A display screen parameter setting optimization method and system

Abstract

This invention relates to the field of display technology and discloses a method and system for optimizing display screen parameter settings. The method introduces the concept of display area and combines user interaction data and content characteristics to evaluate the attention level of each area, thereby realizing the regional and personalized adjustment of display screen parameters.

Description

Technical Field

[0001] This invention relates to the field of display technology, and more specifically, to a method and system for optimizing display screen parameter settings. Background Technology

[0002] In the field of advanced display systems, especially those integrated into high-performance computing platforms, dynamic visual performance is central to the user experience. Traditional monitor settings typically rely on a fixed refresh rate, which users must manually select. However, the frame rate of actual content varies greatly. If the monitor's refresh rate does not match the content's frame rate, visual anomalies such as screen tearing or stuttering will occur. To address this issue, modern display systems have introduced adaptive synchronization technology, which dynamically adjusts the monitor's refresh rate to precisely match the instantaneous frame rate of the content. This ensures that each rendered image is displayed completely and without delay, significantly improving the perceived smoothness and reducing tearing.

[0003] However, even with adaptive refresh rate technology, the pixel response time of the display panel itself remains a limiting factor. If pixels cannot transition from one color state to another quickly enough within the refresh interval, motion blur or ghosting may occur. To mitigate motion blur caused by slow pixel response times, display manufacturers have introduced "overdrive" technology, which applies a temporarily higher-than-normal voltage during color transitions to accelerate the rearrangement of liquid crystal molecules, thereby effectively shortening the pixel response time. Summary of the Invention

[0004] This invention provides a method and system for optimizing display screen parameter settings, aiming to solve the problem of local visual artifacts caused by improper overdrive settings in existing display systems under multi-tasking operation scenarios, which affects user experience.

[0005] The technical solution of this application is as follows: In a first aspect, this application discloses a method for optimizing display screen parameter settings to reduce display screen power consumption and shorten response time, the method comprising: Obtain boundary information of each display area on the display screen. This boundary information is used to define the spatial range of each display area. The display area includes at least one of the following: application window area, video playback area, game screen area, and split-screen display area. Each display area is pixel-scanned to identify the display content characteristics of each display area. These display content characteristics include content type, which includes at least one of the following: static text content, dynamic video content, game screen content, and user interface elements. The interaction data between the user and the display screen is collected through the input interface of the display screen. The interaction data includes at least one of the following: mouse movement trajectory, keyboard input focus, touch operation, and user visual focus. Based on the interactive data and the characteristics of the displayed content, calculate the comprehensive attention score for each display area, and determine the degree of attention for each display area based on the comprehensive attention score; Depending on the level of attention and the characteristics of the displayed content, the source driver or GOA circuit of the display screen outputs the pixel overdrive voltage value or pixel overdrive current value corresponding to each display area, so as to change the response speed of the liquid crystal or the response speed of the organic light-emitting device in the display area. The system monitors the rate of change of pixel overdrive voltage or pixel overdrive current values ​​in adjacent display areas. When the rate of change exceeds a preset threshold, the width of the transition band is dynamically adjusted according to the rate of change. Within the dynamically adjusted transition band, an interpolation algorithm is used to smoothly transition the pixel overdrive voltage or pixel overdrive current values ​​between adjacent display areas.

[0006] This technical solution can dynamically adjust pixel overdrive parameters based on the user's attention level and content characteristics in different display areas, thereby effectively reducing power consumption and shortening response time while ensuring display quality. It also solves the problem of artifacts that may be introduced by traditional overdrive technology in multi-tasking scenarios.

[0007] Furthermore, the steps for identifying the display content characteristics of each display area include: Perform a pixel scan on each display area to obtain the pixel data of the display area; The display area will be divided into multiple sub-regions; Features are extracted from the pixel data of each sub-region, including pixel brightness variation, color distribution, and motion vectors; Perform continuous temporal scanning on sub-regions and determine whether the content type has switched based on the feature differences between consecutive frames; Based on the extracted features and the feature differences between consecutive frames, determine the content type of each sub-region; Based on the content type of each sub-region, assign a content characteristic identifier to the sub-region; and based on the content characteristic identifier of each sub-region, determine the mixed content characteristics of the display area.

[0008] This technical solution enables more precise identification of the mixed content characteristics of the display area, providing a more accurate basis for subsequent overdrive parameter adjustments and avoiding misjudgments that may be caused by judging a single content type, thereby improving the accuracy of overdrive adjustments.

[0009] Based on the above, this application further proposes a step of calculating the comprehensive attention score of each display area according to the characteristics of interactive data and displayed content, and determining the degree of attention of each display area based on the comprehensive attention score, including: The movement trajectory of the mouse cursor is continuously sampled, and the dwell time of the mouse cursor in each display area and the frequency of entering and leaving each display area are calculated. Real-time monitoring of content characteristics changes in each display area; when the category identifier of the content type changes, the display area is marked as an active content area. Using dwell time, frequency, and tags of active content areas as input, time-series analysis is used to generate visual attention trends for each display area; and The degree of attention for each display area is determined based on the temporal changes in visual attention trends.

[0010] This technical solution can comprehensively consider user interaction behavior and dynamic changes in content, and more accurately assess the user's real-time attention to different display areas, providing reliable user attention data for fine-tuning overdrive adjustments.

[0011] Furthermore, the steps for determining the level of attention for each display area based on the temporal changes in visual attention trends include: The screen pixel data of the display area not selected by the user's current operation is scanned in real time to identify the preset visual mode, which includes at least one of the following: flashing highlight mode, red warning mode, pop-up dialog box mode, flashing border mode and dynamic icon mode. Based on the identified visual patterns, a content urgency signal is generated; Based on the preset weighting rules, the fusion weights of the user interaction signals and content urgency signals reflected in the interaction data are determined. The user interaction signals and content urgency signals are then weighted and fused according to the fusion weights to calculate the comprehensive attention score for each display area. Based on the comprehensive attention score, the degree of attention for each display area is determined.

[0012] This technical solution assesses the urgency of content by recognizing visual patterns in areas not directly interacted with by the user, and integrates these patterns with user interaction signals. This makes the judgment of attention level more comprehensive and intelligent, avoiding situations where important visual cues are ignored while relying solely on user operations.

[0013] Preferably, the step of performing real-time scanning of screen pixel data on display areas not currently selected by the user's operation to identify preset visual patterns, and generating a content urgency signal based on the identified visual patterns, includes: Real-time scanning of screen pixel data is performed on display areas not currently selected by the user's operation to identify visual patterns; When multiple visual patterns are detected to appear simultaneously or overlap with each other, the multiple visual patterns are prioritized according to the preset pattern priority rules. Based on the priority ranking results, a content urgency signal is generated; and based on the spatial overlap of visual patterns, the urgency of overlapping areas is weighted and calculated to correct the content urgency signal.

[0014] This technical solution can effectively handle urgency judgment in complex visual patterns. By prioritizing and weighting based on spatial overlap, it ensures the accuracy and rationality of content urgency signals and avoids misjudgments caused by multi-mode conflicts or overlaps.

[0015] In one embodiment, the step of smoothing the transition of pixel overdrive voltage values ​​or pixel overdrive current values ​​between adjacent display areas using an interpolation algorithm within the dynamically adjusted transition band includes: Monitor the rate of change of overdrive target values ​​of pixels in adjacent display areas; When the rate of change meets the preset conditions, the adaptive transition band interpolation mode is triggered; In the adaptive transition band interpolation mode, the overdriven target value of the pixel is temporally smoothed to obtain the smoothed target value. Based on the smoothed target value, calculate the pixel overdrive voltage or pixel overdrive current value within the transition band; and dynamically adjust the width of the transition band according to the rate of change, and perform smooth transition processing on the pixel overdrive voltage or pixel overdrive current value within the dynamically adjusted transition band.

