Liquid crystal screen display optimization method and system

Abstract

The invention relates to the technical field of liquid crystal screen display optimization, and discloses a liquid crystal screen display optimization method and system, and the method can comprehensively master various factors affecting the display effect through obtaining information reflecting an image data source, including data source characteristics, user display preference and display physical state information. On the basis, the method can identify and quantify gray scale information change, offset of a display gray scale response curve, brightness nonuniformity of a display screen area and drift of liquid crystal molecule response characteristics in the data transmission process. The quantized data can accurately reflect various non-linear deviations of the display. Furthermore, nonlinear correction instructions are generated according to the quantized deviations, and the nonlinear correction instructions are combined and applied to accurately adjust the gray scale response of the display.

Description

Technical Field

[0001] This application relates to the field of LCD display optimization technology, and more specifically, to an LCD display optimization method and system. Background Technology

[0002] In professional medical imaging diagnostic environments, the precision requirements for monitors are extremely high, especially when displaying high-resolution medical images. Even the slightest display deviation can directly affect the accuracy of a doctor's diagnosis. Professional-grade medical imaging monitors undergo precise calibration according to the DICOM Part 14 standard before leaving the factory to ensure smooth and natural grayscale transitions, allowing each grayscale level, from the darkest to the brightest, to be accurately distinguished. This initial calibration is the cornerstone of monitor performance, ensuring that the monitor faithfully reproduces the original information of the image under ideal conditions.

[0003] However, in the high-intensity, long-term daily use environment of a hospital radiology department, the monitor's operating state is not static. Users may make multiple fine-tuning adjustments to parameters such as brightness, contrast, and color temperature based on subjective experience, causing the monitor's internal grayscale mapping logic to gradually deviate from the initially set DICOM standard curve, thus disrupting the original grayscale response curve. Furthermore, after several years of use, the LED beads in the backlight module may experience localized inconsistent light attenuation, causing a shift in the actual backlight output intensity in different areas of the screen, further compromising the overall uniformity of the monitor. Simultaneously, during prolonged operation, the heat dissipated by the internal electronic components creates an uneven temperature distribution. Liquid crystal molecules are sensitive to temperature changes; localized temperature differences can slightly alter the deflection characteristics of liquid crystal molecules in that area, introducing another layer of display non-uniformity onto the screen, affecting grayscale accuracy and color balance.

[0004] Furthermore, to improve general compatibility, imaging workstation software loads a generic color profile by default before sending image data to the graphics card. This software-level color management, however, is counterproductive for professional medical monitors that have already undergone precise hardware calibration. It compresses the dark grayscale information of high bit depth images before the raw image data reaches the monitor's internal processing pipeline, resulting in the irreversible loss of a significant amount of dark detail at the source of data transmission.

[0005] Under the cumulative effect of the aforementioned factors, when doctors diagnose high-resolution, high-contrast medical images, they find that the images displayed on the screen exhibit obvious "unnatural color transitions" in dark areas, along with severe "loss of detail in dark areas." Many fine structures that should have been distinguishable are completely blended into the black background, becoming blurry or disappearing entirely. This severe degradation in display performance makes it difficult for doctors to accurately determine lesion boundaries and identify subtle pathological changes, thus seriously affecting the accuracy and efficiency of diagnosis. Existing built-in automatic correction programs are often designed to optimize for single types of aging or drift conditions. When attempting to compensate for the nonlinear deviations caused by the interplay of multiple factors, their processing logic often fails to accurately separate and identify the various influencing sources, instead exacerbating the distortion of the image's grayscale response curve.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] This invention provides a liquid crystal display optimization method and system, aiming to solve the technical problem of image display quality degradation caused by a combination of factors such as changes in grayscale information transmitted through data transmission, shifts in the grayscale response curve of the display, uneven screen brightness, and drift in the response characteristics of liquid crystal molecules during long-term use of professional medical imaging displays. In particular, the loss of details in dark areas and unnatural color transitions affect the accuracy of doctors' diagnoses.

[0008] The technical solution of this application is as follows: In a first aspect, this application discloses a method for optimizing LCD screen display, including: Acquire information reflecting the image data source, including the characteristics of the data source, user display preferences, and the physical state information of the display. Based on the information from the image data source, the changes in grayscale information, the shift of the grayscale response curve of the display, the brightness non-uniformity of the display screen area, and the drift of the liquid crystal molecule response characteristics during the data transmission process are identified and quantified. The corresponding deviation is obtained based on the quantized data, and a nonlinear correction instruction is generated based on the deviation. The non-linear correction instructions are combined and applied to adjust the grayscale response of the display.

[0009] This technical solution comprehensively considers various factors affecting display quality and effectively adjusts the grayscale response of the display through the combined application of nonlinear correction instructions. This solves the problem that a single correction method in the prior art cannot effectively cope with complex display deviations, and significantly improves the display accuracy and diagnostic accuracy of medical images.

[0010] Furthermore, based on the aforementioned LCD display optimization method, and according to information reflecting the image data source, the changes in grayscale information during data transmission, the shift in the display's grayscale response curve, the brightness non-uniformity of the display screen area, and the drift in the response characteristics of liquid crystal molecules are identified and quantified, including: Acquire brightness and temperature data for the display screen area; Time series analysis of brightness output data is performed to identify long-term trends and local differences in brightness decay, and to obtain the corresponding local aging areas of the backlight module. Spatial distribution analysis of temperature data is performed to identify local patterns of the influence of temperature differences on the response characteristics of liquid crystal molecules, and to obtain the spatial existence of the corresponding uneven internal temperature distribution regions. When the local aging area of ​​the backlight module overlaps with the area of ​​uneven internal temperature distribution, the brightness output data and temperature data are correlated and analyzed based on the long-term trend of brightness decay and the local pattern of the influence of temperature difference on the response characteristics of liquid crystal molecules. Based on the correlation analysis results, the screen brightness non-uniformity caused by the local aging area of ​​the backlight module and the liquid crystal molecule response characteristic drift caused by the uneven internal temperature distribution area were separated and quantified.

[0011] Through this technical solution, this application can accurately separate and quantify the display deviation caused by backlight module aging and internal temperature unevenness, avoiding the poor correction effect caused by the confusion of multiple deviations in traditional methods, thus providing a reliable data basis for subsequent accurate correction.

[0012] Furthermore, based on the aforementioned LCD display optimization method, the corresponding deviation is obtained from the quantized data, and a nonlinear correction instruction is generated based on this deviation, including: Real-time monitoring of the frame synchronization signal of the image processing unit inside the display; Based on the frame synchronization signal, the generation process of nonlinear correction instructions is synchronized with the display refresh cycle of the image frame. During the display refresh cycle, the parameters of the nonlinear correction command are calculated and updated based on the currently identified and quantified deviation value. Based on the frame synchronization signal, the application process of the non-linear correction instruction is synchronized with the display refresh cycle of the image frame; During the display refresh cycle, the synchronized non-linear correction instructions are loaded into the display's image processing unit and applied to the currently processed image frame.

[0013] Through this technical solution, this application realizes the real-time generation and synchronous application of correction instructions, ensuring that the correction instructions can be accurately applied to each frame of image, effectively avoiding display artifacts caused by correction lag or asynchrony, thereby ensuring the immediacy and accuracy of the correction effect.

[0014] In some preferred embodiments, based on the above-described LCD display optimization method, information reflecting the image data source is obtained. This information includes the characteristics of the data source, user display preferences, and the physical state information of the display, including: Obtain the electromagnetic noise level inside the display; When the electromagnetic noise intensity exceeds a preset intensity threshold, an interference warning signal is generated; Analyze the real-time readings of the display's multi-point sensor network; Upon receiving an interference warning signal, compare the difference between the real-time reading and the historical stable sensor reading; The differences are matched with pre-stored characteristics of the impact of interference on the sensor; Based on the matching results, identify the impact patterns of transient electromagnetic interference on the sensor; Based on the impact pattern, predictive calibration is performed on the real-time readings of the affected sensors; Based on the predictive calibration of sensor readings, brightness output data and temperature data of the display screen area are acquired to reflect information about the physical state of the display.

[0015] Through this technical solution, this application can effectively identify and calibrate the impact of electromagnetic interference on sensor readings, ensuring the accuracy of the physical state information of the display, thereby avoiding erroneous corrections caused by sensor data distortion and improving the robustness of the entire optimization method.

[0016] As an optional solution, based on the above-mentioned LCD display optimization method, when the electromagnetic noise intensity exceeds a preset intensity threshold, an interference warning signal is generated, including: Obtain the electromagnetic noise level inside the display; The electromagnetic noise level is decomposed into a spectrum to obtain the noise intensity in multiple frequency bands; Identify narrowband interference with specific spectral characteristics based on noise intensity across multiple frequency bands; Interference warning signals are generated based on narrowband interference.

[0017] Through this technical solution, this application can accurately identify narrowband interference through spectrum decomposition, improving the accuracy and sensitivity of interference warning, thereby enabling the calibration mechanism to be activated more promptly and reducing the impact of electromagnetic interference on display performance.

