Method for improving rendering real-time performance through adaptive image quality control

Abstract

The invention discloses a method for improving rendering real-time performance through adaptive image quality control, and the method comprises the steps: S1, carrying out the evaluation of the quality and performance demands of a rendered image according to the requirements of a scene and the evaluation of available resources; s2, formulating a self-adaptive image quality adjustment strategy according to the demand of the scene and the evaluation of available resources; s3, according to an image quality adjustment strategy, performing actual adjustment on each link in the rendering process; and S4, through algorithm optimization and rendering parameter optimization, real-time performance and image quality are further balanced. According to the method and the device, the image quality is dynamically adjusted according to the scene requirements and the availability of resources. And under the condition of limited performance, the resolution of the texture or the calculation intensity of illumination is reduced, so that the real-time performance of rendering is improved. And under the condition of sufficient resources, texture and illumination algorithms with higher quality are selected, so that the quality of the image is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud rendering, and in particular to a method for improving rendering real-time performance through adaptive image quality control. Background Art

[0002] In real-time rendering, there is a certain trade-off between real-time performance and image quality. Traditional methods may not be able to achieve high-quality image rendering under limited resources, while improving image quality often sacrifices real-time performance.

[0003] Therefore, the prior art has defects and needs to be improved. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for improving rendering real-time by adaptive image quality control, so as to dynamically adjust image quality according to scene requirements and resource conditions, and balance real-time and image quality.

[0005] The technical solution of the present invention is as follows: a method for improving rendering real-time performance through adaptive image quality control is provided, comprising the following steps.

[0006] S1: Evaluate the image quality and performance requirements for rendering based on the scene requirements and available resource assessment.

[0007] S2: Develop an adaptive image quality adjustment strategy based on the scene requirements and the evaluation of available resources.

[0008] S3: According to the image quality adjustment strategy, actual adjustments are made to various links in the rendering process. For example, in the material and texture processing stage, the appropriate quality level is selected based on resource evaluation. In lighting calculation, the number of light sources and lighting quality are adjusted based on performance evaluation.

[0009] S4: Further balance real-time performance and image quality by optimizing algorithms and rendering parameters.

[0010] Further, in step S1, the scene requirements are to determine the visual style, lighting conditions, material types, etc. of the scene. For example, an indoor scene may require more sophisticated lighting calculations to simulate complex light propagation, while an outdoor scene may need to consider atmospheric scattering effects. In addition, dynamic elements in the scene, such as moving objects or changing lighting conditions, need to be determined, which will affect rendering performance.

[0011] Furthermore, the available resource evaluation includes the evaluation of available hardware resources (such as CPU, GPU, memory) and software resources (such as rendering engine, shader, etc.); for hardware resources, it is necessary to understand the hardware's processing power, memory capacity, video memory size, etc.; for software resources, it is necessary to understand the features supported by the software, performance limitations, etc. This information will help determine the achievable image quality level.

[0012] Furthermore, in step S1, the rendered image quality and the rendering performance requirement are evaluated by evaluating the device performance, display resolution, lighting requirements, and texture size.

[0013] Device performance: Consider the processing power of the device, including the processing speed of the CPU and GPU, the size of the memory and video memory, etc. High-performance devices can handle more complex rendering tasks, while low-performance devices may need to reduce image quality to maintain smooth rendering speed.

[0014] Display Resolution: The resolution of the display device will also affect the performance and image quality of the rendering. Devices with higher resolutions require more pixels to calculate and display, and thus may slow down the rendering process.

[0015] Lighting requirements: Different lighting models and algorithms have different computational complexity, which will affect rendering performance. For example, global illumination algorithms are usually more complex than local illumination algorithms, so they require more computing resources.

[0016] Texture size: The resolution of the texture also affects rendering performance and image quality. High-resolution textures can provide more detailed visuals, but also require more computing resources.

[0017] Furthermore, the adaptive image quality adjustment strategy is: when resources are limited, reduce the resolution of textures and / or reduce the intensity of lighting calculations and / or use simplified materials; when resources are sufficient, select higher quality textures and / or high-resolution lighting algorithms and / or complex materials.

[0018] Further, the algorithm is optimized to optimize the rendering algorithm, and in the rendering process, those calculations that do not contribute to the final image are identified and eliminated; specifically: through depth sorting and clipping technology, triangles that are blocked by other objects, are not in the field of view, and are too small to be rendered are excluded; and / or, the algorithm is optimized to reduce redundant calculations, and unnecessary repeated calculations are reduced by modifying or reconstructing the implementation of the algorithm; repeated calculations can be avoided by caching intermediate calculation results and using space for time. For example, the cache is used to store already calculated values ​​to avoid repeated calculations; and / or, the algorithm is optimized to use GPU shaders for real-time texture synthesis and effect processing.

