Light Field Display Pupil Tracking for Jitter Reduction
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Solution Overview
Problem
Conventional light field display systems face challenges in providing a stable and comfortable viewing experience due to the high computational demand of continuously updating viewing zone geometries and the noise in pupil location data, leading to issues like image jitteriness and reduced user experience.
Innovation Solution
The system employs predictive techniques based on pupil location and velocity to distinguish between static and dynamic user states, adjusting the rendering geometry only when necessary, thereby minimizing unnecessary updates and maintaining a stable viewing zone during static states and updating during dynamic movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system continuously updates the viewing zone geometry based on pupil location data, then the viewing zone adapts to user movement, but image jitteriness occurs due to noise in pupil location data
Solution Approach 1:
The system performs preliminary classification of user states (static vs. dynamic) before executing geometry updates. By predicting user state in advance and only triggering updates during dynamic movements, the system prevents noise-induced jitter while maintaining adaptability to genuine user movements.
Solution Approach 2:
The system dynamically adjusts the update frequency of viewing zone geometry based on detected user state. During static states, updates are suppressed to maintain stability; during dynamic states, updates are enabled to maintain adaptability. This dynamic control resolves the contradiction between stability and adaptability.
2Adaptability or versatility
If the system continuously updates the rendering geometry to track user movement, then the light field content remains aligned with user position, but computational demand increases significantly
Solution Approach 1:
The system implements periodic updates of rendering geometry only during dynamic user states rather than continuous updates. By gating geometry updates behind user state detection (static vs. dynamic), the system reduces computational energy consumption while maintaining content alignment during movements when it matters most.
Solution Approach 2:
The system changes the update parameter (geometry refresh frequency) based on user state. During static states, the update rate is reduced to zero; during dynamic states, the update rate increases to track movement. This parameter adaptation reduces computational energy while preserving alignment quality.
3Measurement precision
If the system uses high-frequency pupil tracking to maintain accurate gaze direction, then gaze tracking precision improves, but noise in pupil location data increases leading to image jitteriness
Solution Approach 1:
The system performs preliminary user state classification (static vs. dynamic) before applying pupil tracking data to geometry updates. By predicting whether the user is in a static or dynamic state, the system filters out high-frequency noise during static periods while preserving accurate gaze tracking during dynamic movements when precision is needed.
Data Source
AI summary
Described are various embodiments of a pupil tracking system and method, and digital display device and digital image rendering system and method using same. In one embodiment, a computer-implemented method for improving a perceptive experience of light field content projected via a light field display within a light field viewing zone comprises sequentially acquiring a user feature location, and comparing a velocity computed therefrom with a designated threshold velocity. Upon the velocity corresponding with a transition from a relatively dynamic to a relatively static state, a rendering geometry of the light field content is adjusted to project the light field content within an adjusted light field viewing zone in accordance with a newly acquired user feature location.