[0018] Based on the above, this application further proposes, in addition to the above-mentioned LCD display optimization method, to match the differences with pre-stored interference influence characteristics on the sensor, including: The matching weights of the interference impact feature library are dynamically adjusted based on the spectral characteristics, intensity change rate, and interference duration of electromagnetic noise. Based on the spatial distribution and temporal evolution patterns of the differences, select multiple interference impact features that are close to the current interference pattern from the interference impact feature library; Multiple interference impact features are weighted and fused to obtain a composite interference impact feature; The differences were matched with the characteristics of the combined interference.

[0019] Through this technical solution, this application can adaptively adjust the matching weight and perform weighted fusion according to the dynamic characteristics of electromagnetic noise, thereby more accurately identifying and matching complex interference patterns and improving the accuracy of sensor reading calibration.

[0020] Furthermore, based on the aforementioned LCD display optimization method, the matching weights of the interference influence feature library are dynamically adjusted according to the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise, including: Feature extraction was performed on the spectral characteristics, intensity change rate, and interference duration of electromagnetic noise to obtain feature parameters of multiple interference sources; Based on the characteristic parameters of multiple interference sources, the contribution of each interference source to the difference in sensor readings is independently evaluated; Based on the independent evaluation results, calculate the independent adjustment amount of the matching weight for each interference source; The independent adjustment values ​​are superimposed to obtain the matching weights of the feature library affected by interference.

[0021] Through this technical solution, this application can independently assess the contribution of different interference sources to the differences in sensor readings, and dynamically adjust the matching weight accordingly, thereby reflecting the actual interference situation more precisely and further improving the accuracy of interference mode matching.

[0022] Based on the above, this application further proposes, on the basis of the above-mentioned LCD display optimization method, to extract features of the spectral characteristics, intensity change rate, and interference duration of electromagnetic noise, to obtain feature parameters of multiple interference sources, including: Multidimensional time-frequency analysis is performed on electromagnetic noise signals to obtain the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise; Identify nonlinear changes in electromagnetic noise or coupling effects between multiple interference sources, and initiate an adaptive signal separation mechanism; Analyze the energy distribution and interaction modes of multiple interference sources in different time-frequency regions; The separation parameters of the adaptive signal separation mechanism are dynamically adjusted based on energy distribution and interaction modes. Independent feature extraction is performed on the signal after the separation parameters have been adjusted. The spectral characteristics, intensity change rate, and interference duration of each interference source are obtained as characteristic parameters of multiple interference sources.

[0023] Through this technical solution, this application can accurately extract the characteristic parameters of each interference source in complex electromagnetic noise by using multi-dimensional time-frequency analysis and adaptive signal separation mechanism, effectively cope with nonlinear changes and coupling effects, and thus provide more accurate input for subsequent interference identification and calibration.

[0024] Based on the above, this application further proposes, in addition to the above-mentioned LCD display optimization method, to perform multi-dimensional time-frequency analysis on the electromagnetic noise signal to obtain the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise, including: Initial time-frequency analysis is performed on the electromagnetic noise signal to obtain a preliminary time-frequency distribution; Identify whether there are concentrated instantaneous impact regions in the preliminary time-frequency distribution, and dynamically adjust the window length of the time-frequency analysis according to the duration of the instantaneous impact regions; Within the frequency range of the instantaneous impact region, the frequency resolution of the time-frequency analysis is dynamically adjusted; Based on the dynamically adjusted window length and frequency resolution, a local time-frequency reanalysis is performed on the instantaneous impact region. By integrating the analysis results of the instantaneous impact region with those of the non-instantaneous impact region, the spectral characteristics, intensity change rate, and duration of the electromagnetic noise are obtained.

[0025] Through this technical solution, this application can perform local reanalysis of the instantaneous impact region by dynamically adjusting the window length and frequency resolution of the time-frequency analysis, thereby more accurately capturing the transient characteristics of electromagnetic noise, improving the accuracy of time-frequency analysis and the ability to identify complex interference.

[0026] Secondly, this application also discloses a liquid crystal display optimization system, comprising: The input end is used to acquire information reflecting the image data source, including the characteristics of the data source, user display preferences, and the physical status information of the display. The quantization end is used to identify and quantify changes in grayscale information, shifts in the grayscale response curve of the display, brightness non-uniformity of the display screen area, and drift of the liquid crystal molecule response characteristics during the data transmission process based on the information reflected in the image data source. The adjustment end is used to obtain the corresponding deviation based on the quantized data, generate a non-linear correction instruction based on the deviation, and combine the non-linear correction instructions to adjust the grayscale response of the display.

[0027] Through this technical solution, this application provides a system capable of implementing the above-mentioned LCD screen display optimization method. Through modular design, the system can efficiently acquire, quantify, and adjust display parameters, thereby providing users with a continuously optimized display experience and solving the limitations of existing systems in handling complex display deviations. Beneficial effects

[0028] The LCD display optimization method and system disclosed in this application, by acquiring information reflecting the image data source, including data source characteristics, user display preferences, and display physical state information, can comprehensively grasp various factors affecting display effects. Based on this, the method can identify and quantify grayscale information changes during data transmission, shifts in the display's grayscale response curve, brightness non-uniformity in display screen areas, and drifts in the response characteristics of liquid crystal molecules. This quantified data accurately reflects various nonlinear deviations present in the display. Furthermore, this application generates nonlinear correction instructions based on these quantified deviations and combines them for precise adjustment of the display's grayscale response.

[0029] Through the above technical solution, this application effectively solves the display deviation problem caused by a combination of factors during long-term use of professional medical imaging displays in the prior art. Specifically, this method can overcome the compression of dark grayscale information in high bit-depth images by the universal color profile of imaging workstation software, avoiding information loss at the data transmission source; it can correct the problem of grayscale mapping logic deviating from the DICOM standard curve caused by user subjective fine-tuning; it can compensate for screen brightness unevenness caused by local aging of the backlight module; and at the same time, it can correct the drift of liquid crystal molecule response characteristics caused by uneven internal temperature distribution of the display.

[0030] Compared to existing built-in automatic correction programs, which often only optimize for a single type of aging or drift and tend to exacerbate the distortion of the image's grayscale response curve when dealing with nonlinear deviations caused by multiple intertwined factors, the method in this application can accurately separate and identify various influencing sources and generate targeted nonlinear correction instructions for combined application. Therefore, this application can significantly improve the phenomena of "unnatural color transitions" and "loss of detail in dark areas," allowing previously indistinguishable fine structures to be clearly presented, thereby greatly improving the display accuracy and diagnostic precision of medical images and providing doctors with more reliable diagnostic evidence. Attached Figure Description

[0031] Figure 1This is a flowchart illustrating a liquid crystal display optimization method provided in an embodiment of the present invention; Figure 2 This is a flowchart of a method for identifying and quantifying data transmission processes provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a liquid crystal display optimization system provided in an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Reference Figure 1 , Figure 1 This is a flowchart illustrating a liquid crystal display optimization method provided in an embodiment of the present invention, including: S11, Obtain information reflecting the image data source, including the characteristics of the data source, user display preferences, and physical state information of the display. S12, based on the information reflecting the image data source, identify and quantify the grayscale information changes, the shift of the display grayscale response curve, the brightness non-uniformity of the display screen area, and the drift of the liquid crystal molecule response characteristics during the data transmission process. S13, Obtain the corresponding deviation based on the quantized data, and generate a nonlinear correction instruction based on the deviation; S14, combine the nonlinear correction instructions to adjust the grayscale response of the display.

[0034] This application, by comprehensively considering the characteristics of the data source, user preferences, and the physical state of the display, can more comprehensively identify and quantify various complex display deviations, and generate nonlinear correction instructions for combined application, thereby achieving precise adjustment of the display's grayscale response. This effectively solves the problem of insufficient correction under the interplay of multiple factors in traditional methods, and significantly improves the display quality and diagnostic accuracy of medical images.

[0035] The "grayscale information change" mentioned in this application refers to the phenomenon where the grayscale value of an image changes unexpectedly due to various factors (such as color profiles, data compression, transmission protocol conversion, etc.) during the transmission of image data from the source to the display processing unit. This change may result in the loss of details in the dark or bright areas of the image, or an uneven grayscale transition.

[0036] "Gray-to-gray response curve offset" refers to the deviation of the curve showing the relationship between the monitor's actual output brightness and the input gray value from a preset standard (such as the DICOM Part 14 standard). This offset may be caused by monitor aging, manual parameter adjustments by the user, or internal calibration failure, directly affecting image contrast and detail.

[0037] "Uneven brightness across monitor screen areas" refers to differences in brightness output between different areas of the monitor screen, resulting in an uneven visual appearance. This is usually related to aging of the backlight module, localized malfunctions, or design flaws.

