Electronic paper edge optimization method, system, storage medium and program product

Abstract

An edge optimization method, system, storage medium and program product of electronic paper, relating to the field of static display devices, the method comprising: obtaining content display data to be displayed on the electronic paper, and extracting edge feature parameters of a feature type region based on a preset content type feature library, and determining corresponding edge optimization parameters according to the edge feature parameters; optimizing the edge of each feature type region based on the edge optimization parameters to obtain optimized display data; detecting the boundary transition region of adjacent type regions in the optimized display data, and calculating the grayscale difference of adjacent pixels in the boundary transition region; when the grayscale difference is greater than a preset difference threshold, performing smooth transition processing on the boundary transition region to generate transition display data; and performing image display based on the content display data, the optimized display data and the transition display data. Implementation of the present application can optimize edge display based on different types of display content.

Description

Technical Field

[0001] The present application relates to the field of static display devices, and in particular to an edge optimization method, system, storage medium and program product for electronic paper. Background Art

[0002] With the development of display technology, electronic paper has been widely used in e-book readers, smart tags and other fields due to its paper-like visual experience and ultra-low power consumption. In order to improve the display effect, it is necessary to optimize the edges of the displayed content. Especially when electronic paper devices display mixed graphics and text, edge optimization is of great significance to improving display quality and user experience.

[0003] In the related art, edge optimization of electronic paper is mainly achieved by setting fixed optimization parameters. Specifically, the system predefines a set of edge optimization parameters, including grayscale threshold, blur radius, etc., and applies these parameters to pixel processing in the edge area of ​​the image. For the detected edge area, the system adjusts the grayscale values ​​of adjacent pixels according to the preset parameters to achieve edge smoothing.

[0004] However, in actual applications, different types of display content (such as text, images, charts, etc.) have different edge characteristics. When the display content switches frequently, uniform edge processing parameters may cause excessive blurring of text edges or insufficient sharpness of image edges, affecting the overall display effect. Summary of the invention

[0005] The present application provides an edge optimization method, system, storage medium and program product of an electronic paper, which are used to optimize edge display based on different types of display content.

[0006] In a first aspect, the present application provides an edge optimization method for electronic paper, which is applied to an electronic paper system, the method comprising: obtaining content display data to be displayed on the electronic paper, and identifying a feature type area in the content display data based on a preset content type feature library; the feature type area includes at least one of a text area, an image area and a chart area; extracting edge feature parameters of the feature type area, and determining corresponding edge optimization parameters based on the edge feature parameters; the edge feature parameters include the gray value distribution of edge pixels, the edge pixel gradient value and the edge line curvature; the edge optimization parameters include a gray threshold and a blur radius; based on the edge optimization parameters, optimizing the edge of each feature type area to obtain optimized display data; detecting the boundary transition area of ​​adjacent type areas in the optimized display data, and calculating the gray difference of adjacent pixels in the boundary transition area; when the gray difference is greater than the preset difference threshold, performing smooth transition processing on the boundary transition area to generate transition display data; and performing image display based on the content display data, the optimized display data and the transition display data.

[0007] In the above embodiment, the electronic paper system obtains electronic paper display data and identifies different areas based on the content type feature library, extracts edge features in a targeted manner and optimizes the processing, and then smoothly transitions adjacent areas, thereby achieving precise edge optimization of different types of content and a natural transition between areas, improving the overall display effect and visual experience, and optimizing problems such as blurred text or insufficient image sharpness caused by traditional fixed parameter optimization.

[0008] In combination with some embodiments of the first aspect, in some embodiments, based on the edge optimization parameters, the edge of each feature type area is optimized to obtain the step of optimized display data, specifically including: when the feature type area is a text area, performing high contrast enhancement and sharpening processing on the text area; when the feature type area is an image area, performing adaptive smoothing and gradient retention processing on the image area; when the feature type area is a chart area, performing line enhancement and corner retention processing on the chart area; according to the grayscale threshold, nonlinear mapping processing is performed on the grayscale values ​​of the edge pixels of the feature type area, and according to the blur radius, an adaptive kernel function is used to selectively blur the edge area of ​​the feature type area to obtain optimized display data.

[0009] In the above embodiment, the electronic paper system achieves the best display effect for various types of content by adopting differentiated optimization strategies for different feature type areas (high contrast enhancement in text areas, adaptive smoothing in image areas, and line enhancement in chart areas), and combines grayscale threshold and blur radius for nonlinear mapping and selective blur processing, thereby optimizing the uneven display quality problem caused by unified processing parameters.

[0010] In combination with some embodiments of the first aspect, in some embodiments, when the grayscale difference is greater than a preset difference threshold, a step of performing smooth transition processing on the boundary transition area to generate transition display data specifically includes: when the grayscale difference is greater than the preset difference threshold, calculating the grayscale gradient direction and amplitude of adjacent pixels in the boundary transition area, constructing a local gradient tensor, and determining the main direction of the boundary transition based on the local gradient tensor; determining a transition curve function according to the main direction of the boundary transition so that the boundary transition area achieves a smooth transition while maintaining the boundary features; applying anisotropic diffusion filtering to the boundary transition area to generate transition display data; the diffusion coefficient of the anisotropic diffusion filtering has different values ​​depending on the distance between the feature point and the boundary and the grayscale difference.

[0011] In the above embodiment, the electronic paper system calculates the gradient features of the boundary transition area and constructs a local gradient tensor, determines the main transition direction and curve function, and then applies anisotropic diffusion filtering to achieve a smooth transition while maintaining boundary features, thereby avoiding the problem of abrupt display effects at the junction of different types of content.

