A method for correcting HUD image distortion

Abstract

The application discloses a HUD image distortion correction method, which comprises the following steps: acquiring the size and resolution of a display screen to master the original state of an image; simulating the distortion condition under a specific resolution by using optical simulation software to obtain the size, resolution and distortion rate data of a first reference image, which is used for constructing a second reference image; acquiring the pixel point coordinates of the two reference images, analyzing the position change before and after the distortion, splitting the coordinates into the offset amount in X and Y directions, finding the common multiple of each row and column, constructing a matrix, converting the offset amount into reverse offset amount, obtaining a third reference image, and performing normalization, interpolation, magnification compensation and SPR algorithm processing on the third reference image, so as to ensure the consistency of the display effect across devices. The magnification adaptation conversion is carried out based on the existing display screen parameters to prevent distortion. The processed third reference image is applied to the HUD, and the image seen by the human eye is corrected by distortion, so that the display effect is better, and the usability and visual comfort of the image are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and particularly relates to a HUD image distortion correction method. BACKGROUND

[0002] HUD, which stands for Head Up Display, is an advanced augmented reality technology. It projects key driving information, such as speed and navigation directions, directly onto the windshield in front of the driver's line of sight, allowing the driver to access this information without having to look down or turn their head. This helps the driver maintain a high level of concentration on the road, reducing the risk of distraction caused by looking at the instrument panel, and effectively reducing the risk of traffic accidents. However, in the HUD system, the windshield is usually composed of double-layer glass, and the light is reflected on the inner and outer layers of glass, forming two different optical paths. This structure may cause ghosting, which not only affects the visual experience, but also may cause image distortion.

[0003] Currently, there are generally two solutions for image distortion processing in vehicle-mounted HUD. The first structural adjustment method is to add an angle adjustment device to the HUD system to automatically adapt to image changes and reduce distortion. Although this method can reduce distortion to some extent, it has high requirements for accuracy, because even a small angle deviation may cause inaccurate image display. Secondly, this structural adjustment method may not respond quickly to real-time changes in images, especially under dynamic driving conditions, where real-time image adjustment requires fast and accurate response, while traditional mechanical adjustment devices often fail to achieve such response speed. In addition, this method has high cost because it needs to integrate precise adjustment devices, which often contain complex mechanical components or electronic control systems. The large volume of adjustment devices also increases the overall volume of the HUD system, which may affect the layout of the car's interior space. Moreover, due to the complex structure, the response speed of these adjustment devices is usually slow, resulting in time delay in image correction, which not only affects the visual effect, but also may interfere with the driver's real-time judgment.

[0004] The second algorithm correction method, i.e., the algorithm-based distortion correction, mainly corrects the image by linearly adjusting the difference between the reference matrix and the projection matrix. The core of this method is to linearly adjust the dot matrix coordinates according to the offset value between the projection matrix and the deviation matrix, so as to correct the image distortion. However, when dealing with complex nonlinear distortion, the effect of this method is limited, especially in the HUD system with large field of view and high resolution. Simple linear transformation is difficult to fully cover all distortion cases. Specifically, when the field of view of the HUD system is large or the resolution is high, the distortion often presents nonlinear characteristics, which means that the degree and direction of the distortion will change with the position of the image. In this case, the method based on linear adjustment may not accurately correct the distortion of each pixel, resulting in deviation of the corrected image from the actual demand. This is because linear transformation usually assumes that the distortion is uniform and consistent, while in fact the distortion may be variable and complex. SUMMARY

[0005] The present application provides a HUD image distortion correction method that can improve display effect.

[0006] The HUD image distortion correction method provided by the present application comprises the following steps:

[0007] S1, obtaining the size and resolution of the first reference image according to the display content of the display screen, simulating mapping to obtain the distortion rate data under the resolution by using optical simulation software, and constructing a second reference image based on the distortion rate data;

[0008] S2, obtaining the reference coordinates of each pixel point in the first reference image and the second reference image, splitting the reference coordinates into X and Y direction offsets, analyzing the X and Y direction offsets respectively, finding the common multiple of each row and column, and converting the offset at each coordinate into the corresponding inverse offset by constructing a matrix; using the matrix, the inverse offset corresponding to each coordinate point is calculated;

[0009] S3, performing inverse distortion conversion on the first reference image according to the inverse offset to obtain a third reference image with deformation;

[0010] S4, normalizing, interpolating and magnification compensating the data coordinates of the third reference image in shape, performing SPR algorithm processing on the color gray scale of the image display picture based on the shape size in color, performing magnification adaptive conversion on the image under the premise of no distortion of the image according to the resolution of the existing display screen size, and applying the processed image to the HUD to obtain the image corrected by distortion after reaching the human eye.