[0038] "Drift in the response characteristics of liquid crystal molecules" refers to the change in the deflection speed, angle, and other response characteristics of liquid crystal molecules in a liquid crystal display under the influence of factors such as long-term operation or temperature changes. This affects the switching speed and transmittance of pixels, resulting in image ghosting, color distortion, or inaccurate grayscale. The optimization method in this application aims to accurately identify and quantify these complex display deviations and generate corresponding nonlinear correction instructions, in order to achieve fine-tuning of the grayscale response of the display in practical applications and ensure accurate image presentation.

[0039] In practical implementation, the LCD display optimization method of this application first needs to acquire information reflecting the image data source. This information may include the characteristics of the data source, user display preferences, and the physical state information of the display. For example, the characteristics of the data source can be manually input, such as the image type (X-ray, CT, MRI, etc.), bit depth (8-bit, 10-bit, 12-bit, etc.), and color space (sRGB, Adobe RGB, DCI-P3, etc.). User display preferences can be selected through preset profiles or user interfaces; for example, users may prefer higher contrast, warmer color temperature, or a specific gamma curve. The physical state information of the display can be acquired through the display's internal sensor network, such as brightness sensors, color sensors, and temperature sensors, which monitor the display's operating status in real time.

[0040] Next, based on the acquired information reflecting the image data source, the changes in grayscale information, the shift in the display's grayscale response curve, the brightness non-uniformity of the display screen area, and the drift in the liquid crystal molecule response characteristics during data transmission are identified and quantified. For example, grayscale information changes can be identified by analyzing the differences between the metadata of the image data source and the data received by the display. The shift in the display's grayscale response curve can be quantified by periodically running a calibration program to compare the display's actual grayscale response with a standard curve. Brightness non-uniformity of the display screen area can be evaluated by displaying a uniform grayscale image on the screen and measuring different areas using a luminance meter. The drift in the liquid crystal molecule response characteristics can be detected by measuring the pixel response time or using a specialized test pattern.

[0041] Subsequently, the corresponding deviation is obtained based on the quantized data, and a non-linear correction instruction is generated based on the deviation. For example, if the quantization result shows that the grayscale response curve is compressed in dark areas, a non-linear gamma correction instruction can be generated to stretch the grayscale in dark areas. If the brightness in the upper left corner of the screen is too low, a local brightness compensation instruction can be generated. These correction instructions can be in the form of lookup tables (LUTs), mathematical functions, or parameter adjustment sets.

[0042] Finally, the generated non-linear correction instructions are combined and applied to adjust the display's grayscale response. For example, gamma correction instructions can be superimposed with local brightness compensation instructions. These instructions can be loaded into the display's image processing unit (such as an FPGA or ASIC) and processed in real time before each frame of the image is displayed. Through this combined application, multiple complex display deviations can be addressed simultaneously, thereby achieving precise adjustment of the display's grayscale response.

[0043] The LCD display optimization method of this application, by comprehensively considering the characteristics of the data source, user preferences and the physical state of the display, can more comprehensively identify and quantify the changes in grayscale information, the shift of the display's grayscale response curve, the brightness non-uniformity of the display screen area and the drift of the liquid crystal molecule response characteristics during the data transmission process.

[0044] Compared to existing technologies, the liquid crystal display optimization method of this application has significant advantages. Traditional display calibration methods often focus on a single type of display deviation, such as calibrating only the grayscale response curve or adjusting backlight uniformity. When multiple complex display deviations (such as data transmission grayscale changes, grayscale response curve shifts, screen brightness non-uniformity, and liquid crystal molecule response characteristic drift) coexist and intertwine, existing methods struggle to accurately separate and identify each influencing source, resulting in unsatisfactory correction effects and potentially exacerbating image distortion.

[0045] The core innovation of this application lies in its comprehensiveness and non-linear correction capabilities. By acquiring multi-dimensional information such as data source characteristics, user display preferences, and the physical state of the display, this application can comprehensively identify and accurately quantify various complex display deviations. For example, it not only considers the aging and drift of the display itself, but also the loss of grayscale information that may occur during data transmission, as well as the impact of user subjective preferences on the display effect. This comprehensive information acquisition and analysis enables the system to diagnose various deviations more accurately.

[0046] More importantly, this application can generate non-linear correction instructions based on the quantified deviation and apply them in combination. This means that the system can generate highly targeted non-linear adjustment schemes for different types of deviations and the characteristics of different screen areas. For example, for the loss of detail in dark areas, non-linear gamma correction can be used to stretch the grayscale; for local brightness non-uniformity, regional brightness compensation can be used. By combining these non-linear instructions, this application can simultaneously solve multiple complex display problems and achieve fine-grained adjustment of the display's grayscale response. This ability to combine applications allows this application to avoid the problems of "paying attention to one aspect but losing another" or "the more you repair, the worse it gets" that may occur with traditional methods when dealing with non-linear deviations involving multiple factors, thereby significantly improving the display quality and diagnostic accuracy of medical images.

[0047] In some embodiments described above, this application proposes identifying and quantifying grayscale information changes, grayscale response curve shifts, brightness non-uniformity of display screen areas, and liquid crystal molecule response characteristic drifts during data transmission based on information reflecting image data sources. However, in practical applications, brightness non-uniformity of display screen areas and liquid crystal molecule response characteristic drifts can be caused by a variety of complex and interrelated physical factors, such as backlight module aging and uneven internal temperature distribution. If the impact of these specific factors on display performance cannot be effectively separated and accurately quantified, subsequent corrective instructions may not be able to address the root cause, thus affecting the optimization effect. To address this, this application further proposes refining the aforementioned identification and quantification process by acquiring brightness output data and temperature data of the display screen area and performing multi-dimensional analysis and correlation to more accurately identify and quantify display deviations caused by different physical reasons.

[0048] In this regard, refer to Figure 2 , Figure 2 This is a flowchart of a method for identifying and quantifying data transmission processes provided by an embodiment of the present invention, including: S121, acquire brightness output data and temperature data of the display screen area; S122, perform time series analysis on the brightness output data to identify the long-term trend and local differences in brightness decay, and obtain the corresponding local aging area of ​​the backlight module; S123, Perform spatial distribution analysis on temperature data, identify local patterns of the influence of temperature differences on the response characteristics of liquid crystal molecules, and obtain the existence space of the corresponding uneven internal temperature distribution regions. S124, when the local aging area of ​​the backlight module and the area with uneven internal temperature distribution are spatially overlapping, the brightness output data and temperature data are correlated and analyzed based on the long-term trend of brightness decay and the local mode of the influence of temperature difference on the response characteristics of liquid crystal molecules. S125, based on the correlation analysis results, separates and quantifies the screen brightness non-uniformity caused by the local aging area of ​​the backlight module, and the liquid crystal molecule response characteristic drift caused by the uneven internal temperature distribution area.

[0049] Specifically, acquiring brightness and temperature data for different areas of the display screen can be achieved by deploying light and temperature sensors in various regions of the screen. These sensors can collect brightness and temperature values ​​at different points on the screen in real time or periodically, and transmit this data to the processing unit. The brightness output data reflects the actual luminous intensity of different areas of the screen, while the temperature data reflects the heat distribution within the display.

[0050] Furthermore, time-series analysis of brightness output data involves statistically analyzing brightness data collected from the same screen area at different time points. Methods such as moving averages, exponential smoothing, or trend decomposition are used to identify patterns of change over time. This analysis reveals long-term trends of gradual brightness decline, as well as localized brightness fluctuations or differences occurring within specific time periods. These are typically directly related to the aging of LED chips or driver circuits in the backlight module. Therefore, localized aging areas of the backlight module can be accurately identified.

[0051] Meanwhile, spatial distribution analysis of temperature data refers to identifying the internal temperature distribution patterns of the screen by interpolating, creating heatmaps, or performing cluster analysis on temperature data collected from different areas of the screen at the same time. For example, it can identify regions with localized high or low temperatures. These temperature differences directly affect the alignment and response speed of liquid crystal molecules, leading to localized changes in display performance. This allows us to determine the spatial distribution of areas with uneven internal temperature distribution.

[0052] As a preferred implementation, when the localized aging area of ​​the backlight module spatially overlaps with the area of ​​uneven internal temperature distribution, correlation analysis of brightness output data and temperature data is necessary. This overlap indicates that display anomalies in this area may be simultaneously affected by both backlight aging and temperature unevenness. Correlation analysis can employ methods such as multiple regression, correlation analysis, or machine learning models to reveal the interaction between the long-term trend of brightness decay and the local patterns of the influence of temperature differences on the response characteristics of liquid crystal molecules. For example, it can analyze whether the impact of backlight aging on brightness is exacerbated or mitigated at a specific temperature.

[0053] Ultimately, based on the correlation analysis results, it is possible to separate and quantify the screen brightness non-uniformity caused by localized aging areas of the backlight module and the liquid crystal molecule response characteristic drift caused by uneven internal temperature distribution. This means that by establishing a mathematical model or algorithm, the observed total deviation can be decomposed into two parts: brightness caused by backlight aging and liquid crystal response drift caused by temperature unevenness, and their values ​​can be calculated separately. The purpose is to provide a foundation for subsequently generating more accurate nonlinear correction instructions.

