An AR-HUD brightness uniformity optimization method and system, electronic equipment and storage medium

Abstract

The application discloses an AR-HUD brightness uniformity optimization method, and relates to the field of vehicle display, which comprises the following steps: dividing a plurality of target grid areas for the rectangular display field of view of AR-HUD; the target grid area comprises original image pixel values; collecting brightness data in a darkroom environment to obtain measured brightness values; establishing a brightness attenuation model of the optical system according to the measured brightness values; obtaining a brightness deviation coefficient of the target grid area, forming a compensation coefficient matrix according to the brightness deviation coefficient and the distance between the target grid area and the center of the rectangular display field of view; compensating, correcting and dynamically optimizing the original image pixel values according to the compensation coefficient matrix to generate optimized pixel values; judging whether the optimized pixel values meet the test requirements; if not, returning to step S4 to retest, improving the display effect of AR-HUD through the method of optimizing the brightness uniformity algorithm logic and giving users a good visual experience.

Description

Technical Field

[0001] This application relates to the field of vehicle displays, and more particularly to an AR-HUD brightness uniformity optimization method, an AR-HUD brightness uniformity optimization system, an electronic device, and a storage medium. Background Technology

[0002] Local dimming is a technology that dynamically adjusts brightness by controlling the backlight area in separate zones. AR-HUD products using local dimming technology have advantages such as reducing grayscale artifacts, significantly improving image contrast and visual effects, and being compatible with automotive power management for energy saving and reduced consumption.

[0003] However, local dimming technology can also bring the following problems to AR-HUD products: uneven brightness of the backlight zones may be amplified after reflection, resulting in a decrease in the consistency of screen brightness, darker and blurry edge areas, "halo" around bright objects, and horizontal and vertical stripes, which affect information display and driver experience. Therefore, a solution is needed to solve the problem of abnormal display effect of AR-HUD products after using local dimming technology. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide an AR-HUD brightness uniformity optimization method, an AR-HUD brightness uniformity optimization system, an electronic device, and a storage medium, aiming to solve the problem of abnormal display effects in AR-HUD products after using local dmming technology. By optimizing the brightness uniformity algorithm logic, the display effect of AR-HUD is improved, providing users with a better visual experience.

[0005] This invention provides the following solution:

[0006] According to one aspect of the present invention, an AR-HUD brightness uniformity optimization method is provided, comprising the following steps:

[0007] Step S1: Divide the rectangular display field of view of the AR-HUD into multiple target grid areas;

[0008] The target grid region includes the original image pixel values;

[0009] Step S2: Collect brightness data in a dark room environment and obtain the measured brightness value;

[0010] Step S3: Establish a brightness attenuation model for the optical system based on the measured brightness value;

[0011] Step S4: Obtain the brightness deviation coefficient of the target grid region.

[0012] Step S5: Based on the brightness deviation coefficient, and combined with the distance between the target grid area and the center of the rectangular display field of view, a compensation coefficient matrix is ​​formed;

[0013] Step S6: Based on the compensation coefficient matrix, compensate, correct, and dynamically optimize the pixel values ​​of the original image to generate optimized pixel values;

[0014] Step S7: Determine whether the optimized pixel values ​​meet the experimental requirements;

[0015] If not, return to step S4 and repeat the test.

[0016] Furthermore, including:

[0017] Dividing multiple target grid regions includes dividing the area into N×M grid regions, with each grid corresponding to a dimming zone of the backlight module.

[0018] Furthermore, including:

[0019] Step S2 includes: acquiring edge and center brightness data of a pure white image in a dark room environment, and obtaining the measured brightness value L(i,j) of the target grid area.

[0020] Furthermore, including:

[0021] Step S3 includes: establishing a brightness attenuation model of the optical system using multiple sets of measured brightness values;

[0022] The attenuation pattern is based on edge brightness. ≈Center brightness ×(1-k); k is the attenuation coefficient, which is related to the lens collimation angle and the field of view.

[0023] Furthermore, including:

[0024] Step S4 includes: the brightness deviation coefficient is:

[0025] = [目标] / [实测] ;

[0026] in, [目标] To achieve a uniform target brightness, such as 90%-95% of the brightness in the central area.

