Method for processing image data and related apparatus

Abstract

Embodiments of the present application disclose a kind of image data processing method and related device, method is applied to the processor in vehicle electronic rearview mirror system, comprising: receiving first image data and vehicle state information;According to first image data and vehicle state information, identify multiple light sources in first image data, determine the light source information of each light source in multiple light sources, light source information includes light source category and light source intensity;According to light source category and light source intensity, first image data is divided into multiple pixel regions with different glare attributes;For different pixel regions, corresponding glare adjustment strategy is executed, to obtain second image data;Second image data is displayed and adapted processing, to generate target image data.Such, for different pixel regions, glare suppression strategy is executed specifically, while efficiently suppressing glare, dark detail is retained, and the picture quality of electronic rearview mirror under complex light environment is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method and apparatus for processing image data. Background Technology

[0002] With the accelerated development of automotive intelligence and electrification, electronic rearview mirrors are gradually replacing traditional optical rearview mirrors, becoming an industry trend due to their advantages such as wider field of vision, reduced wind resistance, and higher degree of integration. However, in complex driving scenarios such as meeting oncoming traffic at night, tunnel entrances and exits, and suburban areas without streetlights, the high beams of vehicles behind and the strong glare from streetlights create significant halos on the electronic display screen, causing glare for the driver and seriously affecting their judgment of road conditions behind them, thus becoming a core hidden danger to driving safety.

[0003] However, current solutions can only achieve general detection of strong light and cannot accurately distinguish between different types of strong light sources in electronic rearview mirror scenarios, such as moving car lights and fixed street lights, as well as their intensity and movement trajectory. This results in a lack of differentiated adaptation in glare processing, with some light sources being either not completely suppressed or over-processed. Furthermore, the false detection rate is high when facing light sources of different sizes and intensities, affecting the quality of the final image. Summary of the Invention

[0004] In view of this, embodiments of this application provide an image data processing method and related apparatus, which aim to improve the glare suppression capability of electronic rearview mirrors by accurately identifying light sources and implementing targeted glare suppression strategies based on the type of light source.

[0005] In a first aspect, embodiments of this application provide an image data processing method applied to a processor in a vehicle electronic rearview mirror system. The vehicle electronic rearview mirror system includes the processor, an image acquisition device, and a display screen installed inside the vehicle. The processor is electrically connected to the vehicle's central control system. The method includes:

[0006] Receive first image data from the image acquisition device and vehicle status information from the vehicle central control system;

[0007] Based on the first image data and the vehicle status information, multiple light sources in the first image data are identified, and the light source information of each of the multiple light sources is determined, including the light source category and the light source intensity.

[0008] Based on the light source type and the light source intensity, the first image data is divided into multiple pixel regions with different glare properties;

[0009] For different pixel regions, a corresponding glare adjustment strategy is executed to obtain second image data. The glare adjustment strategy is a strategy used to suppress glare in the image.

[0010] The second image data is subjected to display adaptation processing to generate target image data for display on the display screen.

[0011] In one possible embodiment, determining the light source information of each of the plurality of light sources includes: calculating and determining the brightness value of each pixel region in the first image data; filtering out pixel regions with brightness values ​​higher than a first brightness threshold as candidate light source regions, where the first brightness threshold refers to the threshold of background brightness in the current scene; calculating and determining the average brightness, peak brightness, and brightness gradient of the candidate light source regions, and determining the light source intensity of the candidate light source regions based on the average brightness, peak brightness, and brightness gradient of the candidate light source regions; extracting the shape features of the candidate light source regions using a contour detection algorithm, where the shape features characterize the area, roundness, and aspect ratio of the candidate light source regions; and extracting the spectral features of the candidate light source regions; and determining the light source category of the candidate light source regions based on the shape features, the spectral features, and the vehicle state information, where the light source category includes: mobile light sources and stationary light sources.

[0012] In one possible embodiment, determining the light source category of the candidate light source region includes: determining the vehicle's current speed, driving direction, steering angle, and positioning information based on the vehicle status information; calculating the pixel displacement of the candidate light source region in multiple consecutive frames of the first image data; determining the light source category as the moving light source when the vehicle speed is 0 and the pixel displacement is greater than 0; and determining the light source category as the stationary light source when the vehicle speed is not 0, the steering angle is 0°, and the pixel displacement is close to 0.

[0013] In one possible embodiment, the pixel region includes a glare core region, a glare-affected region, and a normal region; dividing the first image data into multiple pixel regions with different glare attributes includes: selecting multiple target light sources in the first image data whose brightness values ​​are greater than a second brightness threshold, where the second brightness threshold is a brightness threshold characterizing glare that will affect the vehicle driver; configuring corresponding light source radii for the multiple target light sources according to their brightness values, and dividing the light source surrounding region according to the light source radius, wherein the higher the brightness value of the target light source, the larger the corresponding light source radius; determining the region in the light source surrounding region whose average brightness value is greater than a third brightness threshold as the glare core region, where the third brightness threshold is greater than the second brightness threshold; and determining the region in the light source surrounding region whose average brightness value is the first brightness threshold as the normal region; and determining the other regions in the light source surrounding region besides the glare core region and the normal region as the glare-affected region.

[0014] In one possible embodiment, the step of executing corresponding glare adjustment strategies for different pixel regions includes: processing the glare core region using a first adjustment strategy, wherein the first adjustment strategy is to suppress the glare core region using a first exposure parameter and a low brightness gain parameter; processing the glare-affected region using a second adjustment strategy, wherein the second adjustment strategy is to suppress the glare-affected region using a second exposure parameter and a medium brightness gain parameter, wherein the exposure duration represented by the second exposure parameter is greater than the exposure duration represented by the first exposure parameter; and processing the normal region using a third adjustment strategy, wherein the third adjustment strategy is to adjust the normal region using a third exposure parameter and a high brightness gain parameter, wherein the exposure duration represented by the third exposure parameter is greater than the exposure duration represented by the second exposure parameter.

[0015] In one possible embodiment, the vehicle central control system includes: an image sensor for acquiring the driver's pupil state; the method further includes: receiving a first control message from the vehicle central control system, the first control message being a control message generated when the driver's pupil dilation state is in a continuously constricted state as acquired by the image sensor; in response to the first control message, reducing the first exposure parameter and the low brightness gain parameter, and reducing the second exposure parameter and the medium brightness gain parameter to enhance the suppression intensity of the glare core region and the glare-affected region; receiving a second control message from the vehicle central control system, the second control message being a control message generated when the driver's pupil dilation state is in a continuously enlarged state as acquired by the image sensor; in response to the second control message, increasing the first exposure parameter and the low brightness gain parameter, and increasing the second exposure parameter and the medium brightness gain parameter to weaken the suppression intensity of the glare core region and the glare-affected region.

