Tablet computer screen energy-saving display method and system

Abstract

The invention discloses an energy-saving display method and system for a tablet computer screen, and relates to the technical field related to screen display, and the method comprises the steps: executing pixel-level display driving analysis processing on the content displayed by the tablet computer screen, and constructing a pixel energy consumption contribution matrix based on brightness, color and time sequence changes; performing regional aggregation analysis in combination with user touch interaction position distribution, display content structure features and pixel display staying time sequence features, and dividing a screen display region into strong / weak perception display regions; and configuring a differentiated degradation strategy, and before execution, performing degradation execution verification and display management through a preset human eye visual perception consistency constraint condition. The technical problems of limited energy-saving effect and poor user visual experience caused by extensive screen energy-saving schemes, lack of user perception guidance and incapability of realizing fine energy efficiency management at a pixel level in the prior art are solved, and the technical effects of reducing the screen energy consumption and improving the visual quality consistency and the user visual experience are achieved.

Description

Technical Field

[0001] This invention relates to the field of screen display technology, specifically to a display method and system for energy saving of tablet computer screens. Background Technology

[0002] While high resolution, high refresh rate, wide color gamut, and high dynamic range (HDR) tablet displays enhance the user's visual experience, they also bring increasingly severe energy consumption challenges. As one of the most power-consuming components in a tablet, the screen typically accounts for 30% to 60% of the device's energy consumption, directly limiting its battery life. Traditional energy-saving methods such as global brightness adjustment and screen timeout often come at the cost of sacrificing the overall visual experience and have limited effectiveness when users actively interact with the device, failing to achieve refined energy efficiency management while maintaining the core visual experience. Content-based adaptive brightness adjustment and dynamic refresh rate adjustment can dynamically adjust the backlight or refresh rate according to the average brightness of the displayed screen or a simple scene. In addition, there are significant differences in the driving power consumption of pixels in different areas, colors and brightness on the screen. Moreover, the sensitivity of the human visual system to content in different spatiotemporal frequencies and areas is not uniform. Existing screen energy-saving displays simply allocate weights based on pixel brightness or color, failing to comprehensively consider the dynamic relationship between the electrical driving characteristics of pixels, the spatiotemporal structure characteristics of content and the perceptual characteristics of the human eye. This may disrupt the color balance of the screen or cause the loss of important visual cues. At the same time, it may produce uncomfortable flickering or blurring of details in the area clicked by the user, affecting the user experience.

[0003] Therefore, current technologies suffer from limitations such as crude screen energy-saving solutions, lack of user awareness guidance, and inability to achieve fine energy efficiency management at the pixel level, resulting in limited energy-saving effects and poor user visual experience. Summary of the Invention

[0004] This application provides a display method and system for saving energy on tablet computer screens, which solves the technical problems of existing screen energy-saving solutions being crude, lacking user awareness guidance, and unable to achieve fine energy efficiency management at the pixel level, resulting in limited energy-saving effects and poor user visual experience. It achieves the technical effect of reducing screen energy consumption and improving visual quality consistency and user visual experience.

[0005] This application provides a display method for energy saving in a tablet computer screen. The method includes: when the tablet computer screen is in display working state, performing pixel-level display driver parsing processing on the screen content to be displayed; assigning corresponding energy consumption contribution weights to each pixel in the screen based on the brightness level, color channel driving characteristics, and pixel display timing changes of each pixel in the screen, and constructing a pixel energy consumption contribution matrix characterizing the energy consumption distribution state of the screen pixels; after reading the distribution of user touch interaction positions, display content structure features, and pixel display dwell timing features, performing regional aggregation analysis on the pixel energy consumption contribution matrix, dividing the screen display area into visually effective display areas, the visually effective display areas including strong perception display areas and weak perception display areas; configuring differentiated degradation strategies based on the pixel energy consumption contribution matrix and the visually effective display areas; before the degradation strategies are executed, performing degradation execution verification through preset human eye visual perception consistency constraints, and performing display management based on the degradation execution verification results.

[0006] In a possible implementation, constructing a pixel energy consumption contribution matrix characterizing the energy consumption distribution of screen pixels includes: taking frame buffer data output by the display driving unit as input, reading the grayscale value of each pixel in each frame, obtaining the driving current range corresponding to the pixel grayscale value using the reading results, and configuring the basic energy consumption value of the pixel; for each pixel, performing sub-pixel energy consumption decomposition and reconstruction on the basic energy consumption value according to the on-state of the red, green, and blue sub-pixels and the sub-pixel driving duty cycle, and constructing the color channel corrected energy consumption value of the pixel; within a continuous display frame, statistically analyzing the number of grayscale changes and the magnitude of changes for each pixel, calculating the display switching frequency coefficient of the pixel using the statistical results, and using the display switching frequency coefficient to perform time-series correction on the color channel corrected energy consumption value; arranging the time-series corrected pixel energy consumption values ​​according to the spatial position of the pixels on the screen to form a pixel energy consumption contribution matrix.

[0007] In a possible implementation, the method for energy-saving display of a tablet computer screen further includes the following processing: performing regional aggregation analysis on the pixel energy consumption contribution matrix to divide the screen display area into visually effective display areas, including: based on the pixel energy consumption contribution matrix, dividing the screen into multiple pixel sub-regions according to a preset spatial partitioning rule, and performing aggregation calculation on the pixel energy consumption contribution weights in each pixel sub-region to obtain the regional energy consumption aggregation value of the corresponding pixel sub-region; using the regional energy consumption aggregation value, combined with the area information of the pixel sub-region, performing energy consumption aggregation normalization processing on each pixel sub-region to form a regional energy consumption distribution feature; performing time-series cumulative statistics on the user touch interaction position to generate... A touch heat map representing the degree of user interaction attention is generated, and the touch heat map is mapped to the corresponding pixel sub-regions to form regional interaction attention features. Structural analysis of the displayed content is performed to identify the main text region, main image region, and interface functional component region. Content structure markers are constructed based on the identification results, and these markers are aggregated to the corresponding pixel sub-regions to form regional content structure features. Based on the regional energy consumption distribution features, regional interaction attention features, and regional content structure features, multi-feature aggregation analysis is performed on each pixel sub-region to calculate the corresponding regional perception relevance index. The screen display area is divided into visually effective display areas according to the regional perception relevance index.

