Image rendering enhancement method, system and equipment and storage medium

Abstract

The invention relates to an image rendering enhancement method, system and device and a storage medium, and the method comprises the following steps: carrying out the pixel information extraction of a game real-time scene graph, generating a scene pixel data set, and carrying out the RGB channel separation processing to obtain a pixel channel separation graph; recognizing a dynamic area based on the image to form a scene dynamic area image, calculating the motion trails of pixels in the image, and establishing an area motion trail table; adaptive special effect types are screened according to the table, an adaptive special effect type table is created, the size of the selected special effect material is adjusted to be matched with the dynamic area, and a size-adaptive special effect graph is obtained. And carrying out pixel fusion and superposition on the size-adaptive special effect graph and the scene dynamic region graph to generate a special effect superposition scene graph, and finally carrying out color consistency calibration on the graph, thereby solving the technical effects of improper special effect occurrence opportunity and form distortion caused by difficulty in accurately identifying a motion region and matching visual feedback conforming to physical logic in a traditional method.

Description

Technical Field

[0001] This invention relates to the field of image technology, and in particular to an image rendering enhancement method, system, device, and storage medium. Background Technology

[0002] With the rapid development of computer graphics and real-time rendering technology, modern video games are increasingly demanding higher levels of visual expressiveness. Players are no longer satisfied with basic graphics presentation but are pursuing a more immersive and dynamic gaming experience. Enhanced image rendering, as a core element in improving visual quality, is widely used in scenarios such as lighting effects, particle effects, and character motion feedback.

[0003] Current technologies for processing real-time game scenes typically treat effect overlay as a post-processing step independent of scene rendering, neglecting in-depth analysis and utilization of dynamic pixel behavior within the scene. For example, when a character moves rapidly or collides, the system struggles to accurately identify the movement area and match physically logical visual feedback, leading to issues such as inappropriate timing, positional misalignment, or shape distortion of effects. Furthermore, due to the lack of refined processing of RGB channel-level pixel information, existing methods often exhibit flaws such as tone mismatch and edge halos during the color blending stage, severely impacting the realism and artistic expression of the final image. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes an enhanced image rendering method, comprising the following steps: Pixel information is extracted from the real-time game scene image to obtain a scene pixel dataset, and the scene pixel dataset is subjected to RGB channel separation processing to obtain a pixel channel separation image; Based on the pixel channel separation map, dynamic region identification is performed on the real-time game scene map to obtain a scene dynamic region map, and pixel motion trajectory is calculated on the scene dynamic region map to obtain a region motion trajectory table. Based on the aforementioned regional motion trajectory table, special effects matching and filtering are performed on the scene dynamic region map to obtain an adaptive special effects type table. Then, the pixel size of the special effects materials in the adaptive special effects type table is adjusted to obtain a size-adapted special effects map. Based on the size-adapted effect map, the scene dynamic area map is pixel-blended and superimposed to obtain the effect superimposed scene map, and the color consistency of the effect superimposed scene map is calibrated to obtain the target rendering effect map.

[0005] Furthermore, the step of extracting pixel information from the real-time game scene image to obtain a scene pixel dataset includes: The pixel coordinates of the real-time game scene are traversed to obtain a pixel coordinate distribution map, and the pixel coordinate distribution map is validated to obtain a valid coordinate point map. Based on the effective coordinate point map, the pixel grayscale values ​​of the real-time game scene map are read to obtain a pixel grayscale value table, and the grayscale value table is filtered by grayscale range to obtain an effective grayscale value map. Based on the effective coordinate point map and the effective grayscale value map, data association and matching are performed to obtain the pixel coordinate grayscale set, and the data format of the pixel coordinate grayscale set is standardized to obtain the scene pixel dataset.

[0006] Furthermore, the step of performing RGB channel separation processing on the scene pixel dataset to obtain a pixel channel separation map includes: The scene pixel dataset is split into color channels to obtain an RGB mixed channel image, and the RGB mixed channel image is then stripped into single channels to obtain the original R channel image, the original G channel image, and the original B channel image. Pixel detail enhancement is performed on the original R-channel image, original G-channel image, and original B-channel image respectively to obtain a channel enhancement image, and edge sharpening processing is performed on the channel enhancement image to obtain a sharpened channel image; Based on the sharpened channel map, pixel coordinates are aligned to obtain a coordinate synchronization channel group, and channel color annotation is performed on the coordinate synchronization channel group to obtain a pixel channel separation map.

[0007] Furthermore, the step of performing dynamic region identification on the real-time game scene map based on the pixel channel separation map to obtain a dynamic scene region map includes: The pixel channel separation map is compared with the real-time game scene map in two channels to obtain a channel difference map, and the pixel change amount is calculated on the channel difference map to obtain a pixel change value map. Threshold filtering is performed based on the pixel variation value map to obtain a high variation pixel map, and neighborhood aggregation is performed on the high variation pixel map to obtain a pixel connected component map. Based on the pixel connected component map, the region boundary is delineated to obtain a dynamic region contour map, and the dynamic region contour map is marked to obtain a scene dynamic region map.

[0008] Furthermore, the step of calculating pixel motion trajectories on the dynamic region map of the scene to obtain a region motion trajectory table includes: Feature pixels are sampled for each dynamic region in the scene dynamic region map to obtain a region sampling point map, and the sampling points in the region sampling point map are marked with coordinates to obtain a sampling point coordinate table; Based on the sampling point coordinate table, the sampling point positions of two consecutive frames of the game real-time scene map are compared to obtain a sampling point displacement map. The displacement direction and distance of the sampling point displacement map are then calculated to obtain the sampling point motion data. Based on the motion data of the sampling points, the motion trends of all sampling points within the same dynamic area are aggregated to obtain a regional motion trend map. The trajectory coordinate sequence of the regional motion trend map is then organized to obtain a regional motion trajectory table.

[0009] Furthermore, the process of performing special effects matching and filtering on the scene dynamic area map based on the area motion trajectory table to obtain an adaptive special effects type table includes: Motion features are extracted from the region motion trajectory table to obtain a region motion feature map, and the region size is measured from the scene dynamic region map to obtain a region size parameter table; Based on the region motion feature map and the region size parameter table, a matching condition set is constructed, and the matching condition set is compared with a preset special effects rule library to obtain a special effects candidate list; Based on the special effects candidate list, the special effects priority of each dynamic area is sorted to obtain a special effects sorting result image. The type information of the special effects sorting result image is then organized to obtain an adaptive special effects type table.