[0016] In one possible embodiment, after receiving the first image data from the image acquisition device, the method further includes: extracting pixels in the first image data whose difference between the brightness of a single pixel and the mean of its neighborhood is greater than a preset threshold, and marking them as suspected bad pixels, wherein the preset threshold is a threshold adaptively adjusted based on the brightness of the current scene environment; if the brightness of the suspected bad pixel remains unchanged in multiple consecutive frames of images, then determining the suspected bad pixel as a target bad pixel; determining the brightness and weight coefficient of the neighborhood pixels of the target bad pixel, and replacing the brightness of the target bad pixel according to the brightness of the neighborhood pixels and the weight coefficient to repair the first image data, wherein the weight coefficient is a coefficient reflecting the distance between the neighborhood pixels and the target bad pixel, and the closer the neighborhood pixels are to the target bad pixel, the higher the weight coefficient.

[0017] Secondly, this application also provides an image data processing device applied to a processor in a vehicle electronic rearview mirror system. The vehicle electronic rearview mirror system includes the processor, an image acquisition device, and a display screen installed in the vehicle. The processor is electrically connected to the vehicle's central control system. The device includes a receiving unit, an identification unit, a segmentation unit, an adjustment unit, and a processing unit. The receiving unit is used to receive first image data from the image acquisition device and vehicle status information from the vehicle's central control system. The identification unit is used to identify multiple light sources in the first image data based on the first image data and the vehicle status information, and to determine the light source information of each of the multiple light sources, the light source information including the light source category and light source intensity. The segmentation unit is used to divide the first image data into multiple pixel regions with different glare attributes based on the light source category and the light source intensity. The adjustment unit is used to execute corresponding glare adjustment strategies for different pixel regions to obtain second image data, the glare adjustment strategy being a strategy for suppressing glare in the image. The processing unit is used to perform display adaptation processing on the second image data to generate target image data for display on the display screen.

[0018] Thirdly, embodiments of this application provide an electronic device, including a processing module, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processing module, and the programs include instructions for performing the steps in the first aspect of embodiments of this application.

[0019] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of embodiments of this application.

[0020] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of embodiments of this application. The computer program product may be a software installation package.

[0021] As can be seen, the image data processing method and related apparatus provided in this application are applied to a processor in a vehicle electronic rearview mirror system. The vehicle electronic rearview mirror system includes a processor, an image acquisition device, and a display screen installed in the vehicle. The processor is electrically connected to the vehicle's central control system. The method includes: first, receiving first image data from the image acquisition device and receiving vehicle status information from the vehicle's central control system; second, identifying multiple light sources in the first image data based on the first image data and the vehicle status information, and determining the light source information of each light source, including the light source category and light source intensity; then, dividing the first image data into multiple pixel regions with different glare attributes based on the light source category and light source intensity; next, executing corresponding glare adjustment strategies for different pixel regions to obtain second image data, whereby the glare adjustment strategy is a strategy used to suppress glare in the image; and finally, performing display adaptation processing on the second image data to generate target image data for display on the display screen. In this way, by accurately identifying light source information and dividing pixel areas according to the light source information, glare suppression strategies are implemented for different pixel areas. While effectively suppressing glare, dark details are preserved, improving the electronic rearview mirror's ability to handle glare and enhancing the image quality of the electronic rearview mirror in complex lighting environments. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below 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.

[0023] Figure 1 This is a schematic diagram of the architecture of a vehicle electronic rearview mirror system provided in an embodiment of this application;

[0024] Figure 2 This is a schematic diagram of an electronic rearview mirror chip provided in an embodiment of this application;

[0025] Figure 3 This is a flowchart illustrating an image data processing method provided in an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of a process for determining light source information provided in an embodiment of this application;

[0027] Figure 5 This is a schematic diagram of the display interface of an electronic rearview mirror provided in an embodiment of this application;

[0028] Figure 6 This is a schematic diagram of a light source-enclosed area provided in an embodiment of this application;

[0029] Figure 7 This is a functional unit block diagram of an image data processing device provided in an embodiment of this application;

[0030] Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of this application;

[0031] Figure 9 This is a schematic diagram of the structure of an electronic rearview mirror chip provided in an embodiment of this application. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0033] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0034] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article indicates that the preceding and following related objects have an "or" relationship.

[0035] In this application's embodiments, "multiple" refers to two or more. In this application's embodiments, "connection" refers to various connection methods, such as direct or indirect connections, to achieve communication between devices; this application's embodiments do not impose any limitations on this.

[0036] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0037] The following describes the relevant content, concepts, meanings, technical issues, technical solutions, and beneficial effects involved in the embodiments of this application.

[0038] With the accelerated development of automotive intelligence and electrification, electronic rearview mirrors are gradually replacing traditional optical rearview mirrors, becoming an industry trend due to their advantages such as wider field of vision, reduced wind resistance, and higher degree of integration. However, in complex driving scenarios such as meeting oncoming traffic at night, tunnel entrances and exits, and suburban areas without streetlights, the high beams of vehicles behind and the strong glare from streetlights create significant halos on the electronic display screen, causing glare for the driver and seriously affecting their judgment of road conditions behind them, thus becoming a core hidden danger to driving safety.

[0039] However, current solutions can only achieve general detection of strong light and cannot accurately distinguish between different types of strong light sources in electronic rearview mirror scenarios, such as moving car lights and fixed street lights, as well as their intensity and movement trajectory. This results in a lack of differentiated adaptation in glare processing, with some light sources being either not completely suppressed or over-processed. Furthermore, the false detection rate is high when facing light sources of different sizes and intensities, affecting the quality of the final image.

[0040] To address the aforementioned issues, embodiments of this application provide an image data processing method and related apparatus, aiming to improve the glare suppression capability of electronic rearview mirrors by accurately identifying light sources and implementing targeted glare suppression strategies based on light source categories.

