A color screen instrument image correction method and system based on RLE decompression algorithm

Abstract

The application discloses a color screen instrument image correction method and system based on an RLE decompression algorithm, relates to the technical field of image processing, and aims to solve the problem of image quality decline of the RLE compression algorithm in the existing vehicle-mounted color screen instrument system when processing complex graphic data. The method comprises the following steps: analyzing graphic elements of a color screen instrument, and extracting design intention parameters of the graphic elements, wherein the design intention parameters are used for representing the generation law of the graphic elements; when the graphic elements need to be displayed, compressed data are read from a vehicle-mounted nonvolatile memory, and run-length encoding decompression is performed on the compressed data to obtain recovered RLE optimization descriptors; high-precision pixel data of the graphic elements are generated in real time according to the recovered RLE optimization descriptors, and the high-precision pixel data are output to the color screen instrument for display.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a method and system for correcting color screen instrument images based on the RLE decompression algorithm. Background Technology

[0002] In modern automotive color instrument cluster systems, image compression technology is widely used to efficiently process and display high-resolution graphic data with limited hardware resources. Run-length encoding (RLE) is a common choice due to its simplicity and fast decompression speed. However, with the rapid development of the automotive industry and consumers' increasing demands for driving experience, the interior design of modern cars, especially the user interface of the instrument panel, is becoming increasingly complex and sophisticated. Designers are beginning to introduce high-resolution textured backgrounds, simulated lighting effects, complex graphic elements, and refined anti-aliased fonts.

[0003] When the Restricted Lens (RLE) compression algorithm is applied to highly complex and detailed graphic data, its inherent limitations become apparent. RLE's compression principle involves recording the length and color value of consecutive identical pixels. For images containing a large number of discontinuous pixels, subtle color variations, or complex textures, RLE's compression efficiency drops sharply, potentially resulting in compressed data exceeding the original data size. More importantly, to achieve a certain compression ratio, RLE may internally perform some form of "simplification" or "quantization" when processing these complex images, introducing new, unconventional compression artifacts. These artifacts are no longer simple color banding or jagged edges, but may manifest as loss of texture detail, pattern distortion, blurring of high-frequency information, and in some cases, even produce unnatural, "mosaic" or "blocky" visual noise.

[0004] Faced with these new artifacts generated by Refresh Image Processing (RLE) when processing complex graphics, existing image correction algorithms are proving inadequate. These algorithms were initially designed to address typical RLE defects (such as gradient banding and jagged edges), with their core logic being to identify and smooth color transitions and sharpen blurred edges. However, when the artifacts introduced by RLE are texture distortion, loss of detail, or unnatural blocky noise, these correction algorithms often fail to effectively identify and repair them. Worse still, they may even misclassify these new artifacts as normal features of the image, or, in attempting to repair them, further exacerbate the image distortion. This "collateral damage" not only fails to improve image quality but may also undermine the designer's original intent, resulting in a final display effect worse than an uncorrected RLE-decompressed image.

[0005] These issues, while perhaps unpleasant in static images, become particularly pronounced in dynamic display scenarios. As dynamic elements with complex textures and details move or update on the screen, the RLE decompression and subsequent correction processes must be completed rapidly within each refresh cycle. If the correction algorithm cannot effectively handle the new artifacts introduced by RLE on complex graphics, these artifacts will flicker, jump, or distort with the dynamic changes in the image. This persistent visual interference not only severely impacts the user experience and diminishes the "premium" feel of the dashboard, but may also, in some cases, indirectly affect the driver's ability to quickly and accurately interpret critical information due to the instability of the displayed information. Summary of the Invention

[0006] This application provides a color screen instrument image correction method and system based on the RLE decompression algorithm, aiming to solve the problem that in existing vehicle color screen instrument systems, the RLE compression algorithm may introduce new artifacts such as texture distortion, loss of detail, or unnatural block noise when processing complex graphic data, resulting in a decline in image quality, which seriously affects user experience and information interpretation, especially in dynamic display scenarios.

[0007] Firstly, to address the aforementioned technical problems, this invention provides a color screen instrument panel image correction method based on the RLE decompression algorithm. The method includes: analyzing the graphic elements of the color screen instrument panel and extracting design intent parameters for the graphic elements, whereby the design intent parameters characterize the generation rules of the graphic elements; generating an RLE optimization descriptor based on the design intent parameters, whereby the RLE optimization descriptor characterizes the instruction set required to reconstruct the graphic elements; performing stroke-length encoding compression on the RLE optimization descriptor to obtain compressed data, and storing the compressed data in an on-board non-volatile memory; when graphic elements need to be displayed, reading the compressed data from the on-board non-volatile memory and performing stroke-length encoding decompression on the compressed data to obtain a recovered RLE optimization descriptor; generating high-precision pixel data of the graphic elements in real time based on the recovered RLE optimization descriptor, and outputting the high-precision pixel data to the color screen instrument panel for display.

[0008] Secondly, this application provides a color screen instrument image correction system based on the RLE decompression algorithm. The system includes: an extraction unit for analyzing the graphic elements of the color screen instrument and extracting design intent parameters for the graphic elements, the design intent parameters being used to characterize the generation rules of the graphic elements; a generation unit for generating an RLE optimization descriptor based on the design intent parameters, the RLE optimization descriptor being used to characterize the instruction set required to reconstruct the graphic elements; an encoding unit for performing stroke-length encoding compression on the RLE optimization descriptor to obtain compressed data, and storing the compressed data in an on-board non-volatile memory; a reading unit for reading the compressed data from the on-board non-volatile memory when graphic elements need to be displayed, and performing stroke-length encoding decompression on the compressed data to obtain the recovered RLE optimization descriptor; and a processing unit for generating high-precision pixel data of the graphic elements in real time based on the recovered RLE optimization descriptor, and outputting the high-precision pixel data to the color screen instrument for display.

[0009] This application discloses at least the following beneficial effects: It provides a method for image correction of color screen instruments based on the RLE decompression algorithm. This method involves in-depth analysis of the graphic elements of the color screen instrument to extract their design intent parameters, which accurately characterize the generation rules of the graphic elements. Subsequently, based on these design intent parameters, an RLE optimization descriptor is generated. This descriptor contains the instruction set required to reconstruct the graphic elements, rather than traditional pixel data. Next, run-length encoding compression is performed on the RLE optimization descriptor to obtain compressed data, which is then stored. When graphic elements need to be displayed, the system reads the compressed data from memory and decompresses it to obtain the recovered RLE optimization descriptor. Finally, based on the recovered RLE optimization descriptor, high-precision pixel data of the graphic elements is generated in real time and output to the color screen instrument for display.