[0010] Furthermore, the step of pixel-by-pixel fusion and overlay of the scene dynamic region map based on the size-adapted effect map to obtain an effect overlay scene map includes: The pixel coordinates of the size adaptation effect map and the scene dynamic area map are matched to obtain a coordinate alignment map, and the area edge alignment of the coordinate alignment map is checked to obtain an edge alignment result map. Based on the edge alignment result map, the special effect pixels and dynamic region pixels are weighted to obtain a pixel weight table, and the pixel weight table is visualized and labeled to obtain a weight label map. The RGB values ​​of the corresponding pixels in the weighted annotation map are weighted to obtain a fused pixel map, and the fused pixel map is stitched together to obtain a special effects overlay scene map.

[0011] The present invention also provides an image rendering enhancement system, comprising: The extraction module is used to extract pixel information from the real-time game scene image to obtain a scene pixel dataset, and to perform RGB channel separation processing on the scene pixel dataset to obtain a pixel channel separation image. The recognition module is used to perform dynamic region recognition on the real-time game scene map based on the pixel channel separation map to obtain a scene dynamic region map, and to calculate the pixel motion trajectory of the scene dynamic region map to obtain a region motion trajectory table. The filtering module is used to perform special effects matching and filtering on the scene dynamic area map based on the area motion trajectory table, to obtain an adaptive special effects type table, and to adjust the pixel size of the special effects materials in the adaptive special effects type table to obtain a size-adapted special effects map. The overlay module is used to perform pixel fusion and overlay on the dynamic area map of the scene based on the size-adapted effect map to obtain an effect overlay scene map, and to perform color consistency calibration on the effect overlay scene map to obtain the target rendering effect map.

[0012] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0014] This invention provides an image rendering enhancement method, comprising the following steps: extracting pixel information from a real-time game scene image to obtain a scene pixel dataset, and performing RGB channel separation processing on the scene pixel dataset to obtain a pixel channel separation image; performing dynamic region recognition on the real-time game scene image based on the pixel channel separation image to obtain a scene dynamic region image, and calculating pixel motion trajectories on the scene dynamic region image to obtain a region motion trajectory table; performing special effects matching and filtering on the scene dynamic region image based on the region motion trajectory table to obtain an adapted special effects type table, and adjusting the pixel size of the special effects materials in the adapted special effects type table to obtain a size-adapted special effects image; and adjusting the pixel size of the special effects materials in the size-adapted special effects type table to obtain a size-adapted special effects image; and performing special effects matching and filtering on the scene dynamic region image based on ... based on the size-adapted special effects type table to obtain a size-adapted special effects image based on the size-adapted special effects image. The method involves pixel-by-pixel fusion and overlay of a dynamic area map of a scene with special effects to obtain a superimposed scene map with special effects. Color consistency calibration is then applied to this superimposed scene map to obtain the target rendering effect. This solves the problem of traditional methods failing to accurately identify moving areas and match visual feedback consistent with physical logic, leading to inappropriate timing and distorted forms of special effects. The method achieves this by introducing a color consistency calibration mechanism after pixel-by-pixel fusion and overlay of a size-adapted special effects map with the original scene. This mechanism automatically adjusts the hue, brightness, and saturation of the overlaid area, eliminating fusion gaps caused by differences in light sources or inconsistent color spaces. This allows the special effects to naturally blend into the original scene, enhancing the overall visual coherence and artistic expression of the image. Attached Figure Description

[0015] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart of the steps of the image rendering enhancement method in an embodiment of the present invention; Figure 2 This is a structural block diagram of the image rendering enhancement system in an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0016] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0018] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0019] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0020] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0021] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0022] Reference Figure 1 This invention provides an image rendering enhancement method, comprising the following steps: Step S1: Extract pixel information from the real-time game scene image to obtain a scene pixel dataset, and perform RGB channel separation processing on the scene pixel dataset to obtain a pixel channel separation image.

[0023] Specifically, the process involves extracting pixel information from the real-time game scene image to obtain a scene pixel dataset, and then performing RGB channel separation processing on this dataset to obtain a pixel channel-separated image. This process first requires acquiring the real-time scene image of the current game frame as input. This image contains information about all visible elements in the game world. Next, by traversing each pixel in the image, its color value is read. These color values ​​consist of three components: red (R), green (G), and blue (B). Each component typically occupies 8 bits, so the color information of each pixel can be stored numerically, forming a dataset containing the color information of all pixels—the scene pixel dataset. After obtaining the scene pixel dataset, to further analyze the impact of each color component on the entire image and to provide a basis for subsequent steps, an RGB channel separation operation needs to be performed on each pixel in this dataset. This means that the color value of each pixel in the dataset is split into independent red, green, and blue components. For example, if the color value of a pixel is (255, 128, 64), then after RGB channel separation, the red component 255, the green component 128, and the blue component 64 will be obtained respectively. In this way, the previously mixed color information is separated, generating a new image version—a pixel channel separation map. In this map, each original color component is presented separately, facilitating subsequent processing tasks such as dynamic region recognition. For example, in a role-playing game scenario, a player is exploring a forest. The game engine captures a frame containing trees, grass, and sky. This frame contains rich color information to depict the realism of the natural environment. When applying the above method, color information is first extracted pixel by pixel from this frame to build a scene pixel dataset. Then, based on this dataset, the color of each pixel is separated according to the three primary colors of RGB. This clearly shows which parts are mainly composed of red and which are dominated by green or blue. For example, the leaves in a forest mostly show varying shades of green. After RGB channel separation, the green component map helps the system more accurately identify the position and outline of the leaves. This is crucial for subsequent recognition of dynamic regions (such as the effect of wind blowing leaves), because different color components react differently to changes in lighting and object movement. Focusing on specific color components helps improve recognition accuracy. This approach not only enhances the understanding of game scenarios but also lays the foundation for adding more realistic visual effects.

[0024] Step S2: Based on the pixel channel separation map, perform dynamic region recognition on the real-time game scene map to obtain a scene dynamic region map, and calculate the pixel motion trajectory on the scene dynamic region map to obtain a region motion trajectory table.