[0027] Furthermore, including:

[0028] Step S5 includes: obtaining brightness attenuation parameters;

[0029] Based on the distance from the brightness attenuation parameter to the center of the screen Proportional relationship;

[0030] Establish function ;

[0031] in, The attenuation coefficient is obtained by fitting experimental data;

[0032] Model the target region and generate a compensation coefficient matrix;

[0033] The target area includes the four corners of the screen and the top and bottom edges.

[0034] Furthermore, including:

[0035] Step S6 includes:

[0036] The pixel values ​​of the original image are compensated;

[0037] Specifically: ;

[0038] in, These are the offset pixel values. These are the original image pixel values;

[0039] Perform gamma correction on the offset pixel values;

[0040] Specifically: ;

[0041] in, The gamma coefficient is, and >1.

[0042] According to a second aspect of the present invention, an AR-HUD brightness uniformity optimization system is provided, the system comprising:

[0043] The system includes a region division module, a brightness data acquisition module, a brightness model establishment module, a deviation coefficient acquisition module, a compensation matrix generation module, a pixel optimization processing module, and a judgment and feedback module.

[0044] The region division module is used to divide the rectangular display field of view of AR-HUD into multiple target grid regions;

[0045] The target grid region includes the original image pixel values;

[0046] The brightness data acquisition module is used to collect brightness data in a dark room environment and obtain the measured brightness value.

[0047] A brightness model establishment module is used to establish a brightness attenuation model of the optical system based on the measured brightness value.

[0048] The deviation coefficient acquisition module is used to obtain the brightness deviation coefficient of the target grid region.

[0049] The compensation matrix generation module is used to form a compensation coefficient matrix based on the brightness deviation coefficient and the distance between the target grid area and the center of the rectangular display field of view.

[0050] The pixel optimization processing module is used to compensate, correct, and dynamically optimize the pixel values ​​of the original image according to the compensation coefficient matrix, so as to generate optimized pixel values.

[0051] The feedback module is used to determine whether the optimized pixel values ​​meet the test requirements.

[0052] If not, return to the deviation coefficient acquisition module and retest.

[0053] According to three aspects of the present invention, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0054] The memory stores a computer program that, when executed by the processor, causes the processor to perform steps of an AR-HUD brightness uniformity optimization method.

[0055] According to four aspects of the present invention, a computer-readable storage medium is provided that stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of an AR-HUD brightness uniformity optimization method.

[0056] Compared with the prior art, the present invention has the following advantages:

[0057] This application verifies the optimized display effect through real vehicle review and brightness uniformity test, solving the problems of dark edges and horizontal and vertical stripes, and improving the overall brightness consistency.

[0058] This application improves the display effect of AR-HUD by optimizing the brightness uniformity algorithm logic, thus providing users with a better visual experience.

[0059] This application establishes a brightness attenuation model for the optical system based on measured brightness data in a darkroom, ensuring that the compensation is based on the actual characteristics of the hardware.

[0060] This application generates a compensation coefficient matrix by combining the distance between the target grid and the center of the field of view, and specifically corrects the distance-related brightness difference caused by optical path attenuation. Attached Figure Description

[0061] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0062] Figure 1 This is a flowchart of an AR-HUD brightness uniformity optimization method provided by one or more embodiments of the present invention.

[0063] Figure 2 This is a structural diagram of an AR-HUD brightness uniformity optimization system provided by one or more embodiments of the present invention.

[0064] Figure 3 This is a flowchart of the algorithm logic for optimizing brightness uniformity according to a specific embodiment of the present invention.

[0065] Figure 4 This is a block diagram of an electronic device structure for an AR-HUD brightness uniformity optimization method provided in one or more embodiments of the present invention. Detailed Implementation

[0066] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Figure 1 This is a flowchart of an AR-HUD brightness uniformity optimization method provided by one or more embodiments of the present invention.

[0068] like Figure 1 As shown, it includes the following steps:

[0069] Step S1: Divide the rectangular display field of view of the AR-HUD into multiple target grid areas;

[0070] Specifically, dividing multiple target grid regions includes: dividing into N×M grid regions, with each grid corresponding to the dimming zone of the backlight module.

[0071] The target grid region includes the original image pixel values;

[0072] Step S2: Collect brightness data in a dark room environment and obtain the measured brightness value;

[0073] Specifically, using a luminance meter or imaging colorimeter, edge and center luminance data of a pure white image are collected in a dark room environment, and the measured luminance value of each grid is recorded.