[0012] In combination with some embodiments of the first aspect, in some embodiments, before obtaining content display data to be displayed on the electronic paper and identifying the feature type area in the content display data based on a preset content type feature library, the method also includes: sampling and testing the display effect of the target electronic paper under different ambient lighting conditions, and establishing a mapping relationship database between ambient lighting and display parameters; the display parameters include driving voltage, refresh timing and temperature compensation coefficient; modeling training is performed based on the physical properties of the target electronic paper and the mapping relationship database to obtain a display effect model; the physical properties include ink particle distribution, response time and grayscale restoration capability; based on the real-time lighting conditions and the display effect model, the real-time display parameters are adjusted.

[0013] In the above embodiment, the electronic paper system establishes a mapping relationship between ambient light and display parameters and performs modeling training in combination with the physical characteristics of electronic paper, thereby achieving environmental adaptive adjustment of the display effect and avoiding the problem of unstable display effects of fixed display parameters in different environments.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of extracting edge feature parameters of feature type areas and determining corresponding edge optimization parameters based on the edge feature parameters, the method also includes: identifying edge areas in each feature type area and extracting feature vectors of the edge areas; the feature vectors include grayscale gradients, directional consistency and texture complexity of edge pixels; based on the feature vectors, an adaptive clustering algorithm is used to classify edge areas into strong edge areas and weak edge areas; the directional consistency of strong edge areas is greater than a preset consistency threshold, and the directional consistency of weak edge areas is less than the preset consistency threshold; constructing an edge propagation map, recording the topological relationship between strong edge areas and weak edge areas, and calculating the error propagation coefficient between edge areas based on the topological relationship; adjusting the edge optimization parameters based on the error propagation coefficient; the edge optimization parameters include a smoothing factor and a sharpening factor, and the smoothing factor is inversely proportional to the error propagation coefficient.

[0015] In the above embodiment, the electronic paper system extracts the feature vector of the edge area and performs adaptive clustering classification to construct an edge propagation graph to calculate the error propagation coefficient, thereby achieving dynamic adjustment of the edge optimization parameters.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after executing the step of image display based on content display data, optimized display data and transition display data, the method also includes: real-time monitoring of the ambient lighting conditions and user viewing data of the target electronic paper during the display process, and generating viewing feedback data; the viewing feedback data includes reading dwell time, gaze point distribution and operation behavior; according to the viewing feedback data, adjusting the image display parameters of the target electronic paper; the image display parameters include contrast, brightness and refresh rate.

[0017] In the above embodiment, the electronic paper system monitors the ambient light and user viewing data in real time, and adjusts the display parameters based on the feedback, thereby achieving real-time optimization of the display effect.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the step of adjusting the image display parameters of the target electronic paper according to the viewing feedback data specifically includes: determining display evaluation parameters based on the viewing feedback data; the display evaluation parameters include reading engagement percentage, regional gaze and operation activity; based on the display evaluation parameters, determining the adjustment step size of the image display parameters of the target electronic paper; based on the adjustment step size, adjusting the image display parameters of the target electronic paper; recording the display evaluation parameters after the image display parameters are adjusted as evaluation correction parameters, and iteratively optimizing the image display parameters based on the evaluation correction parameters.

[0019] In the above embodiment, the electronic paper system determines the evaluation parameters and the adjustment step by analyzing the viewing feedback data, performs iterative optimization of the parameters, and achieves continuous improvement of the display effect.

[0020] In a second aspect, an embodiment of the present application provides an electronic paper system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the electronic paper system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, and when the computer program product runs on an electronic paper system, the electronic paper system executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the instructions are executed on an electronic paper system, the electronic paper system executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] It can be understood that the electronic paper system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. By adopting a method of identifying different display areas based on the content type feature library and extracting edge feature parameters for optimization processing, combined with the smoothing technology of the boundary transition area, it is possible to perform differentiated optimization for the features of different types of content such as text, images, and charts, while ensuring the natural transition of display effects between areas, effectively solving the problems of excessive blurring of text edges or insufficient sharpness of image edges caused by unified parameter processing in the existing technology, thereby achieving precise edge optimization of electronic paper display content and smooth transition between areas, improving the overall display quality and user visual experience.

[0026] 2. By adopting the above technical solution, since the display effect of electronic paper is sampled and tested under different ambient lighting conditions, a database of mapping relationship between ambient lighting and display parameters is established, and modeling training is performed in combination with the physical characteristics of electronic paper, it is possible to achieve adaptive adjustment of the display effect to environmental changes, and dynamically optimize the display parameters according to real-time lighting conditions, which effectively solves the problem in the prior art that fixed display parameters cannot adapt to different ambient lighting conditions, thereby achieving environmental adaptive optimization of the electronic paper display effect, ensuring stable and high-quality display effects in various lighting environments.

[0027] 3. By adopting the above technical solution, due to the real-time monitoring of the ambient light conditions and user viewing data during the electronic paper display process, and the dynamic adjustment of display parameters based on feedback information such as reading dwell time, gaze point distribution and operation behavior, it is possible to optimize parameters such as contrast, brightness and refresh rate in real time according to actual usage, effectively solving the problem that the fixed display effect in the prior art cannot meet the dynamic usage needs, and thus realizing real-time optimization of the display effect based on user experience, thereby improving the comfort of using electronic paper and the reading experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of an edge optimization method for electronic paper in an embodiment of the present application;

[0029] Figure 2 is another flow chart of the edge optimization method of electronic paper in an embodiment of the present application;

[0030] Figure 3 It is a schematic diagram of a physical device structure of the electronic paper system in the embodiment of the present application. DETAILED DESCRIPTION

[0031] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0032] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0033] It should be noted that electronic paper (E-paper or Electronic paper) is a display technology that simulates the display effect of paper, and is also known as electronic ink display (E-ink display) in the industry. This display technology adopts the bi-stable display principle, which means that power is required only when the display content is changed; electronic paper uses a reflective display method, that is, similar to paper, it uses ambient light reflection to display content; electronic paper also has extremely low power consumption characteristics, and consumes almost no power in a static display state.