[0010] Through the above technical solution, this application effectively solves the novel artifact problem introduced by the existing RLE compression algorithm when processing complex and detailed graphic data due to its inherent limitations. These artifacts include loss of texture details, pattern distortion, blurring of high-frequency information, or unnatural blocky noise. Unlike traditional RLE compression, which directly affects pixel data, this application compresses the design intent of graphic elements, rather than the final pixels, fundamentally avoiding the destruction of image details during compression. During display, the system can reconstruct the graphics in real time and with high precision based on these design intent parameters, ensuring the original quality of the image and the complete presentation of the designer's intent. Especially in dynamic display scenarios, this method can guarantee real-time reconstruction and high-precision display of each frame, eliminating the problem of artifacts flickering, jumping, or deforming with dynamic changes in traditional methods. This significantly improves the user experience and the "high-end feel" of the dashboard, and indirectly improves the driver's efficiency in interpreting key information. Therefore, this application provides an innovative solution for efficiently and effectively processing and displaying complex graphic data with limited hardware resources, overcoming the difficulty in correcting RLE compression artifacts in existing technologies, and has significant technological advancements and practical value. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating a color screen instrument image correction method based on the RLE decompression algorithm provided in this application. Detailed Implementation

[0012] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0013] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0014] Traditional in-vehicle color instrument cluster systems commonly employ compression techniques such as Run-Length Encoding (RLE) to handle and display high-resolution graphic data, in order to cope with limited hardware resources. However, the limitations of RLE compression algorithms become apparent when faced with increasingly complex textured backgrounds, intricate lighting effects, complex graphic elements, and anti-aliased fonts in modern automotive interior design. Its compression efficiency drops sharply when processing images containing a large number of discontinuous pixels, subtle color variations, or complex textures, and may even introduce new artifacts such as loss of texture details, pattern distortion, and blurring of high-frequency information. These artifacts not only affect the visual experience but can also cause continuous visual interference in dynamic display scenarios, affecting the driver's interpretation of critical information.

[0015] In view of the above problems, this application provides a color screen instrument panel image correction method based on the RLE decompression algorithm. This application extracts the design intent parameters of graphic elements and generates RLE optimized descriptors based on these parameters. Then, it performs RLE compression and decompression on the descriptors, ultimately generating high-precision pixel data in real time. This method fundamentally avoids the artifacts generated when traditional RLE compression directly processes complex images at the pixel level, ensuring high-quality graphic display with limited resources and significantly improving the visual effect and user experience of in-vehicle color screen instrument panels.

[0016] The following specific embodiments will provide a detailed introduction and explanation of the color screen instrument image correction method and system based on the RLE decompression algorithm provided in this application.

[0017] Reference Figure 1 This application provides a method for color screen instrument image correction based on the RLE decompression algorithm, which may include the following steps: S1. Analyze the graphic elements of the color screen instrument and extract the design intent parameters of the graphic elements.

[0018] Among them, the design intent parameter is used to characterize the generation rules of graphic elements.

[0019] It should be noted that "color instrument cluster" usually refers to an in-vehicle display screen used to show the driver vehicle status, navigation information, entertainment content, etc. Its characteristics include the ability to display rich colors and complex graphics.

[0020] "Graphic elements" refer to the various visual components that make up the color screen instrument display interface, such as background textures, fonts, icons, indicator bars, etc.

[0021] "Design intent parameters" are a set of data used to characterize the generation rules of graphic elements. They are not direct pixel data, but rather a "blueprint" or "instruction set" describing how to generate these pixel data. For example, for a texture, its design intent parameters may include texture type, base color, direction, density, etc.; for a font, they may include font identifier, font size, font weight, Bézier curve control points, etc.

[0022] The method in this application first analyzes the graphic elements of the color screen instrument and extracts the design intent parameters of the graphic elements. This step is fundamental to this application and aims to understand the essence of the graphics at a high level, rather than merely at the pixel level. For example, graphic elements can be manually classified and parameterized, with designers or engineers manually inputting their corresponding design intent parameters based on the visual effects and functions of the graphic elements. While this method requires manual intervention, it ensures the accuracy of the parameters and is particularly suitable for static or infrequently changing graphic elements. Alternatively, image processing algorithms can be used to analyze the graphic elements, such as edge detection, color clustering, and texture analysis, to automatically identify the type and characteristics of the graphic elements and extract the design intent parameters. This method improves processing efficiency and is suitable for batch processing of graphic elements.

[0023] S2. Generate an RLE optimization descriptor based on the design intent parameters.

[0024] The RLE optimization descriptor is used to characterize the instruction set required to reconstruct graphical elements.

[0025] It should be noted that the "RLE Optimization Descriptor" is an intermediate data structure generated based on design intent parameters. It encapsulates the instruction set required to reconstruct graphic elements in an optimized manner. This descriptor is the target of RLE compression, not the original pixel data.

[0026] The RLE optimization descriptor is a crucial link between design intent and RLE compression. For example, extracted design intent parameters can be directly packaged into a data structure as an RLE optimization descriptor. This approach is simple and direct, but may not fully utilize the compression potential of RLE. Alternatively, a series of instructions or code snippets can be generated based on design intent parameters. These instructions or code snippets can guide the reconstruction process of graphical elements. These instruction sets can be organized into RLE optimization descriptors; for example, for a gradient texture, the descriptor might contain instructions such as "from color A to color B, direction D, step size S".

[0027] S3. Perform run-length encoding compression on the RLE optimized descriptor to obtain compressed data, and store the compressed data in the vehicle's non-volatile memory.

[0028] Run-length encoding (RLE) is a simple data compression algorithm that reduces data size by storing the value and number of repetitions of consecutively repeating data. For example, "AAAAABBC" can be encoded as "5A2B1C".

[0029] "Onboard non-volatile memory" refers to a storage device inside a vehicle used to store data that will not be lost after power is cut off, such as flash memory or EEPROM.

[0030] Subsequently, the generated RLE-optimized descriptor undergoes run-length encoding compression to obtain compressed data, which is then stored in the vehicle's non-volatile memory. This step leverages the advantages of the RLE algorithm to efficiently compress structured descriptors. For example, a standard RLE algorithm library or a custom RLE implementation can be used to compress the RLE-optimized descriptor. Because RLE-optimized descriptors typically contain many repetitive patterns (e.g., identical instruction sequences or parameter values), RLE compression achieves a high compression ratio. The compressed data is stored in the vehicle's non-volatile memory for subsequent display.

