Backlight module dynamic test method and system based on machine vision

Abstract

The invention discloses a backlight module dynamic testing method and system based on machine vision, and relates to the technical field of backlight module testing. The method comprises the following steps: constructing a global backlight test scene set and a local backlight test scene set; carrying out a global dimming test on the global backlight test scene set, collecting a global backlight test image, carrying out the analysis of the global brightness uniformity, the global brightness control precision, the global color temperature stability and the global response speed, and generating a global backlight test result; carrying out a local dimming test on the local backlight test scene set, collecting a local backlight test image, carrying out the analysis of the partition precision and the partition response speed, and generating a local backlight test result; and generating a dynamic test result according to the global backlight test result and the local backlight test result. The technical problem of insufficient accuracy and comprehensiveness of the test result caused by single test scene of the backlight module in the prior art is solved, and the technical effect of improving the comprehensiveness and accuracy of the backlight module test is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of backlight module testing, and in particular to a backlight module dynamic testing method and system based on machine vision. Background Art

[0002] As an important component of liquid crystal displays (LCDs) and other display devices, the performance of the backlight unit directly affects the display effect. The quality evaluation of the backlight unit usually relies on a series of tests, including brightness uniformity, color temperature stability, response speed and other aspects. However, existing backlight unit testing methods mostly focus on static test scenarios and lack a comprehensive evaluation of dynamic dimming performance. In addition, the test scene settings during the testing process are too simple and fail to fully consider the diverse conditions in the actual use environment, resulting in the test results being unable to fully and accurately reflect the performance of the backlight unit in real applications. Therefore, a new testing method is urgently needed that can cover different test scenarios and comprehensively evaluate the various performance indicators of the backlight unit to ensure that the test results are more accurate and representative. Summary of the Invention

[0003] The present application provides a dynamic testing method and system for backlight modules based on machine vision, which solves the technical problems in the prior art of single backlight module testing scene, limited testing dimensions, difficulty in accurately covering the differences in backlight control performance during the LCD screen display process, and resulting in insufficient accuracy and comprehensiveness of test results.

[0004] A first aspect of the present application provides a backlight module dynamic testing method based on machine vision, the method comprising: Perform backlight brightness adjustment mining on the backlight module to be tested, and construct a global backlight test scene set and a local backlight test scene set; perform a global dimming test on the global backlight test scene set through a backlight test platform, collect global backlight test images to analyze global brightness uniformity, global brightness control accuracy, global color temperature stability and global response speed, and generate a global backlight test result; perform a local dimming test on the local backlight test scene set through a backlight test platform, collect local backlight test images to analyze partition accuracy and partition response speed, and generate a local backlight test result; generate a dynamic test result based on the global backlight test result and the local backlight test result.

[0005] A second aspect of the present application provides a backlight module dynamic testing system based on machine vision, the system comprising: A test scenario construction module is used to perform backlight brightness adjustment mining on the backlight module to be tested, and to construct a global backlight test scenario set and a local backlight test scenario set; a global analysis module is used to perform a global dimming test on the global backlight test scenario set through a backlight test platform, collect global backlight test images for analysis of global brightness uniformity, global brightness control accuracy, global color temperature stability and global response speed, and generate a global backlight test result; a local analysis module is used to perform a local dimming test on the local backlight test scenario set through a backlight test platform, and collect local backlight test images for analysis of partition accuracy and partition response speed, and generate a local backlight test result; a result generation module is used to generate dynamic test results based on the global backlight test results and the local backlight test results.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, the backlight brightness adjustment of the backlight module to be tested is mined, and a global backlight test scene set and a local backlight test scene set are constructed. Then, a global dimming test is performed on the global backlight test scene set through the backlight test platform, and global backlight test images are collected to analyze the global brightness uniformity, global brightness control accuracy, global color temperature stability and global response speed, and a global backlight test result is generated. Next, a local dimming test is performed on the local backlight test scene set through the backlight test platform, and local backlight test images are collected to analyze the partition accuracy and partition response speed, and a local backlight test result is generated. Finally, dynamic test results are generated based on the global backlight test results and the local backlight test results. This solves the technical problem in the prior art that the backlight module test scene is single, the test dimension is limited, and it is difficult to accurately cover the backlight control performance differences during the LCD display process, resulting in insufficient accuracy and comprehensiveness of the test results, and achieves the technical effect of improving the comprehensiveness and accuracy of the test of the LCD backlight module in display applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0008] Figure 1 A flow chart of a dynamic testing method for a backlight module based on machine vision provided in an embodiment of the present application; Figure 2 A schematic structural diagram of a backlight module dynamic testing system based on machine vision provided in an embodiment of the present application.

