LED display screen debugging method and system

Abstract

The application relates to the technical field of display, in particular to an LED display screen debugging method and system. The method comprises the following steps: acquiring first calibration data representing physical display characteristics of each pixel area of an LED display screen, wherein the first calibration data describes the chromaticity and luminance attenuation caused by long-time operation; extracting a color characteristic abstract of an input source, wherein the color characteristic abstract comprises a main color range, color space parameters and luminance distribution characteristics; acquiring corresponding second calibration data from a preset calibration strategy library according to the color characteristic abstract, wherein the calibration strategy library records calibration schemes for different color characteristics to preferentially guarantee the display accuracy of key colors; and applying the first calibration data and the second calibration data to an input signal of the input source to complete automatic color debugging of the display effect. The method has the advantages of automatic color debugging of the display effect of the LED display screen, improved debugging efficiency and accuracy.

Description

Technical Field

[0001] This application relates to the field of display technology, and more specifically, to a method and system for debugging an LED display screen. Background Technology

[0002] To create a unique brand atmosphere and visual impact, many stores have installed curved LED displays in their core areas. The main task of these displays is to periodically play meticulously produced seasonal advertising videos, live recordings of international fashion weeks, and artistic, variable visual images customized specifically for the flagship store. The sources of these regularly played images vary. For example, advertising videos are usually produced by professional film production companies, using film-grade recording equipment and post-production color grading processes, resulting in images with a specific artistic style and color palette. Fashion show recordings may be from multi-camera live recordings; due to differences in lighting and lens parameters, even after post-processing, the basic color tone and brightness range of the output image may vary slightly. Furthermore, the content displayed on LED screens is not static; new video clips or images are frequently added temporarily. These temporary additions often have short production cycles, and their original color profiles, encoding formats, and color management processes may differ significantly from regular materials. Some materials may even not have undergone rigorous color calibration when provided.

[0003] LED displays typically operate under high-intensity conditions for extended periods after installation. The heat dissipation conditions of LED modules in different areas of the screen may vary during installation. Combined with prolonged continuous operation, some LED units may experience faster luminous efficiency decay or color coordinate drift than others. This uneven aging gradually creates areas of color difference on the screen that are not immediately noticeable to the naked eye but can be measured by instruments. When displaying large areas of solid color or gradient backgrounds, these differences disrupt the overall integrity and detail of the image. Existing calibration methods may not be effective in addressing this localized uneven aging problem and are insufficient for dynamic optimization based on the specific color characteristics of the input content.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide an LED display screen debugging method and system, which has the advantages of automated color debugging of LED display screen display effect, improving debugging efficiency and accuracy.

[0006] This application provides a method for debugging an LED display screen, the technical solution of which is as follows:

[0007] The methods include:

[0008] Obtain first calibration data characterizing the physical display characteristics of each pixel area of ​​the LED display screen. The first calibration data describes the color and brightness attenuation caused by long-term operation.

[0009] Extract a color characteristic summary from the input source, which includes the dominant hue range, color space parameters, and brightness distribution characteristics;

[0010] Based on the color characteristic summary, the corresponding second calibration data is obtained from the preset calibration strategy library. The calibration strategy library records calibration schemes that prioritize the accuracy of key color display for different color characteristics.

[0011] Based on the first and second calibration data, the input signal of the input source is applied to complete the automated color adjustment of the display effect.

[0012] The above solution enables automated color calibration of LED display screens and allows for adaptation based on screen aging and the color characteristics of the input source, thus improving calibration efficiency and accuracy.

[0013] Furthermore, this application also proposes obtaining first calibration data characterizing the physical display characteristics of each pixel area of ​​an LED display screen, including:

[0014] Standardized test patterns are displayed in each pixel area of ​​the LED display screen;

[0015] Obtain the brightness and chromaticity measurements of the standardized test pattern displayed in each pixel area;

[0016] The luminance and chromaticity measurements are compared with preset standard values ​​to obtain the physical attenuation characteristics and luminance attenuation coefficient of each pixel area, and stored as the first calibration data.

[0017] The above scheme provides a specific method for obtaining the physical attenuation characteristics of the screen, making the first calibration data more accurate and reliable.

[0018] Furthermore, this application also proposes extracting a summary of the color characteristics of the input source, including:

[0019] Pixel distribution analysis is performed on the keyframe images of the input source, and color space parameters of the keyframe images are extracted in combination with the metadata information of the input source.

[0020] The dominant hue range and brightness distribution characteristics are calculated based on the color space parameters.

[0021] The color characteristics are summarized by storing the dominant hue range and brightness distribution features in a structured manner.

[0022] The above scheme provides a specific method for extracting the color characteristic summary of the input source, which provides a basis for the selection of subsequent calibration strategies.

[0023] Furthermore, this application also proposes to automatically adjust the color of the display effect based on the first calibration data and the second calibration data applied to the input signal of the input source, including:

[0024] The input signal from the input source is applied with preset basic calibration data to generate a basic image;

[0025] The target pixels corresponding to the preset key colors are identified from the basic image. Based on the color characteristic summary of the target pixels, the corresponding second calibration data is obtained from the calibration strategy library.

[0026] Based on the first calibration data and the second calibration data, the calibration information of the target pixel is calculated;

[0027] The pixels in the base image corresponding to the target pixel position are replaced with the pixels corresponding to the calibration information to complete the automatic color adjustment of the display effect.

[0028] The above scheme provides detailed steps for automated color adjustment based on two calibration data, especially for the processing of key colors.

[0029] Furthermore, this application also proposes to identify target pixels corresponding to preset key colors from a basic image, including:

[0030] Based on the base image, candidate color pixels with color values ​​within the preset key color range are selected;

[0031] Contextual features of candidate color pixels are generated based on the spatial clustering or temporal persistence of candidate color pixels in consecutive frames of the input source.

[0032] The context features are compared with the preset key color determination rules to obtain the comparison results. The key color determination rules define the spatial or temporal thresholds used to distinguish the preset key color from the neighboring colors.

[0033] Candidate color pixels whose comparison results satisfy the key color determination rules are identified as target pixels.

[0034] The above scheme provides a robust method for identifying target pixels corresponding to key colors, combining contextual features such as spatial clustering and temporal persistence.

[0035] Furthermore, this application also proposes to compare contextual features with preset key color determination rules to obtain comparison results, including:

[0036] To obtain dynamic characteristic indicators that characterize the degree of change between consecutive frames;

[0037] Adjust the spatial or temporal thresholds in the key color determination rules based on dynamic characteristic indicators;

[0038] The comparison results are obtained by comparing the contextual features with the adjusted key color determination rules.

[0039] The above scheme enables the key color determination rules to be adaptively adjusted according to the dynamic characteristics of the image, thereby improving the accuracy of key color recognition.

[0040] Furthermore, this application also proposes a dynamic characteristic index for obtaining a representation of the degree of change between consecutive frames in an input source, including:

[0041] Each frame of the input source is divided into multiple frame blocks;

[0042] Calculate the content change of each frame block between consecutive frames and generate the corresponding block change index;

[0043] Based on the change indicators of each block, the screen blocks with similar change indicators are classified into the same dynamic characteristic partition.

[0044] For each dynamic characteristic partition, a partition dynamic indicator is determined, and the partition dynamic indicator is combined as a dynamic characteristic indicator.

