Scene-based sub-pixel rendering method and system

Abstract

The invention belongs to the field of display equipment, and discloses a scene-based sub-pixel rendering method and system, and the method comprises the steps: collecting the electric quantity of a mobile phone and the preference data of SPR parameters of a user in different display scenes in a manual SPR adjustment mode; creating a neural network, and training a machine learning algorithm according to the collected data; in the automatic SPR regulation mode, respectively acquiring battery electric quantity data and an actual display application scene of a user; and inputting the data into a trained machine learning algorithm, and adaptively adjusting SPR parameters. The sub-pixel rendering effect optimization parameter is determined based on the visual effect preference data of the user in different scenes, the display parameter can be automatically adjusted according to the content and scene of the picture so as to achieve the optimal visual effect, meanwhile, the power consumption is reduced, and the use experience of the user is greatly improved.

Description

Technical Field

[0001] The present invention belongs to, but is not limited to, the technical field of display devices, and particularly relates to a scene-based sub-pixel rendering method and system. Background Art

[0002] Organic Light-Emitting Diode (OLED) displays have many advantages such as being thin, light, self-luminous, fast response speed, wide viewing angle, wide color gamut, high brightness, and low power consumption, and have gradually become the third-generation display technology after Liquid Crystal Display (LCD).

[0003] Driven by consumer demands, OLED displays show a development trend of higher resolution and smaller size to provide more delicate and realistic image performance and a more portable and comfortable user experience. However, due to process limitations, it is very difficult to manufacture pixel units with extremely small sizes and high density, which not only affects the yield and increases the manufacturing cost, but also increases the design difficulty of the driving circuit system. Sub Pixel Rendering (SPR) technology improves the perceived resolution by sharing some sub-pixels among adjacent pixels, so that the display can achieve a higher perceived resolution with the same sub-pixel arrangement density, or reduce the requirement for the arrangement density of the display's sub-pixels while maintaining the same perceived resolution. Therefore, the sub-pixel rendering technology provides an effective solution to the above problems, and the sub-pixel rendering algorithm directly affects the image quality of OLED displays.

[0004] The sub-pixel rendering effect depends on subjective evaluation and is directly related to the display screen and user preferences, and there is no unified parameter that satisfies all scenarios. For text and pictures, there is more high-frequency information, so smoothing is required to avoid color aliasing resulting in color fringes. Natural pictures mainly consist of medium and low-frequency information, and a relatively sharp parameter strategy can be adopted to retain the details of the image itself. However, the current sub-pixel rendering methods focus on eliminating color aliasing and use fixed smoothing parameter settings in different scenarios. This setting method is beneficial to improving the display effect of application scenarios mainly composed of text screens, but is not conducive to the true presentation of picture details in other scenarios such as pictures and videos. And because the data window range processed by the smoothing in hardware is relatively large, using it without distinguishing scenarios will also increase power consumption and reduce the user experience.

[0005] In view of the above analysis, the technical problems urgently to be solved in the prior art are:

[0006] Existing sub-pixel rendering methods use fixed parameter settings, which is not conducive to presenting personalized display effects in rich scenarios. Moreover, using them without distinguishing scenarios will also increase power consumption and reduce the user experience. Summary of the Invention

[0007] In view of the problems existing in the prior art, the present invention provides a scene-based sub-pixel rendering method and system.

[0008] The present invention is implemented as follows. A scene-based sub-pixel rendering method, characterized in that the scene-based sub-pixel rendering method specifically includes:

[0009] S1: In the manual SPR adjustment mode, collect the mobile phone battery power and the user's preference data for SPR parameters in different display scenarios.

[0010] S2: Create a machine learning algorithm model and train the machine learning algorithm according to the data collected in S1.

[0011] S3: In the automatic SPR adjustment mode, obtain the battery power data and the user's actual display application scenario respectively.

[0012] S4: Input the data collected in S3 into the trained machine learning algorithm to adaptively adjust the SPR parameters.

[0013] Further, in S1, collect the current battery power data, the user's actual display application scenario, such as reading mode, movie mode, game mode, etc., and the corresponding SPR parameters manually set by the user.