[0009] Description of the accompanying drawings: test scenario construction module 11, global analysis module 12, local analysis module 13, result generation module 14. DETAILED DESCRIPTION

[0010] This application provides a dynamic testing method and system for backlight modules based on machine vision, which solves the technical problems in the prior art that the backlight module testing scenario is single, the testing dimensions are limited, and it is difficult to accurately cover the differences in backlight control performance during the LCD screen display process, resulting in insufficient accuracy and comprehensiveness of the test results.

[0011] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0012] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0013] Example 1, as Figure 1 As shown, the present application provides a dynamic testing method for a backlight module based on machine vision, wherein the method includes: The backlight brightness adjustment of the backlight module to be tested is explored, and a global backlight test scene set and a local backlight test scene set are constructed.

[0014] In an embodiment of the present application, the characteristics of the target liquid crystal display panel matched by the backlight module to be tested, including its size, resolution and brightness requirements, are used as constraints, and the backlight performance under different brightness adjustment scenarios is mined from historical data, including brightness adjustment scenes under different environments (such as different operating temperatures, humidity or lighting conditions); the mined scenes are divided to construct a global backlight test scene set and a local backlight test scene set, wherein the global backlight test scene set includes a brightness adjustment test of the entire area of ​​the display panel, and evaluates performance such as global brightness uniformity, brightness control accuracy, color temperature stability and response speed. The local backlight test scene set focuses on simulating the brightness adjustment of different areas of the display panel, and analyzes characteristics such as local brightness uniformity, color temperature differences and local response speed.

[0015] Furthermore, the backlight brightness adjustment of the backlight module to be tested is mined, and a global backlight test scene set and a local backlight test scene set are constructed, including: Determine the target LCD panel characteristics that match the backlight module to be tested; use the target LCD panel characteristics as constraints to mine historical backlight brightness dynamic adjustment scenarios; classify the historical backlight brightness dynamic adjustment scenarios into global adjustment modes and local adjustment modes to generate the global backlight test scene set and the local backlight test scene set.

[0016] Specifically, the target LCD panel characteristics matched by the backlight module to be tested include panel size, resolution, brightness requirements, and other related characteristics such as contrast and color gamut. These target LCD panel characteristics serve as constraints and provide a foundation for subsequent brightness adjustment scenario mining. Based on the target LCD panel characteristics, historical backlight brightness dynamic adjustment scenario mining is conducted. By analyzing historical adjustment data, the patterns and performance of backlight brightness adjustment under different application environments (such as different temperature, humidity, and brightness conditions) are explored to understand the dynamic adjustment requirements of the backlight module in actual applications. Historical backlight brightness dynamic adjustment scenarios are classified, and the adjustment modes are divided into global adjustment mode and local adjustment mode. Global adjustment mode uniformly adjusts the brightness of the entire display panel, while local adjustment mode independently adjusts different areas of the display panel to evaluate characteristics such as local brightness non-uniformity and local response speed. Based on this classification, a set of global and local backlight test scenarios are generated to ensure comprehensive coverage of the backlight module's adjustment characteristics under different operating conditions, providing sufficient test scenario support for subsequent dynamic testing.

[0017] Furthermore, the global adjustment mode is a mode for adjusting the backlight brightness to be uniform at all positions of the target liquid crystal display panel, and the local adjustment mode is a mode for adjusting the backlight brightness to be different at different positions and partitions of the target liquid crystal display panel.

[0018] Global adjustment mode adjusts backlight brightness uniformly across all locations on the target LCD panel. In global adjustment mode, the backlight unit's brightness is adjusted based on the overall brightness of the LCD panel, ensuring consistent brightness across all areas of the panel and meeting global brightness uniformity requirements. Global adjustment mode is used to test the backlight unit's brightness stability, uniformity, and response speed across the entire display panel area, assessing its global brightness control accuracy and color temperature stability.

[0019] Local dimming mode adjusts the backlight brightness to varying degrees for different areas of the target LCD panel. In local dimming mode, the backlight module adjusts the backlight brightness of each area of ​​the display panel individually, simulating the local dimming requirements of actual use. Local dimming mode is primarily used to test the backlight module's accuracy and response speed in local brightness control, focusing specifically on brightness differences between panel areas, local color temperature changes, and their impact on visual effects, ensuring the backlight module's adaptability and flexibility in complex usage environments.