[0025] Specifically, the process of performing dynamic region identification on the real-time game scene map based on the pixel channel separation map to obtain a scene dynamic region map, and then calculating pixel motion trajectories on the scene dynamic region map to obtain a region motion trajectory table, first requires using the previously obtained pixel channel separation map as input. Since each RGB component is processed separately, the changes in each element in the image can be analyzed more accurately. Specifically, the difference in specific color channels (e.g., the green channel, taking a forest scene as an example) between consecutive frames is used to detect changing regions. This is because dynamic objects change their positions between different frames, causing changes in the color information of the region. Once these changing regions are identified, they can be marked to form the scene dynamic region map. Next, to obtain the region motion trajectory table, the motion trajectory of each pixel or pixel cluster in the obtained scene dynamic region map needs to be calculated. This means tracking the position movement path of each pixel marked as dynamic from one frame to the next. This process involves comparing the changes in pixel coordinates on the dynamic region map between consecutive frames. For each pixel marked as belonging to a dynamic region, its position sequence in consecutive frames is recorded, thus depicting the movement route of this pixel. For example, in role-playing games, when the wind blows through a forest, the leaves sway. By dynamically identifying the main green areas of the leaves and calculating the motion trajectories of pixels within these areas, a motion trajectory table representing the path of the swaying leaves can be constructed. Throughout this process, by meticulously observing and analyzing the subtle differences between each frame, especially focusing on the richly colored and varied parts, such as the green leaves in a forest scene, not only can the presence and movement of dynamic elements be accurately captured, but their movement patterns can also be precisely recorded. This provides valuable data support for subsequent game optimization, such as improving physics engine algorithms, enhancing animation smoothness, or increasing realism, allowing players to experience a more realistic and natural environmental interaction when exploring this virtual forest. At the same time, this technology also helps develop more intelligent game AI, enabling non-player characters to react more reasonably to changes in their surroundings.

[0026] Step S3: Based on the area motion trajectory table, perform special effects matching and filtering on the scene dynamic area map to obtain an adaptive special effects type table, and adjust the pixel size of the special effects materials in the adaptive special effects type table to obtain a size-adapted special effects map.

[0027] Specifically, the process of matching and filtering special effects on the dynamic area map of the scene based on the aforementioned area motion trajectory table to obtain an adaptive special effect type table, and then adjusting the pixel size of the special effect materials in the adaptive special effect type table to obtain a size-adapted special effect map, first requires using the previously generated area motion trajectory table as input data. This table records information such as the motion path, direction, speed change, and coverage of each pixel or pixel region in the dynamic area map of the scene. By analyzing the motion feature parameters in the area motion trajectory table, such as trajectory length representing the movement distance, trajectory density reflecting the intensity of the action, and trajectory direction consistency determining the motion type, these numerical features can be compared with a preset special effect feature database. This database stores the applicable conditions of various special effect materials, such as "gentle breeze" corresponding to a low-speed, small-amplitude swaying trajectory, and "violent collision" corresponding to a high-speed, short-term concentrated displacement trajectory. When the motion feature of a certain dynamic area matches the trigger condition of a certain special effect in the database, the identifier of that special effect is recorded in the adaptive special effect type table, thereby completing the special effect matching and filtering of the dynamic area map of the scene. Subsequently, to ensure that the selected effects accurately match the visual scale of the actual dynamic area, pixel size adjustment is performed on each effect material listed in the adaptive effect type table. This operation scales or crops the image resolution of the original effect material based on the actual pixel dimensions of the corresponding area in the scene's dynamic area map, including width, height, and motion coverage. For example, bilinear interpolation is used to enlarge or reduce the effect texture, ensuring it matches the target area at the pixel level. This avoids issues such as effects being too large to cover irrelevant areas or too small to be effective. For example, in a forest scene of a role-playing game, when the wind blows and leaves sway slightly, the area motion trajectory table shows that the trajectory is short, slow, and dispersed. Based on this, the system filters out "breezy particles" or "gentle swaying" effects from the database and writes them into the adaptation effect type table. Then, according to the actual pixel size of the leaf area, the "breezy particle" effect material is adjusted from the original 64×64 pixels to 48×48 pixels to match the current dynamic area of ​​the leaves. Finally, a size-adapted effect image is generated, so that the particle effect is accurately superimposed around the swaying leaves, neither overflowing nor missing, maintaining visual harmony and realism. The entire process achieves precise application and spatial adaptation of effects without changing the original scene structure.

[0028] Step S4: Based on the size adaptation effect map, pixel fusion and overlay are performed on the scene dynamic area map to obtain the effect overlay scene map, and color consistency calibration is performed on the effect overlay scene map to obtain the target rendering effect map.

[0029] Specifically, the process of pixel-wise fusion and overlay of the scene dynamic area map based on the size-adapted effect map to obtain an effect overlay scene map, and then performing color consistency calibration on the effect overlay scene map to obtain the target rendering effect map, first requires precise pixel-level fusion of the previously obtained size-adapted effect map and the scene dynamic area map. During this operation, the new value of each pixel is calculated based on the transparency, brightness, and color information of the effect material and background image to ensure that the effect is both prominent and naturally integrated into the original image. For example, when combining the "breezy particle" effect with a scene of swaying leaves in a forest, the color of the leaves, the angle and intensity of the light, and the transparency setting of the particle effect itself need to be considered, so that these particles appear to be gently drifting among the leaves with the breeze, rather than simply covering them. By employing techniques such as alpha blending algorithms, a smooth transition between the effect layer and the background layer can be achieved, thereby generating the effect overlay scene map. Next, to ensure the overall color consistency of the overlay scene and avoid visual jarring caused by color inconsistencies between the effects and the original scene, color consistency calibration is required for the entire overlay scene. This step involves analyzing the color distribution of each part after the effects are overlaid and adjusting the color parameters of the effects or background parts accordingly, such as hue, saturation, and brightness, to achieve a harmonious and unified visual effect. Specifically, if the colors of the "wind particles" are found to be too vibrant and out of place with the surrounding environment, their saturation can be reduced or their hue adjusted to better blend them into the background; conversely, if the background appears dull and lackluster due to the addition of the effects, the brightness and contrast of the background can be appropriately increased. Finally, after color consistency calibration, a target rendering effect is obtained that retains the vividness of the effects while maintaining overall harmony and beauty. This allows viewers to experience a natural and smooth visual experience, as if they were in a forest where a gentle breeze is blowing, witnessing the leaves swaying in the wind and seeing tiny particles dancing in the breeze—all of which appears so realistic and vibrant.

