine the second brightness correction parameter of the prediction area of ​​the display interface.

[0088] The prediction area is the display area corresponding to the under-display camera; the reference area is the main display area, which is the display area outside the prediction area in the display interface.

[0089] The parameter prediction model is obtained by training the model on the sample data; the sample data includes the first sample correction parameters of the sample reference region and the second sample correction parameters of the sample prediction region.

[0090] Specifically, the prediction area in the display interface can be Figure 3 The area shown corresponds to the under-display camera (FDC).

[0091] The second brightness correction parameter is used to characterize the brightness-related parameters of the prediction area during the brightness correction process. In the gamma correction of this application embodiment, the second brightness correction parameter can predict the gamma value of the area, which is the voltage value that controls the brightness of the prediction area; optionally, in actual implementation scenarios, the gamma value is the register value of the three color channels of the prediction area of ​​the display interface, namely the register values ​​of the red (R), green (G), and blue (B) color channels; wherein, the register value size is 12 bits, that is, the register value range is 0-4095.

[0092] It should be noted that the second brightness correction parameter can be the initial gamma value of the prediction area, or it can be the target gamma value of the prediction area. Optionally, in this embodiment, the second brightness parameter can be the initial gamma value, and the target gamma value of the prediction area is obtained after gamma correction of the prediction area based on the initial gamma value. The brightness of the prediction area can be controlled by the target gamma value.

[0093] Because the prediction area, i.e., the FDC area, is affected by the camera and has weaker brightness, the gamma correction time for the FDC area is longer than that for the normal area. In this embodiment, the brightness correction parameters of the FDC area can be predicted based on the brightness correction parameters of the normal area of ​​the display interface, that is, the normal area is used as a reference area to predict the brightness correction parameters of the FDC area.

[0094] Optionally, in embodiments of this application, the second brightness correction parameter of the prediction region can be predicted using the first brightness correction parameter and a preset parameter prediction model.

[0095] The parameter prediction model is obtained by training the model on sample data. The sample data includes first sample correction parameters for the sample reference region and second sample correction parameters for the sample prediction region. The first sample correction parameter can be understood as the true brightness correction parameter of the sample reference region, and the second sample correction parameter can be understood as the true brightness correction parameter of the sample prediction region. Thus, during model training, the initial model learns the relationship between the first and second sample correction parameters, that is, the relationship between the true brightness correction parameters of the sample reference region and the true brightness correction parameters of the sample prediction region, and is then trained to obtain the parameter prediction model.

[0096] Step S203: Perform brightness correction processing on the predicted area based on the second brightness correction parameter.

[0097] Optionally, in this embodiment, the second brightness correction parameter can be the initial gamma value of the prediction region. Therefore, the prediction region can be brightness corrected based on the initial gamma value to obtain the target gamma value of the prediction region.

[0098] Specifically, the target gamma value can be the brightness correction parameter corresponding to the target brightness value (or target brightness range), that is, the brightness control voltage value corresponding to the target brightness value (or target brightness range); for example, in actual implementation, the target brightness value can be set to 50 candela, and then the brightness control voltage value corresponding to the target brightness value can be determined.

[0099] In addition, in some embodiments, the second brightness correction parameter can be directly used as the target gamma value of the prediction area, or the second brightness correction parameter can be finely adjusted and used as the target gamma value of the prediction area.

[0100] In summary, in this embodiment, a second brightness correction parameter for the predicted area of ​​the display interface is determined using the first brightness correction parameter and a preset parameter prediction model. Then, based on the second brightness correction parameter, brightness correction processing is performed on the predicted area. The predicted area is the display area corresponding to the under-display camera; the reference area is the main display area; the sample data used to train the parameter prediction model includes correction parameters for the sample reference area and correction parameters for the sample predicted area. Both the correction parameters for the sample reference area and the correction parameters for the sample predicted area are parameters from the actual correction process. By training the parameter prediction model with a large number of parameters from the actual correction process, the parameter prediction model possesses the data characteristics of the actual prediction process. Thus, the accuracy of the second brightness correction parameter predicted using the first brightness correction parameter of the main display area is high. Subsequently, when performing brightness correction processing on the display area corresponding to the under-display camera based on the second brightness correction parameter (e.g., using the second brightness correction parameter as the initial brightness correction parameter), the correction time can be shortened, and the efficiency of the correction processing can be improved.