[0074] Step S3: Establish a brightness attenuation model for the optical system based on the measured brightness value;

[0075] Specifically, based on the measured brightness value of each grid. A dataset of measured brightness from a rectangular display field of view was generated. A brightness attenuation model of the optical system was established using multiple datasets, with the attenuation law being the edge brightness... ≈Center brightness ×(1-k); k is the attenuation coefficient, which is related to the lens collimation angle and the field of view.

[0076] Step S4: Obtain the brightness deviation coefficient of the target grid region.

[0077] Specifically, the brightness deviation coefficient α for each grid is defined. = [目标] / [实测] ,in [目标] To achieve a uniform target brightness (such as 90%-95% of the brightness in the center area, to avoid over-compensation leading to excessive brightness in the center), we assume that the brightness attenuation is proportional to the distance r from the center of the screen, and establish the function α(r)=1+k・r² (k is the attenuation coefficient, which is fitted by actual measurement data).

[0078] Model different areas of the screen, such as the four corners and the top and bottom edges, separately (e.g., center area: α=1.0, top and bottom edges: α=1.2-1.5, four corner areas: α=1.5-2.0 (adjusted according to the degree of attenuation)).

[0079] In existing technologies, display devices (such as various screens) generally suffer from brightness attenuation, with the central area being brighter and the edge / corner areas being darker. This results in significant differences in brightness distribution across different areas, disrupting the consistency and visual harmony of the displayed content.

[0080] If the highest brightness in the central area is used as the target brightness for the entire area for compensation, the central area will become too bright due to over-compensation, which will not only seriously affect visual comfort, but may also cause additional damage to the display device (such as accelerated aging).

[0081] By defining a brightness deviation coefficient, the ratio of the target brightness to the measured brightness is used as the basis for compensation, so that the brightness of each grid area of ​​the screen is aligned with a unified target brightness, such as 90%-95% of the brightness of the center area. This avoids the center from being too bright and effectively improves the uneven phenomenon of bright center and dark edges, thus enhancing the consistency of the overall brightness distribution.

[0082] By combining the law that brightness decay is proportional to the distance r from the center of the screen, and setting different α value ranges for different areas such as the center, top and bottom edges, and four corners (e.g., α≈1.0 for the center, 1.2-1.5 for the edges, and 1.5-2.0 for the four corners), brightness compensation can be performed on different positions to accurately match the degree of brightness decay in each area.

[0083] Uniform and moderate brightness distribution can avoid visual fatigue caused by excessive brightness or darkness in certain areas, making the displayed content such as images and text more visually appealing and consistent across the entire screen area, and significantly improving the overall display quality.

[0084] Step S5: Based on the brightness deviation coefficient, and combined with the distance between the target grid area and the center of the rectangular display field of view, a compensation coefficient matrix is ​​formed;

[0085] Specifically, traditional brightness compensation methods are difficult to design differently for the distance between each grid and the center, and cannot accurately adapt to the degree of brightness attenuation at different locations, resulting in insufficient compensation accuracy.

[0086] The compensation coefficient matrix is ​​designed based on the distance between the grid and the center and the brightness deviation coefficient. It can provide targeted compensation for grids at different locations, effectively reducing the brightness difference between the center and the edges and corners, and making the brightness distribution of the rectangular display field more uniform.

[0087] By utilizing the principle that brightness attenuation is proportional to distance from the center, the compensation coefficient of each grid is precisely matched with the actual degree of brightness attenuation, avoiding the error of indiscriminate compensation and greatly improving the accuracy of brightness correction.

[0088] Step S6: Based on the compensation coefficient matrix, compensate, correct, and dynamically optimize the pixel values ​​of the original image to generate optimized pixel values;

[0089] Specifically, for the original image pixel values Perform linear compensation:

[0090] ,in The offset pixel values ​​must be limited to [0, 255] (if they exceed this range, the maximum value of the range will be used to avoid overexposure); considering the non-linear perception of brightness by the human eye, gamma correction is introduced:

[0091] ,in, >1, this method can avoid noise caused by excessive compensation in bright areas, while enhancing details in dark areas; dynamically adjust the compensation intensity for different scene content (such as dark scene, bright scene): dark scene (average brightness <50): reduce the compensation coefficient (such as α×0.8) to avoid noise amplification; bright scene (average brightness >200): maintain full compensation to ensure that the edge brightness meets the standard.