[0019] Furthermore, the rendering parameters are optimized by using adaptive resolution control to dynamically adjust the rendering resolution according to the device performance and display resolution.

[0020] Further, the adaptive resolution control is implemented by the following steps: A1: resolution evaluation and device detection: perform device detection and obtain the resolution information of the display device, by reading parameters such as the screen size and pixel density of the device, or by querying the device information of the system to obtain relevant data; A2: resource performance detection: evaluate the available resources and performance level of the current device, and determine whether the current device can support higher resolution rendering according to the processing power of the device, the size of the video memory and other relevant resource limitations; A3: adaptive resolution selection strategy: formulate a suitable adaptive resolution selection strategy according to the results of resolution evaluation and resource performance detection; that is, determine whether the resolution needs to be dynamically adjusted according to the performance and resource status of the device and the image quality requirements; A4: dynamic resolution adjustment: dynamically adjust the rendering resolution according to the adaptive resolution selection strategy; when the system performance is low and the resources are tight, reduce the resolution to reduce the rendering load; and when the resources are sufficient, a higher resolution can be selected to improve the image details and quality; A5: adaptive detection and dynamic adjustment: monitor the performance and resource status of the system in real time, and dynamically adjust the resolution as needed; when the system performance fluctuates or the resource availability changes, detect and adjust the resolution to meet the needs of real-time rendering.

[0021] Furthermore, in step A2, it also includes detecting the frame rate and rendering performance of the current device to obtain more detailed performance information.

[0022] By adopting the above scheme, the present invention provides a method for improving rendering real-time performance by adaptive image quality control, which realizes dynamic adjustment of image quality according to scene requirements and resource availability. In the case of limited performance, the texture resolution is reduced or the calculation intensity of lighting is reduced to improve the real-time performance of rendering. In the case of sufficient resources, higher quality texture and lighting algorithms are selected to improve the image quality. The present invention has the following technical effects:

[0023] (1) Improved real-time performance: By dynamically adjusting image quality according to scene requirements and resource availability, the computational and processing burden of the rendering process is reduced.

[0024] (2) Image quality optimization: When resources are sufficient, the method of the present invention selects higher quality textures, high resolution lighting algorithms and complex materials to improve image quality.

[0025] (3) Improved resource utilization: This method adjusts image quality according to scene requirements and resource availability, and makes rational use of available resources.

[0026] (4) Flexibility: This method implements an adaptive image quality adjustment strategy, which can flexibly adjust image quality according to the needs of different scenarios and the availability of resources.

[0027] (5) Balancing real-time performance and image quality: The method of the present invention achieves a balance between real-time performance and image quality by dynamically adjusting image quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0029] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0030] See also Figure 1 The present invention provides a method for improving rendering real-time performance through adaptive image quality control, which includes the following steps.

[0031] S1: Evaluate the image quality and performance requirements for rendering based on the scene requirements and available resource assessment.

[0032] S2: Develop an adaptive image quality adjustment strategy based on the scene requirements and the evaluation of available resources.

[0033] S3: According to the image quality adjustment strategy, actual adjustments are made to various links in the rendering process. For example, in the material and texture processing stage, the appropriate quality level is selected based on resource evaluation. In lighting calculation, the number of light sources and lighting quality are adjusted based on performance evaluation.

[0034] S4: Further balance real-time performance and image quality by optimizing algorithms and rendering parameters.

[0035] In this embodiment, in step S1, the scene requirements are to determine the visual style, lighting conditions, material types, etc. of the scene. For example, an indoor scene may require more sophisticated lighting calculations to simulate complex light propagation, while an outdoor scene may need to consider atmospheric scattering effects. In addition, dynamic elements in the scene, such as moving objects or changing lighting conditions, need to be determined, which will affect rendering performance.

[0036] In this embodiment, the available resource evaluation includes the evaluation of available hardware resources (such as CPU, GPU, memory) and software resources (such as rendering engine, shader, etc.); for hardware resources, it is necessary to understand the hardware processing power, memory capacity, video memory size, etc.; for software resources, it is necessary to understand the features supported by the software, performance limitations, etc. This information will help determine the achievable image quality level.

[0037] In this embodiment, in step S1, the rendered image quality and the rendering performance requirement are evaluated by evaluating the device performance, display resolution, lighting requirements, and texture size.

[0038] Device performance: Consider the processing power of the device, including the processing speed of the CPU and GPU, the size of the memory and video memory, etc. High-performance devices can handle more complex rendering tasks, while low-performance devices may need to reduce image quality to maintain smooth rendering speed.