[0016] This technical solution can adaptively adjust the width of the transition band and perform smooth interpolation according to the rate of change of the pixel overdrive target value, effectively avoiding visual discomfort that may be caused by sudden changes in overdrive parameters in adjacent areas, and improving the overall smoothness and visual experience of the display.

[0017] As a technological improvement, the method also includes: When the user's attention switching speed exceeds a preset threshold, the attention level is smoothed over time. This smoothing process includes the following steps: Get the urgency of the content in each display area; Assign urgency weights to each display area based on the urgency of the content; Calculate the attention switching rate of each display area based on the speed at which the user's attention switches between display areas; Based on the urgency weight and attention switching rate, a corresponding smoothing filter type is selected for each display area. The smoothing filter type includes at least one of the following: a first-order low-pass filter, a second-order Butterworth filter, a Kalman filter, and an exponentially weighted moving average filter. The attention level of each display area is then smoothed in a time sequence according to the selected smoothing filter type.

[0018] This technical solution can intelligently select a suitable smoothing filter based on the dynamic characteristics of user attention switching and the urgency of the content, effectively avoiding frequent fluctuations in overdrive parameters caused by rapid attention switching, thereby improving system stability and the smoothness of user experience.

[0019] To improve the solution, the steps for selecting the appropriate smoothing filter type for each display area based on urgency weight and attention switching rate include: Real-time monitoring of the urgency weight and attention switching rate of each display area; According to the preset filter selection rules, the smoothing filter type corresponding to the urgency weight and attention switching rate of each display area is determined. The filter selection rules define the mapping relationship between different urgency weight intervals and attention switching rate intervals and smoothing filter types. When the urgency weight and attention switching rate are in the boundary area of ​​multiple filter selection rules, weighted interpolation is performed based on the matching distance between the urgency weight and attention switching rate and adjacent rules to determine the final smoothing filter type.

[0020] This technical solution enables the selection of the most suitable smoothing filter in various complex scenarios through precise filter selection rules and weighted interpolation of boundary regions, thereby further improving the system's response accuracy and stability to changes in user attention.

[0021] As a further improvement, the method also includes: Real-time monitoring of the user's attention switching patterns across different display areas, including mouse cursor movement speed, dwell time, and frequency of keyboard input focus changes; Calculate the similarity between the current switching mode and several preset typical switching modes, which include at least one of the following: reading mode, office mode, game mode, and video viewing mode; Identify the type of the current switching mode based on similarity; Based on the type of switching mode identified, adjust the threshold for dividing the urgency weight interval and the threshold for dividing the attention switching rate interval in the mapping relationship defined in the filter selection rule; During the adjustment of the mapping relationship, the matching degree of the filter selection is evaluated; and based on the matching degree, the adjustment range of the urgency weight interval division threshold and the attention switching rate interval division threshold is fine-tuned.

[0022] This technical solution enables the filter selection rules to be dynamically adjusted according to the user's actual switching mode, allowing the system to better adapt to the user's personalized usage habits and scenarios, further optimizing the smoothing effect and improving the intelligence level of the user experience.

[0023] Secondly, this application also discloses a display screen parameter setting optimization system, comprising: The boundary information acquisition module is used to acquire the boundary information of each display area on the display screen. The boundary information is used to define the spatial range of each display area. The display area includes at least one of the following: application window area, video playback area, game screen area, and split-screen display area. The content feature recognition module is used to perform pixel scanning on each display area and identify the display content features of each display area. The display content features include content type, which includes at least one of the following: static text content, dynamic video content, game screen content, and user interface elements. An interactive data acquisition module is used to acquire user interaction data with the display screen through the input interface of the display screen. The interaction data includes at least one of mouse movement trajectory, keyboard input focus, touch operation and user visual focus. The attention level determination module is used to calculate the comprehensive attention score of each display area based on the interaction data and the characteristics of the displayed content, and to determine the attention level of each display area based on the comprehensive attention score; The overdrive intensity adjustment module is used to output the pixel overdrive voltage value or pixel overdrive current value corresponding to each display area to the source driver or GOA circuit of the display screen according to the attention level and display content characteristics of each display area, so as to change the response speed of the liquid crystal or the response speed of the organic light-emitting device in the display area. The smooth transition processing module is used to monitor the rate of change of pixel overdrive voltage or pixel overdrive current values ​​in adjacent display areas, and when the rate of change exceeds a preset threshold, dynamically adjust the width of the transition band according to the rate of change, and use an interpolation algorithm to perform smooth transition processing on pixel overdrive voltage or pixel overdrive current values ​​between adjacent display areas within the dynamically adjusted transition band. Specifically, the output of the boundary information acquisition module is connected to the content feature recognition module and the attention level determination module; the output of the content feature recognition module is connected to the attention level determination module and the overdrive intensity adjustment module; the output of the interactive data acquisition module is connected to the attention level determination module; the output of the attention level determination module is connected to the overdrive intensity adjustment module; and the output of the overdrive intensity adjustment module is connected to the smooth transition processing module.

[0024] This technical solution provides a system that integrates boundary information acquisition, content characteristic recognition, interactive data collection, attention level determination, overdrive intensity adjustment, and smooth transition processing. It enables intelligent and regional optimization of display parameters, effectively solves the artifact problem of traditional display systems in multi-tasking scenarios, and improves the user experience.

[0025] Beneficial Effects: The display screen parameter setting optimization method and system disclosed in this application obtains the boundary information of each display area on the display screen, performs pixel scanning on each display area to identify its display content characteristics, and calculates the comprehensive attention score of each display area by combining user interaction data with the display screen, thereby determining the attention level of each display area. Based on this, according to the attention level and display content characteristics, the method outputs pixel overdrive voltage or pixel overdrive current values ​​corresponding to each display area to the source driver or GOA circuit of the display screen to change the response speed of the liquid crystal or organic light-emitting device within the display area. Furthermore, the method monitors the rate of change of pixel overdrive voltage or pixel overdrive current values ​​in adjacent display areas, and dynamically adjusts the width of the transition band when the rate of change exceeds a preset threshold. Within the dynamically adjusted transition band, an interpolation algorithm is used to smoothly transition the pixel overdrive voltage or pixel overdrive current values ​​between adjacent display areas. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a method for optimizing display screen parameters according to an embodiment of the present invention; Figure 2 This is a flowchart of a method for identifying the display content characteristics of each display area according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a display screen parameter setting optimization system provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Reference Figure 1 , Figure 1 This is a flowchart illustrating a method for optimizing display screen parameters according to an embodiment of the present invention. The method includes: S11, Obtain boundary information of each display area on the display screen. The boundary information is used to define the spatial range of each display area. The display area includes at least one of the following: application window area, video playback area, game screen area, and split-screen display area. S12, perform pixel scanning on each of the display areas to identify the display content characteristics of each display area. The display content characteristics include content type, and the content type includes at least one of the following: static text content, dynamic video content, game screen content, and user interface elements. S13, collect user interaction data with the display screen through the input interface of the display screen, the interaction data including at least one of mouse movement trajectory, keyboard input focus, touch operation and user visual focus; S14, calculate the comprehensive attention score of each display area based on the interaction data and the characteristics of the display content, and determine the degree of attention of each display area based on the comprehensive attention score; S15, based on the level of attention and the characteristics of the display content, output the pixel overdrive voltage value or pixel overdrive current value corresponding to each display area to the source driver or GOA circuit of the display screen, so as to change the response speed of the liquid crystal or the response speed of the organic light-emitting device in the display area. S16, monitor the rate of change of the pixel overdrive voltage value or pixel overdrive current value in adjacent display areas, and when the rate of change exceeds a preset threshold, dynamically adjust the width of the transition band according to the rate of change, and use an interpolation algorithm to perform smooth transition processing on the pixel overdrive voltage value or pixel overdrive current value between adjacent display areas within the dynamically adjusted transition band.