[0045] The above scheme provides a specific method for obtaining dynamic characteristic indicators of images, which can quantify the dynamic changes in different areas.

[0046] Furthermore, this application also proposes classifying image blocks with similar block change indices into the same dynamic characteristic partition based on the block change indices, including:

[0047] Statistically analyze the block change index of all frame blocks corresponding to the current frame in a continuous frame, and obtain the numerical distribution of the block change index.

[0048] Based on the numerical distribution, determine the cluster centers of indicators representing different dynamic levels;

[0049] Based on the proximity of each block's change index to the cluster center of each index, the screen blocks are categorized into the corresponding dynamic characteristic partitions.

[0050] The above scheme provides a specific method for dynamic characteristic partitioning based on block change indicators, which can identify areas with different dynamic levels in the image.

[0051] Furthermore, this application also proposes determining the cluster centers of indicators representing different dynamic levels based on numerical distribution, including:

[0052] The numerical distributions are analyzed cyclically. Based on the local density characteristics of the numerical distributions, multiple local clustering regions are identified and used as the clustering centers of basic indicators. The identification of local density characteristics includes calculating the central tendency of the local neighborhood of each block change indicator and adjusting its contribution weight in the identification of local density characteristics based on the degree of deviation of each block change indicator from the central tendency calculation.

[0053] When the value of the basic indicator cluster center meets the preset stability condition, the basic indicator cluster center will be used as the final indicator cluster center.

[0054] The above scheme provides a specific method for determining the cluster center of dynamic level indicators, making the dynamic characteristic partitioning more accurate.

[0055] Furthermore, this application also proposes an LED display screen debugging system, comprising:

[0056] The acquisition module is used to acquire first calibration data that characterizes the physical display characteristics of each pixel area of ​​the LED display screen. The first calibration data describes the color and brightness attenuation caused by long-term operation.

[0057] The parameter extraction module is used to extract a color characteristic summary of the input source, which includes the dominant hue range, color space parameters, and brightness distribution features.

[0058] The calibration strategy module is used to obtain the corresponding second calibration data from the preset calibration strategy library based on the color characteristic summary. The calibration strategy library records calibration schemes that prioritize the accuracy of key color display for different color characteristics.

[0059] The debugging module is used to automatically adjust the color of the display effect by applying the first calibration data and the second calibration data to the input signal of the input source.

[0060] The above scheme provides a system for implementing the above debugging method, which is convenient for practical application.

[0061] As can be seen from the above, the LED display debugging method and system provided in this application solves the problem in the prior art of being unable to perform content adaptation and key color priority automatic color debugging on irregularly shaped aging screens within a limited time by acquiring screen aging data, extracting the color characteristics of the input source and combining it with a calibration strategy library for automatic debugging. It has the advantages of being able to achieve automatic color debugging of the display effect of LED display screens and being able to adapt to the screen aging condition and the color characteristics of the input source, thereby improving debugging efficiency and accuracy. Attached Figure Description

[0062] Figure 1 This is a flowchart illustrating an LED display debugging method provided in one embodiment of this application.

[0063] Figure 2 This is one of the flowcharts illustrating an LED display debugging method provided in another embodiment of this application.

[0064] Figure 3 This is a second schematic flowchart of an LED display debugging method provided in another embodiment of this application.

[0065] Figure 4 This is a third schematic flowchart of an LED display debugging method provided in another embodiment of this application.

[0066] Figure 5 This is the fourth flowchart illustrating an LED display debugging method according to another embodiment of this application.

[0067] Figure 6 The fifth schematic flowchart illustrates an LED display debugging method according to another embodiment of this application.

[0068] Figure 7 This is a sixth flowchart illustrating an LED display debugging method according to another embodiment of this application.

[0069] Figure 8 This is the seventh flowchart illustrating an LED display debugging method according to another embodiment of this application.

[0070] Figure 9 This is the eighth flowchart illustrating an LED display debugging method provided in another embodiment of this application.

[0071] Figure 10 A flowchart of an LED display debugging system provided in another embodiment of this application.

[0072] In the diagram: 1. Acquisition module; 2. Parameter extraction module; 3. Calibration strategy module; 4. Debugging module. Detailed Implementation

[0073] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0074] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0075] Traditional methods for debugging curved LED displays struggle to accurately compensate for uneven color and brightness degradation caused by long-term operation. Furthermore, existing methods are ill-suited for quickly adapting to frequent switching between input sources with significantly different color characteristics, resulting in poor display quality. Especially within limited maintenance timeframes, manual or semi-automatic debugging processes are inefficient, failing to guarantee the accuracy of key brand colors and hindering automated debugging to handle rapid content updates and reduce reliance on operator skills.

[0076] Reference Figure 1 This application proposes a method for debugging an LED display screen, the method comprising:

[0077] S1000: Acquire first calibration data characterizing the physical display characteristics of each pixel area of ​​the LED display screen. The first calibration data describes the chromaticity and brightness attenuation caused by long-term operation.

[0078] S2000: Extracts a summary of the color characteristics of the input source, including the dominant hue range, color space parameters, and brightness distribution features;

[0079] S3000: Based on the color characteristic summary, retrieve the corresponding second calibration data from the preset calibration strategy library. The calibration strategy library records calibration schemes that prioritize the accuracy of key color display for different color characteristics.

[0080] S4000: Based on the first calibration data and the second calibration data, it applies the input signal of the input source to complete the automatic color adjustment of the display effect.

[0081] In this embodiment, the first calibration data refers to data characterizing the physical display characteristics of each pixel area of ​​the LED display screen. It can be obtained by measuring the brightness and chromaticity of each pixel area through a measuring device and comparing them with preset standard values. It is mainly used to describe the chromaticity and brightness attenuation caused by long-term operation. The color characteristic summary refers to a general description of the color characteristics of the input source. It can be obtained by performing pixel distribution analysis on the key frame image of the input source and extracting color space parameters by combining metadata information. It is mainly used to identify the color style and key color information of the input source. The calibration strategy library refers to a preset database containing multiple calibration schemes. It can be obtained by storing lookup tables (LUTs) or parameter sets corresponding to different color characteristics. It is mainly used to provide targeted calibration schemes based on the color characteristics of the input source, prioritizing the accuracy of key color display. The second calibration data refers to the calibration data obtained from the calibration strategy library that corresponds to the color characteristic summary of the input source. It can be obtained by directly reading the lookup table or parameter set in the calibration strategy library that matches the color characteristic summary. It is mainly used to adjust the display effect according to the content of the input source, especially the display of key colors.

[0082] This application's solution acquires data on the physical attenuation characteristics of the display screen itself. Based on this, it analyzes the color characteristics of the current input source and selects the most suitable calibration scheme from a pre-set strategy library according to the analysis results. Subsequently, the first calibration data representing the physical state of the screen is organically combined with the second calibration data representing the content adaptation strategy and applied to the input signal of the input source. It is precisely because of this calibration method that combines the screen's own state and content characteristics that the final display effect can simultaneously compensate for the uneven aging of the screen and specifically optimize the color performance of the currently played content, especially ensuring the accurate reproduction of key brand colors. This solves the problem of efficient and accurate color calibration of irregularly shaped and aging displays in complex environments. The entire process requires minimal manual intervention, achieving automated operation.