[0101] In one embodiment of this application, obtaining the first brightness correction parameter of the reference area of ​​the display interface of the electronic device includes:

[0102] Obtain the third brightness correction parameters of the reference area of ​​the display interface under a preset brightness level and / or a preset grayscale level;

[0103] The first brightness correction parameter is obtained by preprocessing the third brightness correction parameter.

[0104] Optionally, in this embodiment, the brightness correction parameter for gamma correction can be the brightness control voltage value of the reference area of ​​the display interface under a preset brightness level and / or a preset grayscale level, which is the third brightness correction parameter in this embodiment.

[0105] In practical implementation scenarios, the third brightness correction parameter can be the brightness correction parameter of the target color channel of the reference area of ​​the display interface, that is, the brightness control voltage value corresponding to the target color channel of the reference area. The target color channel includes a first target color channel, a second target color channel, and a third target color channel, which are the red (R), green (G), and blue (B) color channels, respectively. The brightness control voltage values ​​corresponding to the three RGB color channels are the register values ​​of the three color channels.

[0106] In addition, the preset brightness level can include multiple brightness levels (bands). For example, the preset brightness level can include 10 brightness levels, where each brightness level can correspond to a predetermined brightness value range. Optionally, level 1 can be used as the brightest brightness level, level 10 as the darkest brightness level, and so on.

[0107] The preset grayscale levels can also include multiple grayscale levels. For example, the preset grayscale levels can include 19 grayscale levels, where each grayscale level can correspond to a predetermined grayscale value range. Optionally, level 1 can be used as the level with the smallest grayscale value, and level 19 can be used as the level with the largest grayscale value. In practical scenarios, the various grayscale levels of the display interface can be achieved using images with different grayscale levels.

[0108] Furthermore, in order to enable the model to learn the relationship between multiple sample data during the training process of the parameter prediction model, the embodiments of this application can convert the input data of the model into image form; in specific implementation, the first brightness correction parameter in image form can be obtained by preprocessing the third brightness correction parameter.

[0109] For example, when the third brightness correction parameter includes the brightness correction parameter of the target color channel of the reference area of ​​the display interface under a preset brightness level and a preset grayscale level,

[0110] The process of preprocessing the third brightness correction parameter to obtain the first brightness correction parameter includes:

[0111] The third brightness correction parameter is normalized and graphically processed to obtain the brightness image parameter corresponding to the third brightness correction parameter, wherein the first brightness correction parameter includes the brightness image parameter.

[0112] Specifically, the register values ​​of the three RGB color channels are between 0 and 4095. Dividing the register values ​​of the three RGB color channels by 4095 and then multiplying by 255 will normalize the register values ​​of the three RGB color channels to between 0 and 255. Through the above normalization process, the computational load of the network can be reduced.

[0113] Then, the normalized register values ​​of the three RGB color channels are graphically processed to obtain the image-based first luminance correction parameters. As an example, the image-based first luminance correction parameters can be found in [reference]. Figure 4 As shown, where, Figure 4 The three images in the image represent the first brightness correction parameters for the three RGB color channels. The horizontal axis in the image represents the brightness level, and the vertical axis represents the grayscale level.