[0030] In a specific embodiment, the step of extracting pixel information from the real-time game scene graph to obtain a scene pixel dataset includes: The pixel coordinates of the real-time game scene are traversed to obtain a pixel coordinate distribution map, and the pixel coordinate distribution map is validated to obtain a valid coordinate point map. Based on the effective coordinate point map, the pixel grayscale values ​​of the real-time game scene map are read to obtain a pixel grayscale value table, and the grayscale value table is filtered by grayscale range to obtain an effective grayscale value map. Based on the effective coordinate point map and the effective grayscale value map, data association and matching are performed to obtain the pixel coordinate grayscale set, and the data format of the pixel coordinate grayscale set is standardized to obtain the scene pixel dataset.

[0031] Specifically, the process of extracting pixel information from the real-time game scene image to obtain a scene pixel dataset first requires traversing the pixel coordinates of the real-time game scene image. This means starting from the top left corner of the image, visiting each pixel one by one in a left-to-right, top-to-bottom order, thereby constructing a detailed pixel coordinate distribution map. During this process, each pixel is assigned a unique coordinate identifier to record its specific position in the image. Next, the pixel coordinate distribution map needs to be validated for coordinate validity. This step aims to remove coordinate points that exceed the boundaries of the real-time game scene or are invalid for other reasons, such as some coordinate points that may not display valid image content due to occlusion or transparency settings. In this way, a valid coordinate point map is finally obtained, which contains only valid coordinate information that can be used for subsequent processing. Next, based on the valid coordinate point map, the pixel grayscale values ​​of the real-time game scene image are read. This refers to reading the grayscale value of the pixel corresponding to each valid coordinate point. The grayscale value reflects the brightness level of the pixel, and the value range is usually between 0 and 255, where 0 represents black and 255 represents white. This operation yields an effective grayscale value table reflecting the brightness distribution of various parts of the game scene. However, not all grayscale values ​​are valuable for analysis. Therefore, the pixel grayscale value table needs to be filtered to remove grayscale values ​​that do not meet specific conditions, such as areas that are too bright or too dark to provide meaningful information. This results in an effective grayscale value map, containing only the grayscale information crucial for understanding the game scene. Based on this, data association matching is performed between the effective coordinate point map and the effective grayscale value map. This requires finding the direct relationship between each pair of effective coordinates and its corresponding grayscale value, forming sets of corresponding coordinate-grayscale data pairs, i.e., pixel coordinate grayscale sets. This step ensures that each coordinate point not only knows its position in the image but also clearly defines the grayscale characteristics it represents, which is crucial for further analysis and processing. To facilitate subsequent data processing and analysis, the pixel coordinate grayscale set also needs to undergo data format standardization, including measures such as unifying the data format and adjusting the data structure, so that all data can be presented in a consistent form, thereby obtaining the final scene pixel dataset. These datasets comprehensively and accurately reflect the visual features of real-time game scenes, laying the foundation for further image processing, analysis, and even machine learning applications. For example, in an adventure game, when a player explores an ancient forest, the game engine needs to perform the aforementioned series of processes on the current real-time scene to better render lighting effects and optimize the gaming experience. By traversing the pixel coordinates of the real-time game scene map, the specific location information of every tree and leaf in this ancient forest can be obtained; then, after coordinate validity verification, it is determined which trees and leaves are visible, thus obtaining a valid coordinate point map.Subsequently, the grayscale value corresponding to each coordinate is read from the effective coordinate point map to determine which parts of the trees are brighter or darker, thus obtaining an effective grayscale value map. The effective coordinate point map and the effective grayscale value map are then correlated and matched to understand the position and brightness of each tree in the scene, generating a pixel coordinate grayscale set. Finally, this data is formatted to form a complete scene pixel dataset. This not only helps the game engine more accurately simulate the changes in forest under natural light but also provides players with a more realistic and immersive experience, allowing them to feel the mystery and beauty of that ancient forest as if they were actually there. Throughout the entire process, all operations are closely linked and interconnected, working together to achieve the final goal.

[0032] In a specific embodiment, the step of performing RGB channel separation processing on the scene pixel dataset to obtain a pixel channel separation map includes: The scene pixel dataset is split into color channels to obtain an RGB mixed channel image, and the RGB mixed channel image is then stripped into single channels to obtain the original R channel image, the original G channel image, and the original B channel image. Pixel detail enhancement is performed on the original R-channel image, original G-channel image, and original B-channel image respectively to obtain a channel enhancement image, and edge sharpening processing is performed on the channel enhancement image to obtain a sharpened channel image; Based on the sharpened channel map, pixel coordinates are aligned to obtain a coordinate synchronization channel group, and channel color annotation is performed on the coordinate synchronization channel group to obtain a pixel channel separation map.