[0083] In some preferred embodiments, firstly, a measurement unit scans and measures the LED display screen to obtain brightness and chromaticity attenuation data for different areas of the screen, generating first calibration data. Simultaneously, a content analysis unit receives the input source signal, analyzes keyframes of the video stream, extracts its dominant hue range, color space parameters, and brightness distribution characteristics, and generates a color characteristic summary. A strategy selection unit searches for a matching calibration scheme in a pre-built calibration strategy library based on this color characteristic summary, obtaining the corresponding second calibration data. Finally, a calibration processing unit takes the input source signal, the first calibration data, and the second calibration data as input, and processes the input signal in real time, for example, adjusting the color and brightness values ​​of pixels through lookup tables or matrix operations, generating a calibrated output signal to drive the LED display screen. This process can be triggered periodically or automatically when the input source switches, without requiring manual intervention in the measurement and adjustment process.

[0084] Reference Figure 2 In another embodiment of this application, step S1000 further includes:

[0085] S1100: Displays standardized test patterns in each pixel area of ​​the LED display screen;

[0086] S1200: Acquire the brightness and chromaticity measurements of the standardized test pattern displayed in each pixel area;

[0087] S1300: Compare the measured values ​​of brightness and chromaticity with preset standard values ​​to obtain the physical attenuation characteristics and brightness attenuation coefficient of each pixel area, and store them as the first calibration data.

[0088] In this embodiment, the standardized test pattern refers to an image or sequence with a known, stable, and uniform color and brightness distribution, such as a solid color, grayscale, or checkerboard pattern. Its purpose is to provide a controllable and consistent display benchmark for accurate measurement and comparison. The preset standard value refers to the brightness and colorimetric reference values ​​measured under ideal conditions, provided by the manufacturer, or determined according to industry standards, used to measure the performance of the display screen. Its purpose is to provide an objective comparison benchmark to quantify the deviation between the actual measured value and the ideal state. The physical attenuation characteristics and brightness attenuation coefficient are quantitative indicators that characterize the degree of brightness reduction and colorimetric drift of each pixel area of ​​the LED display screen due to long-term use, calculated by comparing the actual measured value with the preset standard value. The brightness attenuation coefficient is one of the specific quantitative parameters, and its purpose is to accurately describe the aging state of each pixel area and provide a basis for subsequent calibration.

[0089] This application's solution ensures the uniformity of the measurement environment and the measured object by displaying standardized test patterns in each pixel area of ​​the LED display screen, eliminating measurement errors caused by differences in displayed content. Next, the brightness and chromaticity measurements of the standardized test patterns displayed in each pixel area are obtained, providing objective data reflecting the actual display effect of the pixel area. Subsequently, the brightness and chromaticity measurements are compared with preset standard values ​​to calculate the physical attenuation characteristics and brightness attenuation coefficient of each pixel area, and this data is stored as the first calibration data. By comparing with the standard values, the influence of individual differences in the LED display screen at the time of manufacture and factors such as ambient light on the measurement results can be effectively eliminated, thus more accurately reflecting the attenuation of the LED display screen due to long-term operation. This accurate first calibration data, combined with the second calibration data obtained in subsequent steps, is applied to the input signal, which can more effectively compensate for the aging and non-uniformity of the display screen, especially in ensuring the accuracy of key color display, providing a reliable data foundation and improving the overall color calibration effect.

[0090] In some preferred embodiments, the specific implementation is as follows: First, standardized test patterns of full white, full red, full green, full blue, and 16 grayscale levels are sequentially displayed on the LED display screen. Using a high-precision luminance and colorimeter, the luminance and chromaticity of each pixel area of ​​the display screen are measured, and the corresponding measurement values ​​are recorded. For example, for the full white pattern, the luminance value and CIE color coordinate value of each pixel area are recorded. Then, these measurement values ​​are compared with the standard measurement values ​​at the factory of this model of display screen. For example, the ratio of the actual luminance of each pixel area to the standard luminance is calculated as the luminance attenuation coefficient. The color difference between the actual color coordinates and the standard color coordinates of each pixel area is calculated as the chromaticity attenuation characteristic. Finally, these calculated luminance attenuation coefficients and chromaticity attenuation characteristic data are stored in the form of a two-dimensional array as the first calibration data, where the array index corresponds to the pixel area position of the display screen.

[0091] Reference Figure 3 In another embodiment of this application, step S3000 further includes:

[0092] S3100: Performs pixel distribution analysis on the keyframe image of the input source and extracts the color space parameters of the keyframe image by combining the metadata information of the input source.

[0093] S3200: The main color range and brightness distribution characteristics are calculated based on the color space parameters;

[0094] S3300: Stores color characteristic summaries in a structured manner, including the primary hue range and brightness distribution features.

[0095] In this embodiment, keyframe images refer to image frames that represent the main content and color characteristics of the input source. Specifically, they can be selected by analyzing the content change rate of the video stream or by fixed time intervals, with the aim of efficiently capturing overall color characteristics with a small number of image samples. Pixel distribution analysis refers to the statistical analysis of the color or brightness values ​​of pixels in the image. Specifically, this can be done by constructing color histograms, brightness histograms, or performing cluster analysis, with the aim of understanding the distribution of different colors and brightness in the image. Metadata information of the input source refers to additional information related to the input source file, specifically including encoding format, color profile, gamma curve information, etc., with the aim of providing prior knowledge about the color characteristics of the input source to assist in more accurately understanding and processing pixel data. Color space parameters refer to parameters describing the color space of the image. The attributes between pixels can include color gamut, white point coordinates, gamma value, etc., with the aim of determining the actual color represented by the pixel value; the dominant color range refers to the most representative or concentrated color area in the input source image, which can be determined by analyzing pixel distribution and identifying high-density areas, with the aim of summarizing the main color tendency of the input source; the brightness distribution feature refers to the overall distribution of pixel brightness in the input source image, which can be described by calculating the statistics of the brightness histogram, with the aim of reflecting the brightness levels and contrast of the image; and structured storage refers to organizing and saving the extracted color characteristic summary information according to a predetermined data format, such as JSON, XML or other custom data structures, with the aim of facilitating the subsequent reading, parsing and utilization of this information by the system.

[0096] This application's solution first selects representative keyframe images and, combined with metadata information from the input source, performs detailed pixel-level analysis on these images to extract accurate color space parameters. Based on these parameters, it further calculates the dominant hue range that summarizes the overall color tendency of the input source and the brightness distribution characteristics reflecting the tonal gradations. Finally, this key color information is stored in a structured format that is easy for machines to process and recognize, forming a color characteristic summary. This process ensures that the extracted summary information is not only based on the visual content of the image itself but also considers its original color coding standard, thus more comprehensively and accurately reflecting the true color characteristics of the input source. Applying this accurately extracted color characteristic summary to subsequent calibration strategy selection allows the system to intelligently match the most suitable calibration scheme according to the specific color style of the input source, such as prioritizing the display accuracy of key colors while also considering the overall color balance of the image. Compared with relying solely on preset calibration or manual judgment, this significantly improves the intelligence level and effectiveness of automated debugging, effectively solving the problem of large differences in color characteristics between different input sources and the difficulty in uniform adaptation, providing a solid foundation for accurate color presentation of LED displays.