[0020] A global dimming test is performed on the global backlight test scene set through the backlight test platform, and global backlight test images are collected to analyze the global brightness uniformity, global brightness control accuracy, global color temperature stability and global response speed to generate global backlight test results.

[0021] The backlight test platform conducts global dimming tests on a set of global backlight test scenes, captures and analyzes global backlight test images, and evaluates global brightness uniformity, global brightness control accuracy, global color temperature stability, and global response speed. By calculating and comparing these performance indicators, global backlight test results are generated to accurately reflect the performance of the backlight module in global dimming mode.

[0022] Furthermore, the backlight testing platform includes the backlight module to be tested, a liquid crystal display panel and an HDR camera.

[0023] The backlight test platform consists of a backlight module to be tested, an LCD panel, and an HDR camera. Specifically, the backlight module to be tested is connected to the LCD panel and located on the test platform, forming the core of the test system. The LCD panel displays the backlight output provided by the backlight module to be tested, while the backlight module adjusts the brightness output through its light source control center, ensuring that the test scenarios cover a wide range of backlight brightness adjustment conditions.

[0024] HDR cameras are used to capture real-time images of the backlight output of LCD panels. They feature high dynamic range imaging capabilities, ensuring accurate recording of image information even under extreme brightness conditions. The high-quality image data captured by HDR cameras effectively analyzes and evaluates backlight unit performance, such as brightness uniformity, color temperature stability, and response speed, ensuring accurate and reliable test results. The backlight test platform enables comprehensive evaluation of backlight units in various adjustment modes, providing support for further optimization of backlight unit designs.

[0025] Furthermore, the backlight module to be tested is connected to the liquid crystal display panel according to a predetermined position structural constraint; and the backlight module to be tested is connected to a light source control center, which is used to adjust the light source output of the backlight module to be tested; and the HDR camera is used to capture the backlight image when the light source output by the backlight module to be tested hits the liquid crystal display panel.

[0026] The backlight module under test is connected to the LCD panel according to predetermined structural constraints, ensuring stable and accurate docking during testing. The backlight module under test is connected to the LCD panel via its light source control center, which precisely adjusts the backlight module's light output and controls brightness to accommodate different testing requirements and scenario settings.

[0027] The light source control center adjusts the backlight module's light output in real time according to a predetermined adjustment strategy, ensuring precise backlight brightness adjustment for the LCD panel during each test phase. During this process, an HDR camera is used to capture the backlight image projected onto the LCD panel by the light source output by the backlight module under test. The HDR camera's high dynamic range imaging capability enables it to accurately capture backlight image data under varying brightness conditions, providing high-quality image information for subsequent analysis of the backlight module's performance indicators, such as brightness uniformity, color temperature stability, and response speed.

[0028] Furthermore, a global dimming test is performed on the global backlight test scene set using a backlight test platform, and global backlight test images are collected to analyze global brightness uniformity, global brightness control accuracy, global color temperature stability, and global response speed, generating global backlight test results, including: Extract the first global backlight test scene in the global backlight test scene set, parse and obtain the first global backlight control parameter and the first expected global backlight feature; input the first global backlight control parameter into the light source control center of the backlight test platform to perform dimming control, and continuously capture the first global backlight test image sequence when the light source hits the liquid crystal display panel through the HDR camera; compare the first global backlight test image sequence with the first expected global backlight feature to determine the first target global backlight test image, and calculate the first global response speed; calculate the global brightness uniformity, global brightness control accuracy, and global color temperature stability of the first target global backlight test image to generate the first global brightness uniformity, the first global brightness control accuracy, and the first global color temperature stability, and construct the first global scene test result; continue to test the remaining global backlight test scenes in the global backlight test scene set, obtain the remaining global scene test results, and perform weighted fusion with the first global scene test result to generate the global backlight test result.

[0029] Specifically, a first global backlight test scene is extracted from the global backlight test scene set, and the first global backlight control parameter and the first expected global backlight feature corresponding to the scene are parsed and obtained. The first global backlight control parameter is used to determine the specific setting of the dimming, and the first expected global backlight feature represents the ideal backlight output performance in the scene. Then, the first global backlight control parameter is input into the light source control center of the backlight test platform for dimming control. Through this control, the backlight test platform adjusts the output light source of the backlight module to be tested. At the same time, the HDR camera is used to continuously capture the first global backlight test image sequence when the light source hits the liquid crystal display panel. The sequence contains backlight images under different brightness adjustment conditions to ensure that the performance of the backlight module is fully captured. Subsequently, the first expected global backlight feature is compared with the first global backlight test image sequence to determine the first target global backlight test image, and the first global response speed is calculated based on the image. Calculate the global brightness uniformity, global brightness control accuracy, and global color temperature stability of the first target global backlight test image; calculate the first global brightness uniformity, first global brightness control accuracy, and first global color temperature stability using indicators such as brightness distribution and color temperature distribution, and construct a first global scene test result. Continue testing the remaining global backlight test scenes in the global backlight test scene set to obtain the corresponding remaining global scene test results, and perform a weighted fusion of these results with the first global scene test result to generate the final global backlight test result.