[0031] S4. When it is necessary to display graphic elements, read compressed data from the vehicle non-volatile memory and perform stroke length encoding decompression on the compressed data to obtain the recovered RLE optimized descriptor.

[0032] When graphic elements need to be displayed, compressed data is read from the vehicle's non-volatile memory, and run-length encoded decompression is performed on the compressed data to obtain the recovered RLE-optimized descriptor. This step is the reverse of the compression process and aims to quickly recover the original RLE-optimized descriptor. For example, the RLE decompression algorithm corresponding to the compression process can be used to read the compressed data from memory and decompress it. Since RLE decompression is typically extremely fast, this makes real-time reconstruction of graphic elements possible.

[0033] S5. Generate high-precision pixel data of graphic elements in real time based on the recovered RLE optimization descriptor, and output the high-precision pixel data to the color screen instrument for display.

[0034] "High-precision pixel data" refers to uncompressed and undistorted raw pixel data that accurately reflects the design intent of graphic elements, and typically has high resolution and color depth.

[0035] Finally, high-precision pixel data of the graphic elements is generated in real time based on the recovered RLE optimization descriptor, and this high-precision pixel data is output to a color screen instrument for display. This is one of the core innovations of this application: generating high-precision pixel data in real time at the display end, rather than decompressing and storing pre-stored low-precision pixel data. For example, based on the instruction set or parameters contained in the recovered RLE optimization descriptor, the color value of each pixel can be calculated and generated in real time using a graphics rendering engine or dedicated hardware accelerator. For example, if the descriptor indicates the generation of a circle, the rendering engine will calculate all the pixels that make up the circle based on the center coordinates and radius. As another example, the graphic outline can be drawn in real time based on the vector data (such as Bézier curve control points) in the recovered RLE optimization descriptor, and then filled and anti-aliased to generate high-precision pixel data. This method ensures that the graphics remain sharp and clear at different resolutions.

[0036] In summary, this application provides a solution for efficiently and with high quality display of complex graphic elements under limited hardware resources by introducing the concepts of design intent parameters and RLE optimization descriptors, and combining them with real-time pixel reconstruction technology. This effectively overcomes the challenges faced by existing RLE compression in processing complex graphics on modern automotive color screen instruments.

[0037] Specifically, the above analysis of the graphic elements of the color screen instrument and the extraction of the design intent parameters of the graphic elements include the following steps: determining the type of the graphic element; when the graphic element is a texture-type graphic element, extracting the texture type identifier, basic color value, texture direction parameter, texture density parameter, illumination reflection parameter, and noise function parameter of the texture-type graphic element, and using the texture type identifier, the basic color value, the texture direction parameter, the texture density parameter, the illumination reflection parameter, and the noise function parameter as the design intent parameters. When the graphic element is a font-type graphic element, extracting the font identifier, font size parameter, font weight parameter, color value, anti-aliasing rule, and Bézier curve control point data of each character in the font-type graphic element, and using the font identifier, the font size parameter, the font weight parameter, the color value, the anti-aliasing rule, and the Bézier curve control point data of each character in the font-type graphic element as the design intent parameters.

[0038] The graphic elements can be of types including, but not limited to, texture-based and font-based graphic elements. The type determination of graphic elements aims to employ targeted parameter extraction strategies based on their inherent generation characteristics. Specifically, when a graphic element is identified as a texture-based graphic element, its design intent parameters are defined as a set of parameters that describe the visual characteristics of the texture. For example, the texture type identifier distinguishes different texture patterns, the base color value sets the texture's tone, the texture direction parameter and texture density parameter jointly determine the texture's arrangement and density, the illumination reflection parameter simulates the effect of light on the texture surface, and the noise function parameter is used to introduce randomness to enhance the texture's realism. These parameters collectively characterize the texture's generation rules. When a graphic element is identified as a font-based graphic element, its design intent parameters focus on the font's structure and display attributes. The font identifier identifies the specific font style, the font size parameter and font weight parameter control the font's size and thickness, the color value specifies the font's display color, the anti-aliasing rule optimizes the smoothness of the font edges, and the Bézier curve control point data for each character is the core of the vector font, precisely defining the character's geometric outline. These parameters collectively characterize the font's generation rules.

[0039] This application's solution first determines the type of graphic element and then extracts corresponding design intent parameters with generational regularity characteristics based on different types. This ensures that the extracted parameters can comprehensively and accurately represent the intrinsic generation mechanism of the graphic element. For example, for texture-type graphic elements, the extracted parameters are directly related to the texture's algorithmic generation process, rather than a simple set of pixels; for font-type graphic elements, the extracted Bézier curve control point data directly defines the font's vector outline. This categorized extraction method allows the subsequently generated RLE optimization descriptor to more accurately instruct the reconstruction of graphic elements, avoiding the problems of inaccurate descriptions or information loss that may result from using a single, general parameter set for different types of graphic elements. This lays a solid foundation for subsequent efficient compression and high-precision real-time reconstruction.

[0040] Through the above technical solution, this application can extract design intent parameters in a targeted manner according to the specific type of graphic element, thereby ensuring that the obtained parameters can more accurately and comprehensively represent the generation rules of different types of graphic elements. This refined parameter extraction method significantly improves the expressive power and reconstruction accuracy of design intent parameters, enabling the subsequent RLE optimization descriptor to more effectively guide the real-time generation of graphic elements, ultimately presenting a higher quality image display effect on the color screen instrument that better matches the original design intent.

[0041] Specifically, in the process of generating RLE optimization descriptors based on design intent parameters, this application proposes a method for structurally packaging design intent parameters.

[0042] The steps for generating an RLE optimization descriptor based on the design intent parameters include: packaging the design intent parameters according to a preset data structure to generate an RLE optimization descriptor containing a texture generation parameter set or a font vector descriptor; wherein the texture generation parameter set corresponds to a texture-type graphic element, and the font vector descriptor corresponds to a font-type graphic element.

[0043] Specifically, the preset data structure can be understood as a predefined data format or template, the purpose of which is to organize and store design intent parameters in a standardized manner. For example, this data structure can be a struct, a class definition, or a specific byte sequence protocol, whose internal fields are predefined to accommodate design intent parameters corresponding to different types of graphic elements. Through this preset data structure, it can be ensured that the design intent parameters of different graphic elements have a consistent format and parsability when packaged.

