A method for improving the brightness and uniformity of digital head-up display characters

Abstract

The application relates to the technical field of airborne head-up display control, in particular to a method for improving the character brightness and uniformity of a digital head-up display, which comprises the following steps: step 1, obtaining a plurality of target processing regions in a dot matrix image with typical characters; step 2, based on the plurality of target processing regions, using an image processing algorithm to pre-process the dot matrix image to obtain a pre-processed dot matrix image; step 3, performing distortion correction on the pre-processed dot matrix image, and counting the gray value distribution information of the character lines after correction; step 4, based on the gray value distribution information of the character lines, using an optimization algorithm to optimize the image processing algorithm, and iteratively repeating steps 2 to 3 to obtain an optimal image processing algorithm; and step 5, using the optimal image processing algorithm to pre-process the dot matrix image. The method can improve the character brightness and uniformity on the head-up display without improving the backlight brightness of the liquid crystal image source.

Description

Technical Field

[0001] The embodiments of this application relate to the field of airborne head-up display control technology, and in particular to a method for improving the brightness and uniformity of characters on a digital head-up display. Background Technology

[0002] With the development of display technology, airborne head-up displays (HUDs) have gradually evolved from high-power, bulky analog CRTs (cathode ray tubes) to low-power, compact liquid crystal digital displays. Liquid crystal displays are essentially spatial discrete sampling systems, spatially limited by pixel grid density, which can produce aliasing effects such as jagged edges and spikes on displayed lines / curves. Furthermore, to compensate for optical distortion in HUDs, optical pre-distortion correction processing is required on the liquid crystal display, which further exacerbates jagged edges and spikes. Therefore, characters observed on HUDs exhibit low brightness and poor brightness uniformity.

[0003] Therefore, how to achieve a significant improvement in the brightness and uniformity of characters on a head-up display without increasing the backlight brightness of the liquid crystal image source is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] In order to improve the brightness and uniformity of characters on a head-up display without increasing the backlight brightness of the liquid crystal image source, embodiments of this application propose a method for improving the brightness and uniformity of characters on a digital head-up display. To achieve the above objectives, embodiments of this application propose a method for improving the brightness and uniformity of characters on a digital head-up display, the method comprising the following steps: Step 1: Obtain multiple target processing regions from a dot matrix image containing typical characters; the degree of distortion varies among different target processing regions. Step 2: Based on multiple target processing areas, an image processing algorithm for improving the brightness and uniformity of characters on a digital head-up display is used to preprocess the dot matrix image to obtain a preprocessed dot matrix image; wherein, the parameters of the image processing algorithm are set independently according to the characteristics of the target processing area. Step 3: Perform distortion correction on the preprocessed bitmap image and statistically analyze the grayscale distribution information of the corrected character lines; Step 4: Based on the grayscale distribution information of the character lines, an optimization algorithm is used to optimize the image processing algorithm, and steps 2 to 3 are iteratively repeated until the character line display effect meets the preset conditions, thus obtaining the optimal image processing algorithm; wherein, the parameters of the optimal image processing algorithm are set independently according to the characteristics of the processing target area. Step 5: Use the optimal image processing algorithm to preprocess the bitmap image.

[0005] This application provides a method for improving the brightness and uniformity of characters on a digital head-up display. The method involves acquiring multiple target processing regions in a dot matrix image containing typical characters; then, based on these target processing regions, an image processing algorithm is used to preprocess the dot matrix image to obtain a preprocessed dot matrix image; subsequently, distortion correction is performed on the preprocessed dot matrix image, and the grayscale value distribution information of the corrected character lines is statistically analyzed; based on the grayscale value distribution information of the character lines, an optimization algorithm is used to optimize the image processing algorithm, and the above preprocessing and distortion correction steps are iteratively repeated until the character line display effect meets preset conditions, thus obtaining the optimal image processing algorithm; finally, the optimal image processing algorithm is applied. The image processing algorithm is preprocessed to improve the brightness and uniformity of characters on the digital head-up display. Since the distortion levels vary across different target processing areas, the parameters of the image processing algorithm are set independently according to the characteristics of each target processing area. This allows for differentiated processing strategies for target processing areas with varying degrees of distortion. Because the image processing algorithm is used to improve the brightness and uniformity of characters on the digital head-up display, distortion correction is performed on the preprocessed image. Based on the grayscale distribution information of the character lines, the image processing algorithm is optimized to obtain the optimal image processing algorithm. This optimal algorithm is then used to preprocess the image, thereby improving the brightness and uniformity of characters on the head-up display without increasing the backlight brightness of the LCD image source.

[0006] To achieve the above objectives, embodiments of this application propose a device for improving the brightness and uniformity of characters on a digital head-up display, characterized in that it is applied to a head-up display and includes: The acquisition module is used to acquire multiple target processing regions in a dot matrix image with typical characters; among them, the degree of distortion varies in different target processing regions; The preprocessing module is used to preprocess the dot matrix image based on multiple target processing areas, using image processing algorithms designed to improve the brightness and uniformity of characters on a digital head-up display, to obtain a preprocessed dot matrix image; wherein, the parameters of the image processing algorithm are set independently according to the characteristics of the target processing areas. The correction module is used to perform distortion correction on the preprocessed bitmap image and to statistically analyze the grayscale value distribution information of the corrected character lines. The optimization module is used to optimize the image processing algorithm based on the gray value distribution information of the character lines. It iteratively repeats the work of the preprocessing module and the correction module until the character line display effect meets the preset conditions, thus obtaining the optimal image processing algorithm. The parameters of the optimal image processing algorithm are set independently according to the characteristics of the target processing area. The final execution module is used to preprocess the raster image using the optimal image processing algorithm.