[0014] Further, the training of the machine learning algorithm can be carried out offline or on a cloud server. The trained machine learning algorithm is built locally on the terminal and completed with the help of the neural network processing unit (Neural Process Unit, NPU) in the terminal application processor (Application Processor, AP).

[0015] Another object of the present invention is to provide a scene-based sub-pixel rendering system, which specifically includes:

[0016] Data collection module 1, used to collect data in the manual SPR adjustment mode;

[0017] Algorithm module, used to establish a machine learning algorithm model;

[0018] Data collection module 2, used to collect data in the automatic SPR adjustment mode;

[0019] Adaptive adjustment module, used to adaptively adjust the SPR parameters according to the output of the algorithm module.

[0020] Combined with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0021] The present invention proposes a scene-based sub-pixel rendering method and system. By collecting and analyzing the visual effect preference data of users in different display scenarios, the optimal parameter settings for sub-pixel rendering are determined. Based on the actual display application scenarios of users, such as reading mode, movie mode, game mode, etc., combined with the battery power status of the current device, the trained machine learning algorithm model is used to adaptively adjust the sub-pixel rendering parameters, so as to achieve the dynamic balance between the display effect and energy consumption. Through this technical solution, the overall efficiency of device use can be greatly improved on the premise of meeting the user's visual experience.

[0022] Through the intelligent algorithm design of the present invention, user preference data is collected in the manual adjustment mode, and the display parameters are adjusted in real time in the automatic adjustment mode, so that the optimization of sub-pixel rendering can be adapted to different scenarios and contents. For example, in the reading mode, the text information is mainly included, which contains a lot of spatial high-frequency components. A smooth sub-pixel rendering strategy needs to be adopted to reduce the color fringes at the text edges and improve the reading experience; in the game mode, the overall transition of the picture is natural, the spatial high-frequency components are less, and the color fringes of the picture itself are not obvious. Therefore, a sharper sub-pixel rendering strategy is adopted to retain the details of the image itself; and in the game scenario, the refresh rate is generally high and the power consumption is large. Adopting a sharper sub-pixel rendering strategy can save power and extend the use time of the terminal device. Compared with the traditional fixed parameter setting method, the present invention can intelligently adjust the display effect for different scenarios, truly realize a personalized use experience, and avoid the inconvenience of frequent manual operations at the same time.

[0023] The technical solution of the present invention has significant expected benefits, which are mainly reflected in improving the display effect and power consumption optimization ability of the terminal device. By adaptively adjusting the sub-pixel rendering parameters, the device can extend the battery life and provide a higher quality visual experience, meeting the personalized needs of users for the display effect in different scenarios. This technical solution not only improves user satisfaction, but also gives the terminal device a differential advantage in the market competition. Especially in the product fields such as smart phones, tablet computers and televisions, it has significant commercial value and market potential.

[0024] Through its application in actual terminal devices, the present invention can effectively enhance the market competitiveness of products. Its intelligent and scenario-based display optimization function provides a powerful selling point for terminal devices, attracting consumers' demand for high-performance and intelligent devices. At the same time, this technical solution can also reduce product energy consumption, conform to the design trend of green environmental protection, and further expand its application scope and market acceptance. In summary, the implementation of the present invention can not only bring a better user experience to users, but also create significant additional value for terminal device manufacturers. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flowchart of a scenario-based sub-pixel rendering method provided by an embodiment of the present invention;

[0026] Figure 2 is a schematic diagram of the training process of a machine learning algorithm for user data collected in the manual mode provided by an embodiment of the present invention;

[0027] Figure 3 is a schematic diagram of the process of adaptively adjusting the SPR mode in the automatic mode provided by an embodiment of the present invention;

[0028] Figure 4 is a schematic diagram of a scenario-based SPR implementation solution 1 provided by an embodiment of the present invention;

[0029] Figure 5 is a schematic diagram of a scenario-based SPR implementation solution 2 provided by an embodiment of the present invention;