[0092] By using a compensation coefficient matrix to specifically compensate for the brightness deviation of each pixel's region, the effect of uneven brightness on the image is effectively eliminated, making the brightness distribution of the original image more coordinated across the entire screen area (center, edges, corners, etc.), and avoiding the problem of local dark / overbright areas.

[0093] By finely adjusting the compensation coefficient of the pixel in the grid, the screen brightness deviation can be accurately compensated for the interference of pixel display, making the color, brightness and detail of the image closer to the original design intent, and greatly improving the fidelity of the image display.

[0094] Based on compensation and correction, dynamic optimization is added, which can dynamically adjust the compensation strategy according to the content of the picture (such as the picture with strong contrast between light and dark) and the ambient brightness (such as the strong light during the day / the weak light at night), so that the image can maintain a clear and comfortable visual effect in various scenarios, and enhance the adaptability and flexibility of the display system.

[0095] The optimized pixel values ​​make the image display clearer and more uniform, which not only improves the visual comfort of daily viewing (videos, pictures, text, etc.), but also meets the needs of professional scenarios (such as design and drafting, medical imaging diagnosis, etc.) for accurate presentation of image details, thus comprehensively improving display quality and professional value.

[0096] Step S7: Determine whether the optimized pixel values ​​meet the experimental requirements;

[0097] If not, return to step S4 and repeat the test.

[0098] Specifically, by introducing a brightness uniformity evaluation index, brightness uniformity η = (minimum brightness / maximum brightness) × 100%, the target value after optimization is ≥ 90% (usually 70%-80% before optimization).

[0099] The difference in brightness between the edge and the center is ≤5% (e.g., when the center brightness is 200 nits, the edge brightness is ≥190 nits).

[0100] In driving scenarios (such as tunnel entrances and exits, and meeting oncoming traffic at night), the readability of signs such as navigation arrows and vehicle speed values ​​is subjectively evaluated by the human eye to assess the effect of brightness uniformity optimization. Generally, it is required that the visual difference between the edge and center of the subjective evaluation reference object be ≤10%.

[0101] By combining objective quantitative indicators with subjective readability assessments in driving scenarios, this approach defines optimization goals from both technical parameters and verifies effects from real-world usage scenarios. It provides measurable and practical standards for brightness uniformity optimization in fields such as automotive displays, driving the iteration of display quality towards adapting to scenario requirements.

[0102] Furthermore, including:

[0103] Dividing multiple target grid regions includes dividing the area into N×M grid regions, with each grid corresponding to a dimming zone of the backlight module.

[0104] Furthermore, including:

[0105] Step S2 includes: acquiring edge and center brightness data of a pure white image in a dark room environment, and obtaining the measured brightness value L(i,j) of the target grid area.

[0106] Furthermore, including:

[0107] Step S3 includes: establishing a brightness attenuation model of the optical system using multiple sets of measured brightness values;

[0108] The attenuation law is that the edge brightness L ≈ the center brightness L × (1-k); k is the attenuation coefficient, which is related to the lens collimation angle and the field of view.

[0109] Furthermore, including:

[0110] Step S4 includes: the brightness deviation coefficient is:

[0111] =L [目标] / L [实测] ;

[0112] Among them, L [目标] To achieve a uniform target brightness, such as 90%-95% of the brightness in the central area.

[0113] Furthermore, including:

[0114] Step S5 includes: obtaining brightness attenuation parameters;

[0115] Based on the distance from the brightness attenuation parameter to the center of the screen Proportional relationship;

[0116] Establish function ;

[0117] in, The attenuation coefficient is obtained by fitting experimental data;

[0118] Model the target region and generate a compensation coefficient matrix;

[0119] The target area includes the four corners of the screen and the top and bottom edges.

[0120] Furthermore, including:

[0121] Step S6 includes:

[0122] The pixel values ​​of the original image are compensated;

[0123] Specifically: ;

[0124] in, These are the offset pixel values. These are the original image pixel values;

[0125] Perform gamma correction on the offset pixel values;

[0126] Specifically: ;

[0127] in, The gamma coefficient is, and >1.