[0114] In one embodiment of this application, before obtaining the first brightness correction parameter of the reference area of ​​the display interface of the electronic device, the method further includes:

[0115] Obtain training samples;

[0116] The first sample brightness correction parameter of the training sample is input into the initial model to obtain the prediction result corresponding to each training sample; the prediction result includes the prediction brightness correction parameter of the sample prediction region;

[0117] The training loss value is determined based on the predicted brightness correction parameters and the second sample correction parameters;

[0118] Based on the training loss value, the initial model is repeatedly trained until the parameter prediction model that meets the training termination condition is obtained.

[0119] Specifically, before obtaining the first brightness correction parameters of the reference area of ​​the display interface, this application embodiment also includes a step of training an initial model to obtain the parameter prediction model.

[0120] The first sample correction parameter can be understood as the true brightness correction parameter of the sample reference area, and the second sample correction parameter can be understood as the true brightness correction parameter of the sample prediction area.

[0121] During training, the first sample brightness correction parameters, visualized in an image, can be input into the initial model to obtain the predicted brightness correction parameters for each training sample. Then, based on the predicted brightness correction parameters and the second sample correction parameters, a training loss value is determined. The initial model is then repeatedly trained based on the training loss value until a parameter prediction model that meets the training termination condition is obtained.

[0122] Optionally, in this embodiment of the application, the trained parameter prediction model is a Convolutional Neural Network (CNN) model, and the model structure may include at least one of the following:

[0123] At least two convolutional layers;

[0124] Pooling layer;

[0125] Array flattening layer;

[0126] Fully connected layer.

[0127] As an example, see Figure 5As shown, the first brightness correction parameter input prediction model for the reference region is used. The first layer of this model can employ 32 3×3 convolutional kernels with a stride of 1; the second layer uses 64 3×3 convolutional kernels with a stride of 1, and the activation function can be Rectified Linear Unit (ReLU); the third layer uses 128 3×3 convolutional kernels with a stride of 1, and the activation function can be ReLU. Feature extraction is performed using these convolutional kernels. The fourth layer is a pooling layer, specifically 2×2 max pooling, with the activation function being ReLU; this pooling layer reduces the dimensionality of the parameter matrix. The fifth layer is a flattening layer; the flattening layer flattens the array. Finally, the second brightness correction parameter for the predicted region is output through a fully connected network.

[0128] In summary, the overall implementation process of the embodiments of this application can be found in [reference needed]. Figure 6 As shown, the parameter prediction model is obtained by training the model with sample data; then the input data (the first brightness correction parameter of the reference area) is input into the parameter prediction model for prediction processing, and the second brightness correction parameter of the prediction area can be obtained.

[0129] In one embodiment of this application, the step of performing brightness correction processing on the predicted region based on the second brightness correction parameter includes:

[0130] The second brightness correction parameter is determined as the initial correction parameter for performing brightness correction processing on the predicted region;

[0131] The predicted region is subjected to brightness correction processing based on the initial correction parameters to determine the target correction parameters for the predicted region.

[0132] In this embodiment of the application, the second brightness correction parameter can be used as the initial correction parameter for the gamma correction of the prediction region, and the initial correction parameter is the initial gamma value; then, the brightness correction processing of the prediction region can be performed based on the initial gamma value to obtain the target gamma value of the prediction region.

[0133] In this embodiment, the second brightness correction parameter predicted by the parameter prediction model is approximately 10 times more accurate than the prior art which uses the brightness correction parameters of other display interfaces as the second brightness correction parameter. As an example, Figure 7a The error curves of the gamma values ​​corresponding to the three RGB channels are shown in the prior art when the brightness correction parameters of other display interfaces are used as the second brightness correction parameters (i.e., as the initial gamma value for gamma correction). Figure 7bIn this embodiment of the application, when the second brightness correction parameter (i.e., the initial gamma value for gamma correction) is predicted by the parameter prediction model, the error curves of the gamma values ​​corresponding to the three RGB channels are shown.