[0029] This application aims to address the problem of visual artifacts that may be introduced by overdrive settings in multi-tasking scenarios in the prior art. By refining area management and dynamically adjusting, it optimizes the power consumption and response time of the display screen, thereby improving the user experience.

[0030] The core of the display parameter setting optimization method proposed in this application lies in the fine-grained management and dynamic parameter adjustment of the display area. Here, "display area" refers to an independent area on the display screen with a specific function or displaying specific content, such as an application window area, video playback area, game screen area, and split-screen display area. The boundary information of these areas is used to define their spatial range and is the basis for subsequent processing. "Display content characteristics" refer to the attributes of the content presented within the display area, including content type, such as static text content, dynamic video content, game screen content, and user interface elements. These characteristics determine the different requirements of the content for response speed and image quality. "Interactive data" refers to the data generated when the user interacts with the display screen, such as mouse movement trajectory, keyboard input focus, touch operation, and user visual focus; this data directly reflects the distribution of the user's attention. "Pixel overdrive voltage value or pixel overdrive current value" are key parameters used to adjust the response speed of liquid crystal or organic light-emitting devices within the display area; changing these values ​​can accelerate or slow down pixel color transitions. "Transition band" refers to the area between adjacent display areas where the pixel overdrive voltage value or pixel overdrive current value needs to transition smoothly to avoid visual abruptness. This method achieves intelligent optimization of display screen parameters by comprehensively analyzing this information.

[0031] Specifically, the display parameter setting optimization method of this application includes the following steps: First, the system obtains the boundary information of each display area on the screen. This boundary information can be provided by the operating system or application programming interfaces (APIs). For example, the operating system can report the position and size of all currently open application windows, or a video player can provide the coordinates of its playback area. One implementation approach is to identify the boundaries of different application windows through screenshot analysis, or to divide the screen display area using a preset screen layout template. For instance, when a user opens a browser window and a document editing window, the system can obtain the precise coordinates and dimensions of these two windows on the screen, thereby defining the corresponding display areas.

[0032] Secondly, each display area is scanned pixel by pixel to identify the characteristics of the displayed content. These characteristics include content type, such as static text, dynamic video, game graphics, and user interface elements. One approach is to analyze features such as the frequency of color changes, motion vectors, and text density of pixels within the display area to determine the content type. For example, for a video playback area, the system will detect high-frequency pixel color changes and significant motion vectors; while for a static text area, it will detect a lower pixel change rate and high-density text characters.

[0033] Secondly, user interaction data with the display screen is collected through the screen's input interface. This interaction data includes at least one of the following: mouse movement trajectory, keyboard input focus, touch operations, and user visual focus. One implementation method is to use APIs provided by the operating system to obtain the mouse cursor position and movement speed, or to use eye-tracking devices to monitor the user's visual focus in real time. For example, when a user keeps the mouse cursor on a certain display area for an extended period, or when the keyboard input focus is on a text editing area, this data is collected and used for subsequent attention analysis.

[0034] Then, based on the interaction data and the characteristics of the displayed content, a comprehensive attention score is calculated for each display area, and the level of attention for each display area is determined based on the comprehensive attention score. One implementation method is to assign different weights to factors such as mouse dwell time, keyboard input frequency, and content dynamism, and then perform a weighted sum to calculate the comprehensive attention score. For example, if a user is performing intensive keyboard and mouse operations in a game screen area, and the content type of that area is dynamic game visuals, then the comprehensive attention score of that area will be significantly higher than other areas, thus being identified as a high-attention area.

[0035] Next, based on the level of attention and the characteristics of the displayed content, the source driver or GOA circuit of the display screen outputs pixel overdrive voltage or pixel overdrive current values ​​corresponding to each display area, thereby changing the response speed of the liquid crystal or organic light-emitting device within the display area. As one implementation method, a lookup table can be established to map different levels of attention and content characteristics to different pixel overdrive voltage or pixel overdrive current values. For example, for high-attention dynamic video areas, a higher overdrive voltage value can be output to accelerate pixel response and reduce motion blur; while for low-attention static text areas, a lower overdrive voltage value can be output to avoid overshoot artifacts and reduce power consumption.

[0036] Finally, the system monitors the rate of change of pixel overdrive voltage or pixel overdrive current values ​​in adjacent display areas. When the rate of change exceeds a preset threshold, the width of the transition band is dynamically adjusted based on the rate of change. Within the dynamically adjusted transition band, an interpolation algorithm is used to smoothly transition the pixel overdrive voltage or pixel overdrive current values ​​between adjacent display areas. As one implementation method, the overdrive parameters of adjacent areas can be compared in real time. When a significant difference is found, the system determines the width of the transition band based on the magnitude and rate of change of the difference, and uses algorithms such as linear interpolation or cubic spline interpolation to ensure smooth changes in pixel overdrive parameters in the transition area, avoiding visual abruptness. For example, when a high-overdrive game area is adjacent to a low-overdrive static text area, the system creates a transition band of appropriate width and gradually adjusts the overdrive parameters within this area to achieve a seamless visual experience.

[0037] The core innovation of this application lies in its refined area management and dynamic adaptive parameter adjustment mechanism. Traditional methods typically apply a uniform overdrive setting to the entire display screen, which often fails to meet the needs of different areas in multi-tasking scenarios. This can lead to motion blur in high-attention areas and overshoot artifacts in low-attention areas. This application introduces the concept of display areas and combines user interaction data and content characteristics to evaluate the attention level of each area, thereby achieving regionalized and personalized adjustment of display screen parameters.

[0038] In some embodiments described above in this application, a scheme for identifying the display content characteristics of each display area is proposed. Specifically, the steps for identifying the display content characteristics of each display area can be further refined into the following process.

[0039] In this regard, refer to Figure 2 , Figure 2 This is a flowchart of a method for identifying the display content characteristics of each display area according to an embodiment of the present invention. S12 includes: S121, Perform pixel scanning on each of the display areas to obtain pixel data of the display areas; S122, the display area is divided into multiple sub-regions; S123, extract features from the pixel data of each sub-region, the features including pixel brightness variation, color distribution and motion vector; S124, continuously scan the sub-region in the time dimension, and determine whether the content type has switched based on the feature differences between the previous and next frames; S125, Based on the extracted features and the feature differences between the preceding and following frames, determine the content type of each sub-region; S126, Assign a content characteristic identifier to each sub-region according to the content type of each sub-region; S127, determine the mixed content characteristics of the display area based on the content characteristic identifiers of each sub-region.

[0040] Specifically, when scanning each display area pixel by pixel, the raw data of all pixels within that area can be obtained. This pixel data contains basic information such as brightness and color, which forms the basis for subsequent analysis.

[0041] Furthermore, to analyze the displayed content more precisely, the display area is divided into multiple sub-regions. For example, an application window area can be divided into multiple smaller grid-like sub-regions, or divided according to content boundaries (such as text blocks or image blocks). This division facilitates the accurate identification of local content.