[0039] Display Resolution: The resolution of the display device will also affect the performance and image quality of the rendering. Devices with higher resolutions require more pixels to calculate and display, and thus may slow down the rendering process.

[0040] Lighting requirements: Different lighting models and algorithms have different computational complexity, which will affect rendering performance. For example, global illumination algorithms are usually more complex than local illumination algorithms, so they require more computing resources.

[0041] Texture size: The resolution of the texture also affects rendering performance and image quality. High-resolution textures can provide more detailed visuals, but also require more computing resources.

[0042] In this embodiment, the adaptive image quality adjustment strategy is: when resources are limited, reduce the resolution of the texture and / or reduce the intensity of the lighting calculation and / or use simplified materials; when resources are sufficient, select higher quality textures and / or high-resolution lighting algorithms and / or complex materials.

[0043] In this embodiment, the algorithm is optimized to optimize the rendering algorithm. During the rendering process, calculations that do not contribute to the final image are identified and eliminated. Specifically, triangles that are blocked by other objects, out of view, or too small to be rendered are excluded through depth sorting and clipping techniques. And / or, the algorithm is optimized to reduce redundant calculations. Unnecessary repeated calculations are reduced by modifying or reconstructing the implementation of the algorithm. Repeated calculations can be avoided by caching intermediate calculation results and using space for time. For example, a cache is used to store already calculated values ​​to avoid repeated calculations. And / or, the algorithm is optimized to use GPU shaders for real-time texture synthesis and effect processing.

[0044] In this embodiment, the rendering parameters are optimized by using adaptive resolution control to dynamically adjust the rendering resolution according to device performance and display resolution.

[0045] In this embodiment, adaptive resolution control is implemented by the following steps: A1: resolution evaluation and device detection: perform device detection and obtain the resolution information of the display device, by reading parameters such as the screen size and pixel density of the device, or by querying the device information of the system to obtain relevant data; A2: resource performance detection: evaluate the available resources and performance level of the current device, and determine whether the current device can support higher resolution rendering according to the processing power of the device, the size of the video memory and other relevant resource limitations; A3: adaptive resolution selection strategy: formulate a suitable adaptive resolution selection strategy according to the results of resolution evaluation and resource performance detection; that is, determine whether the resolution needs to be dynamically adjusted according to the performance and resource status of the device and the image quality requirements; A4: dynamic resolution adjustment: dynamically adjust the rendering resolution according to the adaptive resolution selection strategy; when the system performance is low and the resources are tight, reduce the resolution to reduce the rendering load; and when the resources are sufficient, a higher resolution can be selected to improve the image details and quality; A5: adaptability detection and dynamic adjustment: monitor the performance and resource status of the system in real time, and dynamically adjust the resolution as needed; when the system performance fluctuates or the resource availability changes, detect and adjust the resolution to meet the needs of real-time rendering.

[0046] In this embodiment, step A2 also includes detecting the frame rate and rendering performance of the current device to obtain more detailed performance information.

[0047] The following is a specific embodiment, in a mobile virtual reality application, a user uses a head mounted display device to experience a game. According to the method of the present invention, adaptive image quality control is achieved to improve the effect of rendering real-time. Specifically, the following steps are performed.

[0048] Step 1: Scenario requirements and resource evaluation: Based on the performance of the device and the resolution of the display device, the game scenario's requirements for image quality and real-time performance were evaluated.

[0049] Step 2, Image quality adjustment strategy: Based on the resource evaluation results, an image quality adjustment strategy was developed. When performance is limited, the texture resolution is reduced, the intensity of lighting calculations is reduced, and simplified materials are used. When resources are sufficient, higher quality textures, high-resolution lighting algorithms, and complex materials are selected.

[0050] Step 3: Image quality adjustment implementation: Image quality adjustment is performed according to the strategy. The desired balance between image quality and real-time performance is achieved by selecting appropriate texture resolution, lighting calculation parameters, and material settings.

[0051] Step 4: Optimize the balance between real-time performance and image quality: Further balance real-time performance and image quality by optimizing algorithms and rendering parameters. Optimize rendering algorithms to reduce redundant calculations; use GPU shaders for real-time texture synthesis and effect processing; use adaptive resolution control to dynamically adjust rendering resolution based on device performance and display resolution.

[0052] Through the above implementation steps and optimization strategies, it is possible to adaptively adjust image quality according to scene requirements and resource conditions, thereby improving the performance and effect of real-time rendering.

[0053] In summary, the present invention provides a method for improving rendering real-time performance by adaptive image quality control, which dynamically adjusts image quality according to scene requirements and resource availability. In the case of limited performance, the texture resolution is reduced or the calculation intensity of lighting is reduced to improve the real-time performance of rendering. In the case of sufficient resources, higher quality texture and lighting algorithms are selected to improve image quality. The present invention has the following technical effects:

[0054] (1) Improved real-time performance: By dynamically adjusting image quality according to scene requirements and resource availability, the computational and processing burden of the rendering process is reduced.