[0008] In a possible implementation, based on the pixel energy consumption contribution matrix, the screen is divided into multiple pixel sub-regions according to a preset spatial partitioning rule, including: dividing the screen into basic pixel grid units according to the screen's physical resolution; using the basic pixel grid units as the smallest partitioning units, performing extended search clustering of adjacent basic pixel grid units based on the pixel energy consumption contribution matrix; and using the extended search clustering results to divide the screen into multiple pixel sub-regions.

[0009] In a possible implementation, dividing the screen display area into visually effective display areas based on the region perception relevance index includes: reading the region segmentation threshold set by the user and obtaining the user's historical preference data; performing scene adaptation filtering on the historical preference data, and then performing threshold compensation on the region segmentation threshold based on the filtering results to establish a calibration threshold; determining whether the region perception relevance index meets the calibration threshold, dividing the pixel sub-regions that meet the calibration threshold into strong perception display areas, and dividing the pixel sub-regions that do not meet the calibration threshold into weak perception display areas.

[0010] In a possible implementation, a differentiated degradation strategy is configured based on the pixel energy consumption contribution matrix and the visually effective display area, including: activating a degradation strategy configuration channel; after receiving the pixel energy consumption contribution matrix and the visually effective display area, using a preprocessing layer within the channel to assign visual sensitivity weights according to the category of the visually effective display area; calling a mapping processing layer to combine each pixel value in the pixel energy consumption contribution matrix with the visual sensitivity weight of its respective pixel sub-region to map it into a pixel degradation coefficient; calling a smoothing processing layer to perform spatial neighborhood filtering on the pixel degradation coefficient of each pixel sub-region, and performing time-weighted averaging processing of the degradation coefficients of consecutive display frames, and configuring a differentiated degradation strategy based on the processing result.

[0011] In a possible implementation, degradation execution verification is performed through preset human visual perception consistency constraints, including: after executing the degradation strategy, the fitted pixel sub-regions are analyzed by pixel brightness difference, color deviation, and contrast change indicators to generate a visual consistency index matrix; the visual consistency index matrix is ​​compared with a preset visual threshold to determine whether the degradation of each pixel sub-region meets the human eye's adaptation range; and degradation verification is completed based on the judgment result.

[0012] This application also provides an energy-saving display system for tablet computer screens. The system includes: a display driver parsing and processing module, used to perform pixel-level display driver parsing and processing on the screen content to be displayed when the tablet computer screen is in display working state, and to assign corresponding energy consumption contribution weights to each pixel in the screen based on the brightness level, color channel driving characteristics, and pixel display timing changes, thereby constructing a pixel energy consumption contribution matrix characterizing the energy consumption distribution state of the screen pixels; a region aggregation analysis module, used to read the distribution of user touch interaction positions, display content structure features, and pixel display dwell timing features, and then perform region aggregation analysis on the pixel energy consumption contribution matrix to divide the screen display area into visually effective display areas, including strong perception display areas and weak perception display areas; a degradation strategy configuration module, used to configure differentiated degradation strategies based on the pixel energy consumption contribution matrix and the visually effective display areas; and a degradation execution verification module, used to perform degradation execution verification before the degradation strategy is executed by using preset human visual perception consistency constraints, and to perform display management based on the degradation execution verification results.

[0013] This application proposes a display method and system for energy saving on tablet computer screens. The method performs pixel-level display driver parsing on the content displayed on the tablet computer screen, constructing a pixel energy consumption contribution matrix based on brightness, color, and time-series changes. It then performs regional aggregation analysis by combining user touch interaction location distribution, display content structural features, and pixel display dwell time characteristics to divide the screen display area into strong / weak perception display zones. Differentiated degradation strategies are configured, and before execution, degradation execution verification and display management are performed using preset human visual perception consistency constraints. This addresses the technical problems of existing screen energy-saving solutions being crude, lacking user perception guidance, and unable to achieve fine energy efficiency management at the pixel level, resulting in limited energy-saving effects and poor user visual experience. The method achieves the technical effect of reducing screen energy consumption and improving visual quality consistency and user visual experience. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0015] Figure 1 This is a schematic flowchart of a display method for energy saving on a tablet computer screen, provided as an embodiment of this application.

[0016] Figure 2 This is a schematic diagram of a power-saving display system for a tablet computer screen, provided as an embodiment of this application.

[0017] Figure labeling: Display driver parsing and processing module 10, region aggregation analysis module 20, degradation strategy configuration module 30, degradation execution verification module 40. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention in order to achieve the intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structures, features and effects of the present invention.

[0019] This application provides a display method for energy saving on a tablet computer screen, such as... Figure 1 As shown, the method includes: Step S100: When the tablet computer screen is in display working state, perform pixel-level display driver parsing processing on the screen content to be displayed. Based on the brightness level, color channel driving characteristics and pixel display timing changes of each pixel on the screen, assign corresponding energy consumption contribution weights to each pixel on the screen and construct a pixel energy consumption contribution matrix that characterizes the energy consumption distribution state of the screen pixels.

[0020] Preferably, before the tablet computer screen is in display working state but the screen display content is actually presented, pixel-level display driver parsing processing is performed on the screen content to be displayed to quantify and predict the precise power consumption value of each pixel on the screen. Here, the screen content to be displayed refers to the frame buffer data generated by the graphics processor and about to be sent to the display driver unit, which directly determines the voltage / current signal required to drive each pixel. The display driver unit refers to the hardware in the tablet computer responsible for converting the digital image signal output by the graphics processor into a driving signal that can directly control the physical pixels of the screen. It is located between the graphics processor and the screen panel. The frame buffer data represents the original digital array of all pixel color information of a frame of screen image, which usually contains the brightness values ​​of the red (R), green (G), and blue (B) sub-pixels of each pixel.