[0030] Figure 6 is a module diagram of a scenario-based sub-pixel rendering system provided by an embodiment of the present invention;

[0031] Figure 7 is a comparison of SPR effects in different scenarios provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0033] As Figure 1 shown, an embodiment of the present invention provides a scenario-based sub-pixel rendering method, which specifically includes:

[0034] S1: In the manual SPR adjustment mode, collect the mobile phone battery power and the preference data of the SPR parameters of the user in different display scenarios;

[0035] S2: Create a machine learning algorithm model and train the machine learning algorithm according to the data collected in S1;

[0036] S3: In the automatic SPR adjustment mode, obtain the battery power data and the user's actual display application scenario respectively;

[0037] S4: Input the data collected in S3 into the trained machine learning algorithm to adaptively adjust the SPR parameters.

[0038] In the manual SPR (Sub-Pixel Rendering) adjustment mode, the system obtains the user's parameter setting behaviors in different usage scenarios through the data collection module 1. For example, the user's adjustment preferences for SPR parameters in scenarios such as reading mode, movie mode, and game mode. At the same time, the system records factors affecting the user experience such as the current battery power and ambient brightness. The collected data is uniformly stored for the training of subsequent machine learning models. This process establishes the association between the user's display habits and the device operating state, providing a basis for automatically adjusting the SPR parameters.

[0039] Based on the SPR parameters manually set by the user and the corresponding display scenarios, the algorithm module creates a machine learning algorithm model and uses machine learning technology for training. The training can be completed offline on a server or in the cloud, and the model is deployed in a lightweight form on the terminal device and relies on the neural network processing unit (NPU) of the terminal to achieve efficient operation. During the training process, the algorithm learns the non-linear relationship between the user's preferences and the display scenarios, and establishes a mapping relationship from the input (battery power and display scenario) to the output (optimal SPR parameters).

[0040] When the user switches to the automatic SPR adjustment mode, the data collection module 2 obtains the current device status in real time, including the battery power, ambient brightness, and the display application scenario the user is using. These data are input into the previously trained machine learning algorithm model, and the model calculates the SPR parameter value most suitable for the current scenario according to the input feature parameters. This calculation process combines the user's historical usage preferences and the device status, ensuring the optimization of the display effect and the consistency of the user experience.

[0041] The adaptive adjustment module adjusts the SPR parameters in real time according to the output result of the machine learning algorithm model to optimize the display effect. For example, in the case of low battery power, the system preferentially selects an energy-saving SPR configuration; in the reading mode with sufficient battery power, the system preferentially selects a smooth SPR setting to achieve the optimal display effect; while when watching a movie or playing a game, the system selects a relatively sharp SPR setting to balance the display effect and power consumption. This adaptive adjustment mechanism not only meets the user's display requirements but also maximizes the device battery life. Through real-time data collection and continuous feedback, the system can gradually update and optimize the user preference model, further improving the display quality and user satisfaction.

[0042] The S1 collects the current battery power data, the actual display application scenarios of the user, such as reading mode, movie mode, game mode, etc., and the corresponding SPR parameters manually set by the user.

[0043] Figure 2 It is a schematic diagram of the machine learning algorithm training process for the user data collected in the manual mode.

[0044] Figure 3 It is a schematic diagram of the process of adaptively adjusting the SPR mode in the automatic mode.

[0045] In the manual SPR adjustment mode, user-related data is collected through the sensors and operation log system of the mobile phone terminal, including the battery power status, the current actual display application scenario of the user (such as reading mode, movie mode, game mode, etc.), and the SPR parameters manually adjusted by the user. The collected data is recorded through timestamps to ensure the temporal consistency in the dynamic environment. In addition, outliers, such as user misoperations or incorrect data collected by sensors, are removed to improve the accuracy of the data.

[0046] The preprocessed data is input into the feature extraction module to analyze the potential relationship between the battery power, application scenario, and SPR parameters. For example, in the reading mode, users tend to have a real, delicate, and color-accurate text display effect, while in the game mode, they focus on the battery life of the device. The extracted features include scene categories, remaining battery percentage, user adjustment frequency, etc. All features are processed through a normalization method to ensure the data consistency and training efficiency of the input neural network.