[0034] Therefore, electronic paper is usually used to display static images or texts, and no deliberate improvement is made to changing parameters such as refresh rate. On this basis, since the electronic paper display content requires reading time and the frequency of image changes is low, the present application has sufficient time to optimize the next image or text to be displayed on the electronic paper, and the solution will not be unimplemented due to the performance bottleneck of the system. In addition, if the electronic paper is in a state of high-speed refresh (display content) for some reason, the method of the present application can be implemented asynchronously, that is, edge optimization is achieved after the image is stabilized, which will not affect the original display effect.

[0035] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0036] A large library launched a digital reading project and needed to display various electronic book contents. These digital contents come in various forms: the main text in literary works requires a clear and comfortable reading experience; various charts in professional books require accurate data presentation; illustrations in art books need to show delicate colors and textures; formulas and annotations in textbooks require accurate display. Readers in different environments (bright reading rooms, dim personal spaces, outdoor sunlight, etc.) all want to get a reading experience like a paper book. Traditional electronic paper devices have difficulty balancing these complex needs, and often have problems such as blurred text, distorted images, and messy mixed content display, which affects readers' reading experience and learning effects.

[0037] In the related art, the display effect of electronic paper can be optimized by using an edge optimization processing method with fixed parameters. For example, a unified grayscale threshold and blur radius are set, the same edge enhancement and smoothing processing algorithm is used for all displayed contents, or the display effect is controlled by using a preset driving voltage and refresh timing parameters. The following describes the scenario of using the edge optimization method of electronic paper in the related art.

[0038] The library initially used traditional electronic paper devices to display electronic books. The devices used uniform display parameters and fixed edge processing solutions. For example, when displaying a popular science book with mixed text and pictures, the fixed sharpening parameters made the small characters in the text blurry; the uniform smoothing made the details of the accompanying pictures blurred; and the data annotations in the charts were difficult to recognize under certain lighting conditions due to the fixed contrast. When readers read at different times and locations, the display effects differed significantly, and sometimes they even needed to adjust their reading posture to see the content clearly, which seriously affected the coherence and comfort of reading.

[0039] The edge optimization method of the electronic paper in the embodiment of the present application is used to establish a mapping relationship between ambient light and display parameters, and to combine content type feature recognition and edge feature extraction to achieve differentiated optimization processing for different types of content, which can not only ensure the clarity of the text area, the detail expression of the image area, and the line quality of the chart area. The following introduces the scenario in which the edge optimization method of the electronic paper in the present application is used.

[0040] After applying this solution, the electronic paper system can intelligently identify different content types in books. For the text area, the system uses high contrast enhancement and appropriate sharpening to ensure clear text; for the picture area, adaptive smoothing and gradient retention processing are used to retain image details; for the chart area, line enhancement and corner retention are performed to ensure accurate data presentation. The system can also adjust display parameters in real time according to ambient light, so that readers can have a comfortable reading experience in any environment. The transition between different areas is also more natural, avoiding the display fault feeling common in traditional devices, making the overall reading experience closer to paper books.

[0041] It can be seen that the edge optimization method of electronic paper in the embodiment of the present application can not only realize the basic display function of electronic paper, but also effectively solve the problem of uneven display effect caused by traditional fixed parameter processing, thereby realizing intelligent display optimization adapted to different content types and environmental conditions.

[0042] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , is a flow chart of the edge optimization method of electronic paper in an embodiment of the present application.

[0043] S101: Acquire content display data to be displayed on the electronic paper, and identify a feature type area in the content display data based on a preset content type feature library.

[0044] Among them, the content display data represents the original data information that needs to be displayed on the electronic paper, including various types of display content such as text, pictures, and charts; the content type feature library refers to a pre-established feature data set for identifying and classifying different types of display content, which contains typical feature patterns of various types of content; the feature type area represents different display areas divided according to content characteristics, such as the text area is used to display text content, the image area is used to display photos or illustrations, and the chart area is used to display data charts, etc.

[0045] When the electronic paper system receives the content to be displayed, it needs to identify the content in different areas so as to carry out targeted optimization processing. Specifically, the electronic paper system first obtains the original data of the content to be displayed, and then matches the data with the feature patterns in the preset content type feature library. Through feature analysis and pattern recognition algorithms, the content is divided into different feature type areas, each of which has its own unique display characteristics and optimization requirements.

[0046] It should be noted that the identification of feature type areas in this application can use a content recognition model, which inputs a large number of labeled electronic paper display content samples during training, including feature data of text areas, image areas and chart areas. A supervised learning method is adopted, with regional classification accuracy as the training goal. The training standards include the accuracy of regional classification and the accuracy of boundary positioning. The content recognition model adopts a deep convolutional neural network structure, including a feature extraction layer, a regional proposal layer and a classification layer. The content recognition model can extract multi-scale content features and achieve accurate regional segmentation and classification. The content recognition model can receive content data to be displayed, output type identification and boundary position information of each area, and provide a basis for subsequent differentiated optimization processing. The content recognition model has good generalization ability and can handle various complex mixed content.

[0047] S102: extract edge feature parameters of the feature type area, and determine corresponding edge optimization parameters according to the edge feature parameters.