[0041] First, combined Figure 1 The vehicle electronic rearview mirror system in the embodiments of this application will be described. Figure 1 This is a schematic diagram of the architecture of a vehicle electronic rearview mirror system provided in an embodiment of this application, such as... Figure 1 As shown, the vehicle electronic rearview mirror system 100 includes a processor 110, an image acquisition device 120, and a display screen 130. The processor 110 is electrically connected to both the image acquisition device 120 and the display screen 130. Specifically, the processor 110 is electrically connected to the vehicle central control system 140 to acquire information such as the vehicle's driving status and the driver's driving status in real time.

[0042] The processor 110 receives first image data from the image acquisition device 120 and vehicle status information from the vehicle central control system 140. Based on the first image data and the vehicle status information, the processor 110 identifies multiple light sources in the first image data and determines the light source information of each light source, including the light source category and light source intensity. Based on the light source category and light source intensity, the processor 110 divides the first image data into multiple pixel regions with different glare attributes. For different pixel regions, the processor 110 executes corresponding glare adjustment strategies to obtain second image data. The glare adjustment strategy is a strategy used to suppress glare in the image. The processor 110 performs display adaptation processing on the second image data to generate target image data for display on the display screen 130.

[0043] Specifically, the processor 110 can be an automotive-grade system-on-a-chip (SoC); it integrates an image signal processor (ISP) for performing operations such as preprocessing of raw images, differential exposure, gain adjustment, and wide dynamic range fusion; it has a built-in computing unit that supports AI algorithms such as real-time light source recognition and dynamic trajectory prediction; and it has multi-interface adaptation capabilities, including a MIPI CSI interface for connecting to the image acquisition device 120, a MIPI CSI interface and LVDS interface for connecting to the display screen 130, and CAN and LIN interfaces for connecting to the vehicle bus. The image acquisition device 120 can be a camera, installed on both sides of the front of the vehicle to replace the left and right rearview mirrors, and at the rear of the vehicle to replace the center rearview mirror or reversing mirror, ensuring coverage of the rear view of the vehicle. The display screen 130 can be an LCD or an OLED display, supporting touch interaction, and can display images from different cameras in split-screen mode, simultaneously overlaying the glare-free optimized image output by the processor 110, ensuring that the driver can clearly observe vehicles, road markings, obstacles, etc. behind the vehicle.

[0044] Specifically, processor 110 may be an electronic rearview mirror chip; please refer to [link / reference]. Figure 2 , Figure 2 This is a schematic diagram of an electronic rearview mirror chip provided in an embodiment of this application, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of the LQ560 electronic rearview mirror chip, that is... Figure 1 The processor 110 shown includes the electronic rearview mirror chip LQ560, which has the name LQ560 and the corresponding number 200M1120324050 101500263502010933-CN.

[0045] Specifically, please refer to Figure 3 , Figure 3 This is a schematic flowchart of an image data processing method provided in an embodiment of this application. Figure 3 The methods in [the document] are applied to, for example... Figure 1The processor 110 shown includes the following steps in its method:

[0046] S310 receives first image data from the image acquisition device and vehicle status information from the vehicle central control system.

[0047] The first image data consists of multiple consecutive frames of raw image data without glare suppression processing, containing complete scene lighting information; the vehicle status information refers to dynamic data from the vehicle CAN bus or central control system, such as vehicle speed, turn signal status, steering wheel angle, gear position, etc., used to assist in judging the driving scenario.

[0048] S320: Based on the first image data and vehicle status information, identify multiple light sources in the first image data and determine the light source information of each of the multiple light sources.

[0049] The light source information includes the light source category and light source intensity. The light source category refers to the classification of the physical properties of the light source, such as moving point light sources like headlights of oncoming or rearward vehicles, and fixed surface light sources like streetlights. The light source intensity refers to the quantified brightness value of the light source in the image, used to determine the severity of glare.

[0050] S330 divides the first image data into multiple pixel regions with different glare properties according to the type and intensity of the light source.

[0051] Based on the light source analysis results, the entire image is divided into different blocks. Specifically, these are divided into: glare core area (center of the light source, requiring strong suppression); glare-affected area (around the halo, requiring transition processing); and normal area (requiring protection or enhancement of details).

[0052] S340 executes corresponding glare adjustment strategies for different pixel areas to obtain second image data.

[0053] The glare adjustment strategy is used to suppress glare in images, employing a combination of image processing algorithms tailored to different pixel regions. Specifically, for example, short exposure times and low gain are used in the core glare area to quickly darken strong light; local HDR (High Dynamic Range) fusion is used in the glare-affected area to preserve details; and long exposure times and high gain are used in normal areas to improve visibility in dark areas.

[0054] S350 performs display adaptation processing on the second image data to generate target image data for display on the screen.

[0055] The display adaptation process involves converting the algorithm-optimized second image data into a format compatible with the specific in-vehicle display hardware. Specifically, this includes: 1. Color space conversion and standard color gamut mapping: Converting the second image data from its original image processing color space, such as YUV or linear RGB, to a color space conforming to the target display's native color gamut, such as sRGB, DCI-P3, and automotive-grade color standards. 2. Tone mapping and brightness adaptation based on display hardware characteristics: Performing final compression and optimization of the second image data's brightness dynamic range based on the display's maximum brightness, contrast ratio, and grayscale response curve. For example, mapping the algorithm-processed high dynamic range (HDR) data to the range the display can actually display. Brightness self-adaptation: Integrating ambient light sensor data, dynamically adjusting the overall brightness and contrast of the final output image. For example, increasing overall brightness in bright daylight to ensure visibility, and reducing global brightness at night to minimize visual interference for the driver. 3. Resolution scaling and sharpening optimization: Scaling the image resolution to precisely match the display's physical pixel matrix. In this process, an adaptive sharpening algorithm optimized for in-vehicle scenarios is used to enhance the edge clarity of key targets such as vehicle outlines and lane lines, while avoiding over-sharpening flat areas such as the sky and road surface to prevent noise.

[0056] As can be seen, in this embodiment, firstly, first image data from an image acquisition device and vehicle status information from a vehicle central control system are received; secondly, based on the first image data and vehicle status information, multiple light sources in the first image data are identified, and the light source information of each light source is determined, including the light source category and light source intensity; then, based on the light source category and light source intensity, the first image data is divided into multiple pixel regions with different glare attributes; next, for different pixel regions, corresponding glare adjustment strategies are executed to obtain second image data, whereby the glare adjustment strategy is a strategy used to suppress glare in the image; finally, the second image data undergoes display adaptation processing to generate target image data for display on a screen. Thus, by accurately identifying light source information and dividing pixel regions based on this information, and specifically executing glare suppression strategies for different pixel regions, glare is effectively suppressed while preserving details in dark areas, improving the glare handling capability of the electronic rearview mirror and enhancing the image quality of the electronic rearview mirror in complex lighting environments.