[0097] In some preferred embodiments, firstly, a frame is selected from the input source video stream every 5 seconds or during scene transitions as a keyframe image. Then, pixel value statistics are performed on the selected keyframe images, such as calculating the histogram of each channel in the RGB color space and analyzing the pixel distribution in color spaces such as HSV or Lab. Simultaneously, the metadata of the input source file is read to obtain its color profile information, such as identifying that the video uses the Rec.709 color standard. Based on the pixel analysis results and Rec.709 color space parameters, a clustering algorithm is used to identify the most concentrated color clusters of pixels and determine the dominant color range. For example, if the image is identified as mainly containing blue and green, the dominant color range corresponds to the color intervals of sky blue and grass green, respectively. Simultaneously, the brightness histogram of the keyframe image is calculated, and its mean, standard deviation, and median are calculated as brightness distribution features. Finally, the extracted dominant color range and brightness distribution features are organized into a JSON object and stored as a color characteristic summary file.

[0098] Reference Figure 4 In another embodiment of this application, step S4000 further includes:

[0099] S4100: Apply preset basic calibration data to the input signal from the input source to generate a basic image;

[0100] S4200: Identifies the target pixel corresponding to the preset key color from the basic image, and obtains the corresponding second calibration data from the calibration strategy library based on the color characteristic summary of the target pixel;

[0101] S4300: Calculate the calibration information of the target pixel based on the first calibration data and the second calibration data;

[0102] S4400: Replaces the pixels in the base image corresponding to the target pixel position with the pixels corresponding to the calibration information, thus completing the automatic color adjustment of the display effect.

[0103] In this embodiment, the preset basic calibration data refers to the preliminary, general color calibration data performed on the display screen before performing fine calibration for key colors. This data can be factory calibration data, general calibration data generated during periodic maintenance, or calibration data based on a certain standard color space. Its purpose is to provide a relatively consistent and standardized image foundation for subsequent key color recognition and fine calibration. The basic image refers to the image obtained after applying the preset basic calibration data to the input signal from the input source. It can be represented in the form of digital image frames or pixel matrices. The preset key color refers to a specific color or color range in the displayed content that requires priority in ensuring its display accuracy. It can be implemented using a specific chromaticity coordinate range (such as Lab, Luv), RGB value range, or a color range defined based on a brand's standard color card. Its purpose is to focus calibration resources and ensure the accuracy of visual core elements. The calibration process involves precise color reproduction; target pixels refer to the set of pixels in the base image that are identified as corresponding to the preset key color. These can be represented as a list of pixel coordinates, a binary mask image, or a set of pixel indices, and their purpose is to accurately locate the area requiring fine-tuning; color characteristic summary refers to a general description of the color attributes of the target pixels. This can be achieved using statistical information such as the average chromaticity, brightness, saturation, color distribution histogram, or texture features of the key color region, and its purpose is to provide a basis for selecting a suitable calibration strategy from the calibration strategy library; calibration information refers to parameters or data calculated based on the first and second calibration data, used to adjust the color of the target pixels. This can be represented as new RGB values ​​of the target pixels, a lookup table applied to the target pixels, or a color transformation matrix, and its purpose is to provide specific instructions for precise color compensation of the target pixels.

[0104] This application's solution generates a pre-adjusted base image by applying preset basic calibration data to the input signal of the input source, providing a standardized starting point for subsequent fine-tuning. Next, the base image identifies target pixels corresponding to preset key colors, and based on the color characteristic summaries of these target pixels, retrieves targeted second calibration data from a calibration strategy library. This process ensures that calibration is no longer blindly applied to the entire image, but rather precisely locates key color areas requiring optimization. Subsequently, based on the first calibration data characterizing the screen's physical attenuation characteristics and the calibration strategy (second calibration data) for the key colors, the calibration information required to compensate these target pixels is calculated. This combination considers the screen's physical state and the characteristics of the key colors in the content, making the calculated calibration information more accurate. Finally, the pixels in the base image corresponding to the target pixel positions are replaced with the pixels corresponding to the calculated calibration information, completing automated color adjustment of the display effect. By replacing only the pixels in the key color areas, high accuracy of key color display can be ensured, while minimizing the impact on the colors of other areas of the image, thus maintaining visual harmony between the overall image color and the key colors. This targeted local calibration method overcomes the problem of color distortion in non-critical color areas or overall image incoordination that may result from directly applying calibration data to the entire input signal. Furthermore, it can make full use of the acquired first and second calibration data, refining their application from the overall level to local critical color areas, thereby improving the overall visual quality of the image while ensuring accurate display of critical colors.

[0105] In some preferred embodiments, it is assumed that the input source is playing a video containing a brand logo (a specific shade of red). First, preset factory calibration data is applied to the input signal of the video to generate a base frame. In this base frame, the system identifies the pixels in the red area where the brand logo is located; these pixels are the target pixels. The system analyzes the color characteristics of these red target pixels, such as calculating their average chromaticity coordinates, and finds that the red they currently display is slightly orange-toned. Based on this "orange-toned" color characteristic summary, the system looks up the corresponding calibration strategy from the calibration strategy library, such as obtaining a lookup table for compensating for the orange-toned color (second calibration data). Simultaneously, the system obtains the physical attenuation data of the screen pixels corresponding to the red area (first calibration data), for example, the brightness of the red sub-pixels in this area is attenuated by 8%. Based on this 8% brightness attenuation data and the lookup table for compensating for the orange-toned color, the system calculates the calibration information for the final adjustment of these red target pixels, such as calculating new RGB values ​​or generating a local lookup table applied to these pixels. Finally, the pixels in the brand logo area of ​​the base frame are replaced with the new pixel values ​​obtained according to the calculated calibration information, thereby completing the precise calibration of the brand logo area color, making it display the standard brand red.

[0106] Reference Figure 5 In another embodiment of this application, it is further proposed that the sub-step of step S4200: identifying the target pixel corresponding to the preset key color from the base image includes:

[0107] S4210: Based on the base image, filter candidate color pixels whose color values ​​are within the preset key color range;

[0108] S4220: Generate contextual features of candidate color pixels based on the spatial clustering or temporal persistence of candidate color pixels in consecutive frames of the input source.

[0109] S4230: Compare the context features with the preset key color determination rules to obtain the comparison results. The key color determination rules define the spatial or temporal thresholds used to distinguish the preset key color from neighboring colors.

[0110] S4240: The candidate color pixels whose comparison results satisfy the key color determination rules are identified as the target pixels.

[0111] In this embodiment, a candidate color pixel refers to a pixel whose color value falls within the color range of a preset key color. This can be achieved by defining a color range in a specific color space (e.g., HSL, Lab, or RGB) (e.g., using center color coordinates and tolerance values). Contextual features refer to information reflecting the distribution or change characteristics of candidate color pixels in their surrounding spatial region or continuous time series. This can be generated by calculating the number and density of pixels with the same or similar colors in the neighborhood of the candidate color pixel, or by tracking the stability of the position, color, or existence state of the candidate color pixel in multiple consecutive frames. Key color determination rules refer to the standard or set of conditions used to determine whether a candidate color pixel truly belongs to the preset key color. These rules define spatial or temporal thresholds that distinguish the preset key color from neighboring colors. For example, a spatial threshold can be set to require that there be a sufficient number of pixels of the same color within a certain range around the candidate color pixel, or a temporal threshold can be set to require that the candidate color pixel exists stably in multiple consecutive frames.