[0042] Feature extraction from the pixel data of each sub-region is a crucial step. These features can include pixel brightness variations, color distribution, and motion vectors. Pixel brightness variations reflect the brightness and contrast of the content; color distribution reveals the color tendency and richness of the content; and motion vectors capture the direction and speed of pixel movement within the sub-region, which is essential for identifying dynamic content (such as videos and games). The extraction of these features can be achieved through image processing algorithms, such as calculating pixel value gradients, histogram analysis, or optical flow methods.

[0043] Furthermore, to identify dynamic content and content transitions, continuous temporal scanning of the sub-region is necessary. By comparing the feature differences extracted between consecutive frames (previous and subsequent frames), it can be determined whether the content type within the sub-region has changed, such as switching from static text to dynamic video, or a rapid change in a game scene.

[0044] Based on the extracted features and the feature differences between the preceding and following frames, the specific content type of each sub-region can be determined. For example, if a sub-region has a large motion vector and rich color distribution, it may be identified as dynamic video content or game screen content; if the pixel brightness changes gradually and the color distribution is uniform, it may be identified as static text content.

[0045] After determining the content type of each sub-region, a content characteristic identifier is assigned to that sub-region. This identifier can be a predefined single tag or a set of tags used to describe the main content attributes of the sub-region.

[0046] Finally, based on the content characteristic identifiers of each sub-region, the mixed content characteristics of the entire display area can be determined. For example, if a display area contains multiple sub-regions, some of which are static text and some are dynamic video, then the mixed content characteristic of that display area will be identified as "text and video mixed". This determination of mixed content characteristics can more comprehensively reflect the actual content composition of complex display areas.

[0047] This application's solution overcomes the accuracy limitations of traditional methods in identifying complex or mixed content areas by refining the display area into sub-regions and performing multi-dimensional and temporal feature extraction and analysis on each sub-region. Raw data is acquired through pixel scanning, and then local focusing is performed through sub-region division, extracting key features such as pixel brightness changes, color distribution, and motion vectors, enabling the system to understand the essence of the content from multiple perspectives. Furthermore, continuous temporal scanning and frame-to-frame difference analysis allow the system to dynamically perceive content changes and transitions, thereby accurately determining the content type. Finally, by integrating the content characteristic identifiers of each sub-region, the mixed content characteristics of the entire display area can be comprehensively and accurately determined. This refined identification mechanism provides a more accurate and reliable basis for subsequent pixel overdrive parameter adjustments, ensuring the targeted and effective nature of parameter adjustments.

[0048] This application further proposes the following steps for calculating the comprehensive attention score of each display area based on interactive data and display content characteristics, and determining the attention level of each display area based on the comprehensive attention score: The movement trajectory of the mouse cursor is continuously sampled, and the dwell time of the mouse cursor in each display area and the frequency of entering and leaving each display area are calculated. Real-time monitoring of content characteristics changes in each display area; when the category identifier of the content type changes, the display area is marked as an active content area. By taking dwell time, frequency, and the marking of active content areas as input, the system generates visual attention trends for each display area through time-series analysis; and determines the degree of attention for each display area based on the time-series changes in visual attention trends.

[0049] Specifically, continuous sampling of the mouse cursor's movement trajectory means that the system continuously records the mouse cursor's position information on the display screen and calculates its dwell time within a specific display area and the number of times the cursor enters and leaves that area based on this position information. Dwell time can be understood as the total time the mouse cursor remains stationary or moves at a speed below a preset threshold within a certain display area, reflecting the user's sustained attention to that area. Entry and exit frequencies represent the number of times the user moves the mouse cursor into or out of a certain display area, reflecting the user's activity level in switching attention between different areas.

[0050] Real-time monitoring of content characteristics changes in each display area refers to the system continuously monitoring changes in the content displayed within each area. When the content type category changes, such as from "static text" to "dynamic video" or from "user interface element" to "game screen," the display area is automatically marked as an active content area. This marking aims to identify areas that may attract user attention due to content updates or interactions, even if the mouse cursor or keyboard focus is not currently pointing directly at that area.

[0051] In practical applications, dwell time, frequency, and the marking of active content areas are used as inputs. Through time-series analysis, visual attention trends for each display area are generated. Time-series analysis can employ various algorithms, such as time-series prediction models or machine learning models, to process this dynamic input data and reveal the dynamic changes and trends in user attention across different display areas. This yields a visual attention trend that changes over time, reflecting the strength and direction of user attention.

[0052] Furthermore, the system determines the level of attention for each display area based on the temporal changes in visual attention trends. This means that the system will comprehensively judge the current user's level of attention to each display area based on real-time data of visual attention trends combined with their historical change patterns. For example, if the visual attention trend of a certain area continues to rise, its level of attention will be judged as high; conversely, if the trend declines, the level of attention will be low.

[0053] This application's solution, by introducing continuous sampling of mouse cursor movement trajectories, real-time monitoring of changes in content characteristics, and generation of visual attention trends based on temporal analysis, can capture the distribution of user attention on the display screen more precisely. Traditional attention assessment may rely solely on single, instantaneous interaction events, making it difficult to reflect the dynamics and continuity of user attention. By continuously sampling the dwell time and entry / exit frequency of the mouse cursor, the intensity of user interest in specific areas and switching behaviors can be quantified. Simultaneously, real-time monitoring of changes in content type category labels can proactively identify "content-active areas" that may attract user attention due to content updates, even if these areas are not currently the focus of the user's direct interaction. Using this dynamic and multi-dimensional information as input, temporal analysis can generate a more predictive and continuous visual attention trend, thus overcoming the limitations of traditional methods in capturing dynamic changes in user attention. It is precisely this multi-dimensional, real-time, and temporal analysis that makes the determination of the degree of attention in each display area more accurate and dynamic.

[0054] This application further proposes the following steps for determining the degree of attention for each display area based on the temporal changes in visual attention trends: The screen pixel data of the display area not selected by the user's current operation is scanned in real time to identify preset visual modes, including at least one of the following: flashing highlight mode, red warning mode, pop-up dialog box mode, flashing border mode, and dynamic icon mode. Based on the identified visual patterns, a content urgency signal is generated; According to the preset weight allocation rules, the fusion weights of the user interaction signals and the content urgency signals reflected in the interaction data are determined respectively. The user interaction signals and the content urgency signals are then weighted and fused according to the fusion weights to calculate the comprehensive attention score of each display area. The attention level of each display area is then determined according to the comprehensive attention score.

[0055] Specifically, real-time scanning of screen pixel data for display areas not currently selected by the user's operation refers to the system continuously analyzing the pixel data of display areas that are not currently receiving mouse cursor, keyboard input focus, or touch operation. The purpose is to proactively detect visual cues that may appear in these areas, indicating important or urgent information. These visual patterns can be understood as predefined display states with specific visual characteristics used to attract user attention. For example, a blinking highlight mode refers to the background or text of a certain area alternating between light and dark at a specific frequency to highlight it; a red warning mode refers to using red or high-contrast colors to mark warning or error messages; a pop-up dialog box mode refers to a window that suddenly appears on the screen and usually requires user interaction; a blinking border mode refers to the border of the display area blinking at a specific frequency; and an animated icon mode refers to icons changing in an animated manner to indicate new messages or activities. The recognition of these visual patterns can be achieved through image processing algorithms, pattern matching techniques, or rule-based detectors.

[0056] Specifically, generating a content urgency signal based on identified visual patterns means that once any of the aforementioned visual patterns is detected, the system generates a quantified urgency signal based on the type and intensity of that pattern. For example, the urgency signal of a red alert pattern may be higher than that of a dynamic icon pattern. This signal reflects the potential demand on the user's attention from the content in that display area.