[0134] Furthermore, by using the second brightness correction parameter from this application embodiment as the initial gamma value, due to its high accuracy, the gamma correction time is shortened by approximately one-third compared to the prior art. As an example, Figure 8a The duration of gamma correction in the prior art when using the brightness correction parameters of other display interfaces as the second brightness correction parameter; Figure 8b In this embodiment of the application, the duration of gamma correction is determined when the second brightness correction parameters are predicted by the parameter prediction model.

[0135] In summary, in this embodiment, a second brightness correction parameter for the predicted area of ​​the display interface is determined using the first brightness correction parameter and a preset parameter prediction model. Then, based on the second brightness correction parameter, brightness correction processing is performed on the predicted area. The predicted area is the display area corresponding to the under-display camera; the reference area is the main display area; the sample data used to train the parameter prediction model includes correction parameters for the sample reference area and correction parameters for the sample predicted area. Both the correction parameters for the sample reference area and the correction parameters for the sample predicted area are parameters from the actual correction process. By training the parameter prediction model with a large number of parameters from the actual correction process, the parameter prediction model possesses the data characteristics of the actual prediction process. Thus, the accuracy of the predicted second brightness correction parameter is high. Subsequently, when performing brightness correction processing on the display area corresponding to the under-display camera based on the second brightness correction parameter (e.g., using the second brightness correction parameter as the initial brightness correction parameter), the correction time can be shortened, and the efficiency of the correction processing can be improved.

[0136] This application provides a brightness correction device, such as... Figure 9 As shown, the brightness correction device 90 may include: an acquisition module 901, a prediction module 902, and a correction module 903, wherein,

[0137] The acquisition module 901 is used to acquire the first brightness correction parameters of the reference area of ​​the display interface of the electronic device;

[0138] The prediction module 902 is used to determine the second brightness correction parameter of the prediction area of ​​the display interface based on the first brightness correction parameter and the preset parameter prediction model.

[0139] Wherein, the prediction area is the display area corresponding to the under-display camera; the reference area is the main display area, which is the display area outside the prediction area in the display interface; the parameter prediction model is obtained based on model training on sample data; the sample data includes the first sample correction parameters of the sample reference area and the second sample correction parameters of the sample prediction area.

[0140] The correction module 903 is used to perform brightness correction processing on the predicted area based on the second brightness correction parameter.

[0141] In one embodiment of this application, the acquisition module is specifically used to acquire the third brightness correction parameters of the reference area of ​​the display interface under a preset brightness level and / or a preset grayscale level;

[0142] The first brightness correction parameter is obtained by preprocessing the third brightness correction parameter.

[0143] In one embodiment of this application, the acquisition module is specifically used to acquire the brightness correction parameters of the target color channel of the reference area of ​​the display interface as the third brightness correction parameter under a preset brightness level and / or a preset grayscale level.

[0144] In one embodiment of this application, the acquisition module is specifically used to perform normalization and graphical processing on the third brightness correction parameter to obtain the brightness image parameter corresponding to the third brightness correction parameter, wherein the first brightness correction parameter includes the brightness image parameter.

[0145] In one embodiment of this application, the apparatus further includes: a training module, configured to, before acquiring the first brightness correction parameters of the reference area of ​​the display interface of the electronic device,

[0146] Obtain training samples;

[0147] The first sample brightness correction parameter of the training sample is input into the initial model to obtain the prediction result corresponding to each training sample; the prediction result includes the prediction brightness correction parameter of the sample prediction region;

[0148] The training loss value is determined based on the predicted brightness correction parameters and the second sample correction parameters;

[0149] Based on the training loss value, the initial model is repeatedly trained until the parameter prediction model that meets the training termination condition is obtained.

[0150] In one embodiment of this application, the model structure of the parameter prediction model includes at least one of the following:

[0151] At least two convolutional layers;

[0152] Pooling layer;

[0153] Array flattening layer;

[0154] Fully connected layer.

[0155] In one embodiment of this application, the correction module is used to determine the second brightness correction parameter as the initial correction parameter for performing brightness correction processing on the predicted region;

[0156] The predicted region is subjected to brightness correction processing based on the initial correction parameters to determine the target correction parameters for the predicted region.