[0112] This application's solution first filters candidate color pixels whose color values ​​fall within the preset key color range based on a base image, thus initially identifying potential target areas and reducing the amount of data required for subsequent processing. Building upon this, contextual features of the candidate color pixels are generated based on their spatial clustering or temporal persistence within consecutive frames of the input source. The introduction of these contextual features allows for judgments that no longer rely solely on the color value of a single pixel, but rather consider the temporal and spatial correlation of pixels. Spatial clustering reflects whether a candidate color pixel is isolated or belongs to a large homogeneous region; temporal persistence reflects whether the candidate color pixel is stable in dynamic images, rather than being transient noise or flickering. By combining this spatial and temporal information, the true attributes of candidate color pixels can be determined more accurately. Next, the generated contextual features are compared with preset key color determination rules. These rules define spatial or temporal thresholds for distinguishing preset key colors from neighboring colors; these thresholds are preset based on analysis of key color characteristics and common interference patterns. By comparing the pixel values ​​with these rules, pixels whose color values ​​are close to the key color but are spatially isolated or temporally unstable, such as noise points or rapidly changing background elements, can be effectively filtered out. Ultimately, only candidate color pixels whose comparison results satisfy the key color determination rules are identified as the true target pixels. This method, which uses initial screening based on color values ​​followed by refined determination based on spatial and temporal context, significantly improves the accuracy of target pixel identification and avoids misidentifying non-key color pixels as target pixels. This accurate target pixel identification provides a reliable foundation for subsequent steps of calculating calibration information and replacing pixels based on the first and second calibration data. It ensures that calibration operations are only applied to the key color areas that truly require calibration, thereby improving the overall accuracy and effectiveness of color adjustment. Its advantages are particularly evident when dealing with complex images containing interfering colors or noise similar to the key color.

[0113] In some preferred embodiments, specifically, the base image can first be traversed pixel by pixel. For each pixel, its color value is checked to see if it falls within a preset key color range (e.g., a specific HSL range for a brand logo color). Pixels that meet the condition are marked as candidate color pixels. Then, for each candidate color pixel, its spatial clustering can be calculated. For example, it can be checked how many pixels in its surrounding 3x3 pixel neighborhood have a color value less than a preset threshold that differs from the candidate color pixel's color value, or the density of pixels of the same color in that neighborhood can be calculated. Simultaneously, the position and color stability of the candidate color pixel in the most recent five consecutive frames can be tracked to calculate its temporal persistence characteristics. For example, it can be counted how many frames it remains in a similar position with minimal color change. These spatial clustering values ​​and temporal persistence values ​​are combined to form the contextual features of the candidate color pixel. Then, the contextual features are compared with preset key color determination rules. For example, the key color determination rule can be set as follows: the spatial clustering value must be greater than a threshold (e.g., there are at least 5 similar pixels in the neighborhood), and the temporal persistence value must be greater than another threshold (e.g., it appears in at least 4 out of 5 consecutive frames). Only candidate color pixels that simultaneously meet these spatial and temporal thresholds will be ultimately determined as target pixels that need to be calibrated.

[0114] Reference Figure 6 In another embodiment of this application, step S4230 further includes:

[0115] S4231: Obtain dynamic characteristic indicators that characterize the degree of change between consecutive frames;

[0116] S4232: Adjust the spatial or temporal thresholds in the key color determination rules based on dynamic characteristic indicators;

[0117] S4233: Compare the contextual features with the adjusted key color determination rules to obtain the comparison results.

[0118] In this embodiment, the dynamic characteristic index refers to a numerical value or set that quantifies the degree of content change between consecutive frames. It can be implemented using techniques such as calculating inter-frame pixel differences, motion vector analysis, or scene transition detection. Its purpose is to provide a quantitative basis for the dynamic degree of the image for subsequent key color determination rule adjustments. The spatial or temporal threshold refers to the critical value used in the key color determination rule to measure the degree of spatial aggregation or temporal persistence of candidate color pixels. It can be represented by specific pixel distance, pixel number, or frame number, etc., and its purpose is to serve as a specific standard for determining key colors. The comparison result refers to the determination conclusion obtained after comparing the context features with the key color determination rule. It can be represented by a Boolean value (yes / no) or a confidence value.

[0119] This application's solution obtains dynamic characteristic indicators representing the degree of change between consecutive frames, adjusts the spatial or temporal thresholds in the key color determination rules based on these indicators, and then compares the contextual features with the adjusted key color determination rules to obtain the comparison result. This solves the problem of misjudgment easily caused by using fixed key color determination rules in dynamic scenes due to rapid changes. It is precisely because the determination thresholds are adaptively adjusted according to the dynamic degree of the scene that the key color determination rules can better adapt to scene content with different dynamic characteristics. For example, when the scene changes rapidly, appropriately relaxing the spatial or temporal thresholds can avoid misjudging fast-moving key color objects as non-key colors; when the scene changes slowly, appropriately tightening the spatial or temporal thresholds can improve the recognition accuracy of static key color areas and reduce interference from dynamic backgrounds. This adaptive adjustment mechanism makes the key color recognition process more robust and accurate, especially when processing video content containing complex motion or scene transitions. This solution, combined with the step of identifying target pixels corresponding to preset key colors in the basic solution, improves the accuracy of target pixel recognition through more accurate key color determination, thereby enhancing the accuracy and effect of subsequent color adjustment.

[0120] In some preferred embodiments, obtaining a dynamic characteristic index characterizing the degree of change between consecutive frames can be achieved by analyzing pixel changes, motion information, or scene switching frequency between consecutive frames to obtain a quantified value. For example, the average pixel difference, optical flow information, or motion vector between two or more consecutive frames can be calculated, and this information can be combined to generate a dynamic characteristic index. The higher the index value, the more drastic the scene change. Based on this dynamic characteristic index, the spatial or temporal thresholds in the key color determination rule can be dynamically adjusted. For example, a threshold adjustment function can be preset, with the dynamic characteristic index as input and adjustment coefficients for the spatial and temporal thresholds as output. When the dynamic characteristic index is low, the adjustment coefficient is close to 1, and the threshold change is small; when the dynamic characteristic index is high, the adjustment coefficient is greater than 1, and the spatial and temporal thresholds will increase accordingly. Specifically, if the dynamic characteristic index indicates drastic scene movement, the spatial aggregation range required for determining the key color can be appropriately expanded, or the temporal persistence requirement can be appropriately reduced. Conversely, if the scene is basically static, the spatial aggregation range can be narrowed, and the temporal persistence requirement can be increased. The adjusted key color determination rule is then used to compare with the contextual features of candidate color pixels. For example, if the adjusted spatial threshold allows for greater spatial dispersion, then even if a key color pixel is surrounded by a small number of non-key color pixels, it may still be identified as a key color as long as its spatial clustering meets the adjusted lower requirement. Similarly, if the adjusted temporal threshold allows for shorter durations, then even if a key color object only appears briefly in the image, it may still be identified as a key color as long as its duration meets the adjusted lower requirement. In this way, the key color determination process can be optimized according to the actual dynamic situation of the image, improving the accuracy of recognition.

[0121] Reference Figure 7 In another embodiment of this application, step S4231 further includes:

[0122] S42311: Divide each frame of the input source into multiple frame blocks;

[0123] S42312: Calculate the content change of each frame block between consecutive frames and generate the corresponding block change index.

[0124] S42313: Based on the change indicators of each block, screen blocks with similar change indicators are classified into the same dynamic characteristic partition.

[0125] S42314: Determine a partition dynamic index for each dynamic characteristic partition, and combine the partition dynamic index as a dynamic characteristic index.