[0007] To achieve the above objectives, embodiments of this application also propose an electronic device, including: a processor and a memory, wherein the memory stores instructions executable by the processor, and the processor is configured to execute the instructions such that the electronic device can implement the method described above for improving the brightness and uniformity of characters on a digital head-up display.

[0008] To achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program that, when executed by a processor, enables a method for improving the brightness and uniformity of characters on a digital head-up display as described above.

[0009] Optionally, multiple processing regions can be obtained from a dot matrix image containing typical characters, including: generating a dot matrix image containing typical characters using a GPU and a software model; determining pre-distortion correction parameters for the dot matrix image based on the distortion characteristics of the head-up display's optical system; and dividing the dot matrix image into regions based on the pre-distortion correction parameters to generate target processing regions with different degrees of distortion.

[0010] Optionally, based on the pre-distortion correction parameters of the bitmap image, the bitmap image is divided into regions to generate target processing regions with different distortion levels, including: performing gridding processing on the bitmap image to obtain multiple grid cells in the bitmap image; determining the distortion rate of each grid cell based on the pre-distortion correction parameters of the bitmap image; wherein the distortion rate is used to represent the degree of distortion of the bitmap image at different field of view positions; dividing the bitmap image into target processing regions with different distortion levels based on the distortion rate of each grid cell and a preset threshold; wherein the target processing regions with different distortion levels include one or more of low distortion regions, medium distortion regions, and high distortion regions; the distortion rate of the low distortion region is less than a first preset threshold, the distortion rate of the high distortion region is greater than a second preset threshold, and the distortion rate of the medium distortion region is greater than or equal to the first preset threshold and less than or equal to the second preset threshold, wherein the first preset threshold is less than the second preset threshold.

[0011] Optionally, the image processing algorithm includes a Gaussian filtering algorithm and a linear brightening algorithm. The Gaussian filtering algorithm is used to improve character uniformity, and the linear brightening algorithm is used to improve character brightness. Based on multiple target processing regions, an image processing algorithm for improving the brightness and uniformity of characters on a digital head-up display is used to preprocess the dot matrix image to obtain a preprocessed dot matrix image. This includes: preprocessing low-distortion regions using a Gaussian filtering algorithm with a first standard deviation and a linear brightening algorithm with a first gain to obtain preprocessed low-distortion regions; preprocessing low-distortion regions using a Gaussian filtering algorithm with a second standard deviation and a linear brightening algorithm with a second gain to obtain preprocessed medium-distortion regions; and preprocessing low-distortion regions using a Gaussian filtering algorithm with a third standard deviation and a linear brightening algorithm with a third gain to obtain preprocessed high-distortion regions. Wherein, the first standard deviation is less than the second standard deviation, the second standard deviation is less than the third standard deviation, the first gain is less than the second gain, and the second gain is less than the third gain.

[0012] Optionally, if the character line display effect meets the preset conditions, it indicates that the optimization algorithm has reached the global optimum. The optimization algorithm is used to optimize the image processing algorithm based on the gray value distribution information of the character lines, and steps 2 to 3 are iteratively repeated to make the character line display effect meet the preset conditions and obtain the optimal image processing algorithm. This includes: using gray value statistics as input parameters, using the brightness uniformity index of character lines in the same target processing area as the first optimization index, and using the line width deviation of character lines between different areas as the second optimization index, the optimization algorithm is used to iteratively optimize the Gaussian filtering algorithm and the linear brightening algorithm. When the global optimum is reached, the optimal parameter combination of the Gaussian filtering algorithm and the linear brightening algorithm is determined. Based on the optimal parameter combination of the Gaussian filtering algorithm and the linear brightening algorithm, the optimal image processing algorithm is obtained.

[0013] Optionally, the optimal image processing algorithm is used to preprocess the bitmap image, including: based on the optimal image processing algorithm and multiple target processing regions, a regional processing algorithm architecture is deployed on the target platform to realize real-time preprocessing and correction of the bitmap image.

[0014] Optionally, the target platform is an FPGA. The step of deploying a regional processing algorithm architecture on the target platform based on the optimal image processing algorithm and multiple target processing regions to realize real-time preprocessing and correction of the dot matrix image includes: in the process of regional dot matrix image preprocessing through the FPGA, dividing the pixel data of the dot matrix image into different processing regions through address mapping; wherein, the address mapping mechanism is used to represent the correspondence between the storage address of the pixel data in the dot matrix image and the distortion rate; and using a pipeline structure to execute the optimal target image processing algorithm and distortion correction operation in parallel, and outputting the corrected dot matrix image. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies of this application will be briefly introduced below. Obviously, the following drawings are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The drawings described herein are only used to explain this application and are not intended to limit this application.