[0057] In practical applications, based on preset weighting rules, the fusion weights of user interaction signals and content urgency signals reflected in the interaction data are determined. These fusion weights are then used to weightedly fuse the user interaction signals and content urgency signals, calculating the comprehensive attention score for each display area. User interaction signals can be quantitative indicators reflecting the degree of user active attention, extracted from interaction data such as mouse movement trajectories, keyboard input focus, and touch operations. The fusion weights are preset based on experience, user preferences, or machine learning models to balance the contributions of user active interaction and content urgency to the comprehensive attention score. For example, in some scenarios, the weight of user active interaction may be higher, while in others, the weight of content urgency may be higher. Through weighted fusion, a more comprehensive and accurate comprehensive attention score can be obtained, which comprehensively considers both the user's active attention and the objective importance of the content.

[0058] Therefore, determining the attention level of each display area based on the comprehensive attention score means using the weighted and fused comprehensive attention score as the final basis to quantify the attractiveness of each display area to the user's attention. The higher the score, the higher the attention level of that area.

[0059] This application's solution introduces real-time scanning of screen pixel data in display areas not currently selected by the user's operation and identifies preset visual patterns to generate content urgency signals. This effectively overcomes the limitations of relying solely on direct user interaction and content activity to assess attention levels. Because it can proactively detect urgent or important visual cues even in areas not directly interacted with by the user, the system can capture the user's potential, non-active attention needs. By weightedly fusing this content urgency signal with traditional user interaction signals, this application generates a more comprehensive and accurate overall attention score. This fusion mechanism ensures that both areas actively focused on by the user and areas requiring attention due to content urgency are accurately identified and assigned corresponding attention levels by the system, thus providing a more reliable basis for subsequent display parameter optimization.

[0060] This application further proposes the above-mentioned step of real-time scanning of screen pixel data of display areas not currently selected by the user's operation to identify preset visual patterns, and generating a content urgency signal based on the identified visual patterns, including: The display area not currently selected by the user's operation is scanned in real time for screen pixel data to identify the visual pattern; When multiple visual patterns are detected to appear simultaneously or overlap with each other, the multiple visual patterns are prioritized according to a preset pattern priority rule. Based on the priority ranking result, the content urgency signal is generated; and based on the spatial overlap of the visual patterns, the urgency of the overlapping areas is weighted and calculated to correct the content urgency signal.

[0061] Specifically, the system performs real-time scanning of screen pixel data in display areas not currently selected by the user's operation. This aims to continuously monitor the visual state of these areas to promptly identify visual patterns that may require the user's attention. These visual patterns may include, but are not limited to, flashing highlight patterns, red warning patterns, pop-up dialog box patterns, flashing border patterns, and dynamic icon patterns. These patterns are typically used to alert the user to important information or events requiring immediate response.

[0062] When the system detects multiple visual patterns appearing simultaneously or overlapping each other—for example, a red warning icon appearing within a flashing border, or a pop-up dialog box covering part of a flashing highlighted area—it needs to prioritize these visual patterns according to a preset pattern priority rule. This pattern priority rule can be set empirically; for example, the red warning pattern has a higher priority than the flashing highlight pattern, and the pop-up dialog box pattern has a higher priority than the background dynamic icon pattern. This rule ensures that in complex scenarios, the system can identify the most critical or urgent visual cues.

[0063] In practical applications, a content urgency signal is generated based on the priority ranking results. This means that if a high-priority visual pattern is identified, its corresponding urgency signal will dominate, or its weight will be higher when fused with the urgency signals of other patterns. Furthermore, to further improve the accuracy of the urgency signal, this application also considers the spatial overlap of visual patterns. For example, if a high-priority visual pattern and a low-priority visual pattern highly overlap spatially, the urgency of the overlapping area can be weighted to correct the final content urgency signal. This weighted calculation ensures that when visual patterns overlap, the urgency signal can more accurately reflect the actual urgency level, avoiding misjudgments caused by simple superposition or ignoring overlap.

[0064] This application's solution addresses the problem that single visual pattern recognition may fail to accurately reflect content urgency in complex display environments by introducing priority ranking of multiple visual patterns and weighted calculation of spatial overlap. When multiple visual patterns coexist or overlap, traditional recognition methods may fail to effectively distinguish their importance or mutual influence. Through preset pattern priority rules, the system intelligently determines which visual pattern represents higher urgency, thus avoiding information overload or user distraction caused by multiple simultaneous prompts. Furthermore, by weighting the spatial overlap of visual patterns, the system can more precisely refine the content urgency signal, ensuring the accuracy of urgency assessment when visual patterns overlap, avoiding biases that may result from simple superposition or ignoring overlapping areas. It is precisely this refined processing that enables the generated content urgency signal to more realistically and accurately reflect the actual urgency of the display area.

[0065] This application further proposes an optimization scheme that uses an interpolation algorithm within the dynamically adjusted transition zone to smoothly transition the pixel overdrive voltage or pixel overdrive current values ​​between the adjacent display areas. The aim is to ensure the smoothness of the transition process, the timeliness of the response, and the visual comfort through more refined control and adaptive mechanisms.

[0066] The step of using an interpolation algorithm to smooth the transition of pixel overdrive voltage or pixel overdrive current values ​​between adjacent display areas within the dynamically adjusted transition band includes: Monitor the rate of change of the overdrive target value of pixels in the adjacent display areas; When the rate of change meets the preset conditions, the adaptive transition band interpolation mode is triggered; In the adaptive transition band interpolation mode, the overdriven target value of the pixel is subjected to temporal smoothing to obtain a smoothed target value; Based on the smoothed target value, calculate the pixel overdrive voltage value or pixel overdrive current value within the transition band; and dynamically adjust the width of the transition band according to the rate of change, and perform smooth transition processing on the pixel overdrive voltage value or pixel overdrive current value within the dynamically adjusted transition band.

[0067] Specifically, monitoring the rate of change of the target pixel overdrive value in adjacent display areas refers to the system continuously tracking and calculating the rate of change over time of the expected target value of the pixel overdrive voltage or pixel overdrive current used to drive the liquid crystal or organic light-emitting diode in adjacent display areas. This target value is calculated based on the attention level and display content characteristics of each display area and represents the expected response speed of that area. The rate of change can be monitored by comparing the differences in the target value within consecutive time frames.

[0068] Specifically, when the rate of change meets preset conditions, an adaptive transition band interpolation mode is triggered. These preset conditions may include a rate of change exceeding a specific threshold or a rate of change lasting longer than a preset duration, indicating a significant or persistent difference in display requirements between adjacent areas. The adaptive transition band interpolation mode is a runtime state specifically designed to handle such rapid or significant changes; in this mode, the system will enable more advanced smoothing and interpolation algorithms.

[0069] Furthermore, in the adaptive transition band interpolation mode, the overdriven target value of the pixel is subjected to temporal smoothing to obtain a smoothed target value. Temporal smoothing aims to eliminate instantaneous fluctuations or noise in the target value over time, making its trend more stable and predictable. For example, algorithms such as moving average filters, low-pass filters, or Kalman filters can be used to process the target value to obtain a smoother target value suitable for interpolation calculation.

[0070] Therefore, based on the smoothed target value, the pixel overdrive voltage or pixel overdrive current value within the transition band is calculated. Within the transition band, the pixel overdrive voltage or pixel overdrive current value is no longer a simple linear interpolation, but is calculated using a more complex interpolation algorithm (e.g., cubic spline interpolation, Bézier curve interpolation, etc.) based on the smoothed target value and the width and position of the transition band, to ensure the continuity and smoothness of the transition curve.