[0011] The HUD image distortion correction method can accurately understand the original state of the image during display by obtaining the size and resolution of the display content of the display screen, simulate the distortion of the image under a specific resolution by using optical simulation software, obtain the size and resolution information of the first reference image, and the corresponding distortion rate data for constructing the second reference image; by obtaining the reference coordinates of each pixel point in the first reference image and the second reference image, the position change of the image before and after distortion can be analyzed. The coordinates are split into X and Y direction offsets, and the offsets are analyzed to find the common multiple of each row and column, and a matrix can be constructed to convert the offset under each coordinate into the corresponding inverse offset. The inverse offset obtained in step S2 is used to obtain a third reference image that has been subjected to inverse distortion processing. Theoretically, the image should be closer to the original image, and the distortion is corrected. The third reference image is subjected to shape normalization, interpolation, magnification compensation processing, and SPR algorithm processing in color, which can ensure the consistency of the display effect of the image on different display devices. At the same time, based on the resolution of the existing display screen size, the image is subjected to magnification adaptation conversion to ensure that the image is not distorted. The processed third reference image is applied to the HUD, and the image seen by the human eye is subjected to distortion correction, and the display effect is better, improving the usability and visual comfort of the image. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 It is a HUD image distortion correction method flow chart.

[0013] Figure 2 It is an image distortion offset comparison chart.

[0014] Figure 3 It is a gray scale diagram of adjacent sub-pixels. DETAILED DESCRIPTION

[0015] As Figure 1 shown, a HUD image distortion correction method includes the following steps:

[0016] S1, obtaining the size and resolution of the first reference image according to the display content of the display screen, simulating the distortion rate data under the resolution by using optical simulation software, and constructing the second reference image based on the distortion rate data;

[0017] S2, obtaining the reference coordinates of each pixel point in the first reference image and the second reference image, splitting the reference coordinates into X and Y direction offsets, respectively analyzing the X and Y direction offsets, finding the common multiple of each row and column, and converting the offset under each coordinate into the corresponding inverse offset by constructing a matrix; using the matrix, the inverse offset corresponding to each coordinate point is calculated;

[0018] S3. Perform reverse distortion transformation on the first reference image based on the reverse offset to obtain a third reference image with deformation;

[0019] S4. In terms of shape, the data coordinates of the third reference image are normalized, interpolated, and magnified. In terms of color, the grayscale of the image display is processed by the SPR algorithm based on the shape size. According to the resolution of the existing display screen size, the image is magnified and converted without distortion. The processed image is applied to the HUD and the image after distortion correction is obtained after reaching the human eye.

[0020] The HUD image distortion correction method described above can accurately understand the original state of the image during display by obtaining the size and resolution of the content displayed on the screen. Optical simulation software is used to simulate the distortion of the image at a specific resolution to obtain the size and resolution information of the first reference image, as well as the corresponding distortion rate data, which are used to construct the second reference image. By obtaining the reference coordinates of each pixel in the first and second reference images, the positional changes of the image before and after distortion can be analyzed. These coordinates are decomposed into offsets in the X and Y directions, and these offsets are analyzed to find the common multiple of each row and column, thus constructing a matrix. This matrix can convert the offset at each coordinate into the corresponding inverse offset. Using the inverse offset obtained in step S2, a third reference image after inverse distortion processing is obtained. This image should theoretically be closer to the original image, with the distortion corrected. Shape normalization, interpolation, and scaling compensation processing, as well as color SPR algorithm processing, are applied to the third reference image to ensure consistent display effects on different display devices. Simultaneously, scaling adaptation conversion is performed on the image based on the resolution of the existing screen size to ensure that the image is not distorted. The processed third reference image is applied to the HUD. The image seen by the human eye has been distorted, resulting in a better display effect and improved image usability and visual comfort.