[0157] In one embodiment of this application, the target color channel includes a first target color channel, a second target color channel, and a third target color channel;

[0158] The brightness correction process includes gamma correction.

[0159] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0160] In this embodiment, a second brightness correction parameter for the predicted area of ​​the display interface is determined using the first brightness correction parameter and a preset parameter prediction model. Then, based on the second brightness correction parameter, brightness correction processing is performed on the predicted area. The predicted area is the display area corresponding to the under-display camera; the reference area is the main display area; the sample data used to train the parameter prediction model includes correction parameters for the sample reference area and correction parameters for the sample predicted area. Both the correction parameters for the sample reference area and the correction parameters for the sample predicted area are parameters from the actual correction process. By training the parameter prediction model with a large number of parameters from the actual correction process, the parameter prediction model possesses the data characteristics of the actual prediction process. Thus, the accuracy of the second brightness correction parameter predicted using the first brightness correction parameter of the main display area is high. Subsequently, when performing brightness correction processing on the display area corresponding to the under-display camera based on the second brightness correction parameter (e.g., using the second brightness correction parameter as the initial brightness correction parameter), the correction time can be shortened, and the efficiency of the correction processing can be improved.

[0161] This application provides an electronic device, comprising: a memory and a processor; at least one program stored in the memory, which, when executed by the processor, can achieve the following compared to the prior art: In this application, a second brightness correction parameter of a predicted area of ​​the display interface is determined using a first brightness correction parameter and a preset parameter prediction model; then, brightness correction processing is performed on the predicted area based on the second brightness correction parameter. The predicted area is the display area corresponding to the under-display camera; the reference area is the main display area; the sample data used to train the parameter prediction model includes correction parameters of the sample reference area and correction parameters of the sample predicted area, and both the correction parameters of the sample reference area and the correction parameters of the sample predicted area are parameters from the actual correction process. By training the parameter prediction model with a large number of parameters from the actual correction process, the parameter prediction model possesses the data characteristics of the actual prediction process; thus, the accuracy of the predicted second brightness correction parameter is high, and when subsequent brightness correction processing is performed on the display area corresponding to the under-display camera based on the second brightness correction parameter (e.g., using the second brightness correction parameter as the initial brightness correction parameter), the correction time can be shortened and the efficiency of the correction processing can be improved.

[0162] In one alternative embodiment, an electronic device is provided, such as Figure 10 As shown, Figure 10 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this application.

[0163] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0164] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0165] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0166] The memory 4003 stores application code (computer program) that executes the solution of this application, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0167] Electronic devices include, but are not limited to: mobile phones, laptops, multimedia players, desktop computers, etc.

[0168] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0169] In this embodiment, a second brightness correction parameter for the predicted area of ​​the display interface is determined using the first brightness correction parameter and a preset parameter prediction model. Then, based on the second brightness correction parameter, brightness correction processing is performed on the predicted area. The predicted area is the display area corresponding to the under-display camera; the reference area is the main display area. The sample data used to train the parameter prediction model includes correction parameters for the sample reference area and correction parameters for the sample predicted area. Both the correction parameters for the sample reference area and the correction parameters for the sample predicted area are parameters from the actual correction process. By training the parameter prediction model with a large number of parameters from the actual correction process, the parameter prediction model possesses the data characteristics of the actual prediction process. Thus, the accuracy of the predicted second brightness correction parameter is high. Subsequently, when performing brightness correction processing on the display area corresponding to the under-display camera based on the second brightness correction parameter (e.g., using the second brightness correction parameter as the initial brightness correction parameter), the correction time can be shortened, and the efficiency of the correction processing can be improved.

[0170] The terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0171] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0172] The above description is only an optional implementation method for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application without departing from the technical concept of this application also fall within the protection scope of the embodiments of this application.