[0128] Figure 2 This is a structural diagram of an AR-HUD brightness uniformity optimization system provided by one or more embodiments of the present invention.

[0129] like Figure 2 As shown, it includes:

[0130] The system includes a region division module, a brightness data acquisition module, a brightness model establishment module, a deviation coefficient acquisition module, a compensation matrix generation module, a pixel optimization processing module, and a judgment and feedback module.

[0131] The region division module is used to divide the rectangular display field of view of AR-HUD into multiple target grid regions;

[0132] The target grid region includes the original image pixel values;

[0133] The brightness data acquisition module is used to collect brightness data in a dark room environment and obtain the measured brightness value.

[0134] A brightness model establishment module is used to establish a brightness attenuation model of the optical system based on the measured brightness value.

[0135] The deviation coefficient acquisition module is used to obtain the brightness deviation coefficient of the target grid region.

[0136] The compensation matrix generation module is used to form a compensation coefficient matrix based on the brightness deviation coefficient and the distance between the target grid area and the center of the rectangular display field of view.

[0137] The pixel optimization processing module is used to compensate, correct, and dynamically optimize the pixel values ​​of the original image according to the compensation coefficient matrix, so as to generate optimized pixel values.

[0138] The feedback module is used to determine whether the optimized pixel values ​​meet the test requirements.

[0139] If not, return to the deviation coefficient acquisition module and retest.

[0140] Specifically, as an in-vehicle augmented reality display device, AR-HUD's rectangular display field of view is affected by optical design (such as lens attenuation and light path offset), generally resulting in high brightness in the center and significant brightness attenuation at the edges / corners. Furthermore, it lacks accurate capture of the brightness attenuation pattern in each area, leading to large differences in brightness and darkness in different areas of key information such as navigation arrows and vehicle speed, resulting in poor readability.

[0141] In typical driving scenarios such as tunnel entrances and exits (where light suddenly brightens and dims) and nighttime oncoming traffic (where strong light shines directly), the uneven brightness problem of AR-HUD, which has not been optimized, will be further amplified: information in dark areas at the edges is easily submerged by ambient light, while the bright areas in the center may be more dazzling due to strong light contrast, causing driver distraction or misjudgment of key information, posing a direct safety hazard.

[0142] Traditional optimization schemes are mostly one-time compensations, lacking standardized regional decomposition, attenuation modeling, and feedback adjustment mechanisms. They cannot establish accurate compensation criteria based on the real brightness data of the AR-HUD optical system, nor can they quantitatively verify and dynamically correct the optimization effect, resulting in low compensation accuracy and poor stability.

[0143] The original image pixel values ​​are based on an ideal uniform display design, without taking into account the actual brightness attenuation characteristics of each grid area of ​​the AR-HUD. When output directly, the brightness loss of the optical system will cause image distortion, further aggravating the uneven display.

[0144] By dividing the display field of view into grids using the region division module, combining the accurate measurements of the brightness data acquisition module in a dark room environment, and then constructing a brightness attenuation model of the optical system using the brightness model building module, the brightness attenuation characteristics of each region of the AR-HUD are visualized and systematically described, providing a scientific basis for subsequent compensation.

[0145] The deviation coefficient acquisition module and compensation matrix generation module based on the attenuation model can generate a differentiated compensation coefficient matrix for the brightness deviation and distance from the center of each grid area. Then, the pixel optimization processing module compensates, corrects and dynamically optimizes the original pixel values, so that the brightness uniformity of AR-HUD can reach the quantitative target of η≥90% and the brightness difference between the edge and the center ΔL≤5%, thus eliminating the inherent defects of the center being too bright and the edge being too dark from a technical point of view.

[0146] In complex lighting scenarios such as tunnels and nighttime, the optimized AR-HUD maintains a visual difference of ≤10% between the edge and center of navigation arrows, speed values, and other markers. This significantly improves the clarity of key driving information for the human eye, effectively reducing driver distraction caused by blurry displays and directly lowering driving risks in special scenarios.

[0147] By verifying whether the optimized pixel values ​​meet the test requirements, if they do not meet the standards, the process is traced back to the deviation coefficient acquisition stage for re-optimization. This ensures that the brightness uniformity of the AR-HUD is stable and meets the standards, avoids the problem of one-time optimization failure, and adapts to the stringent requirements of display reliability in automotive scenarios.