[0021] Preferably, based on the brightness level, color channel driving characteristics, and pixel display timing changes of each pixel on the screen, specifically, the grayscale value of each pixel (0-255) is mapped to its corresponding driving current or voltage value. The mapping relationship is predetermined by the characteristics of the screen's driving integrated circuit and the electro-optical conversion curve, reflecting the nonlinear power consumption change caused by the same grayscale change in different brightness ranges. For the red, green, and blue sub-pixels of each pixel, their driving energy consumption is calculated separately. Since the photoelectric efficiency of different color LEDs or liquid crystal materials is different, the brightness and power consumption generated by the same driving signal are different on different color sub-pixels. Based on the luminous efficiency characteristics of the sub-pixels, the total energy consumption is decomposed and weighted by color channel. The additional power consumption caused by the grayscale value state change of the pixel between consecutive frames is calculated. The power consumption in screen driving is mainly divided into static power consumption and dynamic power consumption. The dynamic power consumption is estimated by statistically analyzing the frequency and amplitude of pixel value changes. Then, corresponding energy consumption contribution weights are assigned to each pixel in the screen, and a pixel energy consumption contribution matrix characterizing the screen pixel energy consumption distribution state is constructed. Each element value in this matrix represents the relative or absolute contribution of the corresponding pixel to the total screen energy consumption under the current display content and expected display timing.

[0022] Furthermore, step S100 also includes: taking the frame buffer data output by the display driving unit as input, reading the grayscale value of each pixel in each frame, obtaining the driving current range corresponding to the pixel grayscale value using the reading result, and configuring the basic energy consumption value of the pixel; for each pixel, according to the on-state of the red, green, and blue sub-pixels and the sub-pixel driving duty cycle, performing sub-pixel energy consumption decomposition and reconstruction on the basic energy consumption value to construct the color channel corrected energy consumption value of the pixel; within a continuous display frame, statistically analyzing the number of grayscale changes and the magnitude of changes for each pixel, calculating the display switching frequency coefficient of the pixel using the statistical results, and using the display switching frequency coefficient to perform time-series correction on the color channel corrected energy consumption value; arranging the time-series corrected pixel energy consumption values ​​according to the spatial position of the pixels on the screen to form a pixel energy consumption contribution matrix.

[0023] Preferably, the frame buffer data output by the display driving unit is the original grayscale value of each pixel in the frame of the image to be displayed, such as 8-bit data for each of the R, G, and B channels. Using this frame buffer data as input, the grayscale value of each pixel in each frame is read. Through a pre-stored grayscale-driving current lookup table, the total grayscale value or the grayscale value of each channel of each pixel is mapped to the corresponding driving current range. Each range corresponds to a current value or power consumption value, thereby configuring a basic energy consumption value for each pixel, reflecting the minimum theoretical power consumption required to drive the pixel to achieve a specified display brightness under standard conditions. Each pixel on the screen is composed of red, green, and blue sub-pixels, and the materials, luminous efficiency, and driving current of different color sub-pixels are... The dynamic circuits differ. Based on the on-state of the red, green, and blue sub-pixels corresponding to the pixel, i.e., the actual grayscale value of each color channel of the current pixel, and the sub-pixel driving duty cycle, such as the PWM dimming duty cycle, the independent energy consumption contribution of each color sub-pixel is calculated. Then, the basic energy consumption value is decomposed and reconstructed by sub-pixel energy consumption, that is, the energy consumption contribution of each sub-pixel is multiplied by the corresponding color channel efficiency correction coefficient and summed again to obtain the color channel corrected energy consumption value of each pixel, which more accurately reflects the real power consumption caused by color differences. Among them, the color channel efficiency correction coefficient is obtained by the screen hardware characteristics calibration. For example, driving the blue sub-pixel to achieve the same visual brightness usually consumes more power than the red sub-pixel.

[0024] Preferably, the grayscale value of each pixel is tracked within consecutive display frames, the number of grayscale changes for each pixel and the amplitude of changes between adjacent frames are counted, and the display switching frequency coefficient of the pixel is calculated using the statistical results. This display switching frequency coefficient is usually positively correlated with the switching frequency and amplitude. Then, the display switching frequency coefficient is used to perform time-series correction on the color channel correction energy consumption value. For example, the color channel correction energy consumption value is multiplied by the display switching frequency coefficient of the pixel to obtain the time-series corrected pixel energy consumption value, which includes the static power consumption for maintaining the current brightness and the dynamic power consumption caused by content changes. Finally, the time-series corrected pixel energy consumption values ​​are arranged according to the spatial position of the pixels on the screen to generate a pixel energy consumption contribution matrix mathematically represented as a two-dimensional array. The dimension of this matrix is ​​consistent with the screen resolution, and the value at each coordinate point in the matrix represents the comprehensive estimated power consumption contribution of the pixel at that position in the current frame and the recent time context.

[0025] Step S200: After reading the distribution of user touch interaction positions, structural features of displayed content, and timing features of pixel display dwell time, perform regional aggregation analysis on the pixel energy consumption contribution matrix to divide the screen display area into visually effective display areas, which include strong-sensing display areas and weak-sensing display areas.

[0026] Preferably, the method records and analyzes the location, frequency, and trajectory of user touches on the screen to determine the distribution of user touch interaction locations. Areas frequently touched or long-pressed, such as virtual keyboards, sliders, and operation buttons, are identified as the current interaction focus. The semantic structure of screen content is identified through image analysis or information obtained from the application framework layer to determine the structural characteristics of the displayed content. This includes text-based main areas such as article text and message bubbles, image / video main areas, interface functional component areas such as buttons, icons, and navigation bars, and background / decorative areas. Different structures of content have different visual importance weights, with text being more important than background. The method also calculates the duration for which content in each area remains static, such as static wallpapers that haven't changed for a long time or fixed status bars, to determine the pixel display dwell time sequence characteristics. The novelty and information content of these pixels decrease over time, and human eye attention also decreases accordingly. Then, a regional aggregation analysis is performed on the pixel energy consumption contribution matrix. Specifically, the screen is spatially divided into multiple pixel sub-regions through grid clustering, and the energy consumption contribution values ​​of all pixels within each sub-region are summed or averaged to obtain the regional energy consumption aggregation value.