[0044] The "packaging" refers to the process of extracting design intent parameters from graphic elements and filling them into corresponding fields according to the definition of the preset data structure, thereby forming a complete and structured data unit. For example, for texture-type graphic elements, their texture type identifier, basic color value, texture direction parameter, texture density parameter, illumination reflection parameter, and noise function parameter will be mapped and stored in the corresponding fields of the texture generation parameter set. Similarly, for font-type graphic elements, their font identifier, font size parameter, font weight parameter, color value, anti-aliasing rules, and Bézier curve control point data for each character will be organized into a font vector descriptor.

[0045] The texture generation parameter set is a dataset specifically designed for texture-based graphic elements, containing all the key parameters required for texture reconstruction. These parameters collectively define the visual characteristics and generation algorithm of the texture. The font vector descriptor, on the other hand, is designed for font-based graphic elements, containing all the vector information and rendering rules required for font reconstruction, particularly the Bézier curve control point data, enabling precise description and rendering of the font in vector form.

[0046] The proposed solution generates a structured RLE optimization descriptor by packaging design intent parameters according to a preset data structure. This structured descriptor clearly distinguishes different types of graphic elements and provides a unified and easily parsed storage format for their respective generation parameters. This standardized data organization allows subsequent run-length encoding compression to more efficiently identify and process data patterns. Furthermore, after decompression, the recovered RLE optimization descriptor can be accurately parsed, enabling real-time and high-precision reconstruction of the original graphic elements based on the texture generation parameter set or font vector descriptor contained within, thus ensuring the accuracy and efficiency of image correction.

[0047] Through the above technical solutions, the generation process of RLE optimization descriptors is standardized and structured, enabling the unified and efficient management and processing of design intent parameters for different types of graphic elements. This structured descriptor not only improves the efficiency of data storage and transmission, but more importantly, it provides more regular data input for subsequent run-length encoding compression, thereby optimizing the compression effect. Furthermore, during decompression and real-time generation of high-precision pixel data, the structured descriptor can be parsed quickly and accurately, ensuring the accuracy and real-time performance of graphic element reconstruction, effectively improving the overall performance and display quality of color screen instrument image correction.

[0048] Specifically, this application proposes a specific implementation method for performing run-length encoding compression on the RLE optimized descriptor to obtain compressed data.

[0049] The implementation method includes: identifying consecutively repeating values ​​or data blocks in the RLE optimization descriptor, encoding consecutively repeating values ​​or data blocks into a correspondence between the number of repetitions and the values, so as to generate the compressed data.

[0050] Specifically, "identifying consecutively repeating values ​​or data blocks in the RLE optimization descriptor" refers to scanning the RLE optimization descriptor byte-by-byte or data block-by-data block to detect whether there are consecutive occurrences of the same value or data block. For example, when the RLE optimization descriptor contains a series of identical color values ​​or instruction sequences, this step can accurately locate these repetition patterns.

[0051] "Encoding consecutively repeating values ​​or data blocks as a correspondence between the number of repetitions and the value" means that once a consecutively repeating pattern is identified, instead of storing all the repeating values ​​or data blocks themselves, they are replaced with a pair of information: the number of repetitions and the value or data block itself. For example, if "AAAAA" exists in the RLE optimization descriptor, it can be encoded as "5A", where "5" represents the number of repetitions and "A" represents the repeating value. Thus, the original data is effectively compressed, generating the compressed data.

[0052] This application's solution identifies data redundancy in RLE-optimized descriptors and utilizes run-length encoding to transform continuously repeating data sequences into a more compact representation. This encoding method significantly reduces the amount of data that needs to be stored, and is particularly suitable for RLE-optimized descriptors containing a large number of repeating patterns. By replacing repeated data with combinations of repetition counts and data values, effective compression of the original data is achieved, thereby reducing storage space requirements.

[0053] The above technical solution effectively compresses RLE-optimized descriptors, significantly reducing the space required to store them. This compression mechanism is particularly suitable for environments with limited storage resources, such as automotive non-volatile memory, helping to optimize storage efficiency and laying the foundation for subsequent fast read and decompression operations.

[0054] In some embodiments described above, this application proposes to generate high-precision pixel data of graphic elements in real time based on the recovered RLE optimized descriptor. However, in practical applications, graphic elements are diverse, such as texture-type graphic elements and font-type graphic elements, each with significant differences in their visual characteristics and generation logic. Without providing specific pixel data generation mechanisms to address these differences, the complexity of the generation process may increase, making it difficult to guarantee generation efficiency and the quality of the final displayed image.

[0055] In response, this application further proposes the above-mentioned method of generating high-precision pixel data of the graphic elements in real time based on the recovered RLE optimization descriptor, including: when the recovered RLE optimization descriptor is a texture generation parameter set, calculating the color value of each pixel in the display area in real time according to the basic color value, texture direction parameter, texture density parameter, illumination reflection parameter and noise function parameter in the texture generation parameter set, generating texture pixel data, and using the texture pixel data as the high-precision pixel data; when the recovered RLE optimization descriptor is a font vector descriptor, drawing the vector outline of the character in real time according to the Bézier curve control point data in the font vector descriptor, and performing color interpolation on the character edge pixels according to the anti-aliasing rules in the font vector descriptor to generate smooth font pixel data, and using the font pixel data as the high-precision pixel data.

[0056] Specifically, the recovered RLE optimization descriptor is generated by packaging the design intent parameters of the graphic elements. It can be a texture generation parameter set, used to describe the generation rules of texture-type graphic elements; or a font vector descriptor, used to describe the generation rules of font-type graphic elements. When the recovered RLE optimization descriptor is recognized as a texture generation parameter set, the high-precision pixel data generation process focuses on real-time calculation of the color value of each pixel within the display area. The base color value provides the texture's tone; texture direction parameters and texture density parameters jointly determine the texture's geometric structure and repetition pattern; illumination reflection parameters are used to simulate the reflection effect of light on the texture surface, such as highlights or diffuse reflection, thereby enhancing the texture's three-dimensionality and realism; and noise function parameters can introduce randomness, making the texture exhibit more natural details and irregularities. By comprehensively using these parameters, the final color value of each pixel can be accurately calculated, thus generating high-quality texture pixel data. When the recovered RLE optimization descriptor is recognized as a font vector descriptor, the high-precision pixel data generation process mainly involves the drawing of vector graphics and anti-aliasing processing. The Bézier curve control point data contained in the font vector descriptor is crucial for defining the character shape. These control points allow for the precise drawing of the character's vector outline, ensuring the font remains sharp and smooth at different scaling ratios. Furthermore, to eliminate potential jagged edges on the font, this application employs an anti-aliasing rule. This rule uses color interpolation on the character edge pixels to smoothly transition the character color with the background color, thereby generating visually smoother and more natural font pixel data.