[0148] It is worth noting that although only some basic functional modules are disclosed in this embodiment, it does not mean that the composition of this system is limited to the above-mentioned basic functional modules. On the contrary, what this embodiment intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. The fact that this embodiment only discloses a few basic functional modules does not mean that the scope of protection of the claims of this invention is limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described separately according to its functions as various units and modules. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0149] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0150] Figure 3 This is a flowchart of the algorithm logic for optimizing brightness uniformity according to a specific embodiment of the present invention.

[0151] like Figure 3 As shown, it includes the following steps:

[0152] (1) For the HUD display area (usually a rectangular field of view), it is divided into N×M grid areas, each grid corresponding to the dimming zone of the backlight module; using a luminance meter or imaging colorimeter, the edge and center luminance data of the pure white screen are collected in a dark room environment, and the measured luminance value L(i,j) of each grid is recorded. A luminance attenuation model of the optical system is established through multiple sets of measured data. The attenuation law is that the edge luminance L≈ the center luminance L×(1-k); k is the attenuation coefficient, which is related to the lens collimation angle and the field of view.

[0153] (2) Define the brightness deviation coefficient α(i,j) for each grid as L[target](i,j) / L[measured](i,j), where L[target] is the uniform target brightness (e.g., 90%-95% of the brightness in the central area, to avoid overcompensation leading to excessive brightness in the center); assuming that the brightness attenuation is proportional to the distance r from the center of the screen, establish the function α(r)=1+k・r² (k is the attenuation coefficient, fitted by measured data). Model different areas such as the four corners and the top and bottom edges of the screen separately (e.g., central area: α=1.0, top and bottom edges: α=1.2-1.5, four corners: α=1.5-2.0 (adjusted according to the degree of attenuation)).

[0154] Taking the compensation coefficient matrix of a 10×10 grid as an example:

[0155]

[0156] (3) Linear compensation is applied to the original image pixel value P(i,j): P'(i,j) = P(i,j) × α(i,j), where P'(i,j) is the offset pixel value, which needs to be limited to [0,255] (if it exceeds the range, the maximum value of the range is taken to avoid overexposure); Considering the nonlinear perception of brightness by the human eye, gamma correction is introduced: P'(i,j) =255×[(P(i,j) / 255)×α(i,j)]^γ (γ>1). This method can avoid noise caused by excessive compensation in bright areas, while enhancing details in dark areas; The compensation intensity is dynamically adjusted for different scene content (such as dark scenes and bright scenes): Dark scene (average brightness <50): Reduce the compensation coefficient (such as α×0.8) to avoid noise amplification; Bright scene (average brightness >200): Maintain full compensation to ensure that the edge brightness meets the standard.

[0157] (4) Evaluation index of brightness uniformity:

[0158] a. Brightness uniformity η = (minimum brightness / maximum brightness) × 100%, the target value after optimization is ≥90% (usually 70%-80% before optimization);

[0159] b. The difference in brightness between the edge and the center ΔL ≤ 5% (e.g., when the center brightness is 200 nits, the edge brightness is ≥ 190 nits).

[0160] In driving scenarios (such as tunnel entrances and exits, and meeting oncoming traffic at night), the readability of signs such as navigation arrows and vehicle speed values ​​is subjectively evaluated by the human eye to assess the effect of brightness uniformity optimization. Generally, it is required that the visual difference between the edge and center of the subjective evaluation reference object be ≤10%.

[0161] In another embodiment,

[0162] If you encounter issues such as horizontal or vertical stripes and dark edges in the reversing camera and screen projection, try the following methods to resolve them:

[0163] 1. For the HUD display area, divide it into grid areas, collect actual brightness data of the edge and center of the pure white screen in a dark room environment, and confirm the attenuation coefficient;

[0164] 2. Define the brightness deviation coefficient α for each grid, and model different areas such as the four corners and top and bottom edges of the screen separately;

[0165] 3. Linear compensation is applied to the original image pixel values ​​P(i,j), and correction is introduced to avoid noise caused by excessive compensation in bright areas, while enhancing details in dark areas; the compensation intensity is dynamically adjusted for different image content.

[0166] 4. The optimized display effect was verified through real vehicle review and brightness uniformity test, which solved the problems of dark edges and horizontal and vertical stripes, and improved the overall brightness consistency.

