Real-Time Viewport Prediction With Adaptive Error Correction
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Solution Overview
Problem
Existing viewport prediction algorithms in virtual reality (VR) and augmented reality (AR) suffer from performance limitations, leading to quality loss in the field of view (FOV) when user viewport moves quickly, negatively affecting immersive experiences.
Innovation Solution
Implementing adaptive correction techniques based on viewport trajectory and error between predicted and actual viewports, using historical error descriptors to refine viewport prediction models, and employing multiple model ranking for precise final predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If viewport prediction algorithms are used to pre-fetch content, then bandwidth and latency challenges are addressed, but prediction accuracy is insufficient leading to quality loss in FOV
Solution Approach 1:
The system incorporates feedback mechanisms by continuously monitoring actual viewport positions and comparing them with predicted positions. Error descriptors are generated from these comparisons and fed back into the prediction model to refine future predictions. This closed-loop feedback enables the system to learn from past prediction errors and improve accuracy over time, directly addressing the insufficient prediction accuracy problem.
Solution Approach 2:
The invention dynamically adjusts prediction parameters based on user behavior patterns and contextual information. The system modifies prediction time horizons, resolution levels, and content selection parameters in real-time to optimize for current user needs. This adaptive parameter adjustment allows the system to maintain high prediction accuracy under varying conditions, resolving the contradiction between reliability and precision.
2Reliability
If multiple prediction models are employed, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The prediction system is segmented into multiple specialized models, each handling specific aspects of viewport prediction (e.g., short-term motion prediction, long-term trend prediction, content-based prediction). Each model processes independent data streams and contributes specific predictions to the final result. This segmentation allows the system to achieve high overall accuracy through specialized processing while managing complexity by dividing the prediction task into manageable components.
Solution Approach 2:
The system employs multiple prediction models with varying levels of complexity and time horizons, using partial predictions from simpler models and more comprehensive predictions from complex models as needed. Not all models are used equally for every prediction task; the system selectively activates appropriate models based on current conditions, reducing unnecessary computational overhead while maintaining high accuracy when required.
3Productivity
If viewport prediction is performed in real-time, then immersive experience is enhanced, but computational resources are consumed
Solution Approach 1:
The system performs preliminary predictions and content preparation in advance based on predicted viewport movements. By pre-fetching content for predicted viewports before the user actually needs it, the system reduces real-time computational requirements during actual rendering. This preliminary action allows high-quality content to be ready ahead of time, enhancing immersive experience while lowering instantaneous computational resource consumption.
Solution Approach 2:
The prediction system dynamically adjusts its computational intensity based on user behavior patterns and contextual factors. During periods of stable user position, the system uses lighter prediction models and lower computational thresholds. When detecting rapid viewport changes or new user interactions, the system automatically increases computational resources to improve prediction accuracy. This dynamic resource allocation maintains real-time performance while optimizing energy consumption according to actual needs.
Data Source
AI summary
Techniques related to viewport selection in immersive video contexts are discussed. Such techniques include generating multiple viewport predictions each for a future time interval and based on different prediction models, ranking the viewport predictions using error descriptors of the prediction models, selecting a viewport prediction for the future time intervals using the ranking, and correcting the selected viewport predictions using the error descriptors.