[0033] Specifically, the process of performing RGB channel separation processing on the scene pixel dataset to obtain a pixel channel separation map first requires splitting the scene pixel dataset into color channels. This means decomposing the color information of each pixel from its comprehensive representation into a combination of three basic color channels: red (R), green (G), and blue (B). In this process, the original RGB mixed color information contained in each pixel is decomposed, forming an RGB mixed channel map that can display the RGB mixed state of all these pixels. Subsequently, the RGB mixed channel map needs to be stripped into individual channels, that is, extracting separate original R channel images, original G channel images, and original B channel images. This way, each image only displays the information of its corresponding color channel, making the features of each color channel more clearly visible. Based on the obtained original R channel images, original G channel images, and original B channel images, the next step is to perform pixel detail enhancement. This means enhancing the contrast and brightness attributes of each pixel in each color channel to highlight the detailed features of the image, generating a channel enhancement map. To sharpen image edges, edge sharpening is applied to the channel enhancement map. This increases image clarity by emphasizing color variations in boundary areas, resulting in a sharpened channel map. This step is crucial for improving image quality, as it not only enhances visual effects but also provides more accurate data support for subsequent analysis. Next, pixel coordinate alignment is performed based on the sharpened channel map. This ensures that the pixel coordinates in each color channel perfectly match the corresponding coordinates in the original scene pixel dataset, avoiding potential offsets or distortions during processing, resulting in a synchronized channel group. This step guarantees precise matching between color channels, playing a key role in maintaining image realism and integrity. Finally, channel color annotation is applied to the synchronized channel group. This step aims to reintegrate the various color channels and add corresponding color identifiers, ensuring that the final pixel channel separation map retains the original color information while clearly displaying the independent characteristics of each color channel. For example, in an adventure game, when a player explores an ancient forest, the game engine can use the above method to perform RGB channel separation processing on the current real-time scene image. First, the scene pixel dataset is split into color channels to obtain an RGB mixed channel image. This is then further split into original R, G, and B channel images to allow for more detailed observation of the color distribution of different objects in the forest. Next, pixel detail enhancement is performed on each channel image, such as making tree textures more pronounced and leaf colors more vibrant. Edge sharpening is then applied to highlight tree outlines, generating a sharpened channel image. After ensuring that the pixel coordinates of each channel are consistent with their corresponding coordinates in the original scene, pixel coordinate alignment is completed, forming a coordinate-synchronized channel group.Finally, channel color annotation was performed on the coordinate synchronization channel group to restore and optimize the color performance of the original image. This clearly showcased the unique contribution of each RGB channel, bringing every detail of this ancient forest to life for the player, greatly enhancing the game's immersion and visual enjoyment. The entire process was interconnected and closely collaborative, working together to transform the original scene pixel dataset into a refined pixel channel separation map, providing a solid technical foundation for improving the quality of the game's visuals.

[0034] In a specific embodiment, the step of performing dynamic region identification on the real-time game scene map based on the pixel channel separation map to obtain a dynamic scene region map includes: The pixel channel separation map is compared with the real-time game scene map in two channels to obtain a channel difference map, and the pixel change amount is calculated on the channel difference map to obtain a pixel change value map. Threshold filtering is performed based on the pixel variation value map to obtain a high variation pixel map, and neighborhood aggregation is performed on the high variation pixel map to obtain a pixel connected component map. Based on the pixel connected component map, the region boundary is delineated to obtain a dynamic region contour map, and the dynamic region contour map is marked to obtain a scene dynamic region map.

[0035] Specifically, the process of obtaining a dynamic region map of the game's real-time scene based on the pixel channel separation map first requires a dual-channel comparison between the pixel channel separation map and the game's real-time scene map. This means comparing the color information differences between the two images at the same coordinate positions to obtain a channel difference map that reflects the changes between them. During this process, by comparing the data of the two images pixel by pixel, it is possible to identify which parts have changed, and these changes often represent dynamic elements in the scene, such as the movement of game characters or changes in the environment. Next, to quantify the degree of this change, the pixel change amount needs to be calculated on the channel difference map to generate a pixel change value map. Here, the change amount of each pixel is converted into a numerical form, making areas with significant changes more intuitively visible. Subsequently, a threshold filtering is performed based on the pixel change value map. This means setting a specific threshold to distinguish between high-change pixels and low-change pixels. Only when the pixel change value exceeds this threshold is the pixel considered a high-change pixel, ultimately forming a high-change pixel map. This step is crucial for filtering out minor and unimportant changes, as it ensures that subsequent processing only focuses on those truly meaningful dynamic changes. Next, neighborhood aggregation is performed on the highly variable pixel map to connect adjacent highly variable pixels, forming coherent pixel blocks, i.e., a pixel connected region map. This step helps determine the specific range and shape of dynamic changes, providing a foundation for further analysis. Then, region boundaries are delineated based on the pixel connected region map, meaning contour lines are drawn along the edges of the connected regions, clearly defining the boundaries of each dynamic region and generating a dynamic region contour map. This process not only makes the shape of the dynamic regions clear at a glance but also prepares for accurate labeling of these regions. Finally, the dynamic region contour map is labeled by assigning unique identifiers to different dynamic regions so that they can be individually identified and tracked in the scene, thus obtaining the final scene dynamic region map. This not only helps developers better understand the behavior patterns of various dynamic elements in the game but also provides players with a richer and more realistic interactive experience. For example, in an adventure game, when a player explores an ancient forest, the system can use the above method to perform dynamic region identification on the current real-time scene image. First, a dual-channel comparison is performed between the pixel channel separation map and the real-time game scene map to obtain a channel difference map. This may reveal image changes caused by wind-induced leaf swaying or animals moving through the forest. Next, the pixel variation values ​​of these changes are calculated, and those with significant changes are selected based on a preset threshold to generate a high-variability pixel map. For example, a fox suddenly running by would become part of the high-variability pixel map. Then, neighborhood aggregation is used to connect all the high-variability pixels around the fox, forming a pixel connected component map representing the fox's movement trajectory.Building upon this foundation, regional boundaries are delineated, clearly marking the dynamic areas where the fox resides, generating dynamic area outline maps. Finally, these dynamic areas are uniquely labeled, completing the creation of the scene's dynamic area map. This allows both the game engine and the player to clearly identify the changes occurring in the forest, enhancing the game's realism and interactive enjoyment. It also provides crucial information for the game's AI decision-making, such as triggering specific events or adjusting enemy behavior strategies. The entire process is tightly interconnected and interdependent, collectively achieving the transformation from raw images to accurately capturing dynamic area information, significantly improving the game's expressiveness and technical sophistication.

[0036] In a specific embodiment, the step of calculating pixel motion trajectories of the dynamic region map of the scene to obtain a region motion trajectory table includes: Feature pixels are sampled for each dynamic region in the scene dynamic region map to obtain a region sampling point map, and the sampling points in the region sampling point map are marked with coordinates to obtain a sampling point coordinate table; Based on the sampling point coordinate table, the sampling point positions of two consecutive frames of the game real-time scene map are compared to obtain a sampling point displacement map. The displacement direction and distance of the sampling point displacement map are then calculated to obtain the sampling point motion data. Based on the motion data of the sampling points, the motion trends of all sampling points within the same dynamic area are aggregated to obtain a regional motion trend map. The trajectory coordinate sequence of the regional motion trend map is then organized to obtain a regional motion trajectory table.