[0071] Simultaneously, the width of the transition band is dynamically adjusted according to the rate of change, and within the dynamically adjusted transition band, the pixel overdrive voltage value or pixel overdrive current value undergoes a smooth transition. This means that the width of the transition band is no longer fixed, but is adjusted in real time according to the rate of change of the target overdrive value of adjacent pixels. For example, when the rate of change is fast, the width of the transition band can be appropriately increased to provide a longer smoothing distance; when the rate of change is slow, the width of the transition band can be decreased to maintain clear boundaries between regions. This dynamic adjustment, combined with the interpolation algorithm, ensures optimal smoothing effects at different rates of change.

[0072] This application's solution addresses the issues of uneven transitions or lag that may occur in basic solutions when handling complex or rapidly changing display content by introducing monitoring of the rate of change of pixel overdrive target values ​​and triggering an adaptive transition band interpolation mode. Through this technical solution, the application significantly improves the visual smoothness and user experience when switching between different display areas. Compared to the basic solution, this solution effectively avoids visual artifacts, ghosting, or discontinuities that may occur when pixel overdrive values ​​change rapidly by introducing an adaptive transition band interpolation mode and timing smoothing processing. Dynamically adjusting the transition band width further enhances the system's adaptability, enabling the display to achieve a more natural and smooth transition effect when handling the boundaries between static and dynamic content, and between high-attention and low-attention areas. This ensures display performance while further reducing power consumption and shortening response time, thus improving overall display quality and user comfort.

[0073] This application further proposes a scheme for temporal smoothing of attention levels to address the challenges posed by rapid shifts in user attention. The method also includes: When the user's attention switching speed exceeds a preset threshold, the attention level is subjected to time-series smoothing processing, which includes the following steps: Get the urgency of the content in each display area; Based on the urgency of the content, assign urgency weights to each display area; Calculate the attention switching rate of each display area based on the speed at which the user's attention switches between display areas; Based on the urgency weight and the attention switching rate, a corresponding smoothing filter type is selected for each display area, wherein the smoothing filter type includes at least one of the following: a first-order low-pass filter, a second-order Butterworth filter, a Kalman filter, and an exponentially weighted moving average filter; and Based on the selected smoothing filter type, the attention level of each display area is processed in a time-series smoothing manner.

[0074] Specifically, when the system detects that the user's attention is switching between different display areas at a speed exceeding a preset threshold, it is considered that the user is rapidly shifting their attention. At this time, it is necessary to perform time-series smoothing on the attention level of each display area to avoid instability of display parameters caused by drastic fluctuations in attention level.

[0075] Content urgency refers to the importance or timeliness of content displayed within a given area. For example, a flashing notification window or a critical system notification typically has high urgency. This urgency can be determined based on content type, visual patterns (such as flashing highlight, red alert, pop-up dialog, flashing border, and dynamic icon modes), or preset priority rules. Urgency weight is a numerical value assigned to each display area based on content urgency, used to quantify the area's priority in attention smoothing. Areas with higher urgency generally have a larger weight, indicating that changes in attention should be reflected more quickly and accurately, while areas with lower urgency can be smoothed more extensively. Attention switching rate refers to the speed at which a user's attention shifts from one display area to another. This rate can be calculated by monitoring interactive data such as mouse cursor movement speed, frequency of keyboard input focus changes, changes in touch operation areas, or rapid movements of the user's visual focus.

[0076] Smoothing filter types are algorithmic models used to smooth the temporal distribution of attention levels. First-order low-pass filters are suitable for simple smoothing needs and effectively filter out high-frequency noise; second-order Butterworth filters provide a steeper cutoff frequency, better preserving low-frequency signals while maintaining smoothness; Kalman filters are optimal estimators suitable for noisy dynamic systems, enabling more accurate estimation and prediction of attention levels based on system models and measurement data; exponentially weighted moving average filters achieve smoothing by assigning higher weights to recent data, responding quickly to the latest changes. By selecting appropriate filter types based on different urgency weights and attention switching rates, adaptive smoothing effects can be achieved.

[0077] This application's solution introduces content urgency, urgency weight, and attention switching rate, and dynamically selects an appropriate smoothing filter type based on these factors to perform temporal smoothing of the attention level in each display area. When the user's attention switches rapidly, the system no longer simply calculates the attention level based on instantaneous interaction data, but comprehensively considers both the urgency of the content and the speed of the user's switching.

[0078] This application further proposes the following steps for selecting the corresponding smoothing filter type for each display area based on urgency weight and attention switching rate: The urgency weight and attention switching rate of each display area are monitored in real time. According to preset filter selection rules, a smoothing filter type corresponding to the urgency weight and attention switching rate of each display area is determined, wherein the filter selection rules define the mapping relationship between different urgency weight intervals and attention switching rate intervals and smoothing filter types; and When the urgency weight and the attention switching rate are in the boundary region of multiple filter selection rules, weighted interpolation is performed based on the matching distance between the urgency weight and the attention switching rate and the adjacent rules to determine the final smoothing filter type.

[0079] Specifically, real-time monitoring of the urgency weight and attention switching rate of each display area means that the system continuously acquires and updates the current values ​​of these parameters. The urgency weight reflects the importance or urgency of the displayed content; for example, a flashing alert box may have a high urgency weight. The attention switching rate indicates how quickly the user's attention shifts between different display areas; for example, rapid mouse cursor movement or frequent keyboard input focus switching may result in a high attention switching rate. Real-time monitoring of these parameters forms the basis for subsequent filter selection.

[0080] The process of determining the smoothing filter type corresponding to the urgency weight and attention switching rate of each display area based on preset filter selection rules refers to the system storing a set of rules. These rules define the mapping relationship between different urgency weight ranges and attention switching rate ranges and specific smoothing filter types. For example, when the urgency weight is low and the attention switching rate is slow, a first-order low-pass filter can be selected for smoothing; when the urgency weight is high and the attention switching rate is fast, a Kalman filter may be needed to provide faster response and stronger noise immunity. These filter selection rules can be preset and optimized based on empirical data, user testing, or machine learning models.

[0081] In practical applications, when the urgency weight and attention switching rate are located at the boundary of multiple filter selection rules, weighted interpolation is performed based on the matching distance between the urgency weight and attention switching rate and adjacent rules to determine the final smooth filter type. This means that when a pair of parameter values ​​do not explicitly fall into the range defined by a single rule, the system does not simply select one rule, but considers its "distance" from adjacent rules. For example, if a parameter combination is located at the boundary of rule A and rule B, the system can calculate a weighted average or perform weighted interpolation based on its distance from the centers of rules A and B, thus obtaining a filter selection that is a "hybrid" or "smooth transition" between the filter types suggested by rule A and rule B. This weighted interpolation ensures that the filter type selection does not jump drastically when parameter values ​​change slightly, thus providing smoother and more stable attention time-series smoothing. The matching distance can be calculated using Euclidean distance, Manhattan distance, or other suitable distance metrics.

[0082] This application's solution, by real-time monitoring of urgency weights and attention switching rates, combined with preset filter selection rules, can initially determine the appropriate smoothing filter type. However, to address the potential issue of discontinuous or inaccurate filter type selection when parameter values ​​are within the rule boundary region, this application further introduces a weighted interpolation mechanism. It is precisely because of weighted interpolation in the boundary region that the system can dynamically and smoothly adjust the filter selection based on the matching distance between the parameter and adjacent rules, avoiding abrupt changes that may result from simple rule switching. This ensures that the temporal smoothing of attention levels remains stable and adaptable under various complex user interactions and content change scenarios, thereby effectively improving the accuracy of display parameter setting optimization and the smoothness of the user experience.

