HUD image distortion correction method

Abstract

The invention discloses an HUD image distortion correction method. The method comprises the steps that the size and resolution of a display screen are acquired, and the original state of an image is mastered; simulating a distortion condition under a specific resolution by using optical simulation software to obtain size, resolution and distortion rate data of the first reference image for constructing a second reference image; the method comprises the following steps: acquiring coordinates of pixel points of two reference images, analyzing position changes before and after distortion, splitting the coordinates into offsets in X and Y directions, finding a common multiple of each row and column, constructing a matrix, converting the offsets into reverse offsets, obtaining a third reference image, and performing normalization, interpolation, magnification compensation and SPR algorithm processing on the third reference image to ensure consistency of cross-device display effects. Multiplying power adaptation conversion is carried out based on existing display screen parameters, and distortion is prevented. The processed third reference image is applied to the HUD, the image seen by human eyes is subjected to distortion correction, the display effect is better, and the availability and visual comfort of the image are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method for correcting HUD image distortion. Background Art

[0002] HUD, or Head Up Display, is an advanced augmented reality technology. It projects key driving information, such as vehicle speed and navigation instructions, directly onto the windshield in front of the driver's line of sight, so that the driver can obtain this information without lowering or turning his head, helping the driver to maintain a high degree of concentration on the road, reduce distractions caused by looking at the dashboard, and effectively reduce the risk of traffic accidents. However, in the HUD system, since the windshield is usually made of double-layer glass, light is reflected on the inner and outer layers of glass respectively, forming two different optical paths. This structure may cause ghosting, which not only affects the visual experience, but also may cause image distortion.

[0003] At present, there are generally two solutions for image distortion processing in vehicle HUD. The first structural adjustment method is to automatically adapt to image changes by adding an angle adjustment device to the HUD system to reduce distortion. Although this method can reduce distortion to a certain 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 be able to respond quickly to real-time changes in the image, especially under dynamic driving conditions, where real-time adjustment of the image requires fast and accurate response, and traditional mechanical adjustment devices often find it difficult to achieve such a response speed. In addition, this method is costly because it requires the integration of precise adjustment devices, which often contain complex mechanical components or electronic control systems. Large adjustment devices will also increase the overall volume of the HUD system, which may affect the interior space layout of the car. Moreover, due to the complex structure, the response speed of these adjustment devices is usually slow, resulting in a time delay in image correction, which not only affects the visual effect, but may also interfere with the driver's real-time judgment.

[0004] The second algorithmic correction method, namely algorithm-based distortion correction, mainly performs image correction by linearly adjusting the difference between the reference matrix and the projection matrix. The core of this method is to linearly adjust the lattice coordinates according to the offset value between the projection matrix and the deviation matrix in the hope of correcting the image distortion. However, this method has limited effect when dealing with complex nonlinear distortions, especially in HUD systems with large field of view and high resolution, where simple linear transformations are difficult to fully cover all distortion situations. Specifically, when the field of view of the HUD system is large or the resolution is high, the distortion often exhibits nonlinear characteristics, which means that the degree and direction of the distortion vary with the position of the image. In this case, the linear adjustment-based method may not be able to accurately correct the distortion of each pixel, resulting in a deviation between the corrected image and the actual demand. This is because linear transformations usually assume that the distortion is uniform and consistent, while in reality the distortion may be variable and complex. Summary of the invention

[0005] The present invention provides a HUD image distortion correction method capable of improving display effect.

[0006] The HUD image distortion correction method of the present invention comprises the following steps: S1, obtaining the size and resolution of the first reference image according to the display content of the display screen, using optical simulation software to simulate mapping to obtain distortion rate data at the resolution, and constructing a second reference image based on the distortion rate data; S2, obtaining the reference coordinates of each pixel point in the first reference image and the second reference image, splitting the reference coordinates into offsets in the X and Y directions, analyzing the offsets in the X and Y directions respectively, finding the common multiples of each row and column, and converting the offset under each coordinate into the corresponding inverse offset by constructing a matrix; using this matrix, calculating the inverse offset corresponding to each coordinate point; S3, performing inverse distortion transformation on the first reference image according to the inverse offset to obtain a third reference image with deformation; S4. In terms of shape, the data coordinates of the third reference image are normalized, interpolated, and magnification compensated. In terms of color, the color grayscale of the image display screen is processed by the SPR algorithm based on the shape size. According to the resolution of the existing display screen size, the image is converted to a magnification adaptation without distortion. The processed image is applied to the HUD, and the distortion-corrected image is obtained after reaching the human eye.

[0007] The HUD image distortion correction method can accurately understand the original state of the image when it is displayed by obtaining the size and resolution of the display content, and use optical simulation software 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, 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 the distortion can be analyzed. These coordinates are split into offsets in the X and Y directions, and these offsets are analyzed to find the common multiples of each row and column, so that a matrix can be constructed, which can convert the offset under each coordinate into the corresponding inverse offset, and the inverse offset obtained in step S2 is used to obtain a third reference image after inverse distortion processing, which should be closer to the original image in theory, and the distortion is corrected. The third reference image is normalized, interpolated, and processed by the magnification compensation process in shape, and the SPR algorithm process in color 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 converted to a magnification adaptation 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 is distortion-corrected, and the display effect is better, which improves the image usability and visual comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 The figure is a flow chart of a HUD image distortion correction method.

[0009] Figure 2 This is a comparison chart of image distortion and offset.