[0016] Figure 1 This is a schematic diagram of the structure of a platform for improving the brightness and uniformity of characters on a digital head-up display, provided in one embodiment of this application; Figure 2 This is a schematic diagram illustrating the evaluation of typical dot matrix character image effects generated by a GPU, provided in one embodiment of this application; Figure 3 This is a schematic diagram of an optical distortion rate-based partitioning method provided in an embodiment of this application; Figure 4 This is an example image of the statistical results of grayscale values ​​of character lines provided in an embodiment of this application; Figure 5 This is a schematic diagram of a dot matrix character image preprocessing process provided in an embodiment of this application; Figure 6 This is a schematic diagram of a character brightness and line width test position provided in an embodiment of this application; Figure 7 This is a schematic diagram of a device for improving the brightness and uniformity of characters on a digital head-up display, provided in another embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. Those skilled in the art will understand that many technical details have been presented in the embodiments of this application to facilitate better understanding. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of this application. The following embodiments can be combined with and referenced by each other without contradiction.

[0018] With the development of display technology, airborne head-up displays (HUDs) have gradually evolved from high-power, bulky analog CRT displays to low-power, compact liquid crystal digital displays. Liquid crystal displays are essentially spatial discrete sampling systems, spatially limited by pixel grid density, which can produce aliasing effects such as jagged edges and spikes on displayed lines / curves. Furthermore, to compensate for optical distortion in HUDs, optical pre-distortion correction processing is required on the liquid crystal display, which further exacerbates jagged edges and spikes. Therefore, characters observed on HUDs exhibit low brightness and poor brightness uniformity.

[0019] Therefore, how to achieve a significant improvement in the brightness and uniformity of characters on a head-up display without increasing the backlight brightness of the liquid crystal image source is a technical problem that urgently needs to be solved.

[0020] To improve character brightness and uniformity, optimization measures need to be taken in the character preprocessing stage, taking into account the character rendering effects of the GPU and software model and the requirements of optical pre-distortion processing.

[0021] In view of this, embodiments of this application provide a method for improving the brightness and uniformity of characters on a digital head-up display, which significantly improves the brightness and uniformity of characters on the head-up display without increasing the backlight brightness of the liquid crystal image source.

[0022] One embodiment of this application proposes a method for improving the brightness and uniformity of characters on a digital head-up display (HUD), applied to an electronic device, wherein the electronic device can be a terminal or a server. This embodiment and the following embodiments will use a server as an example for description. The implementation details of the method for improving the brightness and uniformity of characters on a digital head-up display proposed in this embodiment will be described in detail below. The following implementation details are provided for ease of understanding and are not essential for implementing this solution.

[0023] The specific process of the method for improving the brightness and uniformity of characters on a digital head-up display proposed in this embodiment can be described as follows: Figure 1 As shown, it includes: Step 1: Obtain multiple target processing regions from a dot matrix image containing typical characters.

[0024] The degree of distortion varies in different target processing regions.

[0025] For example, typical characters can include a variety of basic graphic primitives, such as horizontal lines, vertical lines, diagonal lines, arcs, and English characters; among them, horizontal lines are used to test the uniformity of lines in the horizontal direction; vertical lines are used to evaluate the display effect in the vertical direction; diagonal lines are used to detect jagged effects (such as burrs and breaks) in discrete sampling systems; arcs are used to check the quality of curve drawing; and English characters can represent actual text display requirements to ensure the clarity of letters and numbers.

[0026] For example, the degree of distortion can also be called the distortion rate, which refers to the proportion of image distortion caused by the optical system relative to the ideal image.

[0027] In one possible embodiment, step 1 above includes: generating a dot matrix image with typical characters using a GPU and a software model; determining pre-distortion correction parameters for the dot matrix image based on the distortion characteristics of the head-up display optical system; and dividing the dot matrix image into regions based on the pre-distortion correction parameters to generate target processing regions with different degrees of distortion.

[0028] For example, a typical character display can be drawn using the GPU and software model (e.g., an OpenGL driver) in a head-up display, including basic primitives such as horizontal lines, vertical lines, diagonal lines, arcs, and English letters. After receiving instructions, the GPU can convert the character vector data into a bitmap image (e.g., a pixel matrix). Because liquid crystal displays are discrete sampling systems, aliasing effects (such as jagged edges) may occur during GPU rendering due to pixel grid limitations. The bitmap image covers the entire field of view of the head-up display, such as... Figure 2 As shown, Figure 2 This is a schematic diagram illustrating the evaluation of a typical dot matrix character image effect generated by a GPU, provided in an embodiment of this application.

[0029] For example, since the degree of distortion can reflect the degree of deformation in different target processing regions, a raster image can be divided into multiple target processing regions based on the degree of distortion; for example, such as Figure 3 As shown, Figure 3 This is a schematic diagram of an embodiment of the present application based on optical distortion rate partitioning. The bitmap image may include regions 1 to 4. Among them, region 1 is a near-zero distortion region, regions 2 and 3 are positive distortion regions (deflection + magnification), and region 4 is a negative distortion region (deflection + reduction).

[0030] In one possible embodiment, the bitmap image is divided into regions based on the pre-distortion correction parameters of the bitmap image to generate target processing regions with different distortion levels. This includes: performing gridding processing on the bitmap image to obtain multiple grid cells in the bitmap image; determining the distortion rate of each grid cell based on the pre-distortion correction parameters of the bitmap image; and dividing the bitmap image into target processing regions with different distortion levels based on the distortion rate of each grid cell and a preset threshold.