[0057] In one possible embodiment, determining the light source information of each of the multiple light sources includes: calculating and determining the brightness value of each pixel region in the first image data; filtering out pixel regions with brightness values ​​higher than a first brightness threshold as candidate light source regions, where the first brightness threshold refers to the threshold of background brightness in the current scene; calculating and determining the average brightness, peak brightness, and brightness gradient of the candidate light source regions, and determining the light source intensity of the candidate light source regions based on the average brightness, peak brightness, and brightness gradient of the candidate light source regions; extracting the shape features of the candidate light source regions using a contour detection algorithm, where the shape features are used to characterize the area, roundness, and aspect ratio of the candidate light source regions; and extracting the spectral features of the candidate light source regions; and determining the light source category of the candidate light source regions based on the shape features, spectral features, and vehicle state information, where the light source categories include: mobile light sources and stationary light sources.

[0058] Please refer to details. Figure 4 , Figure 4 This is a flowchart illustrating a method for determining light source information, as provided in an embodiment of this application. Figure 4 As shown, determining the light source information for each of multiple light sources includes the following steps:

[0059] S401, Calculate and determine the brightness value of each pixel region in the first image data.

[0060] S402, filter out pixel areas with brightness values ​​higher than the first brightness threshold as candidate light source areas.

[0061] The first brightness threshold refers to the threshold of background brightness in the current scene. Specifically, the first brightness threshold can be a dynamically calculated brightness value, determined based on the average brightness of the background in the current scene, such as the sky or road. For example, the first brightness threshold might be set to twice the average background brightness to ensure effective detection in different environments. By adopting a dynamic brightness threshold, the system can reliably and initially capture potential glare sources in various environments, reducing the problem of fixed thresholds being ineffective during the day and overly sensitive at night, and making it more adaptable to complex and changing driving lighting environments.

[0062] Among them, the candidate light source region is a bright connected region with abnormal brightness.

[0063] S403, calculate and determine the average brightness, peak brightness, and brightness gradient of the candidate light source region, and determine the light source intensity of the candidate light source region based on the average brightness, peak brightness, and brightness gradient of the candidate light source region.

[0064] The brightness gradient refers to the rate and direction of brightness change within the candidate light source region. It reflects how the brightness decays from the center to the edge of the light source. A steep gradient may indicate a small, concentrated point light source, such as a car headlight, while a gentle gradient may indicate a large, diffuse area light source, such as the halo under a streetlight.

[0065] S404 uses a contour detection algorithm to extract the shape features of the candidate light source region.

[0066] Among them, shape features are used to characterize the area, roundness, and aspect ratio of the candidate light source region. Specifically, roundness measures how close the shape of the region is to a circle. High roundness may correspond to high beams, while low roundness may correspond to strip LED daytime running lights or long strip street lights.

[0067] S405, extract the spectral features of the candidate light source region.

[0068] Among them, spectral characteristics refer to the color information of a light source, which is usually analyzed by its distribution in the RGB or HSV color space. For example, sodium streetlights have a characteristic yellow color, LED vehicle lights may be cool white, and brake lights are red, used to distinguish traffic lights, vehicle taillights, etc.

[0069] S406. Based on shape characteristics, spectral characteristics, and vehicle status information, determine the light source category of the candidate light source region.

[0070] The light sources are categorized into two types: mobile light sources and fixed light sources. For example, if a high-brightness, highly circular area cannot be determined by shape features to be a fixed streetlight or the high beam of a distant vehicle, further analysis using spectral features can increase the likelihood that it is a streetlight if its spectrum matches the color temperature of common streetlights. Finally, based on vehicle status information, if a vehicle is traveling at high speed in a straight line, and the light source's position in the image is continuously and rapidly moving, it can be strongly inferred that it is the headlight of an oncoming or same-direction moving vehicle, rather than a fixed streetlight.

[0071] As can be seen, in this embodiment, candidate light source regions are first determined using dynamic brightness thresholds, then various features of these regions are analyzed, and finally, vehicle status information is combined to determine the light source category of the candidate light source regions. This improves the accuracy and rationality of determining the light source category, providing data support for subsequent implementation of targeted glare suppression measures for different light source categories.

[0072] In one possible embodiment, determining the light source category of the candidate light source region includes: determining the vehicle's current speed, driving direction, steering angle, and positioning information based on vehicle status information; calculating the pixel displacement of the candidate light source region in multiple consecutive frames of the first image data; when the vehicle speed is determined to be 0 and the pixel displacement is greater than 0, the light source category is determined to be a moving light source; when the vehicle speed is determined to be not 0, the steering angle is 0°, and the pixel displacement is close to 0, the light source category is determined to be a stationary light source.

[0073] Please refer to details. Figure 5 , Figure 5 This is a schematic diagram of the display interface of an electronic rearview mirror provided in an embodiment of this application, as shown below. Figure 5 As shown, the display interface 50 includes a fixed light source 501. Figure 5 The image shows a fixed street light source, while the mobile light source 502 is another type of light source. Figure 5 The middle part represents the headlights of the vehicle behind.

[0074] In this scenario, when the vehicle speed is 0, the camera is stationary relative to the road. At this point, any movement of any object in the image is reflected as its actual motion; if the pixel displacement is greater than 0, the light source is classified as a moving light source. When the vehicle is traveling straight with a 0° turn angle and is moving, the camera is also moving. In this case, the pixel displacement of distant fixed objects, such as streetlights and billboards, will be very slow, even approaching zero, especially for streetlights installed parallel to the lane. However, the headlights of vehicles moving in the same direction will produce a significant pixel displacement. Therefore, when a pixel displacement close to 0 is detected, the light source can be inferred to be a stationary light source. Pixel displacement calculated solely from the image sequence cannot directly determine the absolute category of the light source. Introducing vehicle speed and turn angle information helps to accurately determine the light source category.