[0027] Preferably, the calculated aggregated regional energy consumption value is weighted and fused with the touch interaction heat, content structure importance tag, and content stillness duration mapped to the same region to calculate a comprehensive regional perception relevance index, which is used to quantify the visual effectiveness and importance of the region to the current user. Based on the regional perception relevance index, all sub-regions are binary-classified to obtain visually effective display areas, including strong-perception display areas and weak-perception display areas. Strong-perception display areas refer to areas with high regional perception relevance indices. These areas simultaneously have high interaction frequency, display core content, frequent content updates, and / or significant energy consumption contributions. They must maintain the highest display quality, and energy-saving degradation strategies are strictly limited or completely avoided. Weak-perception display areas refer to areas with low regional perception relevance indices. These areas typically have no user interaction, display background or secondary decorative content, have content that remains still for extended periods, and / or have low energy consumption contributions. They are the primary targets for implementing proactive energy-saving strategies such as reducing brightness, color depth, and refresh rate, offering the greatest energy-saving potential with the least impact on the user experience.

[0028] Furthermore, step S200 also includes: based on the pixel energy consumption contribution matrix, dividing the screen into multiple pixel sub-regions according to a preset spatial partitioning rule, and aggregating and calculating the pixel energy consumption contribution weights in each pixel sub-region to obtain the region energy consumption aggregation value of the corresponding pixel sub-region; using the region energy consumption aggregation value, combined with the area information of the pixel sub-region, performing energy consumption aggregation and normalization processing on each pixel sub-region to form a region-level energy consumption distribution feature; performing time-series cumulative statistics on the user touch interaction position to generate a touch heat distribution map representing the degree of user interaction attention, and mapping the touch heat distribution map to... The data is projected onto the corresponding pixel sub-regions to form region-level interactive attention features; structural analysis of the displayed content is performed to identify the main text region, main image region, and interface functional component region; content structure markers are constructed based on the identification results, and these content structure markers are aggregated to the corresponding pixel sub-regions to form region-level content structure features; based on the region-level energy consumption distribution features, region-level interactive attention features, and region-level content structure features, multi-feature aggregation analysis is performed on each pixel sub-region to calculate the corresponding region perception relevance index; the screen display area is divided into visually effective display areas according to the region perception relevance index.

[0029] Preferably, the preset spatial partitioning rule is a fixed grid or dynamic clustering based on energy consumption distribution. Based on the pixel energy consumption contribution matrix, the computer tablet display screen is divided into multiple pixel sub-regions as a unified spatial unit for regional analysis. For each pixel sub-region, the energy consumption contribution weights of all pixels contained therein are summed and aggregated to obtain the regional energy consumption aggregate value of the corresponding pixel sub-region. Since the area of ​​each pixel sub-region may be different, the regional energy consumption aggregate value is combined with the area information of the pixel sub-region to perform energy consumption aggregation normalization processing on each pixel sub-region. That is, the regional energy consumption aggregate value of each pixel sub-region is divided by the number of pixels in that region to obtain the energy consumption density per unit area, i.e., the regional energy consumption distribution characteristics, so as to achieve the comparability of energy consumption intensity between regions of different sizes.

[0030] Preferably, the location coordinates of user touch events such as touch and swipe are statistically recorded within a time window to determine the temporal accumulation of user touch interaction locations. A touch heat map corresponding to the screen resolution is generated, representing the degree of user interaction attention. The value of each point in the map represents the frequency of interaction at that location. The values ​​on the touch heat map are then aggregated to each pixel sub-region according to their corresponding coordinates. For example, the average heat or maximum heat within the region is calculated to obtain regional-level interaction attention features, which are used to quantify the degree of active attention of the user to that region.

[0031] Preferably, the structure of the displayed content is analyzed using computer vision techniques such as edge detection, texture analysis, and connected component analysis. This involves semantic segmentation of the current screen content, identifying and marking the main text region, main image region, interface functional component region (such as buttons and input boxes), and the default background region. Then, based on the recognition results, a content structure label is constructed, assigning importance weights to each identified content region, for example, functional component importance > text importance > image main importance > background importance. Furthermore, based on the coverage relationship between each pixel sub-region and each content region, the main content type is determined, or the content weights within the coverage area are aggregated to the corresponding sub-region to form a region-level content structure feature, thereby quantifying the inherent importance of the content in that region.

[0032] Preferably, the regional-level energy consumption distribution characteristics, regional-level interaction attention characteristics, and regional-level content structure characteristics represent power consumption per unit area, user interaction intensity, and inherent content importance, respectively. A multi-feature aggregation analysis is performed on each pixel sub-region using a multi-feature aggregation function, and a comprehensive regional perception relevance index is determined through weighted calculation. The multi-feature aggregation function ensures that for each sub-region, if its energy consumption is high, user interaction is frequent, and it carries important content, its perception relevance is extremely high; conversely, if its energy consumption is high but there is no interaction and the content is secondary, its perception relevance is very low. Finally, the screen display area is divided into visually effective display areas based on the regional perception relevance index. That is, a regional segmentation threshold is configured based on historical data and display requirements. Sub-regions with regional perception relevance indices higher than this threshold are designated as strongly perceptible display areas, representing core visual areas crucial to user experience; conversely, sub-regions with regional perception relevance indices lower than this preset dynamic threshold are designated as weakly perceptible display areas, representing the main safety targets for energy-saving optimization.

[0033] Furthermore, step S200 also includes dividing the screen into basic pixel grid units according to the screen's physical resolution; using the basic pixel grid units as the smallest block units, performing extended search clustering of adjacent basic pixel grid units based on the pixel energy consumption contribution matrix; and using the extended search clustering results to divide the screen into multiple pixel sub-regions.