[0126] In this embodiment, a frame block refers to multiple smaller, independent regions into which each frame is divided, aiming to capture the dynamic changes of different regions in the frame more precisely. Content change refers to a numerical value that quantifies the degree of content difference of a frame block between consecutive frames, which can be achieved by calculating pixel value differences, motion vector amplitude, etc. Block change index refers to the value obtained after quantifying the content change of a frame block, aiming to provide a data basis for subsequent dynamic characteristic partitioning. Dynamic characteristic partitioning refers to a set formed by classifying frame blocks with similar block change indices, aiming to stratify the dynamic features in the frame. Partition dynamic index refers to a summary value of the overall dynamic degree of a dynamic characteristic partition, which can be achieved by calculating the average or weighted average of all block change indices within the partition, aiming to reflect the overall dynamic characteristics of each partition.

[0127] This application's solution divides each frame of the input source into multiple frame blocks, enabling detailed analysis of dynamic changes in different areas of the image. Next, the content change of each frame block across consecutive frames is calculated, generating corresponding block change indices that quantify the degree of change in each area. Based on these block change indices, frame blocks with similar levels of dynamics are grouped into the same dynamic characteristic partition, achieving a hierarchical structure of the image's dynamic features. Subsequently, a partition dynamic index is determined for each dynamic characteristic partition, summarizing the overall dynamic level of each partition, and these partition dynamic indices are combined to form the overall dynamic characteristic index. This block-based, hierarchical approach to obtaining dynamic characteristic indices more accurately reflects the local and overall dynamic features of the image compared to simple global indices. Applying these more accurate dynamic characteristic indices to adjust the spatial or temporal thresholds in the key color determination rules allows the key color recognition process to more effectively adapt to dynamic changes in the image. For example, for areas with drastic dynamic changes, the determination threshold can be appropriately adjusted to reduce false positives or false negatives; for relatively static areas, a stricter threshold can be used to improve recognition accuracy. This rule adjustment based on fine-grained dynamic analysis improves the accuracy of key color recognition in complex dynamic scenes, thereby enhancing the overall color tuning effect.

[0128] In some preferred embodiments, each frame of the input source can be divided into a fixed-size grid, for example, the width and height of the image can be divided into several units to form image blocks. When calculating the content change of each image block between consecutive frames, the absolute value of the average pixel brightness difference between the current frame's image block and the corresponding block in the previous frame can be used as the block change index. Based on the block change indices, image blocks with similar block change indices can be classified into different dynamic characteristic partitions using a clustering algorithm (e.g., density-based clustering), such as slow-changing partitions and fast-changing partitions. When determining a partition dynamic index for each dynamic characteristic partition, the average value of all block change indices within that partition can be calculated. Finally, the partition dynamic indices of all partitions can be combined to form a vector as the dynamic characteristic index.

[0129] Reference Figure 8 In another embodiment of this application, step S42313 further includes:

[0130] A1: Statistically analyze the block change index of all frames corresponding to the current frame in a continuous frame, and obtain the numerical distribution of the block change index.

[0131] A2: Based on the numerical distribution, determine the cluster centers of indicators representing different dynamic levels;

[0132] A3: Based on the proximity of each block's change index to the cluster center of each index, the screen blocks are classified into the corresponding dynamic characteristic partitions.

[0133] In this embodiment, numerical distribution refers to the set of block change index values ​​for all frames corresponding to the current frame in a continuous frame and their distribution on the numerical axis. It can be represented by a histogram, probability density function curve, or discrete numerical list, etc., with the aim of comprehensively understanding the overall dynamic changes of each region in the current frame. Index cluster center refers to the typical numerical point or range representing different dynamic levels in the numerical distribution of block change index. It can be determined by clustering algorithms, peak detection algorithms, or statistical feature-based methods, with the aim of providing a reference benchmark for classifying the dynamic characteristics of frame blocks. Proximity refers to the degree of similarity or difference between the block change index value of a frame block and the value of an index cluster center. It can be quantified by Euclidean distance, Manhattan distance, cosine similarity, or similarity calculation methods based on probability models, with the aim of determining which dynamic characteristic partition a frame block should be classified into.

[0134] This application's solution obtains the numerical distribution of block change indicators for all frames in the current frame, thus comprehensively understanding the dynamic changes of the current frame. Based on this numerical distribution, it further identifies the cluster centers of indicators representing different dynamic levels; these centers represent typical dynamic change patterns in the video content. Subsequently, by calculating the proximity between the block change indicator of each frame block and these cluster centers, the frame blocks are precisely classified into the partitions that best represent their dynamic characteristics. This classification method based on overall distribution and cluster centers, compared to simple fixed threshold division, can more flexibly adapt to the dynamic characteristics of different video content and more accurately identify different regions in the frame, such as stationary, slow-moving, and fast-moving areas, thus providing more refined basic data for subsequent dynamic characteristic indicator calculations. It is precisely because of this more accurate dynamic characteristic partitioning that, when adjusting the key color determination rules according to the dynamic characteristic indicators, it is possible to more accurately identify the key color pixels that need to be prioritized under different motion states, thereby improving the accuracy and adaptability of overall color adjustment.

[0135] In some preferred embodiments, the specific implementation is as follows: First, the block change indexes of all frame blocks in the current frame are statistically analyzed, and a histogram of these index values ​​is generated to visualize their distribution. Next, the K-means clustering algorithm is used to perform cluster analysis on these block change index values. For example, the number of clusters is set to 3, representing low dynamic range, medium dynamic range, and high dynamic range, respectively. The clustering algorithm will automatically calculate the center point values ​​of these three clusters, which are the index cluster centers representing different dynamic range levels. Finally, for each frame block, the Euclidean distance between its block change index value and the values ​​of these three index cluster centers is calculated, and the frame block is classified into the dynamic characteristic partition represented by the nearest index cluster center.

[0136] Reference Figure 9 In another embodiment of this application, step A2 further includes:

[0137] A21: Iteratively analyze each numerical distribution, identify multiple local clustering regions based on the local density characteristics of the numerical distribution, and use them as the clustering centers of basic indicators. Local density characteristic identification includes calculating the central tendency of the local neighborhood of each block change indicator, and adjusting the contribution weight of each block change indicator in local density characteristic identification based on the degree of deviation from the central tendency calculation.

[0138] A22: When the value of the basic indicator cluster center meets the preset stability condition, the basic indicator cluster center will be used as the final indicator cluster center.