Claims

1. A brightness correction method, characterized in that, include: Obtain the first brightness correction parameters for the reference area of ​​the display interface of the electronic device; Based on the first brightness correction parameter and the preset parameter prediction model, the second brightness correction parameter of the prediction area of ​​the display interface is determined. Wherein, the prediction area is the display area corresponding to the under-display camera; the reference area is the main display area, which is the display area in the display interface other than the prediction area; The parameter prediction model is obtained by training the model on sample data; the sample data includes the first sample correction parameters of the sample reference region and the second sample correction parameters of the sample prediction region. Based on the second brightness correction parameter, brightness correction processing is performed on the predicted region.

2. The brightness correction method according to claim 1, characterized in that, The first brightness correction parameter for obtaining the reference area of ​​the display interface of the electronic device includes: Obtain the third brightness correction parameters of the reference area of ​​the display interface under a preset brightness level and / or a preset grayscale level; The first brightness correction parameter is obtained by preprocessing the third brightness correction parameter.

3. The brightness correction method according to claim 2, characterized in that, The step of obtaining the third brightness correction parameters of the reference area of ​​the display interface at a preset brightness level and / or a preset grayscale level includes: Under a preset brightness level and / or a preset grayscale level, the brightness correction parameters of the target color channel of the reference area of ​​the display interface are obtained as the third brightness correction parameters.

4. The brightness correction method according to claim 3, characterized in that, When the third brightness correction parameter includes the brightness correction parameter of the target color channel of the reference area of ​​the display interface under a preset brightness level and a preset grayscale level, The process of preprocessing the third brightness correction parameter to obtain the first brightness correction parameter includes: The third brightness correction parameter is normalized and graphically processed to obtain the brightness image parameter corresponding to the third brightness correction parameter, wherein the first brightness correction parameter includes the brightness image parameter.

5. The brightness correction method according to claim 1, characterized in that, Before obtaining the first brightness correction parameters of the reference area of ​​the display interface of the electronic device, the method further includes: Obtain training samples; The first sample brightness correction parameter of the training sample is input into the initial model to obtain the prediction result corresponding to each training sample; the prediction result includes the prediction brightness correction parameter of the sample prediction region; The training loss value is determined based on the predicted brightness correction parameters and the second sample correction parameters; Based on the training loss value, the initial model is repeatedly trained until the parameter prediction model that meets the training termination condition is obtained.

6. The brightness correction method according to claim 1, characterized in that, The model structure of the parameter prediction model includes at least one of the following: At least two convolutional layers; Pooling layer; Array flattening layer; Fully connected layer.

7. The brightness correction method according to claim 1, characterized in that, The step of performing brightness correction processing on the predicted region based on the second brightness correction parameter includes: The second brightness correction parameter is determined as the initial correction parameter for performing brightness correction processing on the predicted region; The predicted region is subjected to brightness correction processing based on the initial correction parameters to determine the target correction parameters for the predicted region.

8. The brightness correction method according to any one of claims 3 to 4, characterized in that, The target color channel includes a first target color channel, a second target color channel, and a third target color channel; The brightness correction process includes gamma correction.

9. A brightness correction device, characterized in that, include: The acquisition module is used to acquire the first brightness correction parameters of the reference area of ​​the display interface of the electronic device; The prediction module is used to determine the second brightness correction parameter of the prediction area of ​​the display interface based on the first brightness correction parameter and the preset parameter prediction model. Wherein, the prediction area is the display area corresponding to the under-display camera; the reference area is the main display area, which is the display area in the display interface other than the prediction area; The parameter prediction model is obtained by training the model on sample data; the sample data includes the first sample correction parameters of the sample reference region and the second sample correction parameters of the sample prediction region. The correction module is used to perform brightness correction processing on the predicted area based on the second brightness correction parameters.

10. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: perform the brightness correction method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the brightness correction method according to any one of claims 1 to 8.

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