[0037] Specifically, the process of calculating pixel motion trajectories in the dynamic scene area map to obtain the area motion trajectory table first requires sampling feature pixels for each dynamic area in the scene dynamic area map. This means selecting representative pixels as samples within each dynamic area, which reflect the movement of objects within that area. This method yields an area sampling point map. During this process, each sampling point in the area sampling point map is assigned specific coordinate values, generating a sampling point coordinate table, thus providing basic data support for subsequent steps. For example, in an adventure game, when a player controls a character to traverse an ancient forest, the system can extract feature pixels from the areas where each swaying leaf or running fox is located and record their position information. Next, based on the sampling point coordinate table, the sampling point positions of two consecutive frames of the game's real-time scene map are compared. This step requires comparing the corresponding sampling point positions in the two frames to determine if displacement has occurred. Through this comparison, a sampling point displacement map showing the movement of each sampling point between two frames can be obtained. In this process, it is necessary not only to pay attention to the changes in the old and new coordinates of the sampling points but also to deeply analyze the direction and distance of their displacement, thereby forming sampling point motion data. For example, by comparing the selected sampling point positions in adjacent frames, it can be observed that the leaf has moved a distance to the lower right due to the wind. Subsequently, the motion trends of all sampling points within the same dynamic area are aggregated based on the motion data of these sampling points. This involves summarizing the motion information of all sampling points within the same area to depict the overall movement trend of objects within the entire area, thus obtaining a regional motion trend map. This step helps to understand the overall behavior pattern of the dynamic area from a macroscopic perspective, rather than being limited to changes in individual pixels. For example, by integrating the motion trends of all sampling points in the fox's area, it can be clearly seen that the fox is running rapidly to the left and forward. Finally, the trajectory coordinate sequence of the regional motion trend map is organized, meaning that the coordinates of the center points or representative points of each dynamic area are arranged in chronological order, ultimately forming a regional motion trajectory table. This not only makes the movement path of the dynamic area clear at a glance but also provides a basis for adjusting the behavior of other elements in the game, such as NPCs, or triggering events. For example, in the adventure game mentioned above, to achieve a more realistic interactive experience, the developers used the above method to track the fox's movement trajectory. First, feature pixels are sampled from the dynamic area where the fox is located. Key pixels that best represent the fox's form and movements are selected, and their initial coordinates are recorded to create a sampling point coordinate table. Then, during game execution, the sampling point positions are continuously compared between two consecutive frames of the scene image to observe how the fox moves as the animation progresses, and a sampling point displacement map is generated accordingly.Next, the specific displacement direction and distance of each sampling point are calculated to obtain detailed motion data. Based on this, the movement trends of all sampling points on the fox are comprehensively considered to draw the fox's movement tendency within the entire dynamic area, i.e., the regional motion trend map. Finally, the coordinate changes of the fox between frames are organized in chronological order to form a coherent regional motion trajectory table. This trajectory not only accurately reflects the fox's actual walking route in the game world but also provides precise data support for subsequent possible interactions, such as setting traps to catch the fox or guiding players to approach the fox in plot design. The entire process is interconnected, ensuring that the conversion from the original image to the final dynamic regional motion trajectory is both scientific and efficient, greatly enhancing the game's realism and player immersion.

[0038] In a specific embodiment, the step of performing special effects matching and filtering on the scene dynamic area map based on the area motion trajectory table to obtain an adaptive special effects type table includes: Motion features are extracted from the region motion trajectory table to obtain a region motion feature map, and the region size is measured from the scene dynamic region map to obtain a region size parameter table; Based on the region motion feature map and the region size parameter table, a matching condition set is constructed, and the matching condition set is compared with a preset special effects rule library to obtain a special effects candidate list; Based on the special effects candidate list, the special effects priority of each dynamic area is sorted to obtain a special effects sorting result image. The type information of the special effects sorting result image is then organized to obtain an adaptive special effects type table.

[0039] Specifically, the process of matching and filtering special effects in the scene's dynamic region map based on the aforementioned regional motion trajectory table to obtain the appropriate special effect type table first requires extracting motion features from the regional motion trajectory table. This means analyzing the positional changes of each sampling point within the region at different time points to determine how objects within that region move, including information such as speed, acceleration, and direction, thereby generating a regional motion feature map. Simultaneously, it is also necessary to measure the dimensions of each dynamic region in the scene's dynamic region map, recording their length, width, height, or diameter parameters to construct a region size parameter table. For example, in a virtual world, when a player is fighting a flying dragon, they can determine its speed, direction changes, and other characteristics by tracking the dragon's flight path and posture changes, and measure the actual size of the space occupied by the dragon. Next, a matching condition set is constructed based on the regional motion feature map and the region size parameter table. This step requires combining the information obtained in the first two steps to form a complete set of rules describing the characteristics of each dynamic region. These rules cover all necessary information from motion features to physical dimensions, providing a precise search basis for subsequent special effect matching. Subsequently, this set of matching conditions is compared with a preset special effects rule library to find special effects options that match or closely resemble the characteristics of the current dynamic area, thus obtaining a list of special effects candidates. For example, based on the dragon's flight path and size, suitable candidate special effects such as fire or lightning effects can be found to express its powerful aura. Next, based on the special effects candidate list, the special effects of each dynamic area are prioritized. This means comprehensively considering factors such as the visual effect, implementation difficulty, and relevance to the game plot to select the most suitable special effect for each dynamic area and generate a special effects ranking result diagram. In this way, it can be ensured that each dynamic area receives the most appropriate visual enhancement effect. For example, in a dragon battle scene, considering the tension of the plot and visual impact, fire effects may be ranked first, because this effect can not only vividly depict the scene of the dragon breathing fire, but also increase the excitement of the game. Finally, the type information of the special effects ranking result diagram is organized, which means recording the specific type, applicable conditions, and other information of each special effect according to the final selected effect order, forming a table of suitable special effects types. This table not only helps developers quickly understand the best effects choices for each dynamic area, but also provides detailed guidance for the subsequent implementation of effects. For example, in the aforementioned dragon battle game, to make the battle scenes more spectacular, the developers used the above method to add effects to the dragon. First, through detailed analysis of the dragon's flight path, key motion characteristics such as flight speed and turning frequency were extracted. Combined with the actual space occupied by the dragon, a set of matching conditions was established. Next, these conditions were meticulously compared with a preset effect rule library, selecting effects such as flame jets and lightning strikes that might be suitable for the dragon as a candidate effect list.Then, based on factors such as the visual impact of the special effects and their suitability for the battle scene, the candidate special effects are prioritized to determine which effects are most suitable for use on the dragon at specific moments, generating a ranking chart of the special effects. Finally, according to the importance and usage of the special effects, specific types and application guidelines for each type are compiled, forming a table of suitable special effect types. This process not only adds more charm to key characters in the game but also greatly enhances the player's gaming experience, making every battle with the dragon full of unknowns and surprises. The entire process emphasizes a systematic approach from data collection and analysis to special effect selection, ensuring that the selection of special effects is both scientific and highly artistic.