[0057] This application's solution effectively addresses the efficiency and quality issues that traditional methods may encounter when handling diverse graphic elements by determining the type of the recovered RLE-optimized descriptor and employing customized pixel data generation strategies for different descriptor types. Specifically, for texture-type graphic elements, by utilizing the design intent parameters in the texture generation parameter set, the system can programmatically calculate the color value of each pixel in real time, avoiding the need to store a large number of pre-rendered texture images, thus significantly reducing storage overhead and improving generation flexibility. This parameter-based real-time calculation allows textures to dynamically adjust their visual effects as needed, such as changing the lighting direction or texture density, without reloading or generating the entire texture image. For font-type graphic elements, by using the Bézier curve control point data in the font vector descriptor for vector contour drawing and combining anti-aliasing rules for edge pixel color interpolation, this application ensures that the font presents a smooth and clear visual effect at any display resolution. This vectorized processing method avoids the pixelation problem that occurs when traditional bitmap fonts are enlarged, greatly improving the quality and readability of font display. It is precisely because of this meticulous processing that color screen instruments can display high-quality text information and meet users' requirements for visual experience.

[0058] Through the above technical solution, this application can adopt the most suitable pixel data generation mechanism according to the specific type of graphic element, thereby significantly improving the display quality and generation efficiency of color screen instrument images. Specifically, for texture-type graphic elements, dynamic generation and high-precision display of textures are achieved by calculating pixel color values ​​in real time, enabling the instrument interface to present a richer and more realistic visual effect. For font-type graphic elements, vector drawing and anti-aliasing ensure that the font remains smooth and clear at various sizes and resolutions, greatly improving the readability and aesthetics of text information. This targeted pixel data generation method not only optimizes the visual performance of the image, but also reduces the occupation of in-vehicle non-volatile memory by avoiding the storage of a large amount of pre-rendered data, providing the in-vehicle system with more efficient and flexible image processing capabilities.

[0059] In some preferred embodiments, this application is implemented as follows: Suppose a color screen instrument needs to display a background texture with a brushed metal effect. In this case, the recovered RLE optimization descriptor will be identified as a texture generation parameter set. This parameter set may include: a base color value of dark gray (e.g., RGB value [50, 50, 50]), a texture direction parameter set to 45 degrees, a texture density parameter set to a period of 10 pixels, a light reflection parameter set to generate white highlights at a specific angle, and a weak noise function parameter to simulate the subtle irregularities of a metal surface. During display, the system traverses every pixel within the display area. For example, for a pixel with coordinates (x, y), its relative position in the texture period is first calculated based on its location, texture direction, and density. Then, the texture intensity value is calculated based on a preset periodic function (e.g., a sine or cosine function). Next, a small random offset value is generated based on the noise function parameter and superimposed on the base color to obtain an intermediate color value. Finally, based on the light reflection parameters, if the pixel is in a highlight area, the highlight color value is superimposed to obtain the final color value of the pixel. By performing real-time calculations on all pixels, texture pixel data with a realistic brushed metal effect is ultimately generated.

[0060] For example, when a color instrument panel needs to display the vehicle speed "120 km / h", the restored RLE optimized descriptor will be recognized as a font vector descriptor. This descriptor contains the Bézier curve control point data for characters such as "1", "2", "0", " ", "k", "m", " / ", and "h", as well as an anti-aliasing rule (e.g., 256-level grayscale interpolation based on pixel coverage). During display, the system first draws the vector outline of each character in real time based on the Bézier curve control point data. For example, for the number "1", its outline is defined by a series of Bézier curve segments. After drawing, the system identifies the edge pixels covered by the character outline. For these edge pixels, for example, if a pixel is 70% covered by the character outline, the system obtains the background color value (e.g., black) at that pixel location and calculates the mixed color value of the edge pixel by weighting the character color value (e.g., white) and the background color value according to a ratio of 70% character color and 30% background color. For pixels inside the character outline, the character color value (white) is directly assigned. In this way, smooth, clear, and jagged-free font pixel data is ultimately generated, ensuring that speed information is clearly visible on the color screen instrument panel.

[0061] In some embodiments described above in this application, a method is proposed to generate texture pixel data by calculating the color value of a pixel in real time based on a texture generation parameter set. Specifically, for each pixel within the display area, the color value of the pixel is calculated in real time, including the following steps: for each pixel within the display area, the correspondence between the pixel's position and the texture period is determined based on the texture direction parameter and the texture density parameter; the texture intensity value of the pixel is calculated based on a preset periodic function; a random offset value of the pixel is generated based on the noise function parameter, and the texture intensity value and the random offset value are superimposed on the base color value to obtain an intermediate color value; a specular color value is superimposed on the intermediate color value based on the illumination reflection parameter to obtain the final color value of the pixel.

[0062] Specifically, the texture direction parameter and texture density parameter define the spatial arrangement characteristics of the texture, such as the direction of the texture pattern and the density of repetition. These parameters allow for precise determination of the relative position of each pixel within the display area during the texture repetition cycle. The preset periodic function can be understood as a mathematical model that calculates the basic texture intensity value of a pixel based on its position within the texture cycle. For example, a sine function, square wave function, or more complex waveform functions can be used to simulate different texture patterns. The noise function parameter controls the characteristics of the generated random offset values, such as the amplitude, frequency, and type of noise (e.g., Perlin noise, fractal noise). Its purpose is to add natural random variations to the texture, avoiding an overly regular and artificial appearance. The random offset values, generated based on the noise function parameter, are used to fine-tune the base color values, thereby introducing irregularity into the texture and enhancing visual realism. The illumination reflection parameter describes the way the texture surface interacts with light, such as specular reflection, diffuse reflection, and ambient light contribution. Its purpose is to simulate lighting effects, making the texture more three-dimensional.

[0063] This application's solution first establishes the spatial context of each pixel within the display area during the texture period using texture direction and density parameters. Then, the base texture intensity value of that pixel is calculated using a preset periodic function, thus outlining the basic texture pattern. To enhance the realism and naturalness of the texture, a noise function parameter is introduced to generate random offset values, which are then superimposed on the base color value along with the texture intensity value, resulting in an intermediate color value that includes the texture pattern and random variations. Finally, based on the illumination reflection parameters, a specular color value is superimposed on this intermediate color value to simulate the reflection effect under illumination, thereby obtaining the final color value of the pixel. This series of steps ensures a precise conversion from abstract texture parameters to specific pixel color values, resulting in a highly detailed and realistic texture image.