[0030] Furthermore, comparing the first global backlight test image sequence with the first expected global backlight feature to determine a first target global backlight test image and calculating a first global response speed includes: The image features of each frame in the first global backlight test image sequence are sequentially compared with the first expected global backlight features to generate a first similarity sequence; similarity fluctuation analysis of adjacent image frames in the first similarity sequence is performed to locate image frames that meet the preset continuous fluctuation consistency rule as the first target global backlight test image; based on the timestamp information corresponding to the first global backlight test image sequence, the time difference from the dimming control trigger time to the acquisition time of the first target global backlight test image is calculated to generate the first global response speed.

[0031] Specifically, the image features of each frame in the first global backlight test image sequence are sequentially compared with the first expected global backlight feature. By extracting features such as brightness and color temperature from each frame, a similarity calculation formula (e.g., Euclidean distance) is used to calculate the similarity between the image and the expected global backlight feature, generating a first similarity sequence. The first similarity sequence reflects the degree of match between each frame and the expected feature, which is used for subsequent analysis of image changes. A similarity fluctuation analysis is then performed on adjacent image frames in the first similarity sequence. By analyzing the fluctuations between adjacent frames in the similarity sequence, image frames that meet a preset continuous fluctuation consistency rule are located. Specifically, by analyzing the similarity changes between adjacent image frames, it is determined whether the similarity gradually converges to consistency until the similarity fluctuation range falls within a preset deviation threshold. During this process, the similarity may initially fluctuate significantly, but as the image frames continue to change, the difference in similarity gradually decreases and eventually stabilizes. When the similarity changes of multiple consecutive frames all meet the preset fluctuation consistency rule, i.e., the similarity fluctuations are all within a preset deviation threshold, the first frame in the sequence is selected as the first target global backlight test image. Based on the timestamp information corresponding to the first global backlight test image sequence, the time difference from the dimming control trigger time to the acquisition time of the first target global backlight test image is calculated; through this time difference, the first global response speed is generated, which represents the response speed of the backlight module to reach the target brightness after dimming control.

[0032] Furthermore, calculating global brightness uniformity, global brightness control accuracy, and global color temperature stability of the first target global backlight test image to generate a first global brightness uniformity, a first global brightness control accuracy, and a first global color temperature stability includes: The first target global backlight test image is subjected to brightness and color temperature feature extraction to generate a first brightness distribution and a first color temperature distribution. The brightness mean is calculated based on the first brightness distribution, and the first global brightness uniformity is calculated by combining the brightness maximum and brightness minimum. The color temperature distribution standard deviation is calculated based on the first color temperature distribution to generate the first global color temperature stability. The brightness errors at multiple locations in the first target global backlight test image and the first expected global backlight feature are compared and averaged to generate the first global brightness control accuracy. Using image processing technology, the brightness value and color temperature value of each pixel in the first target global backlight test image are extracted to obtain a first brightness distribution and a first color temperature distribution. The first brightness distribution and the first color temperature distribution reflect the overall characteristics of the image in terms of brightness and color temperature.

[0033] Based on the first brightness distribution, the brightness mean is calculated, and combined with the maximum and minimum brightness values, the first global brightness uniformity is calculated. Specifically, the brightness mean of all pixels in the image is first calculated to evaluate the overall brightness level; then, the maximum and minimum values ​​in the brightness distribution are analyzed, and the brightness uniformity index is calculated to evaluate the smoothness of the brightness changes in the image. A smaller uniformity value indicates a more uniform brightness distribution of the image. Brightness uniformity calculation formula: L = (L max -L min ) / L mean , where L mean Indicates the overall brightness of the image, that is, the mean brightness, L max and L min They are the maximum and minimum brightness of the image, respectively, and are used to measure the brightness range. L represents the uniformity of brightness, and a lower value indicates a more uniform brightness distribution.