[0040] In a specific embodiment, the step of pixel-by-pixel fusion and overlay of the scene dynamic region map based on the size-adapted effect map to obtain an effect-overlay scene map includes: The pixel coordinates of the size adaptation effect map and the scene dynamic area map are matched to obtain a coordinate alignment map, and the area edge alignment of the coordinate alignment map is checked to obtain an edge alignment result map. Based on the edge alignment result map, the special effect pixels and dynamic region pixels are weighted to obtain a pixel weight table, and the pixel weight table is visualized and labeled to obtain a weight label map. The RGB values ​​of the corresponding pixels in the weighted annotation map are weighted to obtain a fused pixel map, and the fused pixel map is stitched together to obtain a special effects overlay scene map.

[0041] Specifically, the process of pixel-by-pixel fusion and overlay of the scene dynamic area map based on the size-adapted effect map to obtain the effect overlay scene map first requires pixel coordinate matching between the size-adapted effect map and the scene dynamic area map. This means identifying and recording the position information of corresponding pixels in the two images to ensure that each effect element can be accurately placed in the specified position in the scene, thereby generating a coordinate alignment map. For example, in a virtual battle scene, when adding fire effects to a flying dragon, the corresponding pixels between the fire effect map and the scene map where the dragon is located must first be determined to ensure that the fire effect can accurately cover the dragon's body, rather than floating around it. Next, the coordinate alignment map is checked for edge alignment. This is to further ensure that the effect elements are seamlessly integrated with the dynamic area in the scene, especially to check whether the edges of the effect elements perfectly match the outline of the target object (such as the dragon) in the scene. By adjusting any possible misalignments or deviations, the visual realism is improved, and finally, the edge alignment result map is obtained. In this process, the edges of the fire effect are carefully fine-tuned to make them completely match the edges of the dragon's body, avoiding any unnatural overlaps or gaps. Then, based on the edge alignment result map, weights are assigned to the effect pixels and dynamic area pixels. This means that each pixel is assigned a suitable weight value according to factors such as importance and transparency. These weight values ​​determine how the colors of each pixel are mixed in the final composite image, thus forming a pixel weight table. For example, when adding a fire effect to a dragon, considering the transparency of the fire effect and its blending effect with the dragon's body color, appropriate weights are set at the boundary between the two to make the fire look bright yet realistic. Subsequently, the pixel weight table is visualized, showing the assigned weights intuitively, helping developers to understand the weight distribution of each pixel more clearly, thus obtaining a weight annotation map. Next, the RGB values ​​of the corresponding pixels in the weight annotation map are weighted and calculated. This means that according to the previously defined weights, the red, green, and blue primary color values ​​of the effect pixels and dynamic area pixels are calculated separately to generate a new RGB value. This value reflects the best color blending effect between the effect and the dynamic area of ​​the scene, thus generating a blended pixel map. For example, when processing the fire effects on the dragon, a weighted average is applied to the colors of the fire and the dragon's skin, making the effects not only vivid but also blend naturally with the background. Finally, the merged pixel image is stitched together, meaning all the weighted pixels are recombined to construct a complete overlay scene image. This step ensures a seamless transition between the effects and the original scene, enhancing the realism and appeal of the overall image.For example, after completing the RGB value weighted calculation for the dragon's flame effect, all processed pixels are stitched together to form a complete battle scene image with realistic flame effects, making players feel as if they are in a real fantasy world. The entire process emphasizes a systematic approach from initial alignment to final fusion, ensuring that the addition of effects not only highlights the theme but also harmonizes with the original scene, greatly enhancing the visual experience.

[0042] The above describes the image rendering enhancement method in the embodiments of the present invention. The following describes the image rendering enhancement system in the embodiments of the present invention. Please refer to [link / reference]. Figure 2 One embodiment of the image rendering enhancement system in this invention includes: The extraction module 21 is used to extract pixel information from the real-time game scene image to obtain a scene pixel dataset, and to perform RGB channel separation processing on the scene pixel dataset to obtain a pixel channel separation image. The recognition module 22 is used to perform dynamic region recognition on the real-time game scene map based on the pixel channel separation map to obtain a scene dynamic region map, and to calculate the pixel motion trajectory of the scene dynamic region map to obtain a region motion trajectory table. The filtering module 23 is used to perform special effect matching filtering on the scene dynamic area map based on the area motion trajectory table, obtain the adaptive special effect type table, and adjust the pixel size of the special effect material in the adaptive special effect type table to obtain the size-adapted special effect map. The overlay module 24 is used to perform pixel fusion overlay on the scene dynamic area map based on the size-adapted effect map to obtain an effect overlay scene map, and to perform color consistency calibration on the effect overlay scene map to obtain a target rendering effect map.

[0043] In this embodiment, the specific implementation of each unit in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0044] like Figure 3 As shown in the diagram, this embodiment of the invention provides a structural schematic block diagram of a computer device, including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the above-described enhanced image rendering method.