[0064] Through the above technical solution, this application can achieve high-precision real-time calculation of pixel color values ​​for textured graphic elements. By carefully considering various factors such as texture direction, density, periodicity, random noise, and light reflection, the generated texture pixel data can not only accurately reproduce the design intent, but also present a stronger sense of realism, layering, and dynamic effects visually. This significantly improves the visual quality and user experience of textured images on color screen instruments, making graphic elements look more natural and vivid, and avoiding the texture distortion or lack of detail problems that may occur with traditional methods.

[0065] In some embodiments described above in this application, a method for color interpolation of character edge pixels based on anti-aliasing rules in a font vector descriptor is proposed. Specifically, the steps for color interpolation of character edge pixels based on anti-aliasing rules in a font vector descriptor include: for each edge pixel covered by the character outline, obtaining the background color value corresponding to the location of the edge pixel, wherein the background color value is read from a generated display buffer or obtained from background pixel data output by a graphics reconstruction unit; performing a weighted average calculation based on the area ratio of the edge pixel covered by the character outline, the character color value in the font vector descriptor, and the background color value to obtain a mixed color value for the edge pixel; and assigning the character color value to each pixel inside the character outline.

[0066] Specifically, during color interpolation, the first step is to identify the edge pixels covered by the character outline. For these edge pixels, their color values ​​are not simply set to the character color or the background color, but rather need to be blended. For this, the background color value at the location of each edge pixel needs to be obtained. This background color value can be read directly from the currently generated display buffer or obtained from the background pixel data output by the graphics reconstruction unit to ensure the accuracy of the background color.

[0067] Furthermore, once the background color value of the edge pixel is obtained, a weighted average can be calculated based on the proportion of the edge pixel covered by the character outline, the preset character color value in the font vector descriptor, and the obtained background color value. This proportion reflects the degree of coverage of the character outline on the pixel. For example, if a pixel is 50% covered by the character outline, its blend color value will be a weighted average of the character color and the background color, each accounting for 50%. In this way, the blend color value of the edge pixel can be obtained.

[0068] Furthermore, for each pixel inside the character outline, since it is completely covered by the character outline, its color value is directly assigned to the character color value defined in the font vector descriptor to ensure the clear display of the main body of the character.

[0069] This application's solution effectively solves the jagged edge problem that may occur in rasterized display of vector fonts by performing fine-grained color interpolation on the edge pixels of characters. Specifically, by obtaining the background color value of the edge pixels and performing a weighted average calculation based on the area covered by the character outline, the color of the character edge can smoothly transition to the background color, rather than simply presenting a hard, binarized edge. This gradient effect simulates the visual perception of the human eye when observing fine lines, thereby eliminating the visual jaggedness. At the same time, pixels inside the character outline are directly assigned the character color, ensuring the color consistency and clarity of the main body of the character.

[0070] The above technical solution enables smoothing of the edges of font-type graphic elements, significantly improving the visual quality of font display on color screen instruments. Specifically, by accurately calculating the mixed color values ​​of edge pixels, the character edges no longer appear rough and jagged, but instead exhibit a natural and smooth transition effect, thereby improving the readability and aesthetics of the characters. This refined anti-aliasing treatment is particularly effective for small font sizes or italic characters, providing users with a more comfortable and clear visual experience.

[0071] In some embodiments described above, a color screen instrument image correction method based on the RLE decompression algorithm is proposed. This method analyzes graphic elements, generates RLE optimized descriptors, compresses and stores them, and then decompresses and generates high-precision pixel data in real time when needed for display. However, for common dynamic graphic elements in color screen instruments, such as real-time changing instrument pointers, animation effects, or scrolling text, if pixel data is generated only once or infrequently using the processing method for static graphics, the displayed content may not be updated in a timely manner, resulting in screen stuttering or inaccurate display, thus affecting user experience and the real-time nature of information transmission.

[0072] In response, this application further proposes an optimization scheme: when the graphic element is displayed dynamically on the color screen instrument, the steps from reading the compressed data to generating the high-precision pixel data are re-executed in each display refresh cycle, so as to reconstruct the graphic element in real time in each frame of display.

[0073] Specifically, dynamic display refers to the continuous or periodic changes in the content, position, color, or shape of graphic elements over time. Examples include the real-time swing of a vehicle speedometer needle, the dynamic scale changes of an engine tachometer, real-time updates of navigation maps, and flashing animations of warning messages. The display refresh cycle refers to the time interval between hardware updates to the screen content of a color instrument cluster, typically measured in frame rates (e.g., 30Hz, 60Hz), with each cycle corresponding to the display of one frame. Re-executing the steps from reading the compressed data to generating the high-precision pixel data means that at the beginning of each display refresh cycle, the system again retrieves the compressed data required for the current frame from the vehicle's non-volatile memory, and after decompression and high-precision pixel data generation, ensures that the displayed content of the current frame is up-to-date and accurate. Real-time reconstruction of the graphic elements emphasizes the immediacy of this process, meaning that the complete rendering of the graphic elements is completed within each display refresh cycle to match dynamically changing display requirements.

[0074] This application's solution effectively solves the aforementioned problem of untimely or inaccurate display of dynamic graphic elements by re-executing the steps from reading compressed data to generating high-precision pixel data within each display refresh cycle. Specifically, when graphic elements are displayed dynamically, their design parameters or their representation within the display area may change at different points in time. By re-reading and decompressing the latest compressed data within each display refresh cycle, it ensures that an RLE (Recovery Optimization) descriptor corresponding to the current time or state is obtained. Subsequently, high-precision pixel data is generated in real time based on this latest recovered RLE descriptor, enabling the color screen instrument to present the latest state and precise details of the graphic elements in every frame. This mechanism guarantees smooth, real-time updates of dynamic graphic elements, avoiding display delays or errors caused by outdated data or insufficient rendering frequency.

[0075] Through the above technical solution, this application ensures that dynamic graphic elements on the color instrument panel are displayed in real time with high precision. Compared to the traditional method of updating only once or infrequently when graphic elements change, this application reconstructs the display within each refresh cycle, greatly improving the smoothness and accuracy of dynamic graphic display and eliminating problems such as screen stuttering, delays, or discrepancies between the displayed content and the actual state. This is particularly important for in-vehicle instrument systems that require real-time feedback, significantly improving the timeliness and reliability of information obtained by the driver, thereby enhancing driving safety and user experience.