[0031] The distortion rate is used to represent the degree of distortion of the bitmap image at different field of view positions; the target processing regions with different distortion levels include one or more of low distortion regions, medium distortion regions and high distortion regions; the distortion rate of the low distortion region is less than the first preset threshold, the distortion rate of the high distortion region is greater than the second preset threshold, the distortion rate of the medium distortion region is greater than or equal to the first preset threshold and less than or equal to the second preset threshold, and the first preset threshold is less than the second preset threshold.

[0032] For example, the meshing process can employ a uniform mesh division method, dividing the GPU-generated raster image into multiple rectangular mesh units with a fixed pixel size. Each mesh unit contains a specific number of pixels; for example, the size of each mesh unit is a preset pixel value × a preset pixel value.

[0033] For example, the first preset threshold and the second preset threshold can be values ​​pre-configured by the head-up display or values ​​set manually; this application embodiment does not impose specific limitations on this. For example, the first preset threshold is 0.1, and the second preset threshold is 0.5.

[0034] For example, based on the distortion characteristics of the head-up display's optical system, a pre-distortion mapping function can be used to analyze the distortion ratio (e.g., magnification or compression ratio) of each grid cell. Then, based on the distortion ratio of each grid cell, the bitmap image is divided into different target processing regions. According to the distortion rate of each grid cell, combined with a preset threshold, the image is divided into low-distortion regions, medium-distortion regions, and high-distortion regions. For example, the distortion rate of low-distortion regions (e.g., region 1) is less than 0.1, the distortion rate of medium-distortion regions (e.g., regions 2 and 3) is between 0.1 and 0.5, and the distortion rate of high-distortion regions (e.g., region 4) is greater than 0.5.

[0035] Step 2: Based on multiple target processing areas, an image processing algorithm for improving the brightness and uniformity of characters on a digital head-up display is used to preprocess the dot matrix image to obtain the preprocessed dot matrix image.

[0036] The parameters of the image processing algorithm are set independently according to the characteristics of the target processing region.

[0037] For example, in low-distortion areas, where distortion is slight, the focus can be on improving character sharpness. In medium-distortion areas, balanced filtering and brightening are needed to compensate for moderate distortion. In high-distortion areas, where distortion is significant, strong filtering and high-gain brightening are required to suppress jagged edges.

[0038] For example, image processing algorithms include Gaussian filtering and linear brightening. Gaussian filtering improves character uniformity, while linear brightening improves character brightness. Gaussian filtering smooths the image using a convolution kernel, reducing jagged edges and artifacts. The filtering effect is controlled by the standard deviation parameter: a larger standard deviation results in smoother image quality but may sacrifice detail; a smaller standard deviation preserves more detail but offers limited improvement in uniformity. Linear brightening adjusts pixel grayscale values ​​through gain values, directly increasing brightness. A larger gain value results in higher brightness but overexposure must be avoided.

[0039] For example, the parameters of an image processing algorithm can be set independently based on the distortion characteristics (e.g., the degree of distortion) of the target processing region. For instance, highly distorted regions require stronger smoothing and brightening to compensate for severe deformation, while low-distortion regions use gentler parameters to preserve details.

[0040] In one possible embodiment, the step of preprocessing the dot matrix image based on multiple target processing regions using an image processing algorithm for improving the brightness and uniformity of characters on a digital head-up display to obtain a preprocessed dot matrix image includes: The low-distortion region is preprocessed using a Gaussian filtering algorithm with the first standard deviation and a linear brightening algorithm with the first gain to obtain the preprocessed low-distortion region; the medium-distortion region is preprocessed using a Gaussian filtering algorithm with the second standard deviation and a linear brightening algorithm with the second gain to obtain the preprocessed medium-distortion region; and the high-distortion region is preprocessed using a Gaussian filtering algorithm with the third standard deviation and a linear brightening algorithm with the third gain to obtain the preprocessed high-distortion region. Among them, the first standard deviation is less than the second standard deviation, the second standard deviation is less than the third standard deviation; the first gain is less than the second gain, and the second gain is less than the third gain.

[0041] For example, for low-distortion regions, a Gaussian filtering algorithm with the first standard deviation (small standard deviation, such as σ=0.5) and linear brightening with the first gain (lower gain, such as...) can be used. This allows for slight smoothing and brightening, while avoiding loss of detail.

[0042] For example, in regions with moderate distortion, a Gaussian filter with a second standard deviation can be used (where the standard deviation is moderate, such as...). ) and linear brightening of the second gain (medium gain, such as This allows for a balance between uniformity and brightness, compensating for moderate distortion.

[0043] For example, in regions with high distortion, a Gaussian filter with a third standard deviation (where the standard deviation is relatively large, such as...) can be used. ) and linear brightening with the third gain (higher gain, such as This allows for strong smoothing and brightening to suppress blur and dark areas caused by distortion.

[0044] Step 3: Perform distortion correction on the preprocessed bitmap image and statistically analyze the grayscale distribution information of the corrected character lines.

[0045] For example, distortion correction can be used to compensate for inherent distortions in the optical system of a head-up display (such as barrel or pincushion distortion), so that characters can be perceived by the observer as a distortion-free image after passing through the optical components.