[0034] Based on the first color temperature distribution, a standard deviation of the color temperature distribution is calculated to obtain a first global color temperature stability, wherein the color temperature standard deviation represents the degree of color temperature fluctuation in different areas of the image. The smaller the standard deviation, the higher the color temperature stability, that is, the color temperature remains consistent during the global dimming process.

[0035] The first global brightness control accuracy is generated by comparing the brightness errors at multiple locations in the first target global backlight test image and the first expected global backlight signature, and calculating the mean of these errors. By comparing the brightness differences at multiple key locations between the test image and the expected image, the mean of the brightness control accuracy is calculated to measure the backlight unit's precise control capabilities during the dimming process.

[0036] A local dimming test is performed on the local backlight test scene set through a backlight test platform, and local backlight test images are collected to analyze the partition accuracy and partition response speed to generate local backlight test results.

[0037] The backlight test platform performs local dimming tests on a set of local backlight test scenarios, independently adjusting the brightness of different areas of the LCD panel. During the test, the backlight test platform captures local backlight test images in real time and analyzes the images for both zone accuracy and zone response speed. Zone accuracy analysis assesses whether the brightness adjustment of each zone is consistent with the preset target, ensuring the accuracy of brightness control in each zone. Zone response speed analysis measures the dimming response time of the backlight module in different zones to assess its ability to quickly respond to dimming control signals. Based on this comprehensive analysis of zone accuracy and zone response speed, local backlight test results are generated, providing a basis for evaluating the backlight module's multi-zone dimming performance.

[0038] Furthermore, a local dimming test is performed on the local backlight test scene set using a backlight test platform, and local backlight test images are collected to analyze the partition accuracy and partition response speed, generating local backlight test results, including: Based on the first local backlight test scene in the local backlight test scene set, the first partition backlight control parameters and the first expected partition backlight characteristics are analyzed and obtained, and the first partition backlight test image sequence is collected using the backlight test platform; the first partition backlight test image sequence is compared with the first expected partition backlight characteristics to determine the first target partition backlight test image, and the first partition response speed is calculated; based on the first expected partition backlight characteristics, the brightness control accuracy of each partition boundary of the first target partition backlight test image is analyzed and fused to generate the first partition accuracy; the first local scene test result is generated with the first partition accuracy and the first partition response speed; the remaining local backlight test scenes in the local backlight test scene set are continued to be tested to obtain the remaining local scene test results and perform weighted fusion with the first local scene test result to generate the local backlight test result.

[0039] A first local backlight test scene is selected from the local backlight test scene set, and the first partition backlight control parameters and the first expected partition backlight characteristics of the scene are obtained by parsing. The first partition backlight control parameters and the first expected partition backlight characteristics are used to guide the backlight test platform to perform local dimming control. Based on this information, the backlight test platform collects a first partition backlight test image sequence and records the backlight image data of different areas under local dimming conditions. The first expected partition backlight characteristics are compared with the collected first partition backlight test image sequence to determine the first target partition backlight test image that best meets expectations. Based on the first target partition backlight test image, the response speed of the first partition is calculated, which represents the time difference from the issuance of the dimming control signal to the reaching of the target brightness by the backlight module.

[0040] Using the first expected subarea backlight characteristics as a criterion, the brightness control accuracy of each subarea boundary of the determined first target subarea backlight test image is analyzed. By calculating and fusing the brightness errors of each subarea, a first subarea accuracy is generated, reflecting the backlight module's brightness control accuracy during local dimming. Specifically, based on the captured first target subarea backlight test image, the brightness error of each subarea is calculated by calculating the absolute difference between each subarea's brightness value and the corresponding expected brightness value. The brightness errors of all subareas are then fused and combined using a weighted average to derive the overall first subarea accuracy. The weighted average can assign different weights to each subarea based on its area or importance in the image, giving greater influence to errors in important areas. By calculating and fusing the brightness errors of each subarea, the resulting first subarea accuracy reflects the backlight module's brightness control accuracy for each area during local dimming. Lower first subarea accuracy values ​​indicate that the backlight module can achieve more precise brightness control between subareas, while higher accuracy values ​​indicate that the backlight adjustment process suffers from larger brightness errors and requires further optimization.

[0041] The obtained first partition accuracy and first partition response speed are combined to generate the first local scene test result. The remaining scenes in the local backlight test scene set are tested and the corresponding remaining local scene test results are obtained. These test results are weighted and fused with the first local scene test result to generate the local backlight test result, which comprehensively evaluates the performance of the backlight module in local dimming mode, including important indicators such as brightness control accuracy and response speed.

[0042] A dynamic test result is generated using the global backlight test result and the local backlight test result.