[0076] In some preferred embodiments, it is assumed that the color instrument panel needs to display a real-time changing digital speedometer. During vehicle operation, speed data is continuously updated. At each display refresh cycle (e.g., 60 frames per second), the onboard system re-determines the design intent parameters (e.g., font identifiers, font size parameters, font weight parameters, color values, anti-aliasing rules, and Bézier curve control point data for each character) of the digital speedometer graphic elements based on the current speed data. These design intent parameters are then used to generate an RLE-optimized descriptor, which is then compressed using stroke-length encoding to obtain compressed data. Next, the system reads this compressed data from the onboard non-volatile memory and decompresses it to obtain the recovered RLE-optimized descriptor. Finally, based on the recovered RLE-optimized descriptor, high-precision pixel data for the digital speedometer is generated in real time and output to the color instrument panel for display. By repeating this process at each display refresh cycle, even with rapid changes in vehicle speed, the digital speedometer can be updated smoothly and accurately in real time, providing the driver with immediate and reliable speed information.

[0077] Specifically, in some of the above embodiments, when the recovered RLE optimization descriptor is a font vector descriptor, it is necessary to draw the vector outline of the character in real time based on the Bézier curve control point data in the font vector descriptor. To this end, this application further proposes a step for drawing the vector outline of the character in real time based on the Bézier curve control point data in the font vector descriptor, including: for the Bézier curve control point data of each character in the font vector descriptor, according to the curve order and control point coordinates defined by the Bézier curve control point data, using a preset curve interpolation method, calculating outline interpolation points between adjacent control points, so that the outline interpolation points form a polyline approximating the shape of the Bézier curve; connecting adjacent outline interpolation points in the polyline sequentially to form a closed character vector outline; mapping the character vector outline to the pixel coordinate system of the display area, determining the set of pixels covered by the character vector outline and the boundary position of each pixel covered by the outline.

[0078] Specifically, the Bézier curve control point data is a set of mathematical parameters used to define the character shape. It includes the coordinates of the control points required to describe the curve shape and the order information of the curve. For example, for a quadratic Bézier curve, three control points are usually needed (start point, end point, and one control point); for a cubic Bézier curve, four control points are needed (start point, end point, and two control points). The curve order refers to the mathematical order of the Bézier curve, which determines the complexity and smoothness of the curve. The control point coordinates are the positional information of the key points defining the shape of the Bézier curve on a two-dimensional plane. The preset curve interpolation method can be understood as a mathematical algorithm used to calculate the coordinates of any point on the curve given the control points. For example, the De Casteljau algorithm or the direct calculation method of the parametric equations of the Bézier curve can be used. Its purpose is to transform the abstract definition of the Bézier curve into a series of discrete contour interpolation points that can be used for drawing. In practical applications, the contour interpolation points are a series of discrete points calculated using curve interpolation methods. These points lie on the Bézier curve and are densely packed enough that connecting them forms a polygonal line that approximates the shape of the original Bézier curve. The polygonal line is a sequence of line segments formed by connecting these contour interpolation points sequentially, with the aim of approximating a smooth curve using a finite number of straight line segments.

[0079] Furthermore, by sequentially connecting adjacent contour interpolation points in the polyline, a closed character vector contour is formed. This means that by connecting all calculated interpolation points, the outer boundary of the character can be completely outlined. Additionally, mapping the character vector contour to the pixel coordinate system of the display area means converting the abstract vector representation of the character into a specific pixel position on the screen. This typically involves coordinate transformations and scaling operations to ensure that the character is displayed at the correct size and position on the color screen. Thus, the set of pixels covered by the character vector contour can be determined, i.e., the screen pixel area occupied by the character, and the boundary position of each pixel covered by the contour. This is crucial for subsequent anti-aliasing processing because the color interpolation of edge pixels requires precise knowledge of the proportion covered by the character.

[0080] This application's solution transforms the Bézier curve control point data of a character into a series of discrete contour interpolation points, further forming a closed character vector contour, thereby achieving a precise digital representation of the character shape. It is precisely because of the use of a pre-defined curve interpolation method that a sufficient number of interpolation points can be generated between adjacent control points according to the mathematical definition of the Bézier curve, allowing the polygonal line formed by these interpolation points to highly approximate the smooth shape of the original Bézier curve. Subsequently, by mapping this vector contour to the pixel coordinate system of the display area, each pixel covered by the character and its boundary relationship with the contour can be accurately identified. This provides precise basic data for subsequent pixel-level rendering and anti-aliasing processing, ensuring high precision and visual quality in character display.

[0081] The above technical solution enables precise and efficient drawing of vector outlines for font-type graphic elements. Specifically, by decomposing Bézier curves into a series of approximate polyline segments, the overhead of complex curve calculations at the pixel level is avoided, while maintaining the smoothness of vector graphics. This method not only ensures that characters maintain a clear, anti-aliasing visual effect at different scaling ratios, but also provides accurate input for subsequent anti-aliasing processing by precisely determining the boundary relationship between each pixel and the character outline, thereby significantly improving the visual quality of font display and user experience on color screen instruments.

[0082] In some embodiments, this application proposes a color screen instrument image correction system based on the RLE decompression algorithm, comprising: an extraction unit for analyzing graphic elements of the color screen instrument and extracting design intent parameters of the graphic elements, the design intent parameters being used to characterize the generation rules of the graphic elements; a generation unit for generating an RLE optimization descriptor based on the design intent parameters, the RLE optimization descriptor being used to characterize the instruction set required to reconstruct the graphic elements; an encoding unit for performing stroke-length encoding compression on the RLE optimization descriptor to obtain compressed data, and storing the compressed data in an on-board non-volatile memory; a reading unit for reading the compressed data from the on-board non-volatile memory when the graphic elements need to be displayed, and performing stroke-length encoding decompression on the compressed data to obtain a recovered RLE optimization descriptor; and a processing unit for generating high-precision pixel data of the graphic elements in real time based on the recovered RLE optimization descriptor, and outputting the high-precision pixel data to the color screen instrument for display.

[0083] This system aims to address the inefficiency and novel artifacts introduced by traditional stroke-length encoding (RLE) compression when processing complex graphics in modern automotive color instrument panels. By elevating the processing of graphic elements from the pixel level to the design intent level, the system first uses an extraction unit to analyze the graphic elements and obtain design intent parameters that govern their generation patterns. Subsequently, a generation unit constructs an RLE-optimized descriptor based on these parameters, which encapsulates the instruction set required for graphic reconstruction in a structured manner. An encoding unit performs efficient RLE compression on the descriptor and stores the compressed data in the vehicle's non-volatile memory. When a display request arises, a reading unit quickly retrieves and decompresses the data from memory to restore the RLE-optimized descriptor. Finally, a processing unit generates high-precision pixel data in real time based on the restored descriptor and outputs it to the color instrument panel for display. This collaborative mechanism ensures high-quality, artifact-free image display for automotive color instrument panels with limited hardware resources, significantly improving visual effects and user experience.