[0021] In step S2, the offsets and corrections in the X and Y directions are calculated based on the first reference coordinates of the first reference image and the second reference coordinates of the second reference image. The X-direction offset is: Δx1 = n1 / x1, Δx2 = n2 / x2, ...; the common multiple T is: X-direction correction amount: Δx p1 =T / Δx1, Δx p2 =T / Δx2, ...; as Figure 2As shown, the offset and correction in the Y direction are calculated similarly to those in the X direction. By calculating the offset, the relative positional differences between two reference images in the X and Y directions can be determined, thus achieving precise image alignment. A common multiple T is used to standardize the scale of different offsets, ensuring that offsets between different images or regions can be compared and adjusted. By calculating the correction, the image can be adjusted to align with a standard or reference image in the X and Y directions.

[0022] In step S4, after normalization, the coordinates are interpolated according to the display resolution.

[0023] Interpolate the coordinates: Based on this, the required magnification factor should be controlled according to actual needs.

[0024] By interpolating the coordinates to accurately represent the data points on the display, it is ensured that the data points can transition smoothly when the display is zoomed in or out, rather than simply mapping at the pixel level, thus providing a higher quality image display effect.

[0025] In step S4, color distortion is compensated using the SPR algorithm, which is the Spatial Pixel Rearrangement algorithm.

[0026] This matrix will redistribute the brightness ratio of adjacent sub-pixels based on the interpolated data, color, and pixel position index, such as... Figure 3 As shown; finally passed The algorithm obtains new data for the SPR panel. Using a 3x3 rendering matrix, the brightness ratio of adjacent sub-pixels is redistributed based on the interpolated data, color, and pixel position index. This helps improve image contrast and detail, making the image sharper. Through this process, the algorithm generates new data for the SPR panel. This new data represents the image data after color compensation and brightness ratio redistribution, making it closer to reality or more consistent with the corrected standard than the original data.

Claims

1. A method of correcting for distortion of a HUD image, characterized by, Comprise the following steps: S1, according to the display content of the display screen to obtain the size and resolution of the first reference image, using optical simulation software simulation mapping to obtain the distortion rate data under the resolution, based on the distortion rate data to construct the second reference image; S2, obtain the reference coordinates of each pixel point in the first reference image and the second reference image, split these reference coordinates into X and Y direction offset, respectively, the offset of X and Y direction is analyzed, find the common multiple of each row and column, by constructing a matrix, the offset under each coordinate is converted into the corresponding inverse offset; Using this matrix, the corresponding inverse offset of each coordinate point is calculated; In step S2, the offset and the correction in the X and Y directions are calculated based on the first reference coordinates of the first reference image and the second reference coordinates of the second reference image, wherein the offset in the X direction is calculated as follows: ; the common multiple T: ; the correction in the X direction is calculated as follows: ; wherein the offset and the correction in the Y direction are calculated in the same way as in the X direction; S3, according to the inverse offset to the first reference image for inverse distortion conversion, get the third reference image with deformation; S4, the data coordinates of the third reference image are normalized, interpolated and magnification compensated in shape, the color gray scale of the image display picture is processed based on the shape size SPR algorithm in color, the image is magnification adapted and converted under the premise of image distortion, the processed image is applied to HUD, and the image after distortion correction is obtained after reaching the human eye.

2. The HUD image distortion correction method according to claim 1, characterized by, In step S4, the correction amount data for both directions of the coordinates of the third reference image are subjected to normalization processing: .

3. The HUD image distortion correction method according to claim 1, characterized by, In step S4, after the normalization processing, the coordinates are interpolated according to the resolution of the display screen: On this basis, according to the actual demand control required magnification.

4. The HUD image distortion correction method according to claim 1, characterized by, In step S4, for color distortion, SPR algorithm is used for compensation, ; reassigning the luminance ratio of the neighboring sub-pixels according to the interpolated data, color and pixel position index through the matrix; and finally obtaining new data of the SPR panel.

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