[0034] Preferably, the screen's physical resolution may be 2560×1600 pixels. The screen is divided into fixed-size basic pixel grid units, for example, each unit is set to an 8×8 pixel or 16×16 pixel square, and this is used as the smallest unit. Based on the pixel energy consumption contribution matrix, an extended search clustering of adjacent basic pixel grid units is performed. Specifically, the average energy consumption value corresponding to each basic pixel grid unit is obtained by aggregating and averaging the pixel energy consumption contribution matrix values ​​of all pixels in the unit, representing the average energy consumption intensity of this small area. Then, a basic grid unit that has not yet been assigned to any pixel sub-region is selected as the seed for the current clustering. A similarity judgment criterion is set, such as if the difference in energy consumption values ​​between two adjacent grid units is less than a certain threshold, then they are considered similar. Then, the current seed unit is checked. All spatially adjacent and unassigned cells in a pixel subregion are considered, including top, bottom, left, right, and diagonal elements. If the energy consumption of an adjacent cell is close to the average energy consumption of the current cluster region, that cell is merged into the current cluster region. The newly merged cell is then used as a new edge for the same process, continuing to search for and merge adjacent cells that meet the criteria. When all edges of the current cluster region can no longer merge new similar cells, the clustering is complete, forming a pixel subregion. In the remaining unassigned areas of the screen, new seed cells are selected, and this process is repeated until all basic grid cells are assigned to a pixel subregion. The final output consists of multiple pixel subregions of varying sizes and shapes. Each pixel subregion has relatively uniform energy consumption characteristics, while there are significant differences in energy consumption between different subregions. This non-uniform, content-adaptive spatial partitioning ensures accuracy, effectiveness, and efficiency.

[0035] Furthermore, step S200 also includes reading the user-set region segmentation threshold and obtaining the user's historical preference data; performing scene adaptation filtering on the historical preference data, performing threshold compensation on the region segmentation threshold according to the filtering results, and establishing a calibration threshold; determining whether the region perception relevance index meets the calibration threshold, dividing the pixel sub-regions that meet the calibration threshold into strong perception display areas, and dividing the pixel sub-regions that do not meet the calibration threshold into weak perception display areas.

[0036] Preferably, the user subjectively sets a region segmentation threshold of 0.7 to initially distinguish the importance of regions and obtain the user's historical preference data, i.e., records the user's past behavior data, such as the user's habit of manually adjusting brightness or contrast during reading, gaming, and video playback, the user's past feedback on the system's automatic energy-saving prompts, and historical statistics of the screen areas where user interaction is mainly concentrated in similar scenarios. Then, scenario-adaptive filtering is performed on the historical preference data, i.e., determining the current usage scenario, such as running an e-book reader application in a dimly lit environment, and then filtering out a subset of data that is highly relevant to the current scenario from the historical preference data. For example, in a reading scenario, the user's historical preferences in reading applications are mainly referenced. Based on the filtered scenario-related historical data, the region segmentation threshold set by the user is dynamically adjusted and compensated. Specifically, if historical data shows that the user always prefers higher brightness in the current type of application, indicating a high requirement for visual quality, the region segmentation threshold is increased; conversely, if data shows that the user often accepts lower brightness when reading at night, the region segmentation threshold may be decreased. This generates a calibration threshold suitable for the current specific scenario. Then, the region perception correlation index of each pixel sub-region is compared with the dynamically generated calibration threshold to determine whether the region perception correlation index meets the calibration threshold. All pixel sub-regions with region perception correlation indices greater than or equal to the calibration threshold are classified as strong perception display areas, and all pixel sub-regions with region perception correlation indices less than the calibration threshold are classified as weak perception display areas.

[0037] Step S300: Configure differentiated degradation strategies based on the pixel energy consumption contribution matrix and the visually effective display area.

[0038] Step S300 further includes activating the degradation strategy configuration channel; after receiving the pixel energy consumption contribution matrix and the visually effective display area, using the preprocessing layer within the channel to allocate visual sensitivity weights according to the category of the visually effective display area; calling the mapping processing layer to combine each pixel value in the pixel energy consumption contribution matrix with the visual sensitivity weight of its respective pixel sub-region to map it into a pixel degradation coefficient; calling the smoothing processing layer to perform spatial neighborhood filtering on the pixel degradation coefficient of each pixel sub-region, and performing time-weighted averaging processing of the degradation coefficients of consecutive display frames, and configuring a differentiated degradation strategy based on the processing result.

[0039] Preferably, a differentiated degradation strategy is configured using the pixel energy consumption contribution matrix and the visually effective display area. Specifically, a degradation strategy configuration channel is activated, which is a hierarchical, configurable pixel-level energy-saving strategy generation engine. This engine receives the pixel energy consumption contribution matrix and the visually effective display area, and transforms the abstract energy-saving intention into stable and executable screen driving instructions. This includes a preprocessing layer, a mapping processing layer, and a smoothing processing layer. The preprocessing layer assigns visual sensitivity weights based on the category of the visually effective display area, allocating lower visual sensitivity weights to highly perceptual display areas and higher visual sensitivity weights to weakly perceptual display areas, and outputs a visual sensitivity weight mapping table that corresponds one-to-one with the screen pixel sub-regions. The mapping processing layer combines each pixel value in the pixel energy consumption contribution matrix with the visual sensitivity weight of its corresponding pixel sub-region. The process involves linearly calculating multiple pixel degradation coefficients using a mapping function to determine the theoretically applicable degradation intensity for each pixel, outputting an initial pixel degradation coefficient matrix consistent with the screen resolution. A smoothing layer performs spatial neighborhood filtering on the pixel degradation coefficients of each pixel sub-region. This involves using a Gaussian kernel or mean filter kernel to perform a weighted average calculation on the values ​​of each pixel degradation coefficient itself and its neighboring pixels, obtaining spatially smoothed coefficients to eliminate spatial abrupt changes and temporal flicker in the strategy, ensuring that the final energy-saving adjustment appears smooth and natural to the human eye. Then, the spatially smoothed coefficients calculated for the current frame undergo temporal weighted averaging across consecutive display frames, i.e., a weighted average with historical smoothing coefficient values, where historical values ​​have a higher weight. This generates pixel degradation coefficients optimized through spatiotemporal smoothing as the processing result. Based on the processing result, a differentiated degradation strategy is configured. This involves proportionally adjusting the grayscale value or voltage drive signal sent to each pixel based on its pixel degradation coefficient. This includes significantly reducing brightness and color saturation in areas with high pixel degradation coefficient values, while keeping the drive signal unchanged in areas with low pixel degradation coefficient values.