[0043] The global backlight test results and local backlight test results reflect the performance of the backlight unit in global dimming mode and local dimming mode, respectively. The global backlight test results mainly evaluate the brightness uniformity, brightness control accuracy, color temperature stability and response speed of the backlight unit across the entire display panel, while the local backlight test results focus on the brightness control accuracy and response speed of each area.

[0044] Global and local test results are combined through weighted fusion. Weighted fusion assigns different weights to global and local test results based on actual application requirements, reflecting their importance in the overall dynamic test. For example, if local dimming performance is more critical in a specific application, a higher weight can be given to the local test results. Ultimately, the resulting dynamic test results combine various indicators from global and local test results to comprehensively evaluate the backlight unit's performance under different operating conditions.

[0045] In summary, the embodiments of the present application have at least the following technical effects: First, the backlight brightness adjustment of the backlight module to be tested is mined, and a global backlight test scene set and a local backlight test scene set are constructed. Then, a global dimming test is performed on the global backlight test scene set through the backlight test platform, and global backlight test images are collected to analyze the global brightness uniformity, global brightness control accuracy, global color temperature stability and global response speed, and a global backlight test result is generated. Next, a local dimming test is performed on the local backlight test scene set through the backlight test platform, and local backlight test images are collected to analyze the partition accuracy and partition response speed, and a local backlight test result is generated. Finally, dynamic test results are generated based on the global backlight test results and the local backlight test results. This solves the technical problem in the prior art that the backlight module test scene is single, the test dimension is limited, and it is difficult to accurately cover the backlight control performance differences during the LCD display process, resulting in insufficient accuracy and comprehensiveness of the test results, and achieves the technical effect of improving the comprehensiveness and accuracy of the test of the LCD backlight module in display applications.

[0046] The second embodiment is based on the same inventive concept as the method for dynamic testing of backlight modules based on machine vision in the above embodiment. Figure 2 As shown, the present application provides a backlight module dynamic testing system based on machine vision, wherein the system includes: A test scenario construction module 11 is used to perform backlight brightness adjustment mining on the backlight module to be tested, and to construct a global backlight test scenario set and a local backlight test scenario set; a global analysis module 12 is used to perform a global dimming test on the global backlight test scenario set through a backlight test platform, collect global backlight test images for analysis of global brightness uniformity, global brightness control accuracy, global color temperature stability and global response speed, and generate a global backlight test result; a local analysis module 13 is used to perform a local dimming test on the local backlight test scenario set through a backlight test platform, and collect local backlight test images for analysis of partition accuracy and partition response speed, and generate a local backlight test result; a result generation module 14 is used to generate a dynamic test result based on the global backlight test result and the local backlight test result.

[0047] Furthermore, the test scenario construction module 11 is used to perform the following method: Determine the target LCD panel characteristics that match the backlight module to be tested; use the target LCD panel characteristics as constraints to mine historical backlight brightness dynamic adjustment scenarios; classify the historical backlight brightness dynamic adjustment scenarios into global adjustment modes and local adjustment modes to generate the global backlight test scene set and the local backlight test scene set.

[0048] Furthermore, the test scenario construction module 11 is used to perform the following method: The global adjustment mode is a mode for adjusting the backlight brightness to be consistent at all positions of the target liquid crystal display panel, and the local adjustment mode is a mode for adjusting the backlight brightness to be different at different positions and partitions of the target liquid crystal display panel.

[0049] Furthermore, the global analysis module 12 is configured to perform the following method: The backlight testing platform includes the backlight module to be tested, a liquid crystal display panel and an HDR camera.

[0050] Furthermore, the global analysis module 12 is configured to perform the following method: The backlight module to be tested is connected to the liquid crystal display panel according to a predetermined position structural constraint; and the backlight module to be tested is connected to a light source control center, which is used to adjust the light source output of the backlight module to be tested; the HDR camera is used to capture the backlight image when the light source output by the backlight module to be tested hits the liquid crystal display panel.

[0051] Furthermore, the global analysis module 12 is configured to perform the following method: Extract the first global backlight test scene in the global backlight test scene set, parse and obtain the first global backlight control parameter and the first expected global backlight feature; input the first global backlight control parameter into the light source control center of the backlight test platform to perform dimming control, and continuously capture the first global backlight test image sequence when the light source hits the liquid crystal display panel through the HDR camera; compare the first global backlight test image sequence with the first expected global backlight feature to determine the first target global backlight test image, and calculate the first global response speed; calculate the global brightness uniformity, global brightness control accuracy, and global color temperature stability of the first target global backlight test image to generate the first global brightness uniformity, the first global brightness control accuracy, and the first global color temperature stability, and construct the first global scene test result; continue to test the remaining global backlight test scenes in the global backlight test scene set, obtain the remaining global scene test results, and perform weighted fusion with the first global scene test result to generate the global backlight test result.