[0046] It is understandable that when an optical system displays characters, the lens characteristics cause distortion at the image edges (such as magnification, reduction, or deflection). Distortion correction preprocesses the raster image using an inverse mapping function, introducing distortions opposite to optical distortion beforehand. For example, if the optical system causes image edge magnification, the correction model will first reduce the image edges, so that the characters can be restored to their standard shape after passing through the optical system. Correction parameters (such as mapping function coefficients) can be dynamically adjusted according to the optical distortion rate. It's understandable that distortion correction reduces character line breaks or blurring caused by optical distortion, but it may introduce uneven grayscale values ​​(such as darkened edges). Therefore, the grayscale value distribution must be statistically analyzed after correction to provide a basis for subsequent parameter optimization.

[0047] For example, such as Figure 4 As shown, Figure 4 This is an example diagram illustrating the statistical results of grayscale values ​​for character lines provided in an embodiment of this application. The grayscale value distribution information is used for quantitative analysis of the pre-distortion corrected bitmap image to evaluate the brightness uniformity and line quality of the character lines. For example, the statistical results of the grayscale value distribution information are used to iteratively optimize preprocessing algorithm parameters (such as the gain of Gaussian filtering and linear brightening) to ensure that the character display meets requirements such as a brightness greater than or equal to 12000 candela / m² and a line width accuracy of 1 ± 0.2 milliradians (mrad).

[0048] For example, the statistics of grayscale value distribution information can refer to the statistics of grayscale values ​​of character lines. Grayscale value statistics include the analysis of the mean, variance, and gradient change rate of pixel grayscale values ​​in the horizontal, vertical, and diagonal directions of character lines after pre-distortion correction, and the optimal configuration is determined by comparing the statistical results under different combinations of processing parameters.

[0049] Step 4: Based on the grayscale distribution information of the character lines, an optimization algorithm is used to optimize the image processing algorithm, and steps 2 to 3 are iteratively repeated until the character line display effect meets the preset conditions, thus obtaining the optimal image processing algorithm.

[0050] The parameters of the optimal image processing algorithm are set independently according to the characteristics of the target processing region.

[0051] For example, if the character line display effect meets the preset conditions, it can indicate that the optimization algorithm has reached the global optimum.

[0052] In one possible embodiment, step 4 above includes: using grayscale statistics as input parameters, using the brightness uniformity index of character lines within the same target processing area as the first optimization index, and using the line width deviation of character lines between different areas as the second optimization index, using an optimization algorithm to iteratively optimize the Gaussian filtering algorithm and the linear brightening algorithm, and determining the optimal parameter combination of the Gaussian filtering algorithm and the linear brightening algorithm when the global optimum is reached; and obtaining the optimal image processing algorithm based on the optimal parameter combination of the Gaussian filtering algorithm and the linear brightening algorithm.

[0053] For example, grayscale statistics can be the grayscale values ​​of character lines, used to quantify character display quality. After distortion correction, the distribution of grayscale values ​​of character lines can be statistically analyzed. For instance, by comparing statistical values ​​(such as mean and variance) under different combinations of processing parameters, the algorithm's effectiveness can be intuitively assessed.

[0054] For example, the first optimization index is used to represent the brightness uniformity index of character lines within the same target processing area; the first optimization index quantifies brightness consistency by calculating the variance or standard deviation of the grayscale values ​​of character lines within the area. The lower the index, the better the uniformity, and the goal is to minimize brightness fluctuations within the area and avoid local over-brightness or under-brightness.

[0055] For example, the second optimization metric is used to represent the line width deviation of character lines between different areas. Line width deviation is evaluated by measuring the width variation of character lines in the horizontal, vertical, and diagonal directions to ensure overall display uniformity.

[0056] For example, optimization algorithms (such as particle swarm optimization or genetic algorithms) can be used to iteratively optimize Gaussian filter parameters (such as standard deviation) and linear brightening gain. Initial ranges (such as filter kernel size and gain value) for Gaussian filter and linear brightening parameters are set independently for each target processing region. Then, based on grayscale statistics, the weighted evaluation values ​​of brightness uniformity index and linewidth deviation under the current parameter combination are calculated. New parameter combinations (e.g., Gaussian filter algorithm and linear brightening algorithm) are generated through an optimization algorithm (such as particle swarm optimization for position and velocity updates), and steps 2 to 3 (i.e., region division, preprocessing, and pre-distortion correction) are repeated. Iteration stops when the objective function value converges (e.g., reaches the global optimum) or the character display effect meets preset conditions (such as brightness and linewidth requirements in the test standard), and the optimal parameter combination is output.

[0057] Step 5: Use the optimal image processing algorithm to preprocess the bitmap image.

[0058] In one possible embodiment, step 5 above includes: deploying a regional processing algorithm architecture on the target platform based on the optimal image processing algorithm and multiple target processing regions to achieve real-time preprocessing and correction of the bitmap image before output.

[0059] In one possible embodiment, the target platform is an FPGA. The step of deploying a regional processing algorithm architecture on the target platform based on the optimal image processing algorithm and multiple target processing regions to realize real-time preprocessing and correction of the dot matrix image includes: in the process of performing regional dot matrix image preprocessing through the FPGA, dividing the pixel data of the dot matrix image into different processing regions through address mapping relationship; and using a pipeline structure to execute the optimal target image processing algorithm and distortion correction operation in parallel, and outputting the corrected dot matrix image.