[0075] In one possible embodiment, the pixel region includes a glare core region, a glare-affected region, and a normal region; dividing the first image data into multiple pixel regions with different glare attributes includes: selecting multiple target light sources in the first image data whose brightness values ​​are greater than a second brightness threshold, where the second brightness threshold is a brightness threshold characterizing glare that will affect the vehicle driver; configuring corresponding light source radii for the multiple target light sources according to their brightness values, and dividing the light source-enclosed region according to the light source radius, wherein the higher the brightness value of the target light source, the larger the corresponding light source radius; determining the region in the light source-enclosed region whose average brightness value is greater than a third brightness threshold as the glare core region, where the third brightness threshold is greater than the second brightness threshold; and determining the region in the light source-enclosed region whose average brightness value is a first brightness threshold as the normal region; and determining the other regions in the light source-enclosed region besides the glare core region and the normal region as the glare-affected region.

[0076] The glare core region is a high-brightness pixel area in the first image data formed by direct light emitted from strong light sources, such as the core of a car headlight or the center of a streetlight. This region is prone to causing visual glare and temporary blindness in drivers and is the main source of glare interference. The glare-affected region is a pixel area surrounding the glare core region, affected by strong light scattering, halos, or diffraction effects. Its brightness is lower than the core region, but it will still form a halo if left untreated, interfering with the recognition of adjacent areas.

[0077] Here, the light source radius refers to a dynamically configured spatial influence range value for each identified target light source, expressed in pixels. The higher the brightness value of the light source, the larger the configured radius. The light source enclosure region is a circular candidate region formed by drawing a circle with each target light source as its center and its corresponding light source radius. Please refer to [link to details]. Figure 6 , Figure 6 This is a schematic diagram of a light source-enclosed area provided in an embodiment of this application, such as... Figure 6 As shown, it includes: a light source point 601 extracted from the first image data, a glare core area 602 generated by it, and a glare-affected area 603 generated by it, with the remaining areas being normal areas.

[0078] As can be seen, in this embodiment, by filtering out different light source-enclosed areas based on brightness values, and further dividing these areas into various pixel regions based on the average brightness value of the light source-enclosed areas, the accuracy of dividing the affected areas of different light sources is improved, the naturalness of the image transition is enhanced, the image quality is improved, and data support is provided for the adaptability of subsequent glare suppression strategies.

[0079] In one possible embodiment, a corresponding glare adjustment strategy is executed for different pixel regions, including: for the glare core region, a first adjustment strategy is executed, which refers to suppressing the glare core region using a first exposure parameter and a low brightness gain parameter; for the glare-affected region, a second adjustment strategy is executed, which refers to suppressing the glare-affected region using a second exposure parameter and a medium brightness gain parameter, wherein the exposure duration represented by the second exposure parameter is greater than the exposure duration represented by the first exposure parameter; and for the normal region, a third adjustment strategy is executed, which refers to adjusting the normal region using a third exposure parameter and a high brightness gain parameter, wherein the exposure duration represented by the third exposure parameter is greater than the exposure duration represented by the second exposure parameter.

[0080] The exposure parameter characterizes the exposure time, which is the duration of light collection in different areas. A longer exposure time captures more photons, resulting in a brighter area in the first image data. In this embodiment, the third exposure time > the second exposure time > the first exposure time. The brightness gain parameter is the intensity coefficient used in the image processing pipeline to amplify the electrical signal after photoelectric conversion. A higher gain parameter results in a brighter adjusted image.

[0081] In this process, using long exposure and high gain on the core glare area will result in severe overexposure, strong halo diffusion, and loss of image information. Therefore, the shortest exposure and lowest gain are used. For glare-affected areas, using the same strong suppression strategy as the core area will result in abruptly cut-off halo edges, forming a distinct bright-dark boundary ring that looks unnatural. Using the same strategy as normal areas will result in insufficient halo suppression. Therefore, a compromise parameter can effectively reduce halo brightness while preserving some texture and gradation information, achieving a smooth visual transition. For normal areas, where the light is insufficient, using a global short exposure to suppress glare will plunge these areas into complete darkness, losing safety information such as dark vehicles or pedestrians to the side and rear. By applying the longest exposure time and highest gain to these areas individually, the details in the shadows can be maximized, improving the overall image visibility.

[0082] As can be seen, in this embodiment, by implementing different glare adjustment strategies for different pixel areas, glare suppression is achieved in the core glare area and the glare-affected area, while also adjusting the normal area to improve image quality. While eliminating glare, the visibility of details in dark scenes is significantly improved, enhancing the glare handling capabilities of the electronic rearview mirror and improving the visual experience in complex environments.

[0083] In one possible embodiment, the vehicle central control system includes: an image sensor for acquiring the state of the driver's pupils; the method further includes: receiving a first control message from the vehicle central control system, the first control message being a control message generated when the driver's pupil dilation state is in a continuously constricted state as acquired by the image sensor; in response to the first control message, reducing a first exposure parameter and a low brightness gain parameter, and reducing a second exposure parameter and a medium brightness gain parameter to enhance the suppression intensity of the glare core region and the glare-affected region; receiving a second control message from the vehicle central control system, the second control message being a control message generated when the driver's pupil dilation state is in a continuously enlarged state as acquired by the image sensor; in response to the second control message, increasing the first exposure parameter and the low brightness gain parameter, and increasing the second exposure parameter and the medium brightness gain parameter to weaken the suppression intensity of the glare core region and the glare-affected region.

[0084] Specifically, when the pupil dilation / contraction state is in a continuous contraction state, it means that the pupil diameter decreases continuously within a short period of time, such as 1-2 seconds. This indicates that the driver is exposed to an environment with increased brightness, and the visual system is actively reducing the amount of light entering to adapt to the strong light. When the pupil dilation / contraction state is in a continuous dilation state, it means that the pupil diameter increases continuously within a short period of time. This indicates that the driver is adapting to an overall darker environment, and the visual system is attempting to capture more light. Specifically, the image sensor in the vehicle's central control system used to collect the driver's pupil state can be installed on the vehicle's steering wheel or in other locations in the cabin that can capture real-time images of the driver's face.

[0085] When the pupil continues to constrict, it indicates that the total amount of light entering the eye is still too high, causing glare or discomfort for the driver. This means the system's current suppression intensity is insufficient. The first control message further reduces the exposure and gain of the core and affected areas, thus strengthening suppression and controlling the overall brightness output of the glare region to match the driver's increased visual sensitivity and stabilize the pupil state. When the pupil continues to dilate, it indicates that the overall field of vision is too dark, and the driver is struggling to obtain more light to see the environment clearly. This may be due to over-suppression by the system, resulting in an overall or localized dark image and loss of essential safety information. The second control message appropriately increases the exposure and gain of the core and affected areas, weakening suppression and allowing more details to emerge from the suppressed areas to match the driver's increased light sensitivity in dark adaptation, preventing safety risks caused by missing information.