[0054] The solution presented in this application can more accurately identify and quantify display deviations because it introduces the acquisition and multi-dimensional analysis of brightness output data and temperature data of the display screen area, enabling in-depth exploration of the physical roots causing screen brightness non-uniformity and liquid crystal molecule response characteristic drift. Through the above technical solution, this application can significantly improve the accuracy and effectiveness of LCD display optimization methods. Specifically, by performing time series analysis, spatial distribution analysis, and correlation analysis on brightness output data and temperature data, it is possible to achieve refined identification and quantification of screen brightness non-uniformity and liquid crystal molecule response characteristic drift. This refined processing allows display deviations caused by specific physical reasons such as local aging of the backlight module and uneven internal temperature distribution to be accurately separated and quantified, thereby overcoming the limitations of existing technologies that struggle to distinguish the combined effects of multiple factors. Consequently, the subsequently generated nonlinear correction instructions will be more targeted, more effectively compensating for local performance degradation and drift of the display, ultimately achieving a more uniform and stable display effect, extending the lifespan of the display, and improving the user viewing experience.

[0055] In some of the embodiments described above in this application, although it is proposed to adjust the grayscale response of the display by generating nonlinear correction instructions based on the quantized data acquisition deviation, in actual display process, if the generation and application of correction instructions fail to be precisely synchronized with the image frame refresh cycle of the display, it may cause the correction effect to lag, screen tearing or introduce new visual artifacts, thereby affecting the real-time performance and accuracy of the optimization effect.

[0056] To address this, this application further proposes a scheme to synchronize the generation and application of nonlinear correction instructions with the display refresh cycle of image frames, ensuring the real-time performance and effectiveness of the correction instructions. Specifically, the process of obtaining the corresponding deviation based on the quantized data and generating nonlinear correction instructions based on the deviation includes: Real-time monitoring of the frame synchronization signal of the image processing unit inside the display; According to the frame synchronization signal, the generation process of the nonlinear correction instruction is synchronized with the display refresh cycle of the image frame; During the display refresh cycle, the parameters of the nonlinear correction instruction are calculated and updated based on the currently identified and quantified deviation value. According to the frame synchronization signal, the application process of the nonlinear correction instruction is synchronized with the display refresh cycle of the image frame; During the display refresh cycle, the synchronized nonlinear correction instruction is loaded into the image processing unit of the display and applied to the currently processed image frame.

[0057] Specifically, real-time monitoring of the frame synchronization signal of the image processing unit inside the display refers to continuously acquiring the synchronization signal emitted by the display's image processing unit through hardware or software mechanisms, such as the vertical synchronization signal (VSync) or the horizontal synchronization signal (HSync). These signals mark the start or end of an image frame, and their purpose is to provide a precise time reference for the generation and application of subsequent correction instructions.

[0058] Specifically, the generation process of the nonlinear correction instruction is synchronized with the display refresh cycle of the image frame according to the frame synchronization signal. This can be understood as immediately starting or adjusting the calculation process of the correction instruction after receiving the frame synchronization signal, ensuring that the parameters of the correction instruction are calculated based on the latest data within the current image frame display cycle, with the aim of ensuring the timeliness of the correction instruction.

[0059] In practical applications, within the display refresh cycle, the parameters of the nonlinear correction instruction are calculated and updated based on the currently identified and quantized deviation value. Specifically, this means using a preset algorithm or model to convert the quantized deviation value obtained from the physical state of the display, the data transmission process, etc., into specific nonlinear correction instruction parameters, such as the adjustment coefficient of the gamma curve, the update value of the lookup table (LUT), etc., with the aim of generating instructions that can accurately compensate for the deviation.

[0060] Furthermore, synchronizing the application process of the nonlinear correction instruction with the display refresh cycle of the image frame according to the frame synchronization signal means ensuring that the calculated correction instruction parameters can be loaded into the image processing unit of the display in a timely manner when a new frame synchronization signal is detected. The purpose is to enable the correction effect to be applied immediately to the image frame to be displayed.

[0061] Furthermore, during the display refresh cycle, the synchronized nonlinear correction instructions are loaded into the image processing unit of the display and applied to the currently processed image frame. This means that when the image processing unit processes the pixel data of the current frame, it will call and execute these loaded correction instructions in real time, thereby making nonlinear adjustments to the grayscale values ​​of the pixels. The purpose is to achieve real-time optimization of the display's grayscale response.

[0062] This application's solution ensures precise alignment between the generation and application of correction instructions and the display refresh cycle of image frames by real-time monitoring of the frame synchronization signal of the display's internal image processing unit. Through this technical solution, the application effectively solves the problem of asynchronous generation and application of correction instructions in traditional methods. Real-time synchronous generation and loading of correction instructions ensures that each adjustment of grayscale response is precisely applied to the corresponding image frame, greatly improving the real-time performance and accuracy of the display optimization solution. As a result, the display can adapt to various dynamic changes more quickly and smoothly, effectively avoiding screen tearing, delays, or visual artifacts, thus providing users with a more stable, smooth, and visually superior display experience.

[0063] In some embodiments described above, information reflecting the image data source, including the physical state information of the display, is proposed. However, in practical applications, the environment in which the display is located may be subject to electromagnetic noise interference. This interference may cause inaccurate sensor readings used to acquire physical state information, thereby affecting the accuracy of subsequent display optimization processing. If the above problem is not addressed, grayscale response adjustments based on inaccurate physical state information may not achieve the expected optimization effect and may even introduce new display problems.

[0064] In response, this application further proposes a method for acquiring information reflecting an image data source, characterized in that the information reflecting the image data source includes the characteristics of the data source, user display preferences, and physical state information of the display, including: Obtain the electromagnetic noise level inside the display; When the electromagnetic noise intensity exceeds a preset intensity threshold, an interference warning signal is generated; Analyze the real-time readings of the display's multi-point sensor network; Upon receiving the interference warning signal, the difference between the real-time reading and the historically stable sensor reading is compared. The differences are matched with pre-stored characteristics of the impact of interference on the sensor; Based on the matching results, the influence mode of transient electromagnetic interference on the sensor is identified; Based on the described influence pattern, predictive calibration is performed on the real-time readings of the affected sensors; Based on the sensor readings after predictive calibration, the brightness output data and temperature data of the display screen area are obtained to reflect information about the physical state of the display.

[0065] Specifically, acquiring the electromagnetic noise level inside the display refers to real-time monitoring of the intensity and spectrum distribution of electromagnetic waves in the environment using electromagnetic noise sensors integrated inside or near the display. The purpose is to promptly detect external electromagnetic interference that may affect the normal operation of the display. When the electromagnetic noise intensity exceeds a preset threshold, an interference warning signal is generated. This preset threshold is a safety upper limit pre-set based on the display's sensitivity to electromagnetic interference and the sensor network's anti-interference capability. When the detected electromagnetic noise intensity exceeds this threshold, the system automatically triggers a warning signal to indicate a potential interference risk and the need to activate appropriate response mechanisms.

[0066] Furthermore, analyzing the real-time readings of the display's multi-point sensor network refers to collecting and processing real-time data from multiple sensors (such as brightness sensors, temperature sensors, and voltage sensors) distributed across key locations such as the display screen area, backlight module, and drive circuitry. The purpose is to comprehensively understand the various physical parameters of the display under its current operating state. Upon receiving an interference warning signal, the difference between the real-time readings and historically stable sensor readings is compared. Historically stable sensor readings refer to baseline sensor readings obtained through long-term monitoring and statistical analysis under normal operating conditions with no or low levels of electromagnetic interference. By comparing the current real-time readings with these baseline readings, the specific impact of electromagnetic interference on the sensor readings can be quantified.

[0067] Matching the differences with pre-stored interference impact characteristics on sensors involves establishing an interference impact feature library. This library contains the potential impact patterns of different types, intensities, and durations of electromagnetic interference on various sensor readings (for example, noise of a specific frequency might cause generally low readings for brightness sensors, while another type of noise might cause periodic fluctuations in temperature sensor readings). The algorithm compares the currently observed reading differences with the features in the library to accurately identify the nature of the current interference. Based on the matching results, the impact patterns of instantaneous electromagnetic interference on sensors are identified to clarify the specific manifestations of the current electromagnetic interference and its impact patterns on sensor readings.

[0068] In practical applications, predictive calibration of real-time sensor readings based on influence patterns refers to correcting the real-time readings of disturbed sensors using appropriate calibration algorithms (e.g., additive compensation, multiplicative correction, filtering) based on the identified influence patterns. Predictive calibration means that the system not only corrects current readings but also predicts and calibrates sensor readings in the near future based on the evolution trend of the interference patterns. Its purpose is to ensure that the calibrated data accurately reflects the physical state of the display. Ultimately, based on the predictively calibrated sensor readings, brightness output data and temperature data for the display screen area are acquired to reflect information about the display's physical state. This calibrated data serves as reliable input for subsequent display optimization processing.