[0060] The address mapping mechanism is used to represent the correspondence between the storage address of pixel data and the distortion rate in a raster image.

[0061] For example, pixel data regions are divided by an address mapping module (based on distortion rate), and Gaussian filtering, linear brightening, and pre-distortion correction are performed in parallel using a pipeline structure.

[0062] For example, the FPGA can store pixel data of a dot matrix image internally, and the address mapping table associates the pixel coordinates with the distortion rate (for example, region 1 corresponds to low distortion rate, address range 0x0000-0x0FFF; region 4 corresponds to high distortion rate, address range 0x3000-0x3FFF). This enables the optimal image processing algorithm to quickly identify and process different target processing regions.

[0063] For example, the optimized Gaussian filtering and linear brightening algorithms are hardware-based as parallel modules, processing multiple pixels per clock cycle to reduce latency.

[0064] like Figure 5 As shown, Figure 5 This is a schematic diagram of a dot matrix character image preprocessing process provided in an embodiment of this application. The dot matrix characters are generated by a software drawing model driving the GPU through OpenGL instructions. The dot matrix character image is preprocessed by a combination of regionally differentiated Gaussian filtering and linear brightening algorithms implemented in the hardware description language (HDL) logic within the FPGA. After pre-distortion correction processing implemented within the FPGA, the image is output to a digital image source for display.

[0065] It is understandable that by evaluating the effect of typical character dot matrix images generated by the GPU and combining it with the pre-distortion correction processing model of head-up display optical display, a method is adopted that divides the character image into regions based on the distortion rate, and configures the combination of Gaussian filtering algorithm and linear brightening algorithm differently for regions with different distortion levels. The processing parameters are further optimized through grayscale value statistical analysis and optimization algorithm iteration. The final determined partition preprocessing algorithm architecture is implemented in FPGA. The final results of head-up display character brightness and uniformity are: the average brightness of horizontal and vertical lines of characters in 5 typical positions is ≥12000cd / m² under the maximum backlight brightness of the image source, and the character line width is within the range of 1±0.2mrad under the condition of 3400±170cd / m². The embodiments of this application combine Gaussian filtering and linear brightening algorithms with regional differentiation for dot matrix character images generated by GPU and implement them through FPGA hardware acceleration, which can significantly improve the character brightness and uniformity of head-up display without increasing the backlight brightness of the liquid crystal image source.

[0066] like Figure 6 As shown, Figure 6 This diagram illustrates a test position for character brightness and line width in an embodiment of this application. Specifically, it shows the position where a brightness testing device is used to test character brightness and line width at the designed eye position of a head-up display (HUD). The diagram shows five specific coordinate points used for testing at the designed eye position of the HUD. These coordinate points are used to verify whether the character display effect meets preset brightness uniformity and line width accuracy standards. For example, point a is the center of the HUD's field of view (0°, 0°), point b is the upper field of view (8°, 0°), point c is the right field of view (0°, 8°), point d is the lower field of view (0°, -6°), and point e is the lower left field of view (-7°, -5°).

[0067] For example, the test standard of this application embodiment includes that, under the maximum backlight brightness of the image source, the average brightness of the horizontal and vertical lines of the characters at five positions (0,0), (8,0), (0,8), (0,-6), and (-7,-5) within the field of view is ≥12000 cd / m²; when the character brightness is 3400±170 cd / m², the width of the horizontal and vertical lines at the above five positions is within the range of 1±0.2 mrad.

[0068] After operational testing of this implementation example, it was confirmed that the method for improving character brightness and uniformity in digital head-up displays is effective and performs excellently. This was confirmed through separate tests of the above-mentioned methods. Figure 6The brightness and line width of the horizontal and vertical lines at the five points in the display can achieve an average brightness of no less than 12000 cd / m2 at the maximum backlight brightness of the image source; under the condition of character brightness of 3400 cd / m2±170 cd / m2, the line width of the horizontal and vertical lines are both within the range of 1±0.2 mrad (2′45″~4′8″), which significantly improves the brightness and uniformity of the character lines of the digital head-up display.

[0069] This application provides a method for improving the brightness and uniformity of characters on a digital head-up display. The method involves acquiring multiple target processing regions in a dot matrix image containing typical characters; then, based on these target processing regions, an image processing algorithm is used to preprocess the dot matrix image to obtain a preprocessed dot matrix image; subsequently, distortion correction is performed on the preprocessed dot matrix image, and the grayscale value distribution information of the corrected character lines is statistically analyzed; based on the grayscale value distribution information of the character lines, an optimization algorithm is used to optimize the image processing algorithm, and the above preprocessing and distortion correction steps are iteratively repeated until the character line display effect meets preset conditions, thus obtaining the optimal image processing algorithm; finally, the optimal image processing algorithm is applied. The image processing algorithm is preprocessed to improve the brightness and uniformity of characters on the digital head-up display. Since the distortion levels vary across different target processing areas, the parameters of the image processing algorithm are set independently according to the characteristics of each target processing area. This allows for differentiated processing strategies for target processing areas with varying degrees of distortion. Because the image processing algorithm is used to improve the brightness and uniformity of characters on the digital head-up display, distortion correction is performed on the preprocessed image. Based on the grayscale distribution information of the character lines, the image processing algorithm is optimized to obtain the optimal image processing algorithm. This optimal algorithm is then used to preprocess the image, thereby improving the brightness and uniformity of characters on the head-up display without increasing the backlight brightness of the LCD image source.