[0086] As can be seen, in this embodiment of the application, by analyzing the driver's pupil dilation state and adjusting the glare suppression parameters to match the driver's physiological state, it is possible to match the differences in the driver's visual sensitivity under different lighting conditions, provide a matching glare suppression strategy, improve the intelligence of the system, and at the same time help improve the driver's driving safety and optimize the driver's driving experience.

[0087] In one possible embodiment, after receiving first image data from an image acquisition device, the method further includes: extracting pixels in the first image data whose difference between the brightness of a single pixel and the mean of its neighborhood is greater than a preset threshold, and marking them as suspected bad pixels, wherein the preset threshold is a threshold adaptively adjusted based on the brightness of the current scene environment; if the brightness of the suspected bad pixels remains unchanged in multiple consecutive frames of images, then the suspected bad pixels are determined to be target bad pixels; determining the brightness and weight coefficient of the neighborhood pixels of the target bad pixel, and replacing the brightness of the target bad pixel according to the brightness and weight coefficient of the neighborhood pixels to repair the first image data, wherein the weight coefficient is a coefficient reflecting the distance between the neighborhood pixels and the target bad pixel, and the closer the neighborhood pixels are to the target bad pixel, the higher the weight coefficient.

[0088] The preset threshold is automatically adjusted based on the ambient brightness of the current scene. For example, in bright daylight, the threshold is set higher to avoid misclassifying normal highlight areas as dead pixels; in dim nighttime, the threshold is lowered to capture abnormal bright spots that are more noticeable against a dark background. Target dead pixels refer to suspected dead pixels whose positions are fixed and whose brightness values ​​remain constant across multiple consecutive frames. Through continuous testing over time, they can be thoroughly distinguished from moving strong light sources and transient disturbances such as flying insects or raindrops. The weight coefficient of neighboring pixels is an influence parameter assigned to each pixel surrounding the dead pixel in the algorithm used to calculate the replacement value during dead pixel repair. This coefficient is typically inversely proportional to distance; that is, the closer a pixel is to the dead pixel, the greater its contribution to the repair result, and the higher its weight. This ensures a smooth transition between the repaired pixel value and the surrounding area, avoiding harsh repair marks.

[0089] Fixed defects on image sensors manifest as bright or dark spots that remain in the same position but exhibit abnormal brightness. In nighttime scenes, a bright defect can easily be misidentified by subsequent AI light source recognition models as a fixed, strong light source, such as a distant streetlamp. This causes the system to perform unnecessary glare suppression on a non-existent light source, wasting computing power and potentially distorting the real scene. By utilizing the temporal continuity feature, static hardware defects are distinguished from dynamic real objects, avoiding the misidentification of flickering lights and moving reflections as defects.

[0090] As can be seen, in this embodiment, by preprocessing the first image data, target defects in the image are extracted and the first image data is repaired. This avoids false positives by the AI ​​model, reduces the false alarm rate, significantly improves the accuracy of light source recognition, and ensures the accuracy and specificity of subsequent glare suppression strategies.

[0091] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0092] and Figure 3 The implementation is consistent with the previous one; please refer to [link / reference]. Figure 7 , Figure 7This is a functional unit block diagram of an image data processing device provided in an embodiment of this application. The image data processing device 700 includes: a receiving unit 710, an identification unit 720, a division unit 730, an adjustment unit 740, and a processing unit 750. The receiving unit 710 is used to receive first image data from an image acquisition device and vehicle status information from a vehicle central control system. The identification unit 720 is used to identify multiple light sources in the first image data based on the first image data and the vehicle status information, and determine the light source information of each light source, including the light source category and light source intensity. The division unit 730 is used to divide the first image data into multiple pixel regions with different glare attributes based on the light source category and light source intensity. The adjustment unit 740 is used to execute corresponding glare adjustment strategies for different pixel regions to obtain second image data; the glare adjustment strategy is a strategy used to suppress glare in the image. The processing unit 750 is used to perform display adaptation processing on the second image data to generate target image data for display on a screen.

[0093] In one possible embodiment, the identification unit 720 determines the light source information of each of the multiple light sources, specifically by: calculating and determining the brightness value of each pixel region in the first image data; filtering out pixel regions with brightness values ​​higher than a first brightness threshold as candidate light source regions, where the first brightness threshold refers to the threshold of background brightness in the current scene; calculating and determining the average brightness, peak brightness, and brightness gradient of the candidate light source regions, and determining the light source intensity of the candidate light source regions based on the average brightness, peak brightness, and brightness gradient of the candidate light source regions; extracting the shape features of the candidate light source regions using a contour detection algorithm, where the shape features characterize the area, roundness, and aspect ratio of the candidate light source regions; and extracting the spectral features of the candidate light source regions; and determining the light source category of the candidate light source regions based on the shape features, spectral features, and vehicle state information, where the light source categories include: mobile light sources and fixed light sources.

[0094] In one possible embodiment, the identification unit 720 is specifically used to determine the light source category of the candidate light source region by: determining the vehicle's current speed, driving direction, steering angle, and positioning information based on vehicle status information; calculating the pixel displacement of the candidate light source region in multiple consecutive frames of the first image data; determining the light source category as a moving light source when the vehicle speed is 0 and the pixel displacement is greater than 0; and determining the light source category as a stationary light source when the vehicle speed is not 0, the steering angle is 0°, and the pixel displacement is close to 0.

[0095] In one possible embodiment, the pixel region includes a glare core region, a glare-affected region, and a normal region. The first image data is divided into multiple pixel regions with different glare attributes. Specifically, the division unit 730 is used to: filter out multiple target light sources in the first image data whose brightness values ​​are greater than a second brightness threshold, where the second brightness threshold is a brightness threshold characterizing glare that will affect the vehicle driver; configure corresponding light source radii for the multiple target light sources according to their brightness values; divide the light source-enclosed region according to the light source radius, wherein the higher the brightness value of the target light source, the larger the corresponding light source radius; determine the region within the light source-enclosed region whose average brightness value is greater than a third brightness threshold as the glare core region, where the third brightness threshold is greater than the second brightness threshold; and determine the region within the light source-enclosed region whose average brightness value is a first brightness threshold as the normal region; and determine the other regions within the light source-enclosed region besides the glare core region and the normal region as the glare-affected region.