[0052] Furthermore, the global analysis module 12 is configured to perform the following method: The image features of each frame in the first global backlight test image sequence are sequentially compared with the first expected global backlight features to generate a first similarity sequence; similarity fluctuation analysis of adjacent image frames in the first similarity sequence is performed to locate image frames that meet the preset continuous fluctuation consistency rule as the first target global backlight test image; based on the timestamp information corresponding to the first global backlight test image sequence, the time difference from the dimming control trigger time to the acquisition time of the first target global backlight test image is calculated to generate the first global response speed.

[0053] Furthermore, the global analysis module 12 is configured to perform the following method: Perform brightness and color temperature feature extraction on the first target global backlight test image to generate a first brightness distribution and a first color temperature distribution; calculate the brightness mean based on the first brightness distribution, and calculate the first global brightness uniformity in combination with the brightness maximum and brightness minimum; calculate the color temperature distribution standard deviation based on the first color temperature distribution to generate the first global color temperature stability; compare the brightness errors of multiple positions in the first target global backlight test image and the first expected global backlight feature and calculate the mean to generate the first global brightness control accuracy.

[0054] Furthermore, the local analysis module 13 is configured to perform the following method: Based on the first local backlight test scene in the local backlight test scene set, the first partition backlight control parameters and the first expected partition backlight characteristics are analyzed and obtained, and the first partition backlight test image sequence is collected using the backlight test platform; the first partition backlight test image sequence is compared with the first expected partition backlight characteristics to determine the first target partition backlight test image, and the first partition response speed is calculated; based on the first expected partition backlight characteristics, the brightness control accuracy of each partition boundary of the first target partition backlight test image is analyzed and fused to generate the first partition accuracy; the first local scene test result is generated with the first partition accuracy and the first partition response speed; the remaining local backlight test scenes in the local backlight test scene set are continued to be tested to obtain the remaining local scene test results and perform weighted fusion with the first local scene test result to generate the local backlight test result.

[0055] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0057] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A dynamic testing method for backlight modules based on machine vision, characterized in that: The method comprises: Conduct backlight brightness adjustment mining for the backlight module to be tested, and construct a global backlight test scene set and a local backlight test scene set; Performing a global dimming test on the global backlight test scene set using a backlight test platform, collecting global backlight test images to analyze global brightness uniformity, global brightness control accuracy, global color temperature stability, and global response speed, and generating global backlight test results; Performing a local dimming test on the local backlight test scene set through a backlight test platform, and collecting local backlight test images to analyze partition accuracy and partition response speed, thereby generating a local backlight test result; A dynamic test result is generated using the global backlight test result and the local backlight test result.

2. A method and system for dynamic testing of a backlight module based on machine vision according to claim 1, characterized in that: Perform backlight brightness adjustment mining for the backlight module to be tested, and build a global backlight test scene set and a local backlight test scene set, including: Determining target liquid crystal display panel characteristics that match the backlight module to be tested; Taking the target liquid crystal display panel characteristics as constraints, mining historical backlight brightness dynamic adjustment scenarios; The historical backlight brightness dynamic adjustment scenes are classified into a global adjustment mode and a local adjustment mode to generate the global backlight test scene set and the local backlight test scene set.

3. A method and system for dynamic testing of a backlight module based on machine vision as claimed in claim 2, characterized in that: The global adjustment mode is a mode for adjusting the backlight brightness to be consistent at all positions of the target liquid crystal display panel, and the local adjustment mode is a mode for adjusting the backlight brightness to be different at different positions and partitions of the target liquid crystal display panel.

4. The method and system for dynamic testing of a backlight module based on machine vision according to claim 1, wherein: The backlight testing platform includes the backlight module to be tested, a liquid crystal display panel and an HDR camera.

5. A method and system for dynamic testing of a backlight module based on machine vision as claimed in claim 4, characterized in that: The backlight module to be tested is connected to the liquid crystal display panel according to a predetermined position structural constraint; and the backlight module to be tested is connected to a light source control center, which is used to adjust the light source output of the backlight module to be tested; the HDR camera is used to capture the backlight image when the light source output by the backlight module to be tested hits the liquid crystal display panel.