[0055] (2) Image quality optimization: When resources are sufficient, the method of the present invention selects higher quality textures, high resolution lighting algorithms and complex materials to improve image quality.

[0056] (3) Improved resource utilization: This method adjusts image quality according to scene requirements and resource availability, and makes rational use of available resources.

[0057] (4) Flexibility: This method implements an adaptive image quality adjustment strategy, which can flexibly adjust image quality according to the needs of different scenarios and the availability of resources.

[0058] (5) Balancing real-time performance and image quality: The method of the present invention achieves a balance between real-time performance and image quality by dynamically adjusting image quality.

[0059] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adaptive image quality control to improve rendering real-time performance, It is characterized in that include: S1: Evaluate the rendered image quality and performance requirements based on the scene requirements and available resources. S2: Develop an adaptive image quality adjustment strategy based on the scene requirements and the evaluation of available resources; S3: According to the image quality adjustment strategy, actual adjustments are made to each link in the rendering process; S4: Further balance real-time performance and image quality by optimizing algorithms and rendering parameters.

2. According to claim 1, a method for improving rendering real-time by adaptive image quality control, It is characterized in that In step S1 , the scene requirements are to determine the visual style, lighting conditions, and material types of the scene.

3. The method for improving rendering real-time by adaptive image quality control according to claim 1, It is characterized in that The available resource evaluation includes an evaluation of available hardware resources and software resources. For hardware resources, it is necessary to understand the hardware's processing power, memory capacity, and video memory size. For software resources, it is necessary to understand the features and performance limitations supported by the software.

4. The method for improving rendering real-time by adaptive image quality control according to claim 1, It is characterized in that In step S1 , the rendered image quality and the rendering performance requirements are evaluated by evaluating the device performance, display resolution, lighting requirements, and texture size.

5. The method for improving rendering real-time performance by adaptive image quality control according to claim 1, It is characterized in that The adaptive image quality adjustment strategy is: when resources are limited, reduce the resolution of textures and / or reduce the intensity of lighting calculations and / or use simplified materials; when resources are sufficient, choose higher quality textures and / or high-resolution lighting algorithms and / or complex materials.

6. The method for improving rendering real-time by adaptive image quality control according to claim 1, It is characterized in that The algorithm optimization is to optimize the rendering algorithm, and in the rendering process, identify and eliminate those calculations that do not contribute to the final image; specifically: through depth sorting and clipping technology, exclude triangles that are blocked by other objects, are not in the field of view, and are too small to be rendered; And / or, the algorithm is optimized to reduce redundant calculations, and unnecessary repeated calculations are reduced by modifying or reconstructing the implementation of the algorithm; repeated calculations can be avoided by caching intermediate calculation results and using space to exchange time; And / or, the algorithm is optimized to use GPU shaders for real-time texture synthesis and effect processing.

7. The method for improving rendering real-time by adaptive image quality control according to claim 1, It is characterized in that The rendering parameters are optimized by using adaptive resolution control to dynamically adjust the rendering resolution according to device performance and display resolution.

8. The method for improving rendering real-time performance by adaptive image quality control according to claim 7, It is characterized in that Adaptive resolution control is achieved using the following steps: A1: Resolution evaluation and device detection: Perform device detection and obtain the resolution information of the display device by reading the device's screen size and pixel density parameters, or by querying the system's device information to obtain relevant data; A2: Resource performance test: Evaluate the available resources and performance level of the current device, and determine whether the current device can support higher resolution rendering based on the device's processing power and video memory size; A3: Adaptive resolution selection strategy: Develop an appropriate adaptive resolution selection strategy based on the resolution evaluation and resource performance test results; That is, based on the performance and resource status of the device and the image quality requirements, decide whether the resolution needs to be adjusted dynamically; A4: Dynamic resolution adjustment: Dynamically adjust the rendering resolution based on the adaptive resolution selection strategy; when the system performance is low and resources are tight, reduce the resolution to reduce the rendering load; When resources are sufficient, higher resolution can be selected to improve image detail and quality; A5: Adaptive detection and dynamic adjustment: Real-time monitoring of system performance and resource status, and dynamic adjustment of resolution as needed; Detect and adjust resolution to meet real-time rendering needs when system performance fluctuates or resource availability changes.

9. The method for improving rendering real-time performance by adaptive image quality control according to claim 8, It is characterized in that In step A2, the frame rate and rendering performance of the current device are also detected to obtain more detailed performance information.

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