[0045] It is evident that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented in this device embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0046] Furthermore, this application also discloses a computer program product or computer program stored in a computer-readable storage medium. A processor of a computer device can read the computer program from the computer-readable storage medium, execute the computer program, and cause the computer device to perform the aforementioned image rendering enhancement method. Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0047] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An image rendering enhancement method, characterized in that, Includes the following steps: Pixel information is extracted from the real-time game scene image to obtain a scene pixel dataset, and the scene pixel dataset is subjected to RGB channel separation processing to obtain a pixel channel separation image; Based on the pixel channel separation map, dynamic region identification is performed on the real-time game scene map to obtain a scene dynamic region map, and pixel motion trajectory is calculated on the scene dynamic region map to obtain a region motion trajectory table. Based on the aforementioned regional motion trajectory table, special effects matching and filtering are performed on the scene dynamic region map to obtain an adaptive special effects type table. Then, the pixel size of the special effects materials in the adaptive special effects type table is adjusted to obtain a size-adapted special effects map. Based on the size-adapted effect map, the scene dynamic area map is pixel-blended and superimposed to obtain the effect superimposed scene map, and the color consistency of the effect superimposed scene map is calibrated to obtain the target rendering effect map.

2. The image rendering enhancement method according to claim 1, characterized in that, The step of extracting pixel information from the real-time game scene image to obtain a scene pixel dataset includes: The pixel coordinates of the real-time game scene are traversed to obtain a pixel coordinate distribution map, and the pixel coordinate distribution map is validated to obtain a valid coordinate point map. Based on the effective coordinate point map, the pixel grayscale values ​​of the real-time game scene map are read to obtain a pixel grayscale value table, and the grayscale value table is filtered by grayscale range to obtain an effective grayscale value map. Based on the effective coordinate point map and the effective grayscale value map, data association and matching are performed to obtain the pixel coordinate grayscale set, and the data format of the pixel coordinate grayscale set is standardized to obtain the scene pixel dataset.

3. The image rendering enhancement method according to claim 1, characterized in that, The step of performing RGB channel separation processing on the scene pixel dataset to obtain a pixel channel separation map includes: The scene pixel dataset is split into color channels to obtain an RGB mixed channel image, and the RGB mixed channel image is then stripped into single channels to obtain the original R channel image, the original G channel image, and the original B channel image. Pixel detail enhancement is performed on the original R-channel image, original G-channel image, and original B-channel image respectively to obtain a channel enhancement image, and edge sharpening processing is performed on the channel enhancement image to obtain a sharpened channel image; Based on the sharpened channel map, pixel coordinates are aligned to obtain a coordinate synchronization channel group, and channel color annotation is performed on the coordinate synchronization channel group to obtain a pixel channel separation map.

4. The image rendering enhancement method according to claim 1, characterized in that, The process of dynamically identifying regions in the real-time game scene map based on the pixel channel separation map to obtain a dynamic scene region map includes: The pixel channel separation map is compared with the real-time game scene map in two channels to obtain a channel difference map, and the pixel change amount is calculated on the channel difference map to obtain a pixel change value map. Threshold filtering is performed based on the pixel variation value map to obtain a high variation pixel map, and neighborhood aggregation is performed on the high variation pixel map to obtain a pixel connected component map. Based on the pixel connected component map, the region boundary is delineated to obtain a dynamic region contour map, and the dynamic region contour map is marked to obtain a scene dynamic region map.

5. The image rendering enhancement method according to claim 1, characterized in that, The step of calculating pixel motion trajectories in the dynamic region map of the scene to obtain a region motion trajectory table includes: Feature pixels are sampled for each dynamic region in the scene dynamic region map to obtain a region sampling point map, and the sampling points in the region sampling point map are marked with coordinates to obtain a sampling point coordinate table; Based on the sampling point coordinate table, the sampling point positions of two consecutive frames of the game real-time scene map are compared to obtain a sampling point displacement map. The displacement direction and distance of the sampling point displacement map are then calculated to obtain the sampling point motion data. Based on the motion data of the sampling points, the motion trends of all sampling points within the same dynamic area are aggregated to obtain a regional motion trend map. The trajectory coordinate sequence of the regional motion trend map is then organized to obtain a regional motion trajectory table.

6. The image rendering enhancement method according to claim 1, characterized in that, The process of matching and filtering special effects on the dynamic area map of the scene based on the regional motion trajectory table yields a table of suitable special effects types, including: Motion features are extracted from the region motion trajectory table to obtain a region motion feature map, and the region size is measured from the scene dynamic region map to obtain a region size parameter table; Based on the region motion feature map and the region size parameter table, a matching condition set is constructed, and the matching condition set is compared with a preset special effects rule library to obtain a special effects candidate list; Based on the special effects candidate list, the special effects priority of each dynamic area is sorted to obtain a special effects sorting result image. The type information of the special effects sorting result image is then organized to obtain an adaptive special effects type table.

7. The image rendering enhancement method according to claim 1, characterized in that, The step of pixel-by-pixel fusion and overlay of the dynamic area map of the scene based on the size-adapted effect map to obtain an overlaid effect scene map includes: The pixel coordinates of the size adaptation effect map and the scene dynamic area map are matched to obtain a coordinate alignment map, and the area edge alignment of the coordinate alignment map is checked to obtain an edge alignment result map. Based on the edge alignment result map, the special effect pixels and dynamic region pixels are weighted to obtain a pixel weight table, and the pixel weight table is visualized and labeled to obtain a weight label map. The RGB values ​​of the corresponding pixels in the weighted annotation map are weighted to obtain a fused pixel map, and the fused pixel map is stitched together to obtain a special effects overlay scene map.

8. An image rendering enhancement system, characterized in that, include: The extraction module is used to extract pixel information from the real-time game scene image to obtain a scene pixel dataset, and to perform RGB channel separation processing on the scene pixel dataset to obtain a pixel channel separation image. The recognition module is used to perform dynamic region recognition on the real-time game scene map based on the pixel channel separation map to obtain a scene dynamic region map, and to calculate the pixel motion trajectory of the scene dynamic region map to obtain a region motion trajectory table. The filtering module is used to perform special effects matching and filtering on the scene dynamic area map based on the area motion trajectory table, to obtain an adaptive special effects type table, and to adjust the pixel size of the special effects materials in the adaptive special effects type table to obtain a size-adapted special effects map. The overlay module is used to perform pixel fusion and overlay on the dynamic area map of the scene based on the size-adapted effect map to obtain an effect overlay scene map, and to perform color consistency calibration on the effect overlay scene map to obtain the target rendering effect map.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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