[0139] In this embodiment, local density features refer to the density of other numerical points in the neighborhood of a given numerical point in a numerical distribution. This can be measured by calculating the number of numerical points in the neighborhood, using a distance-weighted sum, or by density estimation based on a kernel function. The purpose is to identify regions with high density in the data, which may correspond to different dynamic levels. Local clustering regions refer to a series of continuous or closely spaced numerical ranges with high local density features in a numerical distribution. These can be identified using density-based clustering algorithms or local peak detection methods. The purpose is to initially delineate the range of indicators that may represent different dynamic levels. Basic indicator clustering centers refer to the clusters formed by identifying multiple local clusters. The values ​​initially identified as representing the center locations of each cluster region can be determined by calculating the mean, median, or density peak values ​​within the local cluster region. Their purpose is to provide initial candidate cluster centers for subsequent stability assessments. A local neighborhood refers to the set of change index values ​​of other blocks within a certain range, centered on a specific block's change index value. It can be defined using a fixed-radius neighborhood or a k-nearest neighbor neighborhood, and its purpose is to examine the relationship between a single block's change index and its surrounding data. Central tendency calculation involves statistically calculating the change index values ​​of blocks within a local neighborhood to obtain the typical numerical level of that neighborhood. This can be achieved through... This is achieved by calculating the mean, median, or weighted average within a local neighborhood. The purpose is to provide a reference point to measure the concentration of data within that neighborhood. Deviation refers to the difference between the value of a single block's change index and the calculated central tendency of its local neighborhood. It can be measured using absolute difference, squared difference, or relative difference, aiming to quantify the consistency of a single data point with its local environment. Contribution weight refers to the different importance coefficients assigned to different block change index values ​​during the local density feature identification process. These weights are inversely adjusted according to the deviation of the block change index from the central tendency calculation; the greater the deviation, the lower the weight. This aims to reduce noise or outliers. Impact on local density estimation; Preset stability conditions refer to a series of predetermined standards used to judge whether the basic indicator cluster center has sufficient representativeness and reliability. These may include the position change of the basic indicator cluster center in multiple iterations being less than a specific threshold, the number of change indicators in the blocks contained around the basic indicator cluster center reaching a specific proportion, or the local density of the basic indicator cluster center reaching a specific threshold, etc. The purpose is to ensure that the finally determined indicator cluster center can stably represent the true dynamic level; The final indicator cluster center refers to the indicator value that is determined to accurately represent different dynamic levels after stability judgment, which can be used as the basis for subsequent dynamic characteristic partitioning of the image blocks.

[0140] This application's scheme, through iterative analysis of the numerical distribution of block change indicators, can more comprehensively capture potential clustering patterns in the data. Local density feature identification based on numerical distribution can effectively identify high-density areas in the data, corresponding to different dynamic levels. Crucially, during local density feature identification, by calculating the central tendency of the local neighborhood of each block change indicator and adjusting its contribution weight in local density feature identification based on the deviation of each block change indicator from the central tendency calculation, this scheme can effectively suppress the influence of noisy data or outliers on local density estimation, making the identified local clustering areas more accurate. Subsequently, these accurately identified local clustering areas are used as basic indicator clustering centers. To further ensure the reliability of the determined clustering centers, this scheme sets preset stability conditions; only when the values ​​of the basic indicator clustering centers meet these conditions are they determined as the final indicator clustering centers. This method, combining local density feature identification, contribution weight adjustment, and stability judgment, overcomes the shortcomings of traditional methods that are susceptible to noise interference, accurately identifying indicator clustering centers representing different dynamic levels. Because the index cluster center can be accurately identified, the accuracy of subsequent classification of image blocks into corresponding dynamic characteristic zones based on the proximity of each block's change index to the index cluster center is improved. Accurate dynamic characteristic zoning makes the dynamic indexes determined for each dynamic characteristic zone more reliable, thus making the adjustment of spatial or temporal thresholds in the key color determination rules based on the zone's dynamic indexes more reasonable. This ultimately improves the accuracy of identifying target pixels corresponding to preset key colors, thereby enhancing the precision of calculating target pixel calibration information based on the first and second calibration data, and ultimately achieving more accurate automated color adjustment for the display effect.

[0141] In some preferred embodiments, firstly, a set of statistically obtained block change index values ​​is acquired to form a numerical distribution. Then, the following steps are performed iteratively: based on the current numerical distribution, a density-based clustering method, such as a variant of the DBSCAN algorithm, is used to identify multiple local clustering regions. When calculating local density, for each block change index value, its local neighborhood on the numerical axis is determined, for example, by setting a fixed radius ε. The central tendency of all block change index values ​​within this local neighborhood is calculated, for example, by calculating their medians. Next, the deviation of the block change index value from the calculated median is calculated, for example, by calculating the absolute difference. The contribution weight of the block change index value in the local density calculation is adjusted according to the degree of deviation; the greater the deviation, the lower the contribution weight. Based on the weighted local density calculation results, areas with higher density are identified as local clustering regions. From these local clustering regions, the cluster centers of basic indices are determined, for example, by calculating the average or median of the values ​​within each region. The algorithm determines whether the values ​​of these basic indicator cluster centers meet preset stability conditions. For example, it checks whether the position change of each basic indicator cluster center in the current loop compared to the previous loop is less than a preset small threshold δ, and whether the proportion of the number of change indicators in the blocks surrounding each basic indicator cluster center (e.g., within a range on the numerical axis less than a certain threshold ρ) is greater than a preset minimum proportion τ. If the values ​​of all basic indicator cluster centers meet the preset stability conditions, the current basic indicator cluster center is determined as the final indicator cluster center, and the loop ends. Otherwise, the clustering results are updated based on the current basic indicator cluster center, and the loop continues until the stability conditions are met or the maximum number of loops is reached.

[0142] Reference Figure 10 In another embodiment of this application, an LED display debugging system is further proposed, the system comprising:

[0143] The acquisition module 1 is used to acquire first calibration data that characterizes the physical display characteristics of each pixel area of ​​the LED display screen. The first calibration data describes the color and brightness attenuation caused by long-term operation.

[0144] Parameter extraction module 2 is used to extract a color characteristic summary of the input source. The color characteristic summary includes the dominant hue range, color space parameters, and brightness distribution features.

[0145] The calibration strategy module 3 is used to obtain the corresponding second calibration data from the preset calibration strategy library based on the color characteristic summary. The calibration strategy library records calibration schemes that prioritize the accuracy of key color display for different color characteristics.

[0146] The debugging module 4 is used to apply the first calibration data and the second calibration data to the input signal of the input source to complete the automatic color calibration of the display effect.

[0147] The system comprises the following modules: Acquisition module (using sensors, interface circuits, or data reading programs); Parameter extraction module (using image processing algorithms, data analysis programs, or dedicated processing chips); Calibration strategy module (using database queries, rule matching algorithms, or decision tree models); Debugging module (using signal processors, lookup table application logic, or color transformation circuits); First calibration data (using attenuation coefficient matrices, pixel-level lookup tables, or aging model parameters); Color characteristic summary (using color histograms, color space parameter sets, or key color statistics); Calibration strategy library (using a data structure storing multiple preset calibration schemes, using relational databases, file systems, or in-memory lookup table sets); and Second calibration data (using color transformation matrices, gamma correction curves, or pixel-level adjustment parameters selected from the calibration strategy library for specific calibration tasks).

[0148] In some preferred embodiments, this application is implemented as follows. The acquisition module can be configured as an interface unit that communicates with an external color measurement device. This interface unit periodically receives the measurement results of the brightness and chromaticity of various areas of the LED display screen from the measurement device, processes these results, and stores them as first calibration data. The parameter extraction module can be configured as a software program running on the main control processor. This program analyzes the keyframes of the input video stream, extracts the main color tone range of the image through algorithms such as image segmentation and color clustering, calculates color space parameters (such as gamma value and white point coordinates), and analyzes the brightness histogram, and packages this information to generate a color characteristic summary. The calibration strategy module can be configured as a database stored in the system memory. This database pre-stores a second calibration data set optimized for different color characteristics (such as high-saturation animation, low-contrast documentaries, and advertisements with specific brand colors). The calibration strategy module executes a database query or matching algorithm based on the color characteristic summary output by the parameter extraction module to obtain the corresponding second calibration data. The debugging module can be configured as a dedicated image processing chip. This chip receives the input signal and, based on the first calibration data (for pixel-level physical compensation) provided by the acquisition module and the second calibration data (for content optimization and key color protection) provided by the calibration strategy module, performs real-time color space transformation, gamma correction, brightness adjustment, and other processing on the input signal to generate the final signal driving the LED display. The entire system can be integrated into a standalone hardware device or implemented as part of an LED display control system.