[0070] The steps described above are for clarity only. In implementation, they can be combined into one step, or some steps can be broken down into multiple steps, as long as they involve the same logical relationship, they are all within the scope of protection of this application. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, without changing the core design of the algorithm and process, are also within the scope of protection of this application.

[0071] Another embodiment of this application proposes a device for improving the brightness and uniformity of characters on a digital head-up display. The details of this device for improving the brightness and uniformity of characters on a digital head-up display are described below. The following implementation details are provided for ease of understanding and are not essential for implementing this example. Figure 7 This is a schematic diagram of a device for improving the brightness and uniformity of characters on a digital head-up display, as proposed in this embodiment, including: The acquisition module 710 is used to acquire multiple target processing regions in a dot matrix image with typical characters; wherein, the distortion degree of different target processing regions is different; The preprocessing module 720 is used to preprocess the dot matrix image based on multiple target processing areas, using an image processing algorithm designed to improve the brightness and uniformity of characters on a digital head-up display, to obtain a preprocessed dot matrix image; wherein, the parameters of the image processing algorithm are set independently according to the characteristics of the target processing areas. The correction module 730 is used to perform distortion correction on the preprocessed bitmap image and to statistically analyze the grayscale value distribution information of the corrected character lines. The optimization module 740 is used to optimize the image processing algorithm based on the gray value distribution information of the character lines, and iteratively repeats the work of the preprocessing module and the correction module until the character line display effect meets the preset conditions, thus obtaining the optimal image processing algorithm; wherein, the parameters of the optimal image processing algorithm are set independently according to the characteristics of the processing target area; The final execution module 750 is used to preprocess the bitmap image using the optimal image processing algorithm.

[0072] It is not difficult to see that this embodiment is a system embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details and technical effects mentioned in the above method embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiments.

[0073] It is worth mentioning that all modules and units involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units do not exist in this embodiment.

[0074] Another embodiment of this application provides an electronic device, such as Figure 8 As shown, it includes a processor 81 and a memory 82. The memory 82 stores instructions that the processor 81 can execute. When the processor 81 is configured to execute the instructions, the electronic device can implement a method for improving the brightness and uniformity of characters on a digital head-up display as described in the above method embodiment.

[0075] The memory and processor are connected via a bus, which includes any number of interconnecting buses and bridges, connecting various circuits of one or more processors and the memory. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0076] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0077] Another embodiment of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, can implement a method for improving the brightness and uniformity of characters on a digital head-up display as described in the above method embodiments.

[0078] That is, those skilled in the art will understand that all or part of the steps in the above method embodiments can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (such as a microcontroller, chip, etc.) or processor to execute all or part of the steps of the method described in the method embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0079] Those skilled in the art will understand that the above embodiments are specific implementations of this application, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of this application. For those skilled in the art, several improvements and modifications can be made without departing from the principles of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for improving the brightness and uniformity of characters on a digital head-up display, characterized in that, Applications in head-up displays include: Step 1: Obtain multiple target processing regions from a dot matrix image containing typical characters; the degree of distortion varies among different target processing regions. Step 2: Based on multiple target processing areas, an image processing algorithm for improving the brightness and uniformity of characters on a digital head-up display is used to preprocess the dot matrix image to obtain a preprocessed dot matrix image; wherein, the parameters of the image processing algorithm are set independently according to the degree of distortion of the target processing area. Step 3: Perform distortion correction on the preprocessed bitmap image and statistically analyze the grayscale distribution information of the corrected character lines; Step 4: Based on the grayscale distribution information of the character lines, the image processing algorithm is optimized using an optimization algorithm, and steps 2 to 3 are iteratively repeated until the character line display effect meets the preset conditions, thus obtaining the optimal image processing algorithm; wherein, the parameters of the optimal image processing algorithm are set independently according to the distortion degree of the target processing area. Step 5: Preprocess the dot matrix image using the optimal image processing algorithm; the image processing algorithm includes Gaussian filtering algorithm and linear brightening algorithm; the Gaussian filtering algorithm is used to improve character uniformity, and the linear brightening algorithm is used to improve character brightness. Among them, the character line display effect meeting the preset conditions indicates that the optimization algorithm has reached the global optimum; step 4 specifically includes: Using grayscale statistics as input parameters, the brightness uniformity index of character lines within the same target processing area as the first optimization index, and the line width deviation of character lines between different areas as the second optimization index, an optimization algorithm is used to iteratively optimize the Gaussian filtering algorithm and the linear brightening algorithm. The optimal parameter combination of the Gaussian filtering algorithm and the linear brightening algorithm is determined when the global optimum is reached. The optimal image processing algorithm is obtained by combining the best parameters of the Gaussian filtering algorithm and the linear brightening algorithm.