[0048] Among them, the edge feature parameters represent numerical indicators used to describe the edge characteristics of the region, including the grayscale value distribution reflecting the brightness changes of edge pixels, the edge pixel gradient value indicating the direction and magnitude of the grayscale changes at the edge, and the edge line curvature used to describe the degree of curvature of the edge contour; the edge optimization parameters refer to the control parameters used to adjust the edge display effect, among which the grayscale threshold is used to determine the grayscale mapping range of the edge pixels, and the blur radius indicates the scope of the edge blurring process.

[0049] After completing the feature type area recognition, the electronic paper system needs to analyze the edge features of each area to determine the appropriate optimization strategy. Specifically, the system first scans and analyzes the edge of each feature type area, extracts the grayscale distribution characteristics of the edge pixels, calculates the edge gradient value and curvature characteristics, and then determines the corresponding grayscale threshold and blur radius and other optimization parameters based on these feature parameters and the display requirements of different types of areas.

[0050] In some embodiments, edge feature extraction and optimization parameter determination can be achieved in a variety of ways: Optionally, through a local feature analysis method: use a sliding window to scan the edge area; then calculate the statistical features of the pixels in the window; determine the optimization parameters based on the statistical features; and establish a feature-parameter mapping relationship. Optionally, through a global feature analysis method: construct a feature map of the edge area; perform multi-scale feature decomposition; extract the main feature components; and optimize the parameters based on the feature components. It is understandable that other feature analysis and parameter optimization methods can also be used to achieve edge feature extraction and parameter determination, which are not limited here.

[0051] S103 , based on the edge optimization parameters, optimizing the edge of each characteristic type area to obtain optimized display data.

[0052] The optimization processing refers to performing processing operations such as enhancing or smoothing the edge of the region based on the determined edge optimization parameters; the optimized display data refers to the display data obtained after the edge optimization processing.

[0053] After obtaining the edge optimization parameters, the electronic paper system needs to optimize each feature type area. Specifically, the system uses the corresponding image processing algorithm to optimize the edge according to the optimization parameters of different areas, including edge enhancement, smoothing and other operations, and finally generates optimized display data to make the edge display effect clearer and more natural.

[0054] S104, detecting the boundary transition area between adjacent type areas in the optimized display data, and calculating the grayscale difference between adjacent pixels in the boundary transition area.

[0055] Among them, the adjacent type area refers to the areas of different feature types that are adjacent to each other in the display content; the boundary transition area refers to the boundary area between adjacent areas; and the grayscale difference refers to the grayscale value difference between adjacent pixels.

[0056] After completing the edge optimization of each area, the electronic paper system needs to process the transition effect between areas. Specifically, the system first identifies the boundary positions of adjacent areas in the optimized display data, determines the range of the boundary transition area, and then calculates the grayscale difference between adjacent pixels in the transition area to provide a basis for subsequent transition processing.

[0057] S105 . When the grayscale difference is greater than a preset difference threshold, a smooth transition process is performed on the boundary transition area to generate transition display data.

[0058] The preset difference threshold represents the grayscale difference standard for determining whether transition processing is required; the smooth transition processing refers to the operation of smoothing the boundary area; and the transition display data represents the display data after smoothing.

[0059] When a large grayscale difference is detected between adjacent areas, the electronic paper system needs to perform transition processing to improve the display effect. Specifically, the system compares the calculated grayscale difference with the preset threshold. When the difference exceeds the threshold, a smoothing algorithm is used to process the boundary transition area to generate display data with a natural transition effect, namely, transition display data.

[0060] In some embodiments, smooth transition processing can be achieved in a variety of ways: Optionally, through a gradient transition method: construct a gradient function; calculate the transition coefficient based on the gradient function; apply gradient mapping to optimize the transition effect. Optionally, through a fuzzy diffusion method: set diffusion parameters; perform diffusion operations; control the diffusion range and adjust the diffusion intensity during the diffusion operation. It is understandable that other smoothing algorithms can also be used to optimize the transition effect of the boundary area, which is not limited here.

[0061] S106 , performing image display based on the content display data, the optimization display data and the transition display data.

[0062] The content display data refers to the original display content; the optimized display data refers to the edge-optimized data; and the transition display data refers to the smoothed transition area data.

[0063] After completing all optimization processes, the electronic paper system needs to perform the final display operation. Specifically, the system synthesizes the original content display data, the edge optimized display data and the transition processed display data to generate the final display image, and displays the image through the electronic paper driving circuit.

[0064] In some embodiments, image display can be achieved in a variety of ways: Optionally, through a data fusion method: align multiple layers of display data; after setting the regional weights, perform data fusion; and finally generate a display image. Optionally, through a partitioned display method: divide the display area, and then allocate display data based on the display area; synchronize regional display to optimize the display effect. It is understandable that other display control methods can also be used to achieve the display effect of the final image, which is not limited here.

[0065] In the above embodiment, the method achieves a comprehensive improvement in the display effect of the electronic paper through a multi-level optimization strategy. In practical applications, the method can continuously optimize according to the real-time feedback data of the user, and continuously adjust and improve the display parameters by analyzing the reading dwell time, the distribution of the gaze points and other information, so that the display effect is more in line with the user's needs. The following is a supplement to the scenario of this embodiment.

[0066] The system further integrates intelligent learning functions to adapt to the reading habits of different readers. For example, when the system detects that readers often read at night, it will automatically adjust the contrast and brightness to reduce visual fatigue; when readers frequently view charts, the system will optimize the display effect of the chart area; for content that is quickly browsed, the system will adjust the refresh strategy to provide a smoother page turning experience. The system also records the best display parameters for each reader in different scenarios and establishes a personalized display configuration. When important content (such as key annotations and key charts) is identified, the system will automatically switch to the most suitable display mode.

[0067] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the edge optimization method of electronic paper in an embodiment of the present application.