[0040] Step S400: Before the degradation strategy is executed, degradation execution verification is performed by using preset human visual perception consistency constraints, and display management is performed based on the degradation execution verification results.

[0041] Preferably, before executing the degradation strategy, a differentiated degradation strategy and the frame image data to be displayed are input. The degradation strategy is applied to the frame image data in the software to simulate the actual screen display during the degradation strategy execution, determining the degraded simulation frame. Then, degradation execution is verified using preset human visual perception consistency constraints. These constraints encode the human eye's sensitivity to changes in brightness, color, contrast, spatial frequency, etc. The degraded simulation frame is compared pixel-by-pixel with the original frame to calculate the quantitative differences in key perceptual indicators. For example, perceptible brightness changes are determined based on a brightness contrast sensitivity function, and CIED is used. The E2000 color difference formula determines the perceptible color difference and the loss of visibility of texture or edge details, generating a degradation execution verification result that clearly identifies the screen area where the predicted change exceeds the perceptible difference threshold of human visual perception. Finally, display management is performed based on the degradation execution verification result. If the verification passes, the degradation strategy is deemed to be executed safely, and the complete degradation strategy along with the original image data is sent to the display driver unit for execution, displaying an energy-efficient optimized image. If the verification fails, the original strategy is deemed unsafe, triggering strategy correction and attenuation execution, and the verified safe strategy instruction is sent to the display driver unit for execution, thereby achieving a reliable and adaptive balance between energy saving and visual quality.

[0042] Furthermore, step S400 also includes: after executing the degradation strategy, the fitted pixel sub-region is analyzed by pixel brightness difference, color deviation, and contrast change indicators to generate a visual consistency index matrix; the visual consistency index matrix is ​​compared with a preset visual threshold to determine whether the degradation of each pixel sub-region meets the human eye's adaptation range; and degradation verification is completed based on the judgment result.

[0043] Preferably, the fitted pixel sub-region refers to the expected display state of each pixel sub-region after simulating the application of a differentiated degradation strategy in the software. For each pixel sub-region, the differences between its simulated degradation state and the original display state in key visual dimensions such as pixel brightness difference, color deviation, and contrast change indicators are calculated. This includes the change in average pixel brightness within the calculated region, the change in average pixel color within the calculated region, and the change in the ratio of the brightest to the darkest part within the calculated region using a contrast sensitivity function that is more in line with human eye perception. The calculated brightness difference, color deviation, and contrast change values ​​are combined by region to generate a visual degradation score for each pixel sub-region, and then a visual consistency index matrix is ​​output to quantify the degree of visual distortion that the proposed energy-saving strategy is expected to cause at various parts of the screen. A safety threshold value is pre-calibrated through human eye visual perception experiments to determine a preset visual threshold, which is used to define the degree of brightness, color, and contrast changes that an ordinary observer can perceive or begin to feel uncomfortable about. Then, the visual consistency index matrix is ​​used to further quantify the visual distortion. The indicator matrix is ​​compared with a preset visual threshold. If the visual consistency indicator matrix is ​​lower than the preset visual threshold, the verification is passed, and the expected degradation level of the area is considered to be within the range of human eye adaptation, belonging to imperceptible or acceptable minor changes. If the visual consistency indicator matrix is ​​higher than the preset visual threshold, the verification fails, indicating that the expected degradation of the area may lead to a perceptible and uncomfortable decrease in visual quality and exceed the safe range. If all pixel sub-regions pass the verification, the energy-saving strategy will be approved for execution and its instructions will be sent to the display driver hardware. If the verification fails, the degradation coefficient of the local excess is multiplied by a preset attenuation factor of 0.7 to generate a weaker safe degradation strategy, and the verification is repeated until the maximum executable energy-saving intensity strategy that meets the perceptual consistency constraint for all areas is determined. This fundamentally prevents the degradation of display quality caused by excessive pursuit of energy saving, avoids the risk of pushing the poor visual experience that may cause user complaints onto the real screen, and improves the overall reliability of the product and user satisfaction.

[0044] In the above text, refer to Figure 1 A method for energy-saving display of a tablet computer screen according to an embodiment of the present invention has been described in detail. Next, reference will be made to... Figure 2 This invention describes an energy-saving display system for a tablet computer screen according to an embodiment of the present invention.

[0045] According to an embodiment of the present invention, a display system for saving energy in a tablet computer screen addresses the technical problems of existing screen energy-saving solutions being crude, lacking user-aware guidance, and unable to achieve fine energy efficiency management at the pixel level, resulting in limited energy-saving effects and poor user visual experience. The system achieves the technical effect of reducing screen energy consumption and improving visual quality consistency and user visual experience. Figure 2As shown, a power-saving display system for tablet computer screens includes: a display driver parsing and processing module 10, a region aggregation analysis module 20, a degradation strategy configuration module 30, and a degradation execution verification module 40.

[0046] The display driver parsing and processing module 10 is used to perform pixel-level display driver parsing and processing on the screen content to be displayed when the tablet computer screen is in display working state. Based on the brightness level, color channel driving characteristics and pixel display timing changes of each pixel on the screen, it assigns corresponding energy consumption contribution weights to each pixel on the screen and constructs a pixel energy consumption contribution matrix that characterizes the energy consumption distribution state of the screen pixels. The region aggregation analysis module 20 is used to read the distribution of user touch interaction positions, display content structure features and pixel display dwell timing features, and then perform region aggregation analysis on the pixel energy consumption contribution matrix to divide the screen display area into visually effective display areas, which include strong perception display areas and weak perception display areas. The degradation strategy configuration module 30 is used to configure differentiated degradation strategies based on the pixel energy consumption contribution matrix and the visually effective display areas. The degradation execution verification module 40 is used to perform degradation execution verification by a preset human visual perception consistency constraint before the degradation strategy is executed, and perform display management based on the degradation execution verification results.

