Context-Aware Dynamic Distortion Correction for HMDs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Head-mounted devices (HMDs) face challenges in providing comfortable user experiences due to image distortion, which can cause nausea and discomfort, especially when users' eye alignment with the optical axis changes or when the device shifts on the head, leading to inefficient use of processing, memory, and power resources.
Innovation Solution
Implementing context-aware dynamic distortion correction techniques that adjust distortion based on user eye characteristics, gaze direction, and content type, allowing for selective computation of distortion corrections to conserve system resources and improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If distortion correction is computed for every image to maintain consistent user experience, then user comfort is improved, but processing resources and power consumption increase
Solution Approach 1:
The system dynamically adjusts distortion correction computation based on detected eye movement. When eye movement exceeds a threshold, full distortion correction is applied; when eye movement is minimal, correction is reduced or skipped. This dynamic adaptation resolves the contradiction by making processing intensity proportional to actual user need.
Solution Approach 2:
The system changes the distortion correction parameter (computation intensity) based on eye movement characteristics. By monitoring pupil position changes between frames, the system adjusts whether to apply full correction, reduced correction, or no correction, thereby optimizing power consumption while maintaining user comfort when needed.
2Manufacturing precision
If distortion correction is computed for every image, then image quality is improved, but processing time and computational load increase
Solution Approach 1:
The system dynamically selects between full distortion correction and reduced correction based on real-time eye movement detection. This dynamic approach maintains high image quality when users are actively moving their eyes while reducing processing time during stable viewing periods.
Solution Approach 2:
The system uses a lightweight eye movement detection mechanism that requires minimal processing to determine whether full distortion correction is necessary. This low-cost detection approach enables selective application of computationally expensive distortion correction only when needed.
3Adaptability or versatility
If distortion is adjusted based on eye characteristics, then user experience consistency is improved, but device complexity increases
Solution Approach 1:
The system implements feedback by detecting pupil position in each frame and using this information to adjust distortion correction parameters. This feedback loop enables consistent user experience adaptation without requiring complex predictive models, as the system simply responds to actual observed eye movements.
Solution Approach 2:
The distortion correction system serves itself by automatically adjusting its computation intensity based on detected user behavior. The eye movement detection and correction adjustment occur autonomously without requiring external control or complex configuration, reducing overall device complexity.
Data Source
AI summary
Some implementations provide improved user experiences on head mounted devices (HMDs) that provide near eye viewing, e.g., HMDs that display distorted images and provide lenses that undistort the images for the user. The images are produced using distortion that is corrected dynamically based on context to conserve device resources. To do so, a context associated with a state of the user, the HMD, or content being viewed on the HMD is tracked during the user experience. For example, the device may predict pupil position, eye state, eye gaze direction, or eye fixation, content type, connection mode, and other context. The device uses the tracked context to determine how to correct distortion for the images at different points during the user experience. For example, new distortion corrections may be computed and used while the user's gaze is moving and previously-determined distortion corrections may be used while the user's gaze is fixed.