[0010] Figure 3 Schematic diagram of grayscale of adjacent sub-pixels. DETAILED DESCRIPTION

[0011] like Figure 1 As shown, a HUD image distortion correction method includes the following steps: S1, obtaining the size and resolution of the first reference image according to the display content of the display screen, using optical simulation software to simulate mapping to obtain distortion rate data at the resolution, and constructing a second reference image based on the distortion rate data; S2, obtaining the reference coordinates of each pixel point in the first reference image and the second reference image, splitting the reference coordinates into offsets in the X and Y directions, analyzing the offsets in the X and Y directions respectively, finding the common multiples of each row and column, and converting the offset under each coordinate into the corresponding inverse offset by constructing a matrix; using this matrix, calculating the inverse offset corresponding to each coordinate point; S3, performing inverse distortion transformation on the first reference image according to the inverse offset to obtain a third reference image with deformation; S4. In terms of shape, the data coordinates of the third reference image are normalized, interpolated, and magnification compensated. In terms of color, the color grayscale of the image display screen is processed by the SPR algorithm based on the shape size. According to the resolution of the existing display screen size, the image is converted to a magnification adaptation without distortion. The processed image is applied to the HUD, and the distortion-corrected image is obtained after reaching the human eye.

[0012] The HUD image distortion correction method can accurately understand the original state of the image when it is displayed by obtaining the size and resolution of the display content, and use optical simulation software 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, 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 the distortion can be analyzed. These coordinates are split into offsets in the X and Y directions, and these offsets are analyzed to find the common multiples of each row and column, so that a matrix can be constructed, which can convert the offset under each coordinate into the corresponding inverse offset, and the inverse offset obtained in step S2 is used to obtain a third reference image after inverse distortion processing, which should be closer to the original image in theory, and the distortion is corrected. The third reference image is normalized, interpolated, and processed by the magnification compensation process in shape, and the SPR algorithm process in color 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 converted to a magnification adaptation 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 is distortion-corrected, and the display effect is better, which improves the image usability and visual comfort.

[0013] In step S2, the offset and correction amount in the X and Y directions are calculated according to the first reference coordinates of the first reference image and the second reference coordinates of the second reference image, wherein the X direction offset is: Δx 1 =n 1 / x 1 , Δx 2 =n 2 / x 2 , ...; Common multiple T: ; X-direction correction: Δx p1 =T / Δx 1 , Δx p2 =T / Δx 2 ,……;like Figure 2As shown, the offset and correction in the Y direction are the same as those in the X direction. By calculating the offset, the relative position difference between the two reference images in the X and Y directions can be determined, thereby achieving accurate alignment of the images. Using the common multiple T to unify the scale of different offsets ensures that the offsets between different images or different regions can be compared and adjusted. By calculating the correction, the image can be adjusted to align it with a standard or reference image in the X and Y directions.

[0014] In step S4, after normalization, the coordinates are interpolated according to the resolution of the display screen. Interpolate the coordinates: On this basis, the required magnification is controlled according to actual needs. 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, which can provide a higher quality image display effect.

[0015] In step S4, the color distortion is compensated by using the SPR algorithm, i.e. the Spatial Pixel Rearrangement algorithm. This matrix will redistribute the brightness ratio of adjacent sub-pixels according to the interpolated data, color, and pixel position index, such as Figure 3 As shown; finally passed Get new data for the SPR panel. Use a 3x3 rendering matrix to redistribute the brightness ratio of adjacent sub-pixels based on the interpolated data, color, and pixel position index, which helps improve the contrast and details of the image, making the image clearer. Through the above process, the algorithm generates new data for the SPR panel. This new data represents the image data after color compensation and brightness ratio redistribution, which is closer to the actual situation than the original data, or more in line with the corrected standard.

Claims

1. A HUD image distortion correction method, characterized in that: The following steps are involved: S1, obtaining the size and resolution of the first reference image according to the display content of the display screen, using optical simulation software to simulate mapping to obtain distortion rate data at the resolution, and constructing a second reference image based on the distortion rate data; S2, obtaining the reference coordinates of each pixel point in the first reference image and the second reference image, splitting the reference coordinates into offsets in the X and Y directions, analyzing the offsets in the X and Y directions respectively, 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 this matrix, calculate the reverse offset corresponding to each coordinate point; S3, performing inverse distortion transformation on the first reference image according to the inverse offset to obtain a third reference image with deformation; S4. In terms of shape, the data coordinates of the third reference image are normalized, interpolated, and magnification compensated. In terms of color, the color grayscale of the image display screen is processed by the SPR algorithm based on the shape size. According to the resolution of the existing display screen size, the image is converted to a magnification adaptation without distortion. The processed image is applied to the HUD, and the distortion-corrected image is obtained after reaching the human eye.

2. The HUD image distortion correction method according to claim 1, characterized in that: In step S2, the offsets and corrections in the X and Y directions are calculated according to the first reference coordinates of the first reference image and the second reference coordinates of the second reference image, wherein the X-direction offset is: Δx1=n1 / x1, Δx2=n2 / x2, ...; the common multiple T is: X-direction correction: Δx p1 =T / Δx1, Δx p2 =T / Δx2, ...; the offset and correction in the Y direction are the same as those in the X direction.

3. The HUD image distortion correction method according to claim 1, characterized in that: In step S4, the correction amount data in two directions of the coordinates of the third reference image are normalized:

4. The HUD image distortion correction method according to claim 1, characterized in that: In step S4, after normalization, the coordinates are interpolated according to the resolution of the display screen: On this basis, the required magnification is controlled according to actual needs.

5. The HUD image distortion correction method according to claim 1, characterized in that: In step S4, the color distortion is compensated by using the SPR algorithm. This matrix will redistribute the brightness ratio of adjacent sub-pixels according to the interpolated data, color, and pixel position index; finally, Get new data for the SPR panel.

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