[0047] The specific configuration of the display driver parsing and processing module 10 will be described in detail below. The display driver parsing and processing module 10 further includes: taking the frame buffer data output by the display driver unit as input, reading the grayscale value of each pixel in each frame, obtaining the driving current range corresponding to the pixel grayscale value using the reading result, and configuring the basic energy consumption value of the pixel; for each pixel, according to the on-state of the red, green, and blue sub-pixels and the sub-pixel driving duty cycle, performing sub-pixel energy consumption decomposition and reconstruction on the basic energy consumption value to construct the color channel corrected energy consumption value of the pixel; within a continuous display frame, statistically analyzing the number of grayscale changes and the magnitude of changes of each pixel, calculating the display switching frequency coefficient of the pixel using the statistical results, and using the display switching frequency coefficient to perform time-series correction on the color channel corrected energy consumption value; arranging the time-series corrected pixel energy consumption values ​​according to the spatial position of the pixels on the screen to form a pixel energy consumption contribution matrix.

[0048] The specific configuration of the region aggregation analysis module 20 will be described in detail below. The region aggregation analysis module 20 further includes: based on the pixel energy consumption contribution matrix, dividing the screen into multiple pixel sub-regions according to a preset spatial partitioning rule, and performing aggregation calculations on the pixel energy consumption contribution weights within each pixel sub-region to obtain the region energy consumption aggregation value for the corresponding pixel sub-region; using the region energy consumption aggregation value, combined with the area information of the pixel sub-regions, performing energy consumption aggregation normalization processing on each pixel sub-region to form a region-level energy consumption distribution feature; performing time-series cumulative statistics on user touch interaction positions to generate a touch heat distribution map representing the degree of user interaction attention, and displaying the touch heat distribution map... Mapping to corresponding pixel sub-regions forms region-level interactive attention features; performing structural analysis of the displayed content identifies the main text region, main image region, and interface functional component region; constructing content structure markers based on the identification results; aggregating the content structure markers to the corresponding pixel sub-regions forms region-level content structure features; based on the region-level energy consumption distribution features, region-level interactive attention features, and region-level content structure features, performing multi-feature aggregation analysis on each pixel sub-region to calculate the corresponding region perception relevance index; and dividing the screen display area into visually effective display areas based on the region perception relevance index.

[0049] The specific configuration of the region aggregation analysis module 20 will be described in detail below. The region aggregation analysis module 20 further includes: dividing the screen into basic pixel grid units according to the screen's physical resolution; using the basic pixel grid units as the smallest block unit, performing extended search clustering of adjacent basic pixel grid units based on the pixel energy consumption contribution matrix; and dividing the screen into multiple pixel sub-regions using the extended search clustering results.

[0050] The specific configuration of the region aggregation analysis module 20 will be described in detail below. The region aggregation analysis module 20 further includes: reading the user-set region segmentation threshold and obtaining the user's historical preference data; performing scene adaptation filtering on the historical preference data, and then performing threshold compensation on the region segmentation threshold based on the filtering results to establish a calibration threshold; determining whether the region perception relevance index meets the calibration threshold, classifying the pixel sub-regions that meet the calibration threshold as strong perception display areas, and classifying the pixel sub-regions that do not meet the calibration threshold as weak perception display areas.

[0051] The specific configuration of the degradation strategy configuration module 30 will be described in detail below. The degradation strategy configuration module 30 further includes: activating the degradation strategy configuration channel; after receiving the pixel energy consumption contribution matrix and the visually effective display area, using the preprocessing layer within the channel to allocate visual sensitivity weights according to the category of the visually effective display area; calling the mapping processing layer to combine each pixel value in the pixel energy consumption contribution matrix with the visual sensitivity weight of its respective pixel sub-region to map it into a pixel degradation coefficient; calling the smoothing processing layer to perform spatial neighborhood filtering on the pixel degradation coefficient of each pixel sub-region, and performing time-weighted averaging processing of the degradation coefficients of consecutive display frames; and configuring a differentiated degradation strategy based on the processing result.

[0052] The specific configuration of the degradation execution verification module 40 will be described in detail below. The degradation execution verification module 40 also includes: analyzing the fitted pixel sub-regions after executing the degradation strategy through pixel brightness difference, color deviation, and contrast change indicators to generate a visual consistency index matrix; comparing the visual consistency index matrix with a preset visual threshold to determine whether the degradation of each pixel sub-region meets the human eye's adaptation range; and completing the degradation verification based on the judgment result.

[0053] The energy-saving display system for tablet computer screens provided in this embodiment of the invention can execute the energy-saving display method for tablet computer screens provided in this embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A display method for energy saving in a tablet computer screen, characterized in that, The method includes: When the tablet screen is in display mode, pixel-level display driver parsing is performed on the screen content to be displayed. Based on the brightness level, color channel driving characteristics and pixel display timing changes of each pixel on the screen, corresponding energy consumption contribution weights are assigned to each pixel on the screen, and a pixel energy consumption contribution matrix that characterizes the energy consumption distribution of the screen pixels is constructed. After reading the distribution of user touch interaction positions, structural features of display content, and pixel display dwell time sequence features, the pixel energy consumption contribution matrix is ​​subjected to regional aggregation analysis to divide the screen display area into visually effective display areas, which include strong perception display areas and weak perception display areas. Based on the pixel energy consumption contribution matrix, a differentiated degradation strategy is configured for the visually effective display area. Before the degradation strategy is executed, degradation execution verification is performed by using preset human visual perception consistency constraints, and display management is performed based on the degradation execution verification results.