[0069] This application's solution effectively solves the problem of obtaining accurate physical state information of a display in electromagnetic interference environments by introducing electromagnetic noise monitoring and sensor reading calibration mechanisms. Through the above technical solution, this application can significantly improve the accuracy and reliability of acquiring display physical state information, especially in complex electromagnetic environments. This enables subsequent display optimization algorithms to make decisions and adjustments based on more realistic and accurate data, thereby improving the display effect and stability of the LCD screen, effectively avoiding misjudgments and incorrect corrections caused by inaccurate sensor readings, and ultimately extending the lifespan of the display and improving the user experience.

[0070] Specifically, the steps for generating an interference warning signal when the electromagnetic noise intensity exceeds a preset intensity threshold can be further refined into the following process.

[0071] The process includes: Obtain the electromagnetic noise level inside the display; The electromagnetic noise level is subjected to spectral decomposition to obtain the noise intensity of multiple frequency bands; Based on the noise intensity of the multiple frequency bands, identify narrowband interference with specific spectral characteristics; An interference warning signal is generated based on the narrowband interference.

[0072] Obtaining the electromagnetic noise level inside the monitor refers to monitoring and collecting data on the intensity of electromagnetic radiation generated in the monitor's operating environment and internal circuitry in real time using built-in electromagnetic sensors or dedicated detection circuits. This data is typically expressed in the form of voltage, current, or field strength, reflecting the current electromagnetic interference situation.

[0073] Furthermore, the electromagnetic noise level is subjected to spectral decomposition to obtain the noise intensity of multiple frequency bands. This can be understood as transforming the acquired raw electromagnetic noise signal from the time domain to the frequency domain through Fourier transform or other time-frequency analysis methods, thereby revealing the distribution of noise energy at different frequencies. Through this decomposition, broadband noise can be distinguished from narrowband noise at specific frequencies, and the noise intensity of each frequency band can be quantified.

[0074] Based on this, identifying narrowband interference with specific spectral characteristics according to the noise intensity of the multiple frequency bands refers to analyzing the data after spectral decomposition to find spike signals with significantly higher energy than the background noise at specific frequency points or within a specific frequency range. These spike signals typically correspond to narrowband interference generated by specific interference sources (such as power switching noise, wireless communication signals, high-frequency clock signals, etc.). The identification process may involve threshold comparison, pattern recognition algorithms, or machine learning methods to accurately locate and classify these interferences.

[0075] Finally, generating an interference warning signal based on the narrowband interference means that once narrowband interference with specific spectral characteristics is identified, the system will trigger an early warning mechanism to generate an interference warning signal. This signal may contain information such as the frequency, intensity, and duration of the interference, used to notify subsequent processing modules to take appropriate calibration or suppression measures.

[0076] This application's solution, through refined spectral decomposition of electromagnetic noise levels, can break down complex electromagnetic noise environments into analyzable frequency components. Traditional methods may only determine the presence of interference based on a total intensity threshold, which can lead to misjudgments of broadband noise or omissions of narrowband interference at specific frequencies. By identifying narrowband interference with specific spectral characteristics, this solution can accurately pinpoint the nature and frequency of the interference source, thereby avoiding overreaction to non-critical noise and ensuring timely detection of interference that truly affects display performance. This spectral feature-based identification mechanism makes the generation of interference warning signals more accurate and reliable, providing high-quality input for subsequent sensor reading calibration.

[0077] Through the above technical solution, this application enables refined monitoring and identification of electromagnetic noise within the display. Compared to methods that rely solely on total noise intensity, this solution effectively distinguishes between broadband noise and narrowband interference with specific spectral characteristics, thereby significantly improving the accuracy and specificity of interference warnings. This precise interference identification capability helps avoid performance degradation or unnecessary calibration operations caused by false alarms or missed alarms, thus enhancing the robustness and efficiency of the entire LCD display optimization method and ensuring accurate acquisition of the display's physical state information in complex electromagnetic environments.

[0078] In some embodiments described above, this application proposes matching sensor reading differences with pre-stored interference influence features to identify interference patterns. However, in practical applications, the types and characteristics of electromagnetic interference can be complex and varied; for example, its spectral characteristics, intensity change rate, and duration may all differ significantly. If a static or single matching strategy is used, it may fail to adequately capture these dynamically changing interference features, resulting in inaccurate identification of interference patterns and consequently affecting the calibration effect of sensor readings. To address this, this application further proposes an optimization scheme that aims to more accurately identify the influence patterns of electromagnetic interference on the sensor by dynamically adjusting the matching weights and performing multi-feature fusion.

[0079] The above-mentioned matching of the differences with pre-stored characteristics of the impact of interference on the sensor includes: The matching weights of the interference impact feature library are dynamically adjusted based on the spectral characteristics, intensity change rate, and interference duration of electromagnetic noise. Based on the spatial distribution and temporal evolution pattern of the differences, select multiple interference impact features that are close to the current interference pattern from the interference impact feature library; The multiple interference impact features are weighted and fused to obtain a composite interference impact feature; The differences are matched with the characteristics of the combined interference.

[0080] Specifically, dynamically adjusting the matching weights of the interference impact feature library means that the system adjusts the matching importance of each feature in the pre-stored interference impact feature library in real time based on key parameters such as the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise monitored in real time. For example, when narrowband high-frequency interference is detected, the weights related to spectral characteristics are increased; when transient impulse interference is detected, the weights related to the intensity change rate and interference duration are increased accordingly. The purpose is to enable the matching process to better adapt to the actual situation of the current electromagnetic interference.

[0081] The selection of multiple interference impact features closely resembling the current interference pattern from the interference impact feature library, based on the spatial distribution and temporal evolution pattern of the differences, can be understood as the system considering not only the instantaneous values ​​of sensor reading differences but also analyzing the distribution of these differences at different sensor locations (spatial distribution) and their trends over time (temporal evolution pattern). Based on this information, the system filters out multiple features most similar to the currently observed interference pattern from the feature library, rather than simply selecting a single best-matching feature. The aim is to capture the complexity and multifaceted nature of interference, providing more comprehensive information for subsequent fusion.

[0082] In practical applications, the weighted fusion of multiple interference influence features to obtain a composite interference influence feature specifically involves taking the selected, closely related interference influence features and processing them using a weighted average or a more complex fusion algorithm based on their similarity to the current interference pattern or a preset fusion rule. This generates a composite feature that more comprehensively and accurately represents the current interference situation. The purpose is to comprehensively consider multiple possible interference factors and improve the accuracy of interference pattern recognition.

[0083] Therefore, matching the difference with the composite interference effect characteristics means ultimately comparing the sensor reading difference with the composite interference effect characteristics obtained after dynamic weight adjustment and multi-feature fusion to determine the most accurate interference pattern. The purpose is to ensure the robustness and accuracy of interference pattern recognition, providing a reliable basis for subsequent predictive calibration.

[0084] This application's scheme introduces the spectral characteristics, intensity change rate, and interference duration of electromagnetic noise as the basis for dynamically adjusting the matching weights, enabling the matching process of the interference impact feature library to respond in real time to electromagnetic interference of different types and intensities. When the electromagnetic environment changes, the matching weights can be adaptively adjusted, ensuring that the system always focuses on the features most relevant to the current interference. Furthermore, by analyzing the spatial distribution and temporal evolution patterns of sensor reading differences and selecting multiple similar interference impact features from the feature library, this scheme can more comprehensively capture the complexity of interference and avoid identification bias caused by insufficient matching of a single feature. Subsequently, these selected features are weighted and fused to generate a composite interference impact feature. This makes the final matching no longer limited to a single preset pattern, but can integrate multiple potential interference factors to form a more representative and adaptive interference model. Finally, the sensor reading differences are matched with this composite feature, thereby more accurately identifying the impact patterns of instantaneous electromagnetic interference on the sensor, effectively overcoming the limitations of traditional static matching methods in handling complex and variable interference.

[0085] Through the above technical solutions, this application can significantly improve the accuracy and robustness of electromagnetic interference pattern recognition. Dynamically adjusting the matching weights allows the system to flexibly adapt to different types and intensities of electromagnetic interference, avoiding recognition errors caused by fixed weights. The multi-feature selection and weighted fusion mechanism ensures comprehensive capture and accurate modeling of complex interference patterns, effectively reducing false positive and false negative rates. Therefore, it can more accurately identify the impact patterns of instantaneous electromagnetic interference on sensors, providing a more reliable basis for subsequent predictive calibration of sensor readings, thereby ensuring the accurate acquisition of the display's physical state information and ultimately improving the overall effect of LCD display optimization.

[0086] In some embodiments described above, this application proposes dynamically adjusting the matching weights of the interference influence feature library based on the spectral characteristics, intensity change rate, and interference duration of electromagnetic noise. However, in practical applications, electromagnetic noise is often not a single source, but rather the result of multiple interference sources with different characteristics and influence modes acting together. If only an overall weight adjustment is performed, it may be impossible to accurately distinguish and quantify the specific contribution of each interference source to the difference in sensor readings, thus limiting the matching accuracy of the interference influence feature library and consequently affecting the accurate identification of instantaneous electromagnetic interference patterns and the predictive calibration effect of sensor readings.