[0096] In one possible embodiment, a corresponding glare adjustment strategy is executed for different pixel regions. The adjustment unit 740 is specifically used to: process the glare core region using a first adjustment strategy, which suppresses the glare core region using a first exposure parameter and a low brightness gain parameter; process the glare-affected region using a second adjustment strategy, which suppresses the glare-affected region using a second exposure parameter and a medium brightness gain parameter, wherein the exposure duration represented by the second exposure parameter is greater than the exposure duration represented by the first exposure parameter; and process the normal region using a third adjustment strategy, which adjusts the normal region using a third exposure parameter and a high brightness gain parameter, wherein the exposure duration represented by the third exposure parameter is greater than the exposure duration represented by the second exposure parameter.

[0097] In one possible embodiment, the vehicle central control system includes: an image sensor for acquiring the driver's pupil state; the image data processing device 700 is further configured to: receive a first control message from the vehicle central control system, the first control message being a control message generated when the driver's pupil dilation state is in a continuously constricted state as acquired by the image sensor; in response to the first control message, reduce a first exposure parameter and a low brightness gain parameter, and reduce a second exposure parameter and a medium brightness gain parameter to enhance the suppression intensity of the glare core region and the glare-affected region; receive a second control message from the vehicle central control system, the second control message being a control message generated when the driver's pupil dilation state is in a continuously enlarged state as acquired by the image sensor; in response to the second control message, increase the first exposure parameter and the low brightness gain parameter, and increase the second exposure parameter and the medium brightness gain parameter to weaken the suppression intensity of the glare core region and the glare-affected region.

[0098] In one possible embodiment, after acquiring the first image data from the image acquisition device, the image data processing device 700 is further configured to: extract pixels in the first image data whose difference between the brightness of a single pixel and the average value of its neighborhood is greater than a preset threshold, and mark them as suspected bad pixels, wherein the preset threshold is a threshold that is adaptively adjusted based on the brightness of the current scene environment; if the brightness of the suspected bad pixel remains unchanged in multiple consecutive frames of images, then the suspected bad pixel is determined to be a target bad pixel; determine the brightness and weight coefficient of the neighborhood pixels of the target bad pixel, and replace the brightness of the target bad pixel according to the brightness and weight coefficient of the neighborhood pixels to repair the first image data, wherein the weight coefficient is a coefficient that reflects the distance between the neighborhood pixels and the target bad pixel, and the closer the neighborhood pixels are to the target bad pixel, the higher the weight coefficient is.

[0099] It is understood that since the method embodiments and the device embodiments are different presentations of the same technical concept, the content of the method embodiment section in this application should be adapted to the device embodiment section in a synchronous manner, and will not be repeated here.

[0100] Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of this application. For example... Figure 8 As shown, the electronic device 800 may include one or more of the following components: a processing module 801 and a memory 802 coupled to the processing module 801, wherein the memory 802 may store one or more computer programs, which may be configured to implement the methods described in the examples above when executed by one or more processing modules 801. The electronic device 800 may be as follows: Figure 1 The processor 110 shown.

[0101] The processing module 801 may include one or more processing cores. The processing module 801 connects to various parts within the electronic device 800 using various interfaces and lines. It executes various functions and processes data of the electronic device 800 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 802, and by calling data stored in the memory 802. Optionally, the processing module 801 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processing module 801 may integrate one or more of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. It is understood that the aforementioned modem may also not be integrated into the processing module 801 and may be implemented separately through a communication chip.

[0102] The memory 802 may include random access memory (RAM) or read-only memory (ROM). The memory 802 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 802 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method examples described above. The data storage area may also store data created by the electronic device 800 during use.

[0103] It is understood that the electronic device 800 may include more or fewer structural elements than those shown in the above block diagram, such as a power module, physical buttons, WiFi (Wireless Fidelity) module, speaker, Bluetooth module, sensor, etc., without limitation.

[0104] This application also provides a computer storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements some or all of the steps of any of the methods described in the above method embodiments.

[0105] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0106] This application provides an electronic rearview mirror chip, which is coupled to a memory and used to read and execute program instructions in the memory so that the device containing the electronic rearview mirror chip can implement any of the above-described methods.

[0107] Specifically, please refer to Figure 9 , Figure 9 This is a schematic diagram of the structure of an electronic rearview mirror chip provided in an embodiment of this application, as shown below. Figure 9 As shown, the electronic rearview mirror chip LQ560 includes at least 14 pins, and the correspondence of these 14 pins is represented by Table 1, which is shown below:

[0108] Table 1

[0109]

[0110] Understandable, Figure 9 The table above only lists the key functional pins involved in implementing the glare suppression method of this application. The chip should also include other necessary general pins, such as: a reset pin (RESETn), multiple general purpose input / output pins (GPIO), a memory interface (such as an SPI / eMMC interface) for connecting the memory, and additional power and ground pins required for internal power management of the chip.

[0111] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, and systems can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and there may be other division methods in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can be physically comprised separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0115] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute partial steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, volatile memory, or non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM), etc., which are various media capable of storing program code.

[0116] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A method for processing image data, characterized in that, A processor for use in a vehicle electronic rearview mirror system, the vehicle electronic rearview mirror system including the processor, an image acquisition device, and a display screen installed in the vehicle, the processor being electrically connected to the vehicle's central control system; the method includes: Receive first image data from the image acquisition device and vehicle status information from the vehicle central control system; Based on the first image data and the vehicle status information, multiple light sources in the first image data are identified, and the light source information of each of the multiple light sources is determined, including the light source category and the light source intensity. Based on the light source type and the light source intensity, the first image data is divided into multiple pixel regions with different glare attributes. Each pixel region includes a glare core region, a glare-affected region, and a normal region. This process includes: selecting multiple target light sources in the first image data whose brightness values ​​are greater than a second brightness threshold, where the second brightness threshold represents the brightness threshold that will affect the vehicle driver; configuring corresponding light source radii for the multiple target light sources according to their brightness values; dividing the light source surrounding region according to the light source radius, wherein the higher the brightness value of the target light source, the larger the corresponding light source radius; determining the region within the light source surrounding region whose average brightness value is greater than a third brightness threshold as the glare core region, where the third brightness threshold is greater than the second brightness threshold; determining the region within the light source surrounding region whose average brightness value is a first brightness threshold as the normal region; and determining the other regions within the light source surrounding region besides the glare core region and the normal region as the glare-affected region. For the glare core region, a first adjustment strategy is executed to suppress the glare core region using a first exposure parameter and a low brightness gain parameter. For the glare-affected region, a second adjustment strategy is executed to suppress the glare-affected region using a second exposure parameter and a medium brightness gain parameter, where the exposure duration represented by the second exposure parameter is greater than the exposure duration represented by the first exposure parameter. For the normal region, a third adjustment strategy is executed to adjust the normal region using a third exposure parameter and a high brightness gain parameter, where the exposure duration represented by the third exposure parameter is greater than the exposure duration represented by the second exposure parameter, resulting in second image data. The second image data is subjected to display adaptation processing to generate target image data for display on the display screen.