[0084] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for correcting color screen instrument images based on the RLE decompression algorithm, characterized in that, The method includes: The graphic elements of the color screen instrument are analyzed, and the design intent parameters of the graphic elements are extracted. The design intent parameters are used to characterize the generation rules of the graphic elements. Based on the design intent parameters, an RLE optimization descriptor is generated, which is used to characterize the instruction set required to reconstruct the graphical element; Run-length encoding compression is performed on the RLE optimized descriptor to obtain compressed data, and the compressed data is stored in the vehicle non-volatile memory; When the graphic element needs to be displayed, the compressed data is read from the on-board non-volatile memory, and the compressed data is decompressed by stroke length encoding to obtain the recovered RLE optimized descriptor; Based on the recovered RLE optimization descriptor, high-precision pixel data of the graphic elements is generated in real time, and the high-precision pixel data is output to the color screen instrument for display.

2. The method according to claim 1, characterized in that, The analysis of the graphic elements of the color screen instrument and the extraction of the design intent parameters of the graphic elements include: Determine the type of the graphic element; When the graphic element is a textured graphic element, the texture type identifier, basic color value, texture direction parameter, texture density parameter, illumination reflection parameter, and noise function parameter of the textured graphic element are extracted, and the texture type identifier, the basic color value, the texture direction parameter, the texture density parameter, the illumination reflection parameter, and the noise function parameter are used as the design intent parameter; When the graphic element is a font-type graphic element, the font identifier, font size parameter, font weight parameter, color value, anti-aliasing rule, and Bézier curve control point data of each character in the font-type graphic element are extracted, and the font identifier, font size parameter, font weight parameter, color value, anti-aliasing rule, and Bézier curve control point data of each character in the font-type graphic element are used as the design intent parameter.

3. The method according to claim 2, characterized in that, The step of generating an RLE optimization descriptor based on the design intent parameters includes: The design intent parameters are packaged according to a preset data structure to generate an RLE optimization descriptor containing a texture generation parameter set or a font vector descriptor; wherein, the texture generation parameter set corresponds to a texture-type graphic element, and the font vector descriptor corresponds to a font-type graphic element.

4. The method according to claim 1, characterized in that, The process of performing run-length encoding compression on the RLE optimized descriptor to obtain compressed data includes: Identify consecutively repeating values ​​or data blocks in the RLE optimization descriptor, and encode the consecutively repeating values ​​or data blocks into a correspondence between the number of repetitions and the values ​​to generate the compressed data.

5. The method according to claim 3, characterized in that, The step of generating high-precision pixel data of the graphic elements in real time based on the recovered RLE optimized descriptor includes: When the recovered RLE optimization descriptor is a texture generation parameter set, based on the basic color value, texture direction parameter, texture density parameter, illumination reflection parameter and noise function parameter in the texture generation parameter set, the color value of each pixel in the display area is calculated in real time to generate texture pixel data, and the texture pixel data is used as the high-precision pixel data. When the recovered RLE optimization descriptor is a font vector descriptor, the vector outline of the character is drawn in real time according to the Bézier curve control point data in the font vector descriptor, and the character edge pixels are color interpolated according to the anti-aliasing rules in the font vector descriptor to generate smooth font pixel data, and the font pixel data is used as the high-precision pixel data.

6. The method according to claim 5, characterized in that, The process of calculating the color value of each pixel within the display area in real time includes: For each pixel within the display area, the correspondence between the pixel's position and the texture period is determined based on the texture direction parameter and the texture density parameter. The texture intensity value of the pixel is calculated according to a preset periodic function; Based on the noise function parameters, a random offset value for the pixel is generated, and the texture intensity value and the random offset value are superimposed on the base color value to obtain an intermediate color value; Based on the light reflection parameters, the intermediate color value is superimposed with the highlight color value to obtain the final color value of the pixel.

7. The method according to claim 5, characterized in that, The step of color interpolating character edge pixels according to the anti-aliasing rules in the font vector descriptor includes: For each edge pixel covered by the character outline, obtain the background color value corresponding to the position of the edge pixel. The background color value is read from the generated display buffer or obtained from the background pixel data output by the graphics reconstruction unit. The mixed color value of the edge pixel is obtained by performing a weighted average calculation based on the proportion of the area covered by the character outline of the edge pixel, the character color value in the font vector descriptor and the background color value. For each pixel inside the character outline, assign it the character color value.

8. The method according to claim 1, characterized in that, When the graphic element is displayed dynamically on the color screen instrument, the steps from reading the compressed data to generating the high-precision pixel data are re-executed in each display refresh cycle to reconstruct the graphic element in real time in each frame of display.

9. The method according to claim 5, characterized in that, The step of drawing the vector outline of the character in real time based on the Bézier curve control point data in the font vector descriptor includes: For the Bézier curve control point data of each character in the font vector descriptor, according to the curve order and control point coordinates defined by the Bézier curve control point data, a preset curve interpolation method is used to calculate the contour interpolation points between adjacent control points, so that the contour interpolation points form a polyline that approximates the shape of the Bézier curve. Connect the adjacent contour interpolation points in the polyline in sequence to form a closed character vector contour; The character vector outline is mapped to the pixel coordinate system of the display area to determine the set of pixels covered by the character vector outline and the boundary position of each pixel covered by the outline.

10. A color screen instrument image correction system based on RLE decompression algorithm, characterized in that, include: The extraction unit is used to analyze the graphic elements of the color screen instrument and extract the design intent parameters of the graphic elements. The design intent parameters are used to characterize the generation rules of the graphic elements. The generation unit is used to generate an RLE optimization descriptor based on the design intent parameters. The RLE optimization descriptor is used to characterize the instruction set required to reconstruct the graphic element. The encoding unit is used to perform run-length encoding compression on the RLE optimized descriptor to obtain compressed data, and store the compressed data in the vehicle non-volatile memory; The reading unit is used to read the compressed data from the on-board non-volatile memory when the graphic element needs to be displayed, and to perform stroke length encoding decompression on the compressed data to obtain the recovered RLE optimized descriptor; The processing unit is used to generate high-precision pixel data of the graphic elements in real time based on the recovered RLE optimization descriptor, and output the high-precision pixel data to the color screen instrument for display.

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