[0047] Based on the collected user data, machine learning algorithm models (such as multi-layer perceptrons or convolutional neural networks) are used for training. The model mainly considers the diversity of user display requirements and the complexity of battery power changes, and establishes a prediction model for SPR parameters in different scenarios. During the training process, a supervised learning method is adopted, using the user's historical preference data as labels, and cross-validation and regularization methods are used to prevent overfitting while optimizing the generalization ability of the model.

[0048] In the automatic SPR adjustment mode, the current battery power and application scenario data are obtained through the real-time sensor system of the mobile phone. This data is preprocessed and then input into the trained machine learning model to predict the SPR parameters suitable for the current scenario in real time. Finally, the sub-pixel rendering parameters of the display screen are adjusted through the display driver module of the device to achieve adaptive optimization, which not only meets the user's visual experience but also extends the battery usage time of the device.

[0049] In practical applications, the training of machine learning algorithms can be carried out offline or on cloud servers. The trained machine learning algorithms need to be built locally on the terminal to achieve real-time display scene recognition and parameter adjustment. This solution is completed by means of the Neural Process Unit (NPU) in the terminal Application Processor (AP).

[0050] Solution 1: The NPU obtains images from the Graphics Processing Unit (GPU), performs scene recognition, obtains other parameters from the AP, determines the finally selected SPR parameters according to the pre-established machine learning algorithm, and the sending end transmits the information of the SPR parameters and the image signal output by the GPU to the receiving end of the Display Driver IC (DIC). The receiving end then transmits the two signals to the SPR module in the DIC. The SPR module selects an appropriate rendering method according to the SPR parameters, and the rendered data is output directly or after passing through other image processing modules to the display panel.

[0051] Solution 2: The NPU obtains images from the Graphics Processing Unit (GPU), performs scene recognition, obtains other parameters from the AP, determines the finally selected SPR parameters according to the pre-established machine learning algorithm, and transmits the information of the SPR parameters and the image signal output by the GPU to the SPR module in the AP. The SPR module selects an appropriate rendering method according to the SPR parameters, and the rendered data is transmitted through the sending end of the AP to the receiving end of the DIC. The receiving end outputs the signal directly or after passing through other image processing modules to the display panel.

[0052] Among the two solutions, the formats of the data transmitted from the sending end of the AP to the receiving end of the DIC are different, so the data transmission protocols are also different. Since the manufacturing process of the AP is generally more advanced than that of the DIC, building the SPR module in the AP in Solution 2 can save power consumption.

[0053] Figure 4 、 Figure 5 They are respectively the schematic diagrams of Solution 1 for scene-based SPR implementation and Solution 2 for scene-based SPR implementation.

[0054] As Figure 6 shown, a scene-based sub-pixel rendering system provided by an embodiment of the present invention specifically includes:

[0055] A data collection module 1, configured to collect data in the manual SPR adjustment mode;

[0056] An algorithm module, configured to establish a machine learning algorithm;

[0057] A data collection module 2, configured to collect data in the automatic SPR adjustment mode;

[0058] An adaptive adjustment module, configured to adaptively adjust SPR parameters according to the output of the algorithm module.

[0059] As Figure 6 shown, an embodiment of the present invention provides a scene-based sub-pixel rendering system, aiming to achieve an intelligent balance between display effects and power consumption. The system includes multiple functional modules, and each module cooperates to dynamically adjust sub-pixel rendering parameters according to scene requirements, improving the display effect while reducing power consumption.

[0060] A data collection module 1 is configured to collect relevant data in the manual SPR adjustment mode. Specifically, this module collects battery power data, display application scenario data, and SPR parameters set by the user, and at the same time records the timestamp information of the data, providing training data for the establishment of subsequent machine learning algorithm models.

[0061] An algorithm module is configured to establish a machine learning algorithm neural network model based on the sample data provided by the data collection module 1. By analyzing the collected battery power and display scenario data, this module trains a set of SPR parameters suitable for different scenarios, enabling the system to optimize display performance and power consumption for different usage scenarios.