[0083] This application further proposes a method for dynamically adjusting filter selection rules based on user attention switching patterns to improve the adaptability and accuracy of smoothing processing. The method further includes: Real-time monitoring of the user's attention switching patterns in each display area, including the mouse cursor movement speed, dwell time, and keyboard input focus change frequency; Calculate the similarity between the current switching mode and several preset typical switching modes, wherein the typical switching modes include at least one of the following: reading mode, office mode, game mode, and video viewing mode; Based on the similarity, the type of the current switching mode is identified; Based on the type of the identified switching mode, adjust the urgency weight interval division threshold and the attention switching rate interval division threshold in the mapping relationship defined in the filter selection rule; During the adjustment of the mapping relationship, the matching degree of the filter selection is evaluated; and based on the matching degree, the adjustment range of the urgency weight interval division threshold and the attention switching rate interval division threshold is fine-tuned.

[0084] Specifically, real-time monitoring of user attention switching patterns across different display areas refers to the system continuously tracking user interactions on the screen, such as mouse cursor movement speed, dwell time in specific areas, and frequency of keyboard input focus changes. These data are considered key indicators of the user's current attention switching pattern. For example, rapid mouse movement and frequent keyboard input might indicate gaming mode, while slower mouse movement and longer dwell times might indicate reading mode.

[0085] Calculating the similarity between the current switching mode and several preset typical switching modes can be understood as comparing the real-time monitored user switching modes with predefined typical switching modes representing different user activities (such as reading, working, gaming, and video watching). This similarity calculation can be implemented using various pattern recognition algorithms, such as Euclidean distance, cosine similarity, or machine learning classifiers.

[0086] In practical applications, identifying the type of the current switching mode based on the similarity score means that the system determines which preset typical switching mode the current user's behavior most closely resembles based on the calculated similarity value. For example, if the current mode has the highest similarity to "game mode," the system will identify the current mode as game mode.

[0087] Furthermore, based on the identified type of switching mode, the threshold values ​​for urgency weight interval division and attention switching rate interval division in the mapping relationship defined in the filter selection rule are adjusted. This aims to better adapt the filter selection rule to the current user activity. For example, in game mode, users have higher requirements for response speed; therefore, the threshold values ​​can be adjusted to make the system more inclined to select filter types with faster responses.

[0088] In the process of adjusting the mapping relationship, evaluating the matching degree of the filter selection means that after the system adjusts the threshold, it simulates or actually tests the performance of the selected filter in the current mode to determine whether it can effectively smooth attention changes while maintaining appropriate responsiveness.

[0089] Therefore, based on the matching degree, the adjustment range of the urgency weight interval division threshold and the attention switching rate interval division threshold is finely adjusted. The purpose is to finely control the threshold adjustment, avoid over-adjustment leading to new problems, and ensure the stability and optimization effect of filter selection.

[0090] This application's solution overcomes the limitation of preset filter selection rules being unable to adapt to dynamic user behavior by introducing real-time monitoring and recognition of user attention switching patterns. Through this technical solution, the application can dynamically adjust the filter selection rules according to the user's actual attention switching patterns, overcoming the limitations of traditional fixed rules. This allows the system to more accurately select the smoothing filter type suitable for the current user activity, thereby effectively reducing power consumption while ensuring display response speed.

[0091] refer to Figure 3 , Figure 3 This is a schematic diagram of a display screen parameter setting optimization system provided in an embodiment of the present invention, comprising: The boundary information acquisition module is used to acquire the boundary information of each display area on the display screen. The boundary information is used to define the spatial range of each display area. The display area includes at least one of the following: application window area, video playback area, game screen area, and split-screen display area. The content feature recognition module is used to perform pixel scanning on each of the display areas and identify the display content features of each display area. The display content features include content types, and the content types include at least one of the following: static text content, dynamic video content, game screen content, and user interface elements. An interactive data acquisition module is used to acquire user interaction data with the display screen through the input interface of the display screen. The interaction data includes at least one of mouse movement trajectory, keyboard input focus, touch operation and user visual focus. The attention level determination module is used to calculate the comprehensive attention score of each display area based on the interaction data and the characteristics of the display content, and to determine the attention level of each display area based on the comprehensive attention score; The overdrive intensity adjustment module is used to output a pixel overdrive voltage value or pixel overdrive current value corresponding to each display area to the source driver or GOA circuit of the display screen according to the attention level of each display area and the characteristics of the display content, so as to change the response speed of the liquid crystal or the response speed of the organic light-emitting device in the display area. The smooth transition processing module is used to monitor the rate of change of the pixel overdrive voltage value or the pixel overdrive current value in adjacent display areas, and when the rate of change exceeds a preset threshold, dynamically adjust the width of the transition band according to the rate of change, and use an interpolation algorithm to perform smooth transition processing on the pixel overdrive voltage value or pixel overdrive current value between adjacent display areas within the dynamically adjusted transition band. The output of the boundary information acquisition module is connected to the content feature recognition module and the attention level determination module. The output of the content feature recognition module is connected to the attention level determination module and the overdrive intensity adjustment module. The output of the interactive data acquisition module is connected to the attention level determination module. The output of the attention level determination module is connected to the overdrive intensity adjustment module. The output of the overdrive intensity adjustment module is connected to the smooth transition processing module.

[0092] This system aims to address the problem in existing technologies where overdrive settings can introduce visual artifacts in areas of non-user interest when users multitask simultaneously on the same display. By dividing the display screen into multiple display areas and finely sensing the content characteristics and user attention in each area, this system can dynamically adjust pixel overdrive parameters for different areas. This ensures fast response times in high-interest areas while optimizing power consumption and image quality in low-interest areas. Furthermore, by dynamically adjusting the transition band width and performing smooth transition processing, the system effectively avoids visual discontinuities that may result from abrupt parameter changes between adjacent areas, providing users with a superior and more comfortable visual experience.

[0093] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing display screen parameters, used to reduce display screen power consumption and shorten response time, characterized in that, The method includes: Obtain boundary information of each display area on the display screen. The boundary information is used to define the spatial range of each display area. The display area includes at least one of the following: application window area, video playback area, game screen area, and split-screen display area. Each display area is pixel-scanned to identify the display content characteristics of each display area. The display content characteristics include content type, which includes at least one of the following: static text content, dynamic video content, game screen content, and user interface elements. The interaction data between the user and the display screen is collected through the input interface of the display screen. The interaction data includes at least one of mouse movement trajectory, keyboard input focus, touch operation and user visual focus. Based on the interaction data and the characteristics of the displayed content, calculate the comprehensive attention score for each display area, and determine the degree of attention for each display area based on the comprehensive attention score; Based on the level of attention and the characteristics of the displayed content, the source driver or GOA circuit of the display screen outputs a pixel overdrive voltage value or a pixel overdrive current value corresponding to each display area, so as to change the response speed of the liquid crystal or the response speed of the organic light-emitting device in the display area. The system monitors the rate of change of the pixel overdrive voltage or pixel overdrive current values ​​in adjacent display areas. When the rate of change exceeds a preset threshold, the width of the transition band is dynamically adjusted according to the rate of change. Within the dynamically adjusted transition band, an interpolation algorithm is used to perform smooth transition processing on the pixel overdrive voltage or pixel overdrive current values ​​between adjacent display areas.

