Scenario-Aware Image Rendering for Quality and Frame Rate
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Solution Overview
Problem
Existing rendering technologies in virtual, augmented, and mixed reality systems struggle to balance rendering quality and frame rate effectively, particularly in scenarios where one aspect is prioritized over the other, leading to suboptimal user experiences.
Innovation Solution
An electronic device that recognizes objects and sets weights based on scenario context, performing either a first rendering process to enhance quality or a second process to increase frame rate for portions with higher weights, thereby optimizing resource allocation based on the specific context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a high-quality rendering process is performed on all portions of the object, then the rendering quality is improved, but the frame rate decreases
Solution Approach 1:
The patent applies local quality by differentiating rendering processes for different portions of the object based on their weights. High-weight portions (e.g., eyes, mouth in facial recognition) receive high-quality rendering with detailed processing, while low-weight portions receive simplified rendering. This resolves the contradiction by concentrating computational resources on critical areas rather than uniformly processing the entire object, thereby maintaining high frame rates while improving perceived rendering quality in important regions.
Solution Approach 2:
The patent segments the object into multiple portions and assigns different weights to each portion based on scenario context. This segmentation enables selective application of rendering processes - high-quality rendering for high-weight portions and low-quality rendering for low-weight portions. By dividing the rendering task into segments with different quality requirements, the system achieves both high frame rates and high rendering quality in critical areas simultaneously.
2Productivity
If a high frame rate is prioritized for all portions, then the frame rate is improved, but the rendering quality deteriorates
Solution Approach 1:
The patent applies local quality by differentiating rendering processes for different portions of the object based on their weights. High-weight portions (e.g., eyes, mouth in facial recognition) receive high-quality rendering with detailed processing, while low-weight portions receive simplified rendering. This resolves the contradiction by concentrating computational resources on critical areas rather than uniformly processing the entire object, thereby maintaining high frame rates while improving perceived rendering quality in important regions.
Solution Approach 2:
The patent segments the object into multiple portions and assigns different weights to each portion based on scenario context. This segmentation enables selective application of rendering processes - high-quality rendering for high-weight portions and low-quality rendering for low-weight portions. By dividing the rendering task into segments with different quality requirements, the system achieves both high frame rates and high rendering quality in critical areas simultaneously.
3Manufacturing precision
If rendering quality is improved for all portions, then the rendering quality is improved, but the computational resources increase
Solution Approach 1:
The patent applies local quality by differentiating rendering processes for different portions of the object based on their weights. High-weight portions (e.g., eyes, mouth in facial recognition) receive high-quality rendering with detailed processing, while low-weight portions receive simplified rendering. This resolves the contradiction by concentrating computational resources on critical areas rather than uniformly processing the entire object, thereby maintaining high frame rates while improving perceived rendering quality in important regions.
Solution Approach 2:
The patent segments the object into multiple portions and assigns different weights to each portion based on scenario context. This segmentation enables selective application of rendering processes - high-quality rendering for high-weight portions and low-quality rendering for low-weight portions. By dividing the rendering task into segments with different quality requirements, the system achieves both high frame rates and high rendering quality in critical areas simultaneously.
Data Source
AI summary
An electronic device may, when instructions are executed: recognize an object corresponding to a user in an image frame provided by an application; identify a scenario context of the application; set, on the basis of the scenario context, a weight for each of a plurality of parts of the object; determine whether the scenario context is a first type or a second type; when the scenario context is the first type, perform a first rendering process on the image frame to improve rendering quality of a part having a weight that is greater than a reference value from among the plurality of parts; and when the scenario context is the second type, perform a second rendering process on the image frame to increase the frame rate for a part having a weight that is greater than the reference value from among the plurality of parts.