[0068] S201 , performing sampling tests on the display effects of the target electronic paper under different ambient lighting conditions, and establishing a database of mapping relationships between ambient lighting and display parameters.

[0069] Among them, ambient lighting conditions refer to different natural light or artificial light intensities and light distribution states; display effect refers to the visual performance of electronic paper under specific lighting; sampling test refers to the process of quantitative collection and measurement of display effects; mapping relationship database refers to a data set that stores the correspondence between lighting conditions and display parameters.

[0070] In the initialization phase of the electronic paper system, it is necessary to establish a foundation for environmental adaptability. Specifically, the system collects display effect data, including reflectivity, contrast and other indicators, through professional testing equipment under different light intensity, angle and spectral distribution conditions, and records the corresponding driving voltage, refresh timing and temperature compensation coefficient, and finally establishes a complete mapping relationship database; the driving voltage represents the voltage parameter that controls the electronic ink display; the refresh timing refers to the time sequence for updating the display content; the temperature compensation coefficient is used to adjust the impact of temperature on the display effect.

[0071] In some embodiments, display effect sampling and mapping relationship establishment can be achieved in a variety of ways: Optionally, through an automated testing method: build a lighting simulation environment; set a parameter scanning sequence, collect display effect data, to establish a parameter mapping model; and finally verify the mapping accuracy. Optionally, through a manual evaluation method: plan test scenarios, record subjective scores; organize test data to build a scoring model; and finally optimize the mapping relationship. It is understandable that other testing and modeling methods can also be used to achieve the relationship mapping between ambient lighting and display parameters, which is not limited here.

[0072] S202 , performing modeling training based on the physical properties of the target electronic paper and the mapping relationship database to obtain a display effect model.

[0073] Among them, the physical properties represent the material and structural characteristics of the electronic paper itself, including ink particle distribution, response time and grayscale restoration capability; ink particle distribution refers to the spatial arrangement of particles in electrophoretic ink; response time indicates the state switching speed of electronic ink under the action of an electric field; grayscale restoration capability refers to the accuracy of displaying different grayscale levels.

[0074] After completing the ambient light mapping, the electronic paper system needs to establish a comprehensive model based on the characteristics of the device itself. Specifically, the system combines the physical characteristic parameters of the electronic paper with the mapping relationship database established in the early stage, conducts modeling training through machine learning algorithms, and constructs a mathematical model that can accurately predict the display effect.

[0075] It should be noted that the input data of the display effect model in this application during training includes display effect sampling data (reflectivity, contrast) and corresponding display parameters (driving voltage, refresh timing, temperature compensation coefficient) under different ambient lighting conditions (light intensity, angle, spectral distribution), as well as the physical property data of electronic paper (ink particle distribution, response time, grayscale restoration ability). Through the machine learning algorithm, the mapping relationship between environmental factors and display parameters is constructed with the display effect score as the training target. The training standard is to minimize the error between the predicted display parameters and the actual optimal display parameters. The display effect model is a multi-input and multi-output regression model, which includes three main modules: ambient lighting feature extraction, physical property mapping, and parameter prediction. The display effect model adopts a nonlinear mapping structure and can handle complex environment-parameter relationships. The display effect model can receive the environmental data collected by the light sensor as input in real time, and output the optimal display parameter configuration (driving voltage, refresh timing, etc.) in combination with the physical property parameters of the device. The display effect model can also continuously adaptively optimize according to the feedback of the actual display effect to improve the prediction accuracy.

[0076] S203: Adjust real-time display parameters based on real-time lighting conditions and display effect model.

[0077] Among them, the real-time lighting condition refers to the instantaneous lighting status of the current environment; the real-time display parameter refers to the display control parameter dynamically adjusted according to environmental changes.

[0078] During the display process, the electronic paper system needs to continuously adapt to environmental changes. Specifically, the system collects ambient light data in real time through light sensors, inputs the data into the display effect model, calculates the optimal display parameter configuration, and dynamically adjusts parameters such as the drive voltage to ensure the best display effect.

[0079] In some embodiments, real-time parameter adjustment can be achieved in a variety of ways: Optionally, through a feedback control method: collect environmental data, calculate parameter deviations; perform parameter adjustment based on parameter deviations; verify the adjustment effect, and optimize the control strategy accordingly. Optionally, through a predictive control method: predict illumination changes based on historical illumination data, calculate parameters in advance; then smooth parameter transitions; finally evaluate the display effect and correct the prediction model. It is understandable that other control methods can also be used to achieve real-time optimization of display parameters, which are not limited here.

[0080] S204: Acquire content display data to be displayed on the electronic paper, and identify a feature type area in the content display data based on a preset content type feature library.

[0081] Referring to step S101 , the electronic paper system identifies characteristic type areas.

[0082] S205: extract edge feature parameters of the feature type area, and determine corresponding edge optimization parameters according to the edge feature parameters.

[0083] Referring to step S102 , the electronic paper system determines edge optimization parameters.

[0084] S206: Identify edge regions in each feature type region, and extract feature vectors of the edge regions.

[0085] Among them, the edge area represents the area near the boundary of the feature type area; the feature vector refers to a multidimensional numerical set that describes the edge features, including the grayscale gradient, directional consistency and texture complexity of the edge pixels; the grayscale gradient represents the rate of change of the grayscale value of the edge pixels; the directional consistency refers to the directional continuity of the edge lines; the texture complexity represents the degree of texture change in the edge area.

[0086] After determining the characteristic type area, the electronic paper system needs to further analyze the edge features. Specifically, the system uses an edge detection algorithm to identify the edge position of each area, and then calculates the grayscale gradient, directional consistency, and texture complexity of the edge area to form a feature vector that describes the edge characteristics.