[0167] Figure 4 This is a block diagram of an electronic device structure for an in-vehicle multilingual speech recognition method provided by one or more embodiments of the present invention.

[0168] like Figure 4 As shown, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0169] The memory stores a computer program that, when executed by a processor, causes the processor to perform the steps of an in-vehicle multilingual speech recognition method.

[0170] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of an in-vehicle multilingual speech recognition method.

[0171] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0172] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing brightness uniformity in an AR-HUD, characterized in that, Includes the following steps: Step S1: Divide the rectangular display field of view of the AR-HUD into multiple target grid areas; The target grid region includes the original image pixel values; Step S2: Collect brightness data in a dark room environment and obtain the measured brightness value; Step S3: Establish a brightness attenuation model for the optical system based on the measured brightness value; Step S4: Obtain the brightness deviation coefficient of the target grid region; Step S5: Based on the brightness deviation coefficient, and combined with the distance between the target grid area and the center of the rectangular display field of view, a compensation coefficient matrix is ​​formed; Step S6: Based on the compensation coefficient matrix, compensate, correct, and dynamically optimize the pixel values ​​of the original image to generate optimized pixel values; Step S7: Determine whether the optimized pixel values ​​meet the experimental requirements; If not, return to step S4 and repeat the test.

2. The AR-HUD brightness uniformity optimization method according to claim 1, characterized in that, The division of multiple target grid regions includes: dividing into N×M grid regions, with each grid corresponding to a dimming zone of the backlight module.

3. The AR-HUD brightness uniformity optimization method according to claim 1, characterized in that, Step S2 includes: acquiring edge and center brightness data of a pure white image in a darkroom environment, and obtaining the measured brightness value of the target grid area. .

4. The AR-HUD brightness uniformity optimization method according to claim 1, characterized in that, Step S3 includes: establishing a brightness attenuation model of the optical system using multiple sets of measured brightness values; The attenuation pattern is based on edge brightness. k is the attenuation coefficient, which is related to the lens collimation angle and the field of view.

5. The AR-HUD brightness uniformity optimization method according to claim 1, characterized in that, Step S4 includes: the brightness deviation coefficient is: = [目标] / [实测] ； in, [目标] To achieve a uniform target brightness.

6. The AR-HUD brightness uniformity optimization method according to claim 1, characterized in that, Step S5 includes: obtaining brightness attenuation parameters; Based on the distance from the brightness attenuation parameter to the center of the screen Proportional relationship; Establish function ; in, The attenuation coefficient is obtained by fitting experimental data; Model the target region and generate a compensation coefficient matrix; The target area includes the four corners of the screen and the top and bottom edges.

7. The AR-HUD brightness uniformity optimization method according to claim 5, characterized in that, Step S6 includes: The pixel values ​​of the original image are compensated; Specifically: ; in, These are the offset pixel values. These are the original image pixel values; Perform gamma correction on the offset pixel values; Specifically: ; in, The gamma coefficient is, and >

1.

8. An AR-HUD brightness uniformity optimization system, characterized in that, include: The system includes a region division module, a brightness data acquisition module, a brightness model establishment module, a deviation coefficient acquisition module, a compensation matrix generation module, a pixel optimization processing module, and a judgment and feedback module. The region division module is used to divide the rectangular display field of view of AR-HUD into multiple target grid regions; The target grid region includes the original image pixel values; The brightness data acquisition module is used to collect brightness data in a dark room environment and obtain the measured brightness value. A brightness model establishment module is used to establish a brightness attenuation model of the optical system based on the measured brightness value. The deviation coefficient acquisition module is used to obtain the brightness deviation coefficient of the target grid region. The compensation matrix generation module is used to form a compensation coefficient matrix based on the brightness deviation coefficient and the distance between the target grid area and the center of the rectangular display field of view. The pixel optimization processing module is used to compensate, correct, and dynamically optimize the pixel values ​​of the original image according to the compensation coefficient matrix, so as to generate optimized pixel values. The feedback module is used to determine whether the optimized pixel values ​​meet the test requirements. If not, return to the deviation coefficient acquisition module and retest.

9. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the AR-HUD brightness uniformity optimization method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of an AR-HUD brightness uniformity optimization method as described in any one of claims 1-7.

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