2. The method according to claim 1, characterized in that, Determining the light source information of each of the plurality of light sources includes: Calculate and determine the brightness value of each pixel region in the first image data; Pixel regions with brightness values ​​higher than the first brightness threshold are selected as candidate light source regions. The first brightness threshold refers to the threshold of background brightness in the current scene. The average brightness, peak brightness, and brightness gradient of the candidate light source region are calculated and determined, and the light source intensity of the candidate light source region is determined based on the average brightness, peak brightness, and brightness gradient of the candidate light source region. The shape features of the candidate light source region are extracted using a contour detection algorithm. These shape features characterize the area, roundness, and aspect ratio of the candidate light source region. Extract the spectral features of the candidate light source region; Based on the shape features, the spectral features, and the vehicle state information, the light source category of the candidate light source region is determined, and the light source category includes: mobile light sources and stationary light sources.

3. The method according to claim 2, characterized in that, Determining the light source category of the candidate light source region includes: Based on the vehicle status information, determine the vehicle's current speed, driving direction, steering angle, and positioning information; Calculate the pixel displacement of the candidate light source region in multiple consecutive frames of the first image data; When the vehicle speed is determined to be 0 and the pixel displacement is greater than 0, the light source category is determined to be the moving light source. When it is determined that the vehicle speed is not 0, the steering angle is 0°, and the pixel displacement is close to 0, the light source category is determined to be the fixed light source.

4. The method according to claim 3, characterized in that, The vehicle central control system includes: an image sensor for acquiring the driver's pupil state; the method further includes: The system receives a first control message from the vehicle's central control system. The first control message is generated when the driver's pupils are in a continuously constricted state, as captured by the image sensor. In response to the first control message, the first exposure parameter and the low brightness gain parameter, as well as the second exposure parameter and the medium brightness gain parameter, are reduced to enhance the suppression intensity of the glare core region and the glare-affected region. Receive a second control message from the vehicle central control system. The second control message is a control message generated when the driver's pupils are continuously enlarged, as captured by the image sensor. In response to the second control message, the first exposure parameter and the low brightness gain parameter, as well as the second exposure parameter and the medium brightness gain parameter, are increased to reduce the suppression intensity on the glare core region and the glare-affected region.

5. The method according to claim 1, characterized in that, After receiving the first image data from the image acquisition device, the method further includes: Pixels in the first image data whose difference between the brightness of a single pixel and the mean of its neighborhood is greater than a preset threshold are extracted and marked as suspected bad pixels. The preset threshold is a threshold that is adaptively adjusted based on the brightness of the current scene environment. If the brightness of the suspected bad pixel remains unchanged in multiple consecutive frames of images, then the suspected bad pixel is determined to be the target bad pixel. The brightness and weight coefficient of the neighborhood pixels of the target bad pixel are determined. Based on the brightness of the neighborhood pixels and the weight coefficient, the brightness of the target bad pixel is replaced to repair the first image data. The weight coefficient is a coefficient that reflects the distance between the neighborhood pixels and the target bad pixel. The closer the neighborhood pixels are to the target bad pixel, the higher the weight coefficient is.

6. An image data processing apparatus, characterized in that, A processor is used in a vehicle electronic rearview mirror system, the vehicle electronic rearview mirror system including the processor, an image acquisition device, and a display screen installed in the vehicle, the processor being electrically connected to the vehicle's central control system; the device includes: a receiving unit, a recognition unit, a segmentation unit, an adjustment unit, and a processing unit; wherein... The receiving unit is used to receive first image data from the image acquisition device and vehicle status information from the vehicle central control system; The identification unit is configured to identify multiple light sources in the first image data based on the first image data and the vehicle status information, and determine the light source information of each of the multiple light sources, wherein the light source information includes the light source category and the light source intensity. The segmentation unit is used to divide the first image data into multiple pixel regions with different glare attributes according to the light source type and the light source intensity. The pixel regions include a glare core region, a glare-affected region, and a normal region. The process includes: selecting multiple target light sources in the first image data whose brightness values ​​are greater than a second brightness threshold, where the second brightness threshold is a brightness threshold characterizing glare that will affect the vehicle driver; configuring corresponding light source radii for the multiple target light sources according to their brightness values; dividing the light source-enclosed region according to the light source radius, wherein the higher the brightness value of the target light source, the larger the corresponding light source radius; determining the region in the light source-enclosed region whose average brightness value is greater than a third brightness threshold as the glare core region, where the third brightness threshold is greater than the second brightness threshold; determining the region in the light source-enclosed region whose average brightness value is a first brightness threshold as the normal region; and determining the other regions in the light source-enclosed region besides the glare core region and the normal region as the glare-affected region. The adjustment unit is configured to: execute a first adjustment strategy for the glare core region, wherein the first adjustment strategy is to suppress the glare core region using a first exposure parameter and a low brightness gain parameter; execute a second adjustment strategy for the glare-affected region, wherein the second adjustment strategy is to suppress the glare-affected region using a second exposure parameter and a medium brightness gain parameter, wherein the exposure duration represented by the second exposure parameter is greater than the exposure duration represented by the first exposure parameter; and execute a third adjustment strategy for the normal region, wherein the third adjustment strategy is to adjust the normal region using a third exposure parameter and a high brightness gain parameter, wherein the exposure duration represented by the third exposure parameter is greater than the exposure duration represented by the second exposure parameter, thereby obtaining second image data. The processing unit is used to perform display adaptation processing on the second image data to generate target image data for display on the display screen.

7. An electronic device, characterized in that, It includes a processing module and a memory, the memory being used to store one or more programs and configured to be executed by the processing module, the programs including instructions for performing the steps of the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, A computer program for storing electronic data interchange is provided, wherein the computer program causes a computer to perform the method as described in any one of claims 1-5.

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