[0087] S207 . Based on the feature vector, an adaptive clustering algorithm is used to classify the edge region into a strong edge region and a weak edge region.

[0088] Among them, the adaptive clustering algorithm refers to a clustering method that can automatically adjust classification parameters according to data distribution; the strong edge region refers to the region with obvious edge features; the weak edge region refers to the region with unclear edge features.

[0089] After obtaining the edge feature vector, the electronic paper system needs to classify the edge area. Specifically, the system uses an adaptive clustering algorithm to automatically determine the clustering parameters based on the distribution characteristics of the feature vector, and divides the edge area into two categories: strong edge and weak edge, providing a basis for subsequent optimization processing.

[0090] In some embodiments, edge region classification can be implemented in a variety of ways: Optionally, through a hierarchical clustering method: calculate feature distance; construct a hierarchical tree; determine clustering threshold; divide edge categories; optimize classification results. Optionally, through a density clustering method: estimate feature density; find density center; divide density region; determine edge type; adjust classification boundary. It is understandable that other classification methods can also be used to achieve strong and weak division of edge regions, which is not limited here.

[0091] S208: construct an edge propagation graph, record the topological relationship between the strong edge regions and the weak edge regions, and calculate the error propagation coefficient between the edge regions based on the topological relationship.

[0092] Among them, the edge propagation graph represents the network structure that describes the relationship between edge regions; the topological relationship refers to the spatial connection relationship between edge regions; and the error propagation coefficient represents the influence of edge processing errors on the transmission between regions.

[0093] After completing edge classification, the electronic paper system needs to analyze the correlation between edge regions. Specifically, the system constructs an edge propagation map to represent the spatial relationship between strong and weak edge regions, analyzes the propagation characteristics of errors in the edge network, and calculates the error propagation coefficients between regions.

[0094] In some embodiments, edge propagation analysis can be implemented in a variety of ways: Optionally, through graph analysis methods: establish an adjacency matrix; calculate propagation paths; evaluate propagation impacts; determine propagation coefficients; and optimize propagation models. Optionally, through network flow analysis methods: construct a traffic network; calculate maximum flows; analyze bottlenecks; evaluate propagation capabilities; and optimize network structures. It is understandable that other network analysis methods can also be used to implement error propagation analysis between edge regions, which is not limited here.

[0095] S209: Adjust edge optimization parameters according to the error propagation coefficient.

[0096] The smoothing factor refers to the parameter that controls the degree of edge smoothness; the sharpening factor refers to the parameter that controls the degree of edge sharpness.

[0097] After obtaining the error propagation coefficient, the electronic paper system needs to optimize the edge processing parameters. Specifically, the system dynamically adjusts the smoothing factor and the sharpening factor according to the size of the error propagation coefficient, where the smoothing factor is inversely proportional to the error propagation coefficient to reduce the impact of error propagation.

[0098] In some embodiments, parameter optimization can be achieved in a variety of ways: Optionally, through a gradient optimization method: calculate parameter gradients, update parameter values; evaluate the optimization effect, adjust the optimization step size, and iterate the optimization process. Optionally, through a heuristic optimization method: define the objective function, generate candidate solutions; evaluate the quality of the solution, update the optimal solution; and finally converge the optimization process based on the optimal solution. It is understandable that other optimization methods can also be used to adjust the edge optimization parameters, which are not limited here.

[0099] S210 , optimizing the edge of each characteristic type region based on the edge optimization parameters to obtain optimized display data.

[0100] Referring to step S103 , the electronic paper system generates optimized display data.

[0101] In some embodiments, the electronic paper system will perform high contrast enhancement and sharpening processing on the text area when the feature type area is a text area; perform adaptive smoothing and gradient preservation processing on the image area when the feature type area is an image area; perform line enhancement and corner preservation processing on the chart area when the feature type area is a chart area; perform nonlinear mapping processing on the grayscale values ​​of edge pixels of the feature type area according to the grayscale threshold, and use an adaptive kernel function to selectively blur the edge area of ​​the feature type area according to the blur radius to obtain optimized display data.

[0102] Among them, high contrast enhancement refers to the processing operation of improving the light and dark contrast of the text area; sharpening processing refers to the technology of enhancing the clarity of the text edge; adaptive smoothing refers to the processing of dynamically adjusting the degree of smoothing according to the content characteristics of the image; gradient preservation processing refers to the technology of retaining important edge information of the image during the smoothing process; line enhancement refers to the processing of improving the clarity of lines in the chart; corner preservation processing is a technology used to retain the key node features of the chart; nonlinear mapping processing refers to the operation of nonlinear transformation of grayscale values; selective blur processing refers to the technology of selectively applying blur effects according to regional characteristics.

[0103] After the electronic paper system completes the feature type area recognition, it needs to perform corresponding optimization processing for different types of areas. Specifically, the system first determines the area type. For the text area, the text clarity is improved by adjusting the contrast and edge sharpness; for the image area, an adaptive algorithm is used to smooth the noise while maintaining important details; for the chart area, the line features are enhanced and the key node information is maintained; finally, the edges of all areas are refined, including gray value mapping and selective blurring, to obtain the best display effect.

[0104] S211, detecting the boundary transition area between adjacent type areas in the optimized display data, and calculating the grayscale difference between adjacent pixels in the boundary transition area.

[0105] Referring to step S104 , the electronic paper system calculates the grayscale difference.

[0106] S212: When the grayscale difference is greater than a preset difference threshold, a smooth transition process is performed on the boundary transition area to generate transition display data.

[0107] Referring to step S105 , the electronic paper system generates transition display data.