[0062] The data collection module 2 operates in the automatic SPR adjustment mode, and in real time collects the current battery power data and display application scenario data. The adaptive adjustment module dynamically adjusts the sub-pixel rendering settings of the display screen according to the SPR parameters output by the algorithm module, specifically including adjusting contrast, brightness, refresh rate, sharpness smoothness, and color rendering, so as to meet the display requirements of users in different scenarios.

[0063] The technical solution of the present invention can be applied to terminal devices with a display system such as smart phones, tablet computers, and televisions.

[0064] The data collection module 1 is responsible for collecting user operation-related data in the manual SPR adjustment mode, including battery power, actual display application scenarios (such as reading mode, movie mode, game mode, etc.), and SPR parameters manually adjusted by the user in each scenario. When collecting data, the system automatically records the timestamp, scene classification label, and preprocesses the sensor data, such as denoising and filtering out outliers, to ensure the accuracy and consistency of the collected data.

[0065] The algorithm module uses the data collected in manual mode to generate feature vectors for training through feature extraction. Features include scene type, battery power status, user preference adjustment parameters, etc. Neural network algorithms in machine learning, such as convolutional neural network (CNN) or multi-layer perceptron (MLP), are used to model the data and explore the potential relationship between user display requirements and power status. Supervised learning methods are used in the training process, combined with historical data as labels, and regularization and cross-validation are used to prevent model overfitting and optimize the generalization ability of the algorithm.

[0066] In the automatic SPR adjustment mode, the data collection module 2 collects the battery power status of the current device and displays the application scenario data in real time. The collected data is quickly analyzed by the signal processor in the module, including classifying the current display scene and evaluating the power change trend. The processed data is input into the algorithm module in the form of feature vectors as the input basis for real-time parameter adjustment.

[0067] The adaptive adjustment module dynamically adjusts the sub-pixel rendering (SPR) parameters of the display screen according to the output of the algorithm module. For example, in low-power mode, the system will give priority to the SPR parameter combination that optimizes energy saving; in reading mode, the system will give priority to the SPR parameter configuration with the best text display effect; in movie and game mode, it focuses on the presentation of picture details and the improvement of battery life. The adjusted parameters act on the device screen through the display driver interface to achieve the best visual experience in different scenarios, while balancing battery life performance and user needs.

[0068] In general reading scenarios, there are many words, so there is more high-frequency information in the display screen. If the sharpening parameter setting is used, the brightness of the pixels at the edge of the words will be very different, which may easily cause visual color fringing at the edge of the words. Figure 7 (a) SPR parameter 1 setting is shown. Therefore, in this scenario, a relatively smooth parameter setting is generally used to reduce the brightness difference of the text edge and alleviate the color edge phenomenon, such as Figure 7 (a) SPR parameter 2 is set as shown. It should be noted that Figure 7 The effect is a captured picture, which is more obvious when observed with the naked eye in actual applications. In addition, most users would like to reduce the color fringing phenomenon, and in this scenario, they would select a smooth parameter setting in manual mode, so that when the scene is recognized in automatic mode, the smooth parameter setting is intelligently selected. However, a small number of users are not sensitive to color fringing and prefer a sharp display, so they would select a sharp parameter setting in manual mode, so that when the scene is recognized in automatic mode, the sharp parameter setting is intelligently selected. This technical solution will select parameter settings for application scenarios based on the actual preferences of users to meet personalized needs.

[0069] In the green scenery scene, there is a lot of green background. Due to the law of sub-pixel arrangement, the color edge phenomenon is weak. Therefore, a relatively sharp parameter setting can be adopted to enhance the display of details, such as Figure 7 (b) as shown in SPR parameter setting 1. Similarly, in this scene, there will also be users who prefer a softer display effect, such as Figure 7 (b) as shown in SPR parameter setting 2. This technical solution will select the parameter setting of the application scene according to the actual preferences of users to meet personalized needs.