2. The method according to claim 1, characterized in that, The step of identifying the display content characteristics of each display area includes: Perform a pixel scan on each of the display areas to obtain pixel data for the display areas; The display area is divided into multiple sub-areas; Features are extracted from the pixel data of each sub-region, including pixel brightness variation, color distribution, and motion vector; The sub-region is continuously scanned in the time dimension, and the content type is determined to have switched based on the feature differences between consecutive frames. Based on the extracted features and the feature differences between the preceding and following frames, the content type of each sub-region is determined; Based on the content type of each sub-region, a content characteristic identifier is assigned to the sub-region; and The mixed content characteristics of the display area are determined based on the content characteristic identifiers of each sub-region.

3. The method according to claim 1, characterized in that, The step of calculating the comprehensive attention score of each display area based on the interaction data and the characteristics of the display content, and determining the degree of attention for each display area based on the comprehensive attention score, includes: The movement trajectory of the mouse cursor is continuously sampled, and the dwell time of the mouse cursor in each display area and the frequency of entering and leaving each display area are calculated. The changes in the content characteristics of each display area are monitored in real time. When the category identifier of the content type changes, the display area is marked as an active content area. Using the dwell time, frequency, and markers of the active content areas as input, a time-series analysis is conducted to generate visual attention trends for each display area; and The degree of attention for each display area is determined based on the temporal changes in the aforementioned visual attention trends.

4. The method according to claim 3, characterized in that, The step of determining the degree of attention for each display area based on the temporal changes in the visual attention trend includes: The screen pixel data of the display area not selected by the user's current operation is scanned in real time to identify preset visual modes, including at least one of the following: flashing highlight mode, red warning mode, pop-up dialog box mode, flashing border mode, and dynamic icon mode. Based on the identified visual patterns, a content urgency signal is generated; According to a preset weighting rule, the fusion weights of the user interaction signal and the content urgency signal reflected in the interaction data are determined respectively. Then, the user interaction signal and the content urgency signal are weighted and fused according to the fusion weights to calculate the comprehensive attention score for each display area; and The level of attention for each display area is determined based on the comprehensive attention score.

5. The method according to claim 4, characterized in that, The step of performing real-time scanning of screen pixel data on display areas not currently selected by the user's operation to identify preset visual patterns, and generating a content urgency signal based on the identified visual patterns, includes: The display area not currently selected by the user's operation is scanned in real time for screen pixel data to identify the visual pattern; When multiple visual patterns are detected to appear simultaneously or overlap with each other, the multiple visual patterns are prioritized according to a preset pattern priority rule. Based on the priority ranking result, the content urgency signal is generated; and Based on the degree of spatial overlap of the visual patterns, the urgency of the overlapping areas is weighted and calculated to correct the content urgency signal.

6. The method according to claim 1, characterized in that, The step of using an interpolation algorithm to smooth the transition of pixel overdrive voltage or pixel overdrive current values ​​between adjacent display areas within the dynamically adjusted transition band includes: Monitor the rate of change of the overdrive target value of pixels in the adjacent display areas; When the rate of change meets the preset conditions, the adaptive transition band interpolation mode is triggered; In the adaptive transition band interpolation mode, the overdriven target value of the pixel is subjected to temporal smoothing to obtain a smoothed target value; Based on the smoothed target value, calculate the pixel overdrive voltage value or pixel overdrive current value within the transition band; and The width of the transition band is dynamically adjusted according to the rate of change, and the pixel overdrive voltage value or pixel overdrive current value is smoothly transitioned within the dynamically adjusted transition band.

7. The method according to claim 6, characterized in that, The method further includes: When the user's attention switching speed exceeds a preset threshold, the attention level is subjected to time-series smoothing processing, which includes the following steps: Get the urgency of the content in each display area; Based on the urgency of the content, assign urgency weights to each display area; Calculate the attention switching rate of each display area based on the speed at which the user's attention switches between display areas; Based on the urgency weight and the attention switching rate, a corresponding smoothing filter type is selected for each display area, wherein the smoothing filter type includes at least one of the following: a first-order low-pass filter, a second-order Butterworth filter, a Kalman filter, and an exponentially weighted moving average filter; and Based on the selected smoothing filter type, the attention level of each display area is processed in a time-series smoothing manner.

8. The method according to claim 7, characterized in that, The step of selecting the corresponding smoothing filter type for each display area based on the urgency weight and the attention switching rate includes: The urgency weight and attention switching rate of each display area are monitored in real time. According to preset filter selection rules, a smoothing filter type corresponding to the urgency weight and attention switching rate of each display area is determined, wherein the filter selection rules define the mapping relationship between different urgency weight intervals and attention switching rate intervals and smoothing filter types; and When the urgency weight and the attention switching rate are in the boundary region of multiple filter selection rules, weighted interpolation is performed based on the matching distance between the urgency weight and the attention switching rate and the adjacent rules to determine the final smoothing filter type.

9. The method according to claim 8, characterized in that, The method further includes: Real-time monitoring of the user's attention switching patterns in each display area, including the mouse cursor movement speed, dwell time, and keyboard input focus change frequency; Calculate the similarity between the current switching mode and several preset typical switching modes, wherein the typical switching modes include at least one of the following: reading mode, office mode, game mode, and video viewing mode; Based on the similarity, the type of the current switching mode is identified; Based on the type of the identified switching mode, adjust the urgency weight interval division threshold and the attention switching rate interval division threshold in the mapping relationship defined in the filter selection rule; During the adjustment of the mapping relationship, the matching degree of the filter selection is evaluated; and Based on the matching degree, the adjustment range of the urgency weight interval division threshold and the attention switching rate interval division threshold is fine-tuned.

10. A display screen parameter setting optimization system, characterized in that, include: The boundary information acquisition module is used to acquire the boundary information of each display area on the display screen. The boundary information is used to define the spatial range of each display area. The display area includes at least one of the following: application window area, video playback area, game screen area, and split-screen display area. The content feature recognition module is used to perform pixel scanning on each of the display areas and identify the display content features of each display area. The display content features include content types, and the content types include at least one of the following: static text content, dynamic video content, game screen content, and user interface elements. An interactive data acquisition module is used to acquire user interaction data with the display screen through the input interface of the display screen. The interaction data includes at least one of mouse movement trajectory, keyboard input focus, touch operation and user visual focus. The attention level determination module is used to calculate the comprehensive attention score of each display area based on the interaction data and the characteristics of the display content, and to determine the attention level of each display area based on the comprehensive attention score; The overdrive intensity adjustment module is used to output a pixel overdrive voltage value or pixel overdrive current value corresponding to each display area to the source driver or GOA circuit of the display screen according to the attention level of each display area and the characteristics of the display content, so as to change the response speed of the liquid crystal or the response speed of the organic light-emitting device in the display area. The smooth transition processing module is used to monitor the rate of change of the pixel overdrive voltage value or pixel overdrive current value of adjacent display areas, and when the rate of change exceeds a preset threshold, dynamically adjust the width of the transition band according to the rate of change, and use an interpolation algorithm to perform smooth transition processing on the pixel overdrive voltage value or pixel overdrive current value between adjacent display areas within the dynamically adjusted transition band. The output of the boundary information acquisition module is connected to the content feature recognition module and the attention level determination module. The output of the content feature recognition module is connected to the attention level determination module and the overdrive intensity adjustment module. The output of the interactive data acquisition module is connected to the attention level determination module. The output of the attention level determination module is connected to the overdrive intensity adjustment module. The output of the overdrive intensity adjustment module is connected to the smooth transition processing module.

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