[0108] In some embodiments, when the grayscale difference is greater than a preset difference threshold, the electronic paper system calculates the grayscale gradient direction and amplitude of adjacent pixels in the boundary transition area, constructs a local gradient tensor, and determines the main direction of the boundary transition based on the local gradient tensor; according to the main direction of the boundary transition, determines the transition curve function so that the boundary transition area can achieve a smooth transition while maintaining the boundary features; applies anisotropic diffusion filtering to the boundary transition area to generate transition display data; the diffusion coefficient of the anisotropic diffusion filter has different values ​​depending on the distance between the feature point and the boundary and the grayscale difference.

[0109] Among them, the grayscale gradient direction indicates the direction of change of the pixel grayscale value; the gradient amplitude refers to the intensity of the grayscale change; the local gradient tensor is a mathematical expression used to describe the characteristics of regional gradient distribution; the main direction of boundary transition indicates the main trend of boundary change; the transition curve function refers to the mathematical function that describes the smooth change of boundary grayscale value; anisotropic diffusion filtering refers to filtering processing with different diffusion characteristics in different directions; the diffusion coefficient refers to the parameter that controls the filtering intensity.

[0110] When the electronic paper system detects that there is a significant grayscale difference between adjacent areas, it needs to perform boundary smoothing. Specifically, the system first analyzes the gradient characteristics of the boundary area, constructs a local gradient tensor to describe the boundary change characteristics, and determines the optimal transition direction based on this; then designs a transition curve function that conforms to the boundary characteristics to achieve smooth changes in grayscale values; finally, anisotropic diffusion filtering is applied to dynamically adjust the diffusion coefficient according to the distance from the boundary and the grayscale difference to ensure a natural and harmonious transition effect.

[0111] S213: Execute image display based on the content display data, the optimization display data and the transition display data.

[0112] Referring to step S106 , the electronic paper system performs image display.

[0113] S214: monitor in real time the ambient light conditions and user viewing data of the target electronic paper during the display process, and generate viewing feedback data.

[0114] Among them, the ambient lighting condition refers to the lighting status of the current display environment; user viewing data refers to data describing the user's reading behavior. Viewing feedback data includes reading dwell time, gaze point distribution and operation behavior; reading dwell time refers to the length of time the user stays on a specific content; gaze point distribution refers to the spatial distribution of the user's visual focus; operation behavior refers to the user's interactive operation record.

[0115] During the display process, the electronic paper system needs to continuously monitor the usage status. Specifically, the system collects ambient light data in real time through sensors, and records the user's reading behavior, including stay time, gaze position, and operation actions, to generate comprehensive viewing feedback data.

[0116] S215: Adjust the image display parameters of the target electronic paper according to the viewing feedback data.

[0117] Among them, the image display parameters represent the key parameters that control the display effect, including contrast, brightness and refresh rate; contrast refers to the degree of difference in light and dark of the displayed content; brightness refers to the overall brightness level of the displayed content; and refresh rate refers to the time interval for updating the displayed content.

[0118] After obtaining viewing feedback data, the e-paper system needs to optimize the display effect. Specifically, the system analyzes the user's viewing needs and preferences based on the feedback data and dynamically adjusts display parameters such as contrast, brightness, and refresh rate to provide a better viewing experience.

[0119] In some embodiments, parameter adjustment can be achieved in a variety of ways: Optionally, through an adaptive control method: analyzing feedback data; calculating adjustment amount; performing parameter adjustment; verifying display effect; optimizing control strategy. Optionally, through an intelligent learning method: establishing a user model; predicting parameter requirements; dynamically adjusting parameters; evaluating adjustment effect; updating learning models. It is understandable that other adjustment methods can also be used to achieve dynamic optimization of display parameters, which are not limited here.

[0120] In some embodiments, the electronic paper system determines display evaluation parameters based on viewing feedback data; the display evaluation parameters include reading engagement percentage, regional gaze, and operation activity; based on the display evaluation parameters, the adjustment step size of the image display parameters of the target electronic paper is determined; based on the adjustment step size, the image display parameters of the target electronic paper are adjusted; the display evaluation parameters after the image display parameters are adjusted are recorded as evaluation correction parameters, and the image display parameters are iteratively optimized based on the evaluation correction parameters.

[0121] Among them, the percentage of reading engagement indicates the proportion of time that users focus on reading; regional attention refers to the degree of attention that users pay to a specific area; operation activity is used to describe the frequency of user interaction operations; adjustment step size indicates the magnitude of parameter modification; evaluation correction parameter refers to the indicator used to evaluate the effect of parameter adjustment; iterative optimization refers to the process of gradual improvement through multiple adjustments.

[0122] During the operation of the electronic paper system, the display effect needs to be continuously optimized based on user feedback. Specifically, the system first analyzes the user's viewing behavior data and calculates the evaluation parameters that reflect the user experience; then determines the appropriate parameter adjustment range based on the evaluation parameters; after performing the parameter adjustment, records the new evaluation data, and continuously improves the display effect through iterative optimization to achieve continuous optimization of the system.

[0123] In the embodiment of the present application, due to the use of a differentiated edge optimization strategy based on content type feature recognition, combined with a dynamic parameter adjustment mechanism driven by ambient light adaptation and user feedback, and intelligent classification of edge areas and error propagation control technology, it is possible to achieve accurate edge optimization processing for different types of display content, and dynamically adjust the display effect according to environmental changes and user needs, effectively solving the problems of text blur, image distortion, and abrupt boundaries caused by fixed parameter processing in traditional electronic paper displays, thereby achieving clear presentation of display content, natural transition between regions, and intelligent adjustment of environmental adaptability, improving the display quality and user experience of electronic paper. At the same time, by introducing a user viewing feedback mechanism, the system can continuously optimize the display effect, so that the electronic paper display can be continuously optimized and the user experience can be improved.