2. The display method for energy saving of a tablet computer screen as described in claim 1, characterized in that, Construct a pixel energy consumption contribution matrix to characterize the screen pixel energy consumption distribution state, including: Using the frame buffer data output by the display driving unit as input, the grayscale value of each pixel in each frame is read, and the driving current range corresponding to the pixel grayscale value is obtained by using the reading result, and the basic power consumption value of the pixel is configured. For each pixel, based on the on-state of the corresponding red, green, and blue sub-pixels and the sub-pixel driving duty cycle, the basic energy consumption value is decomposed and reconstructed into sub-pixel energy consumption value to construct the color channel corrected energy consumption value of the pixel. Within a continuous display frame, the number of grayscale changes and the magnitude of the changes for each pixel are statistically analyzed. The display switching frequency coefficient of the pixel is calculated using the statistical results, and the display switching frequency coefficient is used to perform timing correction on the energy consumption value of the color channel. The time-corrected pixel energy consumption values ​​are arranged according to the spatial position of the pixels on the screen to form a pixel energy consumption contribution matrix.

3. The display method for energy saving of a tablet computer screen as described in claim 1, characterized in that, The pixel energy consumption contribution matrix is ​​subjected to region aggregation analysis, and the screen display area is divided into visually effective display areas, including: Based on the pixel energy consumption contribution matrix, the screen is divided into multiple pixel sub-regions according to a preset spatial partitioning rule, and the pixel energy consumption contribution weights in each pixel sub-region are aggregated and calculated to obtain the regional energy consumption aggregate value of the corresponding pixel sub-region. Using the aggregated energy consumption value of the region, combined with the area information of the pixel sub-region, the energy consumption of each pixel sub-region is aggregated and normalized to form a regional energy consumption distribution feature. The user's touch interaction location is statistically accumulated over time to generate a touch heat distribution map that represents the degree of user interaction attention. The touch heat distribution map is then mapped to the corresponding pixel sub-region to form a region-level interaction attention feature. Perform structural analysis of the displayed content, identify the main text region, the main image region, and the interface functional component region, construct content structure markers based on the identification results, and aggregate the content structure markers to the corresponding pixel sub-regions to form region-level content structure features; Based on the regional energy consumption distribution characteristics, regional interactive attention characteristics, and regional content structure characteristics, multi-feature aggregation analysis is performed on each pixel sub-region to calculate the corresponding regional perception relevance index. The screen display area is divided into visually effective display areas based on the area perception correlation index.

4. The display method for energy saving of a tablet computer screen as described in claim 3, characterized in that, Based on the pixel energy consumption contribution matrix, the screen is divided into multiple pixel sub-regions according to a preset spatial partitioning rule, including: The screen is divided into basic pixel grid units based on its physical resolution. Using the basic pixel grid unit as the smallest block unit, an extended search clustering of adjacent basic pixel grid units is performed based on the pixel energy consumption contribution matrix; The screen is divided into multiple pixel sub-regions using the results of extended search clustering.

5. A display method for energy saving of a tablet computer screen as described in claim 3, characterized in that, The screen display area is divided into visually effective display areas based on the aforementioned region perception correlation index, including: Read the user-set region segmentation threshold and obtain the user's historical preference data; After performing scenario adaptation filtering on the historical preference data, threshold compensation is performed on the region segmentation threshold based on the filtering results to establish a calibration threshold; Determine whether the region perception correlation index meets the calibration threshold. If the pixel sub-region that meets the calibration threshold is classified as a strong perception display area, the pixel sub-region that does not meet the calibration threshold is classified as a weak perception display area.

6. The display method for energy saving of a tablet computer screen as described in claim 1, characterized in that, Based on the pixel energy consumption contribution matrix and the configuration of differentiated degradation strategies for the visually effective display area, the following strategies are included: The degradation strategy configuration channel is activated. After receiving the pixel energy consumption contribution matrix and the visually effective display area, the preprocessing layer in the channel is used to allocate visual sensitivity weights according to the category of the visually effective display area. The mapping processing layer is invoked to combine each pixel value in the pixel energy consumption contribution matrix with the visual sensitivity weight of its respective pixel sub-region and map it into a pixel degradation coefficient. The smoothing layer is invoked to perform spatial neighborhood filtering on the pixel degradation coefficients of each pixel sub-region, and time-weighted averaging of the degradation coefficients of consecutive display frames is performed. Differentiated degradation strategies are configured based on the processing results.

7. A display method for energy saving of a tablet computer screen as described in claim 1, characterized in that, Degenerate execution verification is performed using preset constraints on the consistency of human visual perception, including: After implementing the degradation strategy, the fitted pixel sub-regions are analyzed by pixel brightness difference, color deviation, and contrast change indicators to generate a visual consistency index matrix. The visual consistency index matrix is ​​compared with a preset visual threshold to determine whether the degradation of each pixel sub-region meets the human eye's adaptation range. The degradation verification is completed based on the judgment results.

8. A power-saving display system for tablet computer screens, characterized in that, The system is used to implement a display method for energy saving of a tablet computer screen as described in any one of claims 1 to 7, the system comprising: The display driver parsing and processing module is used to perform pixel-level display driver parsing and processing on the screen content to be displayed when the tablet computer screen is in display working state. Based on the brightness level, color channel driving characteristics and pixel display timing changes of each pixel on the screen, it assigns corresponding energy consumption contribution weights to each pixel on the screen and constructs a pixel energy consumption contribution matrix that characterizes the energy consumption distribution state of the screen pixels. The regional aggregation analysis module is used to read the distribution of user touch interaction positions, display content structure features and pixel display dwell time sequence features, and then perform regional aggregation analysis on the pixel energy consumption contribution matrix to divide the screen display area into visually effective display areas, which include strong perception display areas and weak perception display areas. The degradation strategy configuration module is used to configure differentiated degradation strategies based on the pixel energy consumption contribution matrix and the visually effective display area. The degradation execution verification module is used to perform degradation execution verification by means of preset human visual perception consistency constraints before the degradation strategy is executed, and to perform display management based on the degradation execution verification results.

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