[0087] In response, this application further proposes a more refined method for dynamically adjusting the matching weights of the interference influence feature library. By independently evaluating and adjusting multiple interference sources, the accuracy and robustness of the matching can be improved.

[0088] The above-mentioned dynamic adjustment of the matching weights of the interference impact feature library based on the spectral characteristics, intensity change rate, and interference duration of electromagnetic noise specifically includes: Feature extraction is performed on the spectral characteristics of the electromagnetic noise, the rate of intensity change, and the duration of the interference to obtain feature parameters of multiple interference sources; Based on the characteristic parameters of the multiple interference sources, the contribution of each interference source to the difference in sensor readings is independently evaluated; Based on the independent evaluation results, calculate the independent adjustment amount of the matching weight for each interference source; The independent adjustment values ​​are superimposed to obtain the matching weights of the interference influence feature library.

[0089] Specifically, feature extraction of the spectral characteristics, intensity change rate, and duration of electromagnetic noise refers to using signal processing techniques, such as Fourier transform, wavelet analysis, or Hilbert-Huang transform, to separate and identify different interference components from the original electromagnetic noise signal. Each interference component, i.e., an interference source, has its unique spectral distribution, intensity change rate over time, and duration. These characteristics are quantified as feature parameters of the interference source, such as center frequency, bandwidth, peak intensity, rise / fall time, and duration. The aim is to decompose complex composite electromagnetic noise into multiple independently analyzable components.

[0090] This process involves independently evaluating the contribution of each interference source to sensor reading differences based on its characteristic parameters. This can be understood as analyzing the specific impact of each identified interference source on the deviation of the display's multi-point sensor network readings within a specific time period. This can be achieved by establishing a mapping model between interference source characteristics and sensor reading differences, such as using machine learning algorithms or statistical regression analysis, to quantify the sensor reading deviation caused by each interference source acting alone. The aim is to accurately understand the independent impact of each interference source and avoid confusion.

[0091] In practical applications, calculating the independent adjustment amount of each interference source's matching weight based on independent evaluation results refers to determining the weight adjustment magnitude that each interference source should be assigned during the matching process of the interference influence feature library, based on the evaluation results of the differences in sensor readings. For example, if a certain interference source is evaluated as contributing significantly to the differences in readings of a specific sensor, its corresponding matching weight adjustment amount will be increased accordingly to enable it to play a more important role in the matching process. The purpose is to make the weight adjustment more targeted.

[0092] Furthermore, the independent adjustment values ​​are superimposed to obtain the matching weights for the interference influence feature library. This involves accumulating or weighting the weight adjustment values ​​calculated from all independent interference sources to form the final comprehensive weights used for matching the interference influence feature library. This superposition can be linear or non-linear, depending on whether there is a coupling effect between the interference sources. The purpose is to comprehensively consider the influence of all known interference sources to form a comprehensive and accurate matching weight.

[0093] The proposed solution decomposes complex electromagnetic noise into multiple independent interference sources and independently evaluates the characteristics of each source and its contribution to sensor reading differences, thereby enabling a more refined understanding and quantification of the actual impact of electromagnetic interference. This independent evaluation makes it possible to calculate more precise matching weight adjustments for each interference source. By superimposing these independent adjustments, the resulting interference impact feature library's matching weights more accurately reflect the complexity of the current electromagnetic environment, avoiding the errors and inaccuracies that may arise from traditional overall adjustments. Consequently, in the subsequent interference impact feature matching process, the system can more effectively identify the impact patterns of transient electromagnetic interference on the sensor, thus providing a more reliable basis for predictive calibration.

[0094] Through the above technical solution, this application overcomes the limitations of existing technologies in identifying and quantifying complex electromagnetic interference sources. By independently evaluating the contribution of each interference source and adjusting the matching weights accordingly, the matching process of the interference influence feature library becomes more targeted and accurate. This significantly improves the accuracy of the system in identifying instantaneous electromagnetic interference patterns, thereby enhancing the predictive calibration accuracy of real-time readings from affected sensors. Ultimately, with more accurate sensor data, the acquisition of the display's physical state information becomes more reliable, providing a solid data foundation for subsequent grayscale response optimization, thus achieving a more stable and accurate LCD display optimization effect.

[0095] In some embodiments described above, this application proposes feature extraction of the spectral characteristics, intensity change rate, and interference duration of electromagnetic noise to obtain feature parameters of multiple interference sources, thereby dynamically adjusting the matching weights of the interference impact feature library. However, in practical applications, electromagnetic noise environments are often complex and variable, with multiple interference sources potentially existing, and these sources may exhibit nonlinear variations or coupling effects. If only traditional feature extraction methods are used, it may be difficult to accurately separate and identify the independent features of these complex interference sources, resulting in inaccurate adjustment of the matching weights of the interference impact feature library, and consequently affecting the predictive calibration effect of sensor readings. Therefore, this application further proposes a more refined and robust feature extraction method, employing multidimensional time-frequency analysis and an adaptive signal separation mechanism to more accurately obtain the feature parameters of multiple interference sources.

[0096] In response, this application further proposes the above-mentioned feature extraction of electromagnetic noise spectral characteristics, intensity change rate, and interference duration to obtain characteristic parameters of multiple interference sources, including: Multidimensional time-frequency analysis is performed on the electromagnetic noise signal to obtain the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise; The system identifies nonlinear changes in the electromagnetic noise or coupling effects between the multiple interference sources and initiates an adaptive signal separation mechanism. Analyze the energy distribution and interaction modes of the multiple interference sources in different time-frequency regions; The separation parameters of the adaptive signal separation mechanism are dynamically adjusted based on the energy distribution and the interaction mode. Independent feature extraction is performed on the signal after the separation parameters are adjusted; The spectral characteristics, intensity change rate, and interference duration of each interference source are obtained as characteristic parameters of the plurality of interference sources.

[0097] Specifically, multidimensional time-frequency analysis refers to the joint analysis of electromagnetic noise signals across multiple dimensions, such as time and frequency, to reveal their dynamic spectral characteristics, instantaneous intensity changes, and duration information. This can be achieved using advanced time-frequency analysis tools such as wavelet transform, short-time Fourier transform, or Wigner-Ville distribution, with the aim of providing a more comprehensive view of noise characteristics than single-dimensional analysis.

[0098] Identifying nonlinear variations in electromagnetic noise or coupling effects between multiple interference sources involves further analyzing the results of multidimensional time-frequency analysis to determine whether the noise signal deviates from a linear superposition model or whether different interference sources interact in a specific time-frequency region. Once such complex situations are identified, an adaptive signal separation mechanism is activated. This mechanism can be understood as an intelligent algorithm, such as independent component analysis, blind source separation, or nonnegative matrix factorization, aiming to decompose the mixed electromagnetic noise signal into multiple independent or relatively independent interference source signals.

[0099] In practical applications, analyzing the energy distribution and interaction patterns of multiple interference sources in different time-frequency regions refers to continuously monitoring the energy concentration area, bandwidth, center frequency, and whether there are energy crossovers or synchronous changes among the signals of each separated interference source on the time-frequency graph during the operation of the adaptive signal separation mechanism. The purpose is to provide a basis for subsequent dynamic adjustment of separation parameters.

[0100] Furthermore, dynamically adjusting the separation parameters of the adaptive signal separation mechanism based on energy distribution and interaction patterns refers to optimizing the internal parameters of the adaptive signal separation algorithm in real time based on the analysis results of the energy distribution and interaction patterns of the interference sources. Examples include the iteration step size for independent component analysis, convergence criteria, or basis matrix update strategy for nonnegative matrix factorization. The aim is to ensure that, under complex and variable noise environments, the separation mechanism can continuously and effectively decompose the mixed signal into signals that best represent the independent interference sources.

[0101] Therefore, independent feature extraction of the signal after separation parameter adjustment refers to extracting the spectral characteristics, intensity change rate, and interference duration of each independent interference source signal obtained after the optimized separation mechanism. The purpose is to ensure that the feature parameters of each interference source are independent and accurate.

[0102] Finally, the spectral characteristics, intensity change rate, and duration of interference for each interference source are obtained as feature parameters for multiple interference sources. The purpose is to provide accurate input for the matching weight adjustment of the subsequent interference impact feature library, thereby improving the recognition accuracy of instantaneous electromagnetic interference impact patterns.

[0103] The solution proposed in this application effectively solves the limitations of traditional feature extraction methods in accurately separating and identifying multiple nonlinear or coupled interference sources when facing complex electromagnetic noise environments by introducing multidimensional time-frequency analysis and adaptive signal separation mechanism.