[0124] The electronic paper system in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of the electronic paper system in an embodiment of the present application.

[0125] It should be noted that Figure 3 The structure of the electronic paper system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0126] like Figure 3 As shown, the electronic paper system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0127] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0128] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0129] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0130] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0131] Specifically, the electronic paper system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the electronic paper edge optimization method provided in the above embodiment is implemented.

[0132] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the electronic paper system described in the above embodiment; or may exist independently without being assembled into the electronic paper system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the electronic paper system, the electronic paper system implements the electronic paper edge optimization method provided in the above embodiment.

[0133] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0134] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0135] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. An edge optimization method for electronic paper, characterized in that: Applied to an electronic paper system, the method comprises: Sampling and testing the display effect of the target electronic paper under different ambient lighting conditions, and establishing a database of mapping relationships between ambient lighting and display parameters; the display parameters include driving voltage, refresh timing and temperature compensation coefficient; Modeling training is performed based on the physical properties of the target electronic paper and the mapping relationship database to obtain a display effect model; the physical properties include ink particle distribution, response time and grayscale restoration capability; Adjusting real-time display parameters based on real-time lighting conditions and the display effect model; Acquire content display data to be displayed on the electronic paper, and identify a feature type area in the content display data based on a preset content type feature library; the feature type area includes at least one of a text area, an image area, and a chart area; Extracting edge feature parameters of the feature type area, and determining corresponding edge optimization parameters according to the edge feature parameters; the edge feature parameters include gray value distribution of edge pixels, edge pixel gradient value and edge line curvature; the edge optimization parameters include gray threshold and blur radius; When the feature type area is a text area, performing high contrast enhancement and sharpening processing on the text area; When the feature type region is an image region, performing adaptive smoothing and gradient preservation processing on the image region; When the feature type area is a chart area, performing line enhancement and corner point preservation processing on the chart area; According to the grayscale threshold, nonlinear mapping processing is performed on the grayscale values ​​of edge pixels of the characteristic type area, and according to the blur radius, an adaptive kernel function is used to selectively blur the edge area of ​​the characteristic type area to obtain optimized display data; Detecting a boundary transition area between adjacent type areas in the optimized display data, and calculating a grayscale difference between adjacent pixels in the boundary transition area; When the grayscale difference is greater than a preset difference threshold, performing smooth transition processing on the boundary transition area to generate transition display data; Image display is performed based on the content display data, the optimization display data, and the transition display data.

2. The method according to claim 1, characterized in that When the grayscale difference is greater than a preset difference threshold, the step of performing smooth transition processing on the boundary transition area to generate transition display data specifically includes: When the grayscale difference is greater than a preset difference threshold, the grayscale gradient direction and amplitude of adjacent pixels in the boundary transition area are calculated, a local gradient tensor is constructed, and a main direction of the boundary transition is determined based on the local gradient tensor; Determining a transition curve function according to the main direction of the boundary transition so that the boundary transition area can achieve a smooth transition while maintaining boundary features; Anisotropic diffusion filtering is applied to the boundary transition area to generate transition display data; the diffusion coefficient of the anisotropic diffusion filtering has different values ​​depending on the distance between the feature point and the boundary and the grayscale difference.

3. The method according to claim 1, characterized in that After the step of extracting edge feature parameters of the feature type area and determining corresponding edge optimization parameters according to the edge feature parameters, the method further includes: Identifying edge regions in each of the feature type regions, and extracting feature vectors of the edge regions; the feature vectors include grayscale gradients, directional consistency, and texture complexity of edge pixels; Based on the feature vector, an adaptive clustering algorithm is used to classify the edge region into a strong edge region and a weak edge region; the directional consistency of the strong edge region is greater than a preset consistency threshold, and the directional consistency of the weak edge region is less than the preset consistency threshold; Constructing an edge propagation graph, recording the topological relationship between the strong edge region and the weak edge region, and calculating the error propagation coefficient between the edge regions based on the topological relationship; According to the error propagation coefficient, the edge optimization parameters are adjusted; the edge optimization parameters include a smoothing factor and a sharpening factor, and the smoothing factor is inversely proportional to the error propagation coefficient.

4. The method according to claim 1, characterized in that: After the step of performing image display based on the content display data, the optimization display data and the transition display data, the method further includes: Real-time monitoring of the ambient light conditions and user viewing data of the target electronic paper during display, and generating viewing feedback data; the viewing feedback data includes reading dwell time, gaze point distribution and operation behavior; According to the viewing feedback data, the image display parameters of the target electronic paper are adjusted; the image display parameters include contrast, brightness and refresh rate.

5. The method according to claim 4, characterized in that The step of adjusting the image display parameters of the target electronic paper according to the viewing feedback data specifically includes: Determining display evaluation parameters based on the viewing feedback data; the display evaluation parameters include reading engagement percentage, regional gaze, and operation activity; Based on the display evaluation parameter, determining an adjustment step size of an image display parameter of the target electronic paper; Based on the adjustment step size, adjusting the image display parameters of the target electronic paper; The display evaluation parameters after the image display parameters are adjusted are recorded as evaluation correction parameters, and the image display parameters are iteratively optimized based on the evaluation correction parameters.

6. An electronic paper system, characterized in that: The electronic paper system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the electronic paper system to execute the method as described in any one of claims 1-5.

7. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the electronic paper system, the electronic paper system executes the method according to any one of claims 1 to 5.

8. A computer program product, characterized in that When the computer program product runs on an electronic paper system, the electronic paper system is enabled to execute the method according to any one of claims 1 to 5.

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