6. A method and system for dynamic testing of a backlight module based on machine vision as claimed in claim 4, characterized in that: A global dimming test is performed on the global backlight test scene set using a backlight test platform. Global backlight test images are collected to analyze global brightness uniformity, global brightness control accuracy, global color temperature stability, and global response speed, and global backlight test results are generated, including: Extracting a first global backlight test scene from the global backlight test scene set, and parsing and obtaining a first global backlight control parameter and a first expected global backlight feature; Inputting the first global backlight control parameter into the light source control center of the backlight test platform to perform dimming control, and continuously capturing a first global backlight test image sequence when the light source hits the liquid crystal display panel through an HDR camera; comparing the first global backlight test image sequence with the first expected global backlight feature to determine a first target global backlight test image, and calculating a first global response speed; Calculate the global brightness uniformity, global brightness control accuracy, and global color temperature stability of the first target global backlight test image to generate a first global brightness uniformity, a first global brightness control accuracy, and a first global color temperature stability, and construct a first global scene test result; Continue to test the remaining global backlight test scenes in the global backlight test scene set to obtain the remaining global scene test results and perform weighted fusion on the remaining global scene test results to generate the global backlight test result.

7. A method and system for dynamic testing of a backlight module based on machine vision as claimed in claim 6, characterized in that: Comparing the first global backlight test image sequence with the first expected global backlight feature to determine a first target global backlight test image, and calculating a first global response speed, including: performing a similarity comparison between the image features of each frame in the first global backlight test image sequence and the first expected global backlight features in sequence to generate a first similarity sequence; performing similarity fluctuation analysis on adjacent image frames of the first similarity sequence, and locating an image frame that meets a preset continuous fluctuation consistency rule as the first target global backlight test image; Based on the timestamp information corresponding to the first global backlight test image sequence, a time difference from the dimming control triggering time to the acquisition time of the first target global backlight test image is calculated to generate the first global response speed.

8. The method and system for dynamic testing of a backlight module based on machine vision according to claim 6, wherein: Calculating global brightness uniformity, global brightness control accuracy, and global color temperature stability for the first target global backlight test image to generate a first global brightness uniformity, a first global brightness control accuracy, and a first global color temperature stability, including: Extracting brightness and color temperature features of the first target global backlight test image to generate a first brightness distribution and a first color temperature distribution; Calculating a brightness mean based on the first brightness distribution, and calculating the first global brightness uniformity in combination with a brightness maximum and a brightness minimum; Calculating a color temperature distribution standard deviation based on the first color temperature distribution to generate the first global color temperature stability; The brightness errors at multiple positions in the first target global backlight test image and the first expected global backlight feature are compared and averaged to generate the first global brightness control accuracy.

9. The method and system for dynamic testing of a backlight module based on machine vision according to claim 1, wherein: The local backlight test scene set is subjected to a local dimming test via a backlight test platform, and local backlight test images are collected to analyze the partition accuracy and partition response speed, thereby generating local backlight test results, including: Based on a first local backlight test scene in the local backlight test scene set, analyzing and obtaining a first subarea backlight control parameter and a first expected subarea backlight feature, and using the backlight test platform to collect a first subarea backlight test image sequence; Comparing the first subarea backlight test image sequence with the first expected subarea backlight feature to determine a first target subarea backlight test image, and calculating a first subarea response speed; Based on the first expected subarea backlight feature as a standard, performing brightness control accuracy analysis and fusion on each subarea boundary of the first target subarea backlight test image to generate a first subarea accuracy; generating a first local scene test result with a first partition accuracy and a first partition response speed; Continue to test the remaining local backlight test scenes in the local backlight test scene set to obtain the remaining local scene test results and perform weighted fusion with the first local scene test result to generate the local backlight test result.

10. A backlight module dynamic testing system based on machine vision, characterized in that: A system for implementing a backlight module dynamic testing method based on machine vision according to any one of claims 1 to 9, comprising: The test scenario construction module is used to perform backlight brightness adjustment mining on the backlight module to be tested and construct a global backlight test scenario set and a local backlight test scenario set; A global analysis module is used to perform a global dimming test on the global backlight test scene set through a backlight test platform, collect global backlight test images to analyze global brightness uniformity, global brightness control accuracy, global color temperature stability and global response speed, and generate global backlight test results; A local analysis module is used to perform a local dimming test on the local backlight test scene set through a backlight test platform, and collect local backlight test images to analyze the partition accuracy and partition response speed, and generate local backlight test results; The result generating module is configured to generate a dynamic test result based on the global backlight test result and the local backlight test result.

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