[0104] Through the above technical solution, this application can significantly improve the accuracy and robustness of extracting feature parameters of multiple interference sources in complex electromagnetic noise environments. Compared with methods that only perform simple feature extraction, this application can effectively address the nonlinear changes in electromagnetic noise and the coupling effects between interference sources, ensuring that the feature parameters of each interference source are independently and accurately identified. This precise acquisition of feature parameters provides a more reliable basis for adjusting the matching weights of the subsequent interference impact feature library, thereby making the identification of the impact patterns of instantaneous electromagnetic interference on sensors more accurate, improving the effectiveness of predictive calibration, and ultimately optimizing the accuracy of acquiring the physical state information of the display, providing a more solid data foundation for LCD display optimization.

[0105] In some of the above embodiments, in order to accurately identify the impact patterns of transient electromagnetic interference on the sensor, multidimensional time-frequency analysis of the electromagnetic noise signal is required to obtain its spectral characteristics, intensity change rate, and interference duration. However, traditional fixed-parameter time-frequency analysis methods may struggle to simultaneously meet the requirements of time resolution and frequency resolution when dealing with complex and variable electromagnetic noise, especially that containing transient impulses or nonlinear changes. This results in insufficiently accurate extraction of noise features, which in turn affects the accurate assessment of the interference source and the subsequent sensor calibration effect.

[0106] To address this, this application further proposes a step of performing multidimensional time-frequency analysis on the electromagnetic noise signal to obtain the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise, including: An initial time-frequency analysis is performed on the electromagnetic noise signal to obtain a preliminary time-frequency distribution; Identify whether there are concentrated instantaneous impact regions in the preliminary time-frequency distribution, and dynamically adjust the window length of the time-frequency analysis according to the duration of the instantaneous impact regions; Within the frequency range of the instantaneous impact region, the frequency resolution of the time-frequency analysis is dynamically adjusted; Based on the dynamically adjusted window length and frequency resolution, a local time-frequency reanalysis is performed on the instantaneous impact region. By integrating the analysis results of the instantaneous impact region with those of the non-instantaneous impact region, the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise are obtained.

[0107] Specifically, initial time-frequency analysis of electromagnetic noise signals aims to obtain a preliminary overview of their time-frequency distribution. This can be achieved using conventional time-frequency analysis methods such as short-time Fourier transform and wavelet transform. The preliminary time-frequency distribution can reveal the approximate energy distribution of the noise signal in time and frequency.

[0108] Identifying the presence of concentrated, transient impact regions within the initial time-frequency distribution involves analyzing the energy density in the time-frequency distribution map to locate areas with short durations but high energy intensity. These regions typically correspond to sudden electromagnetic interference events. Dynamically adjusting the time-frequency analysis window length based on the duration of the transient impact region optimizes the capture capability for transient events. For example, a shorter analysis window improves temporal resolution and more accurately pinpoints the occurrence time of short-duration transient impacts; conversely, a longer window enhances frequency resolution for longer-duration, stable noise.

[0109] Furthermore, within the frequency range of the transient impact region, the frequency resolution of the time-frequency analysis is dynamically adjusted. The aim is to improve the ability to identify frequency components within the transient impact region while maintaining time resolution. For example, for transient impacts containing narrowband interference, the frequency resolution can be increased to accurately identify its center frequency; for broadband transient impacts, the frequency resolution can be appropriately reduced to cover a wider frequency range.

[0110] Therefore, by performing local time-frequency reanalysis on the instantaneous impact region based on the dynamically adjusted window length and frequency resolution, more refined and accurate analysis of these key regions can be carried out, avoiding the limitations of fixed parameter analysis.

[0111] Finally, the analysis results of the instantaneous impact region are integrated with those of the non-instantaneous impact region to form a comprehensive and high-precision description of the time-frequency characteristics of electromagnetic noise, thereby obtaining the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise.

[0112] The solution proposed in this application effectively addresses the limitations of traditional fixed-parameter time-frequency analysis in processing complex electromagnetic noise, especially transient impulse noise, by introducing a dynamic adjustment mechanism for time-frequency analysis parameters. When there are transient impulse regions with concentrated energy in electromagnetic noise signals, traditional analysis methods often struggle to simultaneously achieve high time resolution and high frequency resolution, resulting in inaccurate identification of the occurrence time, duration, and internal frequency components of transient events.

[0113] Through the above technical solution, this application can significantly improve the analysis accuracy of complex electromagnetic noise signals, especially noise containing instantaneous impulses or nonlinear changes. Compared with time-frequency analysis methods using fixed parameters, this solution, by dynamically adjusting the window length and frequency resolution of the time-frequency analysis, can more accurately capture the occurrence time, duration, and internal frequency composition of instantaneous electromagnetic interference, thereby obtaining more refined and comprehensive electromagnetic noise spectrum characteristics, intensity change rate, and interference duration. This high-precision feature extraction makes the identification and quantification of interference sources more accurate, and thus enables more effective predictive calibration of affected sensor readings, significantly improving the reliability and accuracy of acquiring physical state information of the display, and ultimately optimizing the display effect of the LCD screen.

[0114] refer to Figure 3 , Figure 3 This is a schematic diagram of a liquid crystal display optimization system provided in an embodiment of the present invention, comprising: The input terminal is used to acquire information reflecting the image data source, including the characteristics of the data source, user display preferences, and the physical state information of the display. The quantization end is used to identify and quantify changes in grayscale information, shifts in the grayscale response curve of the display, brightness non-uniformity of the display screen area, and drift of liquid crystal molecule response characteristics during data transmission based on the information reflecting the image data source. The adjustment end is used to obtain the corresponding deviation based on the quantized data, generate nonlinear correction instructions based on the deviation, and combine the nonlinear correction instructions to adjust the grayscale response of the display.

[0115] This application's LCD screen display optimization system, through the establishment of an input end, a quantization end, and an adjustment end, forms a collaborative overall architecture, aiming to solve various display deviation problems that arise in existing LCD screens under long-term use and complex environments. The input end is responsible for comprehensively collecting the characteristics of the image data source, user display preferences, and the physical state information of the display, providing a multi-dimensional data foundation for subsequent precise analysis. Based on this information, the quantization end can precisely identify and quantify grayscale information changes during data transmission, the offset of the display's grayscale response curve, the brightness non-uniformity of the display screen area, and the drift of liquid crystal molecule response characteristics, thereby accurately diagnosing various nonlinear deviations in the display. The adjustment end, based on the deviations generated by the quantization results, intelligently generates and combines nonlinear correction instructions to achieve precise adjustment of the display's grayscale response. Through this systematic design, this application can effectively address the insufficient correction problems of traditional methods under multiple intertwined factors, significantly improving the display quality and diagnostic accuracy of medical images, ensuring that doctors can obtain faithfully reproduced image information.

[0116] The specific steps of the LCD screen display optimization method have been described in the above embodiments and will not be repeated here. It should be emphasized that the LCD screen display optimization system of this application implements the above method through specific functional modules.

[0117] Specifically, the input terminal can be configured in various forms. For example, it can be a hardware module integrating multiple physical interfaces (such as HDMI, DisplayPort, USB, etc.) to receive image data and metadata from an image data source. Simultaneously, the input terminal can also include one or more sensor interfaces for connecting to internal display sensors such as brightness, color, and temperature sensors to acquire real-time physical status information of the display. Furthermore, the input terminal can interact with the user through a user interface module, receiving manually input display preferences or selecting preset display profiles. As a preferred embodiment, the input terminal can be a software module running on the display's main control chip, acquiring information by reading data streams and sensor data from the system bus and parsing user configuration information.

[0118] The quantization end can be implemented as a dedicated digital signal processor (DSP) or field-programmable gate array (FPGA) module. This module is programmed to execute complex algorithms to analyze various types of information acquired from the input end, thereby identifying and quantifying grayscale information changes, grayscale response curve shifts in the display, brightness non-uniformity in the display screen area, and drift in the liquid crystal molecule response characteristics during data transmission. For example, the quantization end can include an image analysis engine to compare the original image data with the data received by the display to detect grayscale loss; it can also include a calibration analyzer to evaluate the deviation between the display's actual grayscale response curve and a standard curve; simultaneously, it can integrate spatial analysis and temporal analysis units to process sensor data to identify screen brightness non-uniformity and drift in the liquid crystal molecule response characteristics. Alternatively, the quantization end can be a software service running in the display's operating system, utilizing multi-threaded processing technology to perform parallel analysis and quantization of the collected data.

[0119] The adjustment end can be a high-performance image processing unit (IPU), such as a custom application-specific integrated circuit (ASIC) or an FPGA configured with specific logic. This adjustment end receives deviation data output from the quantization end and generates non-linear correction instructions based on this deviation data. These correction instructions can exist in the form of lookup tables (LUTs), mathematical model parameters, or control signal sequences. For example, it can generate a multi-segment gamma-correction LUT or a region-based brightness compensation matrix. Subsequently, the adjustment end combines these non-linear correction instructions and applies them in real-time to the image frame data stream that is about to be displayed on the screen. For example, it can perform real-time grayscale mapping, brightness adjustment, and color correction on pixel data before it reaches the display panel driver. In some simpler implementations, the adjustment end can also be a software module that adjusts grayscale response by modifying parameters of the display driver or directly manipulating frame buffer data.