[0149] 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 debugging an LED display screen, characterized in that, The method includes: Obtain first calibration data characterizing the physical display characteristics of each pixel area of ​​the LED display screen. The first calibration data describes the chromaticity and brightness attenuation caused by long-term operation. Extract a color characteristic summary from the input source, which includes the dominant hue range, color space parameters, and brightness distribution characteristics; Based on the color characteristic summary, the corresponding second calibration data is directly retrieved from the preset calibration strategy library. The calibration strategy library records calibration schemes that prioritize the accuracy of key color display for different color characteristics. The calibration scheme includes a lookup table or parameter set, which records the second calibration data for the corresponding color characteristics. Based on the first calibration data and the second calibration data, the input signal of the input source is applied to complete the automatic color adjustment of the display effect; The color space parameters refer to the attributes describing the color space of the image, specifically including the color gamut, white point coordinates, and gamma value; the dominant color range refers to the most representative or concentrated color area in the input source image; and the brightness distribution characteristics refer to the overall distribution of pixel brightness in the input source image. The step of applying the first calibration data and the second calibration data to the input signal of the input source to complete the automated color adjustment of the display effect includes: A basic image is generated by applying preset basic calibration data to the input signal of the input source; the input signal of the input source refers to the content of the input source that needs to be displayed on the LED display screen. The target pixel corresponding to the preset key color is identified from the basic image, and the corresponding second calibration data is obtained from the calibration strategy library based on the color characteristic summary of the input source; Based on the first calibration data and the second calibration data, the calibration information of the target pixel is calculated; The pixels in the base image corresponding to the target pixel position are replaced with the pixels corresponding to the calibration information to complete the automatic color adjustment of the display effect.

2. The LED display screen debugging method according to claim 1, characterized in that, The acquisition of the first calibration data characterizing the physical display characteristics of each pixel area of ​​the LED display screen includes: A standardized test pattern is displayed in each pixel area of ​​the LED display screen; Obtain the brightness and chromaticity measurement values ​​of the standardized test pattern displayed in each pixel area; The brightness and chromaticity measurements are compared with preset standard values ​​to obtain the chromaticity attenuation characteristics and brightness attenuation coefficient of each pixel region, and stored as the first calibration data.

3. The LED display screen debugging method according to claim 1, characterized in that, The extracted color characteristic summary of the input source includes: Pixel distribution analysis is performed on the keyframe image of the input source, and color space parameters of the keyframe image are extracted in combination with the metadata information of the input source; The dominant hue range and brightness distribution characteristics are calculated based on the color space parameters. The color characteristic summary is obtained by storing the main color range and brightness distribution features in a structured manner.

4. The LED display screen debugging method according to claim 1, characterized in that, The target pixels identified by the base image and corresponding to the preset key colors include: Based on the base image, candidate color pixels whose color values ​​are within the preset key color range are selected; The contextual features of the candidate color pixels are generated based on the spatial clustering or temporal persistence of the candidate color pixels in consecutive frames of the input source. The context features are compared with the preset key color determination rules to obtain the comparison results. The key color determination rules define spatial or temporal thresholds for distinguishing the preset key color from neighboring colors. The candidate color pixels whose comparison results satisfy the key color determination rules are determined as the target pixels.

5. The LED display screen debugging method according to claim 4, characterized in that, The context features are compared with preset key color determination rules to obtain the comparison result, including: Obtain dynamic characteristic indicators that characterize the degree of change between the consecutive frames; Based on the dynamic characteristic index, adjust the spatial or temporal thresholds in the key color determination rule; The comparison result is obtained by comparing the contextual features with the adjusted key color determination rules.

6. The LED display screen debugging method according to claim 5, characterized in that, The acquisition of dynamic characteristic indicators characterizing the degree of change between consecutive frames includes: Each frame of the input source is divided into multiple frame blocks; Calculate the content change of each of the aforementioned image blocks between consecutive frames to generate corresponding block change indices; Based on the change indicators of each block, the screen blocks with similar change indicators are classified into the same dynamic characteristic partition. For each of the dynamic characteristic partitions, a partition dynamic index is determined, and the partition dynamic index is combined as the dynamic characteristic index.

7. The LED display screen debugging method according to claim 6, characterized in that, The step of classifying screen blocks with similar change indices into the same dynamic characteristic partition based on the change indices of each block includes: The block change index of all the frame blocks corresponding to the current frame in the continuous frame is statistically analyzed, and the numerical distribution of the block change index is obtained. Based on the numerical distribution, determine the cluster centers of indicators representing different dynamic levels; Based on the proximity of each block change index to the cluster center of each index, the screen blocks are classified into the corresponding dynamic characteristic partitions.

8. The LED display screen debugging method according to claim 7, characterized in that, The step of determining the cluster centers of indicators representing different dynamic levels based on the numerical distribution includes: The numerical distributions are analyzed cyclically, and multiple local clustering regions are identified based on the local density features of the numerical distributions and used as the clustering centers of basic indicators. The identification of local density features includes calculating the central tendency of the local neighborhood of each block change indicator and adjusting its contribution weight in the identification of local density features based on the degree of deviation of each block change indicator from the central tendency calculation. When the value of the basic indicator cluster center meets the preset stability condition, the basic indicator cluster center is taken as the final indicator cluster center.

9. An LED display screen debugging system, characterized in that, The system includes: The acquisition module is used to acquire first calibration data that characterizes the physical display characteristics of each pixel area of ​​the LED display screen. The first calibration data describes the color and brightness attenuation caused by long-term operation. The parameter extraction module is used to extract a color characteristic summary of the input source, which includes the dominant hue range, color space parameters, and brightness distribution characteristics. The calibration strategy module is used to directly retrieve the corresponding second calibration data from a preset calibration strategy library based on the color characteristic summary. The calibration strategy library records calibration schemes that prioritize the accuracy of key color display for different color characteristics. The calibration scheme includes a lookup table or parameter set, which records the second calibration data for the corresponding color characteristics. The debugging module is used to automatically adjust the color of the display effect based on the input signal of the input source, using the first calibration data and the second calibration data. The color space parameters refer to the attributes describing the color space of the image, specifically including the color gamut, white point coordinates, and gamma value; the dominant color range refers to the most representative or concentrated color area in the input source image; and the brightness distribution characteristics refer to the overall distribution of pixel brightness in the input source image. The step of applying the first calibration data and the second calibration data to the input signal of the input source to complete the automated color adjustment of the display effect includes: A basic image is generated by applying preset basic calibration data to the input signal of the input source; the input signal of the input source refers to the content of the input source that needs to be displayed on the LED display screen. The target pixel corresponding to the preset key color is identified from the basic image, and the corresponding second calibration data is obtained from the calibration strategy library based on the color characteristic summary of the input source; Based on the first calibration data and the second calibration data, the calibration information of the target pixel is calculated; The pixels in the base image corresponding to the target pixel position are replaced with the pixels corresponding to the calibration information to complete the automatic color adjustment of the display effect.

Images