[0070] In ordinary natural scenes, since there is less high-frequency information in the display screen itself, the difference in the display effects of sharpened parameters and smoothing parameters is small and not easily noticeable to the naked eye, such as Figure 7 (c) as shown. However, under the setting of smoothing parameters, the SPR algorithm needs to store the data of other rows additionally during implementation, and the calculated data is relatively large, so the power consumption is also large. In this display scene, the SPR parameter setting can be selected according to the actual battery power of the device to improve the power consumption utilization rate and extend the device usage time.

[0071] Embodiment 1: Sub-pixel rendering optimization based on reading mode

[0072] In the manual SPR adjustment mode, the system collects the display parameter setting data of users in the reading mode, including the battery power status, scene classification labels (such as e-book readers or document browsers), and the SPR parameters manually adjusted by users (such as different filtering ranges and corresponding weights). According to the collected data, a neural network model is trained to extract the display preference characteristics of users at different battery power levels. In the automatic SPR adjustment mode, when the user opens the e-book application, the system obtains the current battery power (such as 90% high battery power) and the scene label in real time. After calculation by the neural network model, the SPR parameters suitable for the reading scene are output, such as smoothing parameters to improve the text display effect of the display screen and achieve an efficient reading experience.

[0073] Embodiment 2: Sub-pixel rendering optimization based on game mode

[0074] In the manual SPR adjustment mode, the system records the SPR parameter adjustment data of users in the game scene and the corresponding battery power status. The system establishes a neural network model by collecting data multiple times to learn the relationship between user preferences and battery power levels. In the automatic SPR adjustment mode, when the user enters the game application, the system detects the current battery power (such as 30% low battery power) and the application scene in real time, and uses the trained neural network to select the most suitable SPR parameters, such as relatively sharp parameters to enhance the picture details and optimize the battery energy consumption performance, providing a more realistic game experience.

[0075] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those of ordinary skill in the art can understand that the above devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits of programmable hardware devices such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above hardware circuits and software such as firmware.

[0076] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A scene-based sub-pixel rendering method, characterized in that: The method comprises the following steps: S1: In the manual SPR adjustment mode, collect the data on the mobile phone power and the user's preference for SPR parameters in different display scenarios; S2: Create a machine learning algorithm model and train the machine learning algorithm based on the data collected in S1; S3: In the automatic SPR adjustment mode, the battery power data and the user's actual display application scenario are obtained respectively; S4: Input the data collected by S3 into the trained machine learning algorithm and adaptively adjust the SPR parameters. S5: Adjust sub-pixel rendering based on SPR parameters.

2. The method according to claim 1, characterized in that The SPR parameter preference data includes timestamp information and SPR parameters set by the user.

3. The method according to claim 1, characterized in that The display application scenario data includes at least one of a reading mode, a movie mode and a game mode.

4. The method according to claim 1, characterized in that: The acquired battery power data is normalized into a percentage form after preprocessing.

5. The method according to claim 1, characterized in that The adjusting sub-pixel rendering based on the SPR parameters includes dynamically adjusting at least one of the contrast, brightness, refresh rate, sharpness and smoothness, color presentation and power consumption of the display screen.

6. The method according to claim 1, characterized in that The machine learning algorithm model performs supervised learning training based on historical display data and SPR parameter data sets.

7. The method according to claim 1, characterized in that The sub-pixel rendering adjustment process further incorporates temperature data to optimize rendering parameters.

8. A scene-based sub-pixel rendering system, characterized in that: The system includes: Data collection module 1, used to collect user's display scene, SPR parameter settings and battery power data in manual SPR adjustment mode; Algorithm module, used to build machine learning algorithm models and train machine learning algorithms; Data collection module 2, used for collecting device status data in real time in automatic SPR adjustment mode; The adaptive adjustment module is used to adjust the SPR parameters in real time according to the results output by the algorithm module.

9. The scene-based sub-pixel rendering system of claim 8, wherein: The data collection module 2 is integrated with the terminal's sensor to obtain real-time information on device power, usage environment, and display scene changes, and use it for SPR parameter optimization and adjustment.

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