[0120] Compared to existing technologies, the liquid crystal display optimization system of this application represents a significant advancement. Traditional display calibration systems often optimize only a single type of display deviation, such as calibrating only the grayscale response curve or adjusting backlight uniformity. In professional medical imaging diagnostic environments, when multiple complex display deviations (such as changes in grayscale information during data transmission, shifts in the display's grayscale response curve, brightness non-uniformity in screen areas, and drift in the response characteristics of liquid crystal molecules) coexist and intertwine, existing systems struggle to accurately separate and identify each influencing source. Their correction effects are often unsatisfactory and may even exacerbate image distortion, leading to problems such as loss of detail in dark areas and unnatural color transitions.

[0121] The LCD display optimization system of this application provides a more comprehensive and refined solution through the coordinated operation of its input, quantization, and adjustment ends. The input end can acquire multi-dimensional information, including data source characteristics, user display preferences, and the physical state of the display, enabling the system to more accurately diagnose various potential deviations. The quantization end can precisely identify and quantify these complex, multi-factor intertwined display deviations, rather than simply making fuzzy judgments. More importantly, the adjustment end can generate highly targeted non-linear correction instructions based on the quantized deviations and apply them in combination. This means the system can simultaneously handle multiple complex display problems; for example, a correction instruction may simultaneously include compensation for grayscale loss in data transmission, correction of grayscale response curve offset, and local adjustment of screen brightness uniformity. This systematic, non-linear combined correction capability allows this application to effectively avoid the problems of "paying attention to one aspect while neglecting another" or "the more you correct, the worse it gets" that may occur with traditional methods when dealing with multi-factor intertwined deviations, thereby significantly improving the display quality and diagnostic accuracy of medical images and ensuring that doctors can obtain faithfully reproduced image information.

[0122] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing LCD screen display, characterized in that, include: Acquire information reflecting the image data source, including the characteristics of the data source, user display preferences, and the physical state information of the display; Based on the information reflecting the image data source, the grayscale information changes, the shift of the display grayscale response curve, the brightness non-uniformity of the display screen area, and the drift of the liquid crystal molecule response characteristics during the data transmission process are identified and quantified. The corresponding deviation is obtained based on the quantified data, and a nonlinear correction instruction is generated based on the deviation. The nonlinear correction instructions are combined and applied to adjust the grayscale response of the display.

2. The LCD screen display optimization method according to claim 1, characterized in that, The step of identifying and quantifying grayscale information changes, grayscale response curve shifts, brightness non-uniformity of display screen areas, and liquid crystal molecule response characteristic drifts during data transmission based on the information reflecting the image data source includes: Acquire the brightness output data and temperature data of the display screen area; Time series analysis is performed on the brightness output data to identify the long-term trend and local differences in brightness decay, and to obtain the corresponding local aging areas of the backlight module. Spatial distribution analysis is performed on the temperature data to identify local patterns of the influence of temperature differences on the response characteristics of liquid crystal molecules, and to obtain the spatial existence of the corresponding uneven internal temperature distribution regions. When the local aging area of ​​the backlight module spatially overlaps with the area of ​​uneven internal temperature distribution, the brightness output data and the temperature data are correlated based on the long-term trend of brightness decay and the local pattern of the influence of temperature difference on the response characteristics of liquid crystal molecules. Based on the correlation analysis results, the screen brightness non-uniformity caused by the local aging area of ​​the backlight module and the liquid crystal molecule response characteristic drift caused by the uneven internal temperature distribution area are separated and quantified.

3. The LCD screen display optimization method according to claim 1, characterized in that, The step of obtaining the corresponding deviation based on the quantized data and generating a nonlinear correction instruction based on the deviation includes: Real-time monitoring of the frame synchronization signal of the image processing unit inside the display; According to the frame synchronization signal, the generation process of the nonlinear correction instruction is synchronized with the display refresh cycle of the image frame; During the display refresh cycle, the parameters of the nonlinear correction instruction are calculated and updated based on the currently identified and quantified deviation value. According to the frame synchronization signal, the application process of the nonlinear correction instruction is synchronized with the display refresh cycle of the image frame; During the display refresh cycle, the synchronized nonlinear correction instruction is loaded into the image processing unit of the display and applied to the currently processed image frame.

4. The LCD screen display optimization method according to claim 1, characterized in that, The acquisition of information reflecting the image data source includes the characteristics of the data source, user display preferences, and the physical state information of the display, including: Obtain the electromagnetic noise level inside the display; When the electromagnetic noise intensity exceeds a preset intensity threshold, an interference warning signal is generated; Analyze the real-time readings of the display's multi-point sensor network; Upon receiving the interference warning signal, the difference between the real-time reading and the historically stable sensor reading is compared. The differences are matched with pre-stored characteristics of the impact of interference on the sensor; Based on the matching results, the influence mode of transient electromagnetic interference on the sensor is identified; Based on the described influence pattern, predictive calibration is performed on the real-time readings of the affected sensors; Based on the sensor readings after predictive calibration, the brightness output data and temperature data of the display screen area are obtained to reflect information about the physical state of the display.

5. The LCD screen display optimization method according to claim 4, characterized in that, When the electromagnetic noise intensity exceeds a preset intensity threshold, an interference warning signal is generated, including: Obtain the electromagnetic noise level inside the display; The electromagnetic noise level is subjected to spectral decomposition to obtain the noise intensity of multiple frequency bands; Based on the noise intensity of the multiple frequency bands, identify narrowband interference with specific spectral characteristics; An interference warning signal is generated based on the narrowband interference.

6. The LCD screen display optimization method according to claim 4, characterized in that, The step of matching the difference with pre-stored characteristics of the impact of interference on the sensor includes: The matching weights of the interference impact feature library are dynamically adjusted based on the spectral characteristics, intensity change rate, and interference duration of electromagnetic noise. Based on the spatial distribution and temporal evolution pattern of the differences, select multiple interference impact features that are close to the current interference pattern from the interference impact feature library; The multiple interference impact features are weighted and fused to obtain a composite interference impact feature; The differences are matched with the characteristics of the combined interference.

7. The LCD screen display optimization method according to claim 6, characterized in that, The step of dynamically adjusting the matching weights of the interference influence feature library based on the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise includes: Feature extraction is performed on the spectral characteristics of the electromagnetic noise, the rate of intensity change, and the duration of the interference to obtain feature parameters of multiple interference sources; Based on the characteristic parameters of the multiple interference sources, the contribution of each interference source to the difference in sensor readings is independently evaluated; Based on the independent evaluation results, calculate the independent adjustment amount of the matching weight for each interference source; The independent adjustment values ​​are superimposed to obtain the matching weights of the interference influence feature library.

8. The LCD screen display optimization method according to claim 7, characterized in that, The process involves extracting features from the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise to obtain feature parameters for multiple interference sources, including: Multidimensional time-frequency analysis is performed on the electromagnetic noise signal to obtain the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise; The system identifies nonlinear changes in the electromagnetic noise or coupling effects between the multiple interference sources and initiates an adaptive signal separation mechanism. Analyze the energy distribution and interaction modes of the multiple interference sources in different time-frequency regions; The separation parameters of the adaptive signal separation mechanism are dynamically adjusted based on the energy distribution and the interaction mode. Independent feature extraction is performed on the signal after the separation parameters are adjusted; The spectral characteristics, intensity change rate, and interference duration of each interference source are obtained as characteristic parameters of the plurality of interference sources.

9. The LCD screen display optimization method according to claim 8, characterized in that, The multidimensional time-frequency analysis of the electromagnetic noise signal to obtain the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise includes: An initial time-frequency analysis is performed on the electromagnetic noise signal to obtain a preliminary time-frequency distribution; Identify whether there are concentrated instantaneous impact regions in the preliminary time-frequency distribution, and dynamically adjust the window length of the time-frequency analysis according to the duration of the instantaneous impact regions; Within the frequency range of the instantaneous impact region, the frequency resolution of the time-frequency analysis is dynamically adjusted; Based on the dynamically adjusted window length and frequency resolution, a local time-frequency reanalysis is performed on the instantaneous impact region. By integrating the analysis results of the instantaneous impact region with those of the non-instantaneous impact region, the spectral characteristics, intensity change rate, and interference duration of the electromagnetic noise are obtained.

10. A liquid crystal display optimization system, characterized in that, include: The input terminal is used to acquire information reflecting the image data source, including the characteristics of the data source, user display preferences, and the physical state information of the display. The quantization end is used to identify and quantify changes in grayscale information, shifts in the grayscale response curve of the display, brightness non-uniformity of the display screen area, and drift of liquid crystal molecule response characteristics during data transmission based on the information reflecting the image data source. The adjustment end is used to obtain the corresponding deviation based on the quantized data, generate nonlinear correction instructions based on the deviation, and combine the nonlinear correction instructions to adjust the grayscale response of the display.

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