2. The method according to claim 1, characterized in that, Multiple processing regions are obtained from a bitmap image containing typical characters, including: Using GPUs and software models, raster images with typical characters are generated; Based on the distortion characteristics of the head-up display optical system, the pre-distortion correction parameters of the dot matrix image are determined. Based on the pre-distortion correction parameters of the bitmap image, the bitmap image is divided into regions to generate target processing regions with different degrees of distortion.

3. The method according to claim 2, characterized in that, The pre-distortion correction parameters based on the bitmap image are used to divide the bitmap image into regions and generate target processing regions with different degrees of distortion, including: The raster image is processed into a grid to obtain multiple grid cells in the raster image; Based on the pre-distortion correction parameters of the raster image, the distortion rate of each grid cell is determined; whereby the distortion rate is used to represent the degree of distortion of the raster image at different field of view positions; Based on the distortion rate of each grid cell and a preset threshold, the bitmap image is divided into target processing regions with different distortion levels. The target processing regions with different distortion levels include one or more of low distortion regions, medium distortion regions, and high distortion regions. The distortion rate of the low distortion region is less than the first preset threshold, the distortion rate of the high distortion region is greater than the second preset threshold, and the distortion rate of the medium distortion region is greater than or equal to the first preset threshold and less than or equal to the second preset threshold. The first preset threshold is less than the second preset threshold.

4. The method according to claim 3, characterized in that, The image processing algorithm, based on multiple target processing regions and used to improve the brightness and uniformity of characters on a digital head-up display, preprocesses the dot matrix image to obtain a preprocessed dot matrix image, including: The low-distortion region is preprocessed using a Gaussian filtering algorithm with the first standard deviation and a linear brightening algorithm with the first gain to obtain the preprocessed low-distortion region. The medium distortion region is preprocessed using a Gaussian filtering algorithm with the second standard deviation and a linear brightening algorithm with the second gain to obtain the preprocessed medium distortion region. The high distortion region is preprocessed using a Gaussian filtering algorithm with the third standard deviation and a linear brightening algorithm with the third gain to obtain the preprocessed high distortion region. Among them, the first standard deviation is less than the second standard deviation, the second standard deviation is less than the third standard deviation; the first gain is less than the second gain, and the second gain is less than the third gain.

5. The method according to claim 1, characterized in that, The optimal image processing algorithm is used to preprocess the raster image, including: Based on the optimal image processing algorithm and multiple target processing regions, a regional processing algorithm architecture is deployed on the target platform to achieve real-time preprocessing and correction of raster images.

6. The method according to claim 5, characterized in that, The target platform is an FPGA. Based on the optimal image processing algorithm and multiple target processing regions, a regional processing algorithm architecture is deployed on the target platform to achieve real-time preprocessing and correction of the bitmap image before output, including: In the process of preprocessing a regionalized dot matrix image using FPGA, the pixel data of the dot matrix image is divided into different processing regions through address mapping; the address mapping mechanism is used to represent the correspondence between the storage address of the pixel data and the distortion rate in the dot matrix image. The optimal target image processing algorithm and distortion correction operation are executed in parallel using a pipeline structure, and the corrected bitmap image is output.

7. A device for improving the brightness and uniformity of characters on a digital head-up display, characterized in that, Applications in head-up displays include: The acquisition module is used to acquire multiple target processing regions in a dot matrix image with typical characters; among them, the degree of distortion varies in different target processing regions; The preprocessing module is used to preprocess the bitmap image based on multiple target processing areas, using image processing algorithms designed to improve the brightness and uniformity of characters on a digital head-up display, to obtain a preprocessed bitmap image; wherein, the parameters of the image processing algorithm are set independently according to the degree of distortion of the target processing area. The correction module is used to perform distortion correction on the preprocessed bitmap image and to statistically analyze the grayscale value distribution information of the corrected character lines. The optimization module is used to optimize the image processing algorithm based on the gray value distribution information of the character lines using an optimization algorithm, and iteratively repeats the work of the preprocessing module and the correction module until the character line display effect meets the preset conditions, thus obtaining the optimal image processing algorithm; wherein, the parameters of the optimal image processing algorithm are set independently according to the distortion degree of the target processing area. The final execution module is used to preprocess the bitmap image using the optimal image processing algorithm; the image processing algorithm includes Gaussian filtering algorithm and linear brightening algorithm; the Gaussian filtering algorithm is used to improve character uniformity, and the linear brightening algorithm is used to improve character brightness; Among them, the character line display effect meeting the preset conditions indicates that the optimization algorithm has reached the global optimum; the optimization module is specifically used to: take the grayscale statistical results as input parameters, take the brightness uniformity index of character lines in the same target processing area as the first optimization index, take the line width deviation of character lines between different areas as the second optimization index, and use the optimization algorithm to iteratively optimize the Gaussian filtering algorithm and the linear brightening algorithm, and determine the optimal parameter combination of the Gaussian filtering algorithm and the linear brightening algorithm when the global optimum is reached; based on the optimal parameter combination of the Gaussian filtering algorithm and the linear brightening algorithm, the optimal image processing algorithm is obtained.

8. An electronic device, characterized in that, include: A processor and a memory, the memory storing instructions executable by the processor, the processor being configured to, when executing the instructions, enable the electronic device to implement the method for improving the brightness and uniformity of characters on a digital head-up display as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it can implement the method for improving the brightness and uniformity of characters on a digital head-up display as described in any one of claims 1 to 6.

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