Smart Glasses Region-of-Interest Processing for Power Efficiency
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Solution Overview
Problem
Existing smart glasses face limitations in image clarity, battery life, and heat dissipation due to their small camera and display form factors, leading to short run-times and discomfort for continuous wear, and struggle to provide augmented reality and extended reality experiences effectively.
Innovation Solution
The implementation of a high-resolution sensor with a telephoto lens and eye-tracking capabilities for region-of-interest based processing, allowing for reduced data processing and power consumption by focusing on the user's gaze point and using neural network processing to identify and crop images efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution sensors and processing capabilities are implemented in smart glasses, then image clarity and AR/XR experience quality improve, but power consumption increases and battery life decreases
Solution Approach 1:
The patent divides the visual field into regions of interest (ROI) and non-ROI areas. Only ROI regions are processed at high resolution and transmitted to the companion device, while non-ROI areas use lower resolution. This segmentation approach maintains image clarity for important areas while significantly reducing overall power consumption and data processing requirements.
Solution Approach 2:
The system applies different processing qualities to different regions of the visual field. High-resolution processing is applied locally to identified regions of interest (such as faces, text, or objects the user is looking at), while peripheral or less important areas use lower resolution. This local quality differentiation resolves the contradiction by providing high clarity only where needed.
2Measurement precision
If full-field high-resolution processing is applied, then image quality improves, but processing complexity and computational load increase
Solution Approach 1:
The patent extracts only the necessary portions of the visual field (regions of interest) for high-resolution processing. By identifying and extracting ROI areas using eye-tracking and gesture detection, the system avoids processing the entire visual field at high resolution, thereby reducing computational complexity while maintaining image quality for important areas.
Solution Approach 2:
Instead of applying full-field high-resolution processing (excessive action), the system applies processing only to the extent necessary for the user's current focus (partial action). This means processing only the ROI regions at high resolution while using lower resolution for other areas, reducing overall processing complexity while maintaining sufficient image quality.
3Productivity
If continuous high-resolution camera operation is implemented, then AR/XR experience quality improves, but battery runtime decreases
Solution Approach 1:
The system uses periodic eye-tracking monitoring to determine when high-resolution processing is needed based on user gaze patterns. Instead of continuous high-resolution operation, the system periodically checks user focus and activates high-resolution processing only when ROI regions are detected, thereby extending battery runtime while maintaining AR/XR experience quality during relevant periods.
4Ease of operation
If small camera and display form factors are used, then device wearability and comfort improve, but image clarity and processing capability deteriorate
Solution Approach 1:
The patent introduces the dimension of selective processing through eye-tracking and gesture-based ROI identification. This allows the system to achieve high image clarity not through hardware size but through intelligent processing of specific regions. The small form factor is compensated by processing efficiency in the temporal and spatial dimensions, maintaining wearability while improving effective image clarity.
Data Source
AI summary
Systems, apparatus, and methods for augmenting vision with region-of-interest based processing. In one specific example, smart glasses may use an eye-tracking camera to monitor the user's gaze and determine the user's gaze point. When triggered, the camera assembly captures a high-resolution image. The high-resolution image may be cropped to a much smaller region-of-interest (ROI) image based on computer-vision analysis of the user's gaze point. For example, if the smart glasses detect a human face at the gaze point, then the ROI is cropped to the human face. In this manner, the smart glasses may leverage specific capabilities of the smart glasses to augment the user experience; for example, telephoto lenses provide long distance vision, or computer-assisted search may direct the user to interesting activity. Other aspects may include e.g., external database assisted operation and/or ongoing cataloging throughout the day.