Dynamic Stabilization Adjustment for Video Motion Artifacts
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Solution Overview
Problem
Existing video stabilization techniques fail to effectively address the jerky or shaky motion in videos captured by image capture devices in motion, as a single tuning of stabilization may not work for all types of motion.
Innovation Solution
A dynamic stabilization adjustment system that assesses the context of video capture, including motion types and environmental factors, to determine and adjust stabilization parameter values in real-time, ensuring optimal stabilization throughout the capture duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single tuning of stabilization is applied to all video content, then the stabilization parameters remain constant and simple to implement, but the stabilization effectiveness deteriorates when different types of motion are present
Solution Approach 1:
The patent implements dynamic stabilization by continuously adjusting stabilization parameters based on real-time analysis of camera motion characteristics. The system transitions from static single-tuning stabilization to dynamic multi-parameter adjustment, analyzing motion types (e.g., walking, running, vehicle-mounted) and corresponding motion frequencies to adaptively select optimal stabilization settings during video capture
Solution Approach 2:
The system changes stabilization parameters (such as stabilization strength, smoothing intensity, and frame interpolation settings) based on detected motion characteristics. Different motion types trigger different parameter configurations, allowing the stabilization algorithm to optimize performance for each specific capture scenario rather than using a fixed parameter set
2Reliability
If dynamic adjustment of stabilization parameters is implemented based on capture context, then stabilization effectiveness improves for different motion types, but system complexity increases
Solution Approach 1:
The stabilization system performs self-adjustment by automatically analyzing camera motion characteristics and selecting appropriate stabilization parameters without user intervention. The system services itself by monitoring its own operational context (motion type, capture duration, environmental conditions) and autonomously optimizing stabilization settings, eliminating the need for manual parameter tuning or complex user interfaces
Solution Approach 2:
The system implements a feedback loop where stabilization performance is continuously monitored and used to adjust parameters in real-time. Motion detection data, stabilization output quality, and capture context information feed back into the parameter adjustment algorithm, creating a closed-loop control system that adapts to changing conditions during video capture
3Adaptability or versatility
If stabilization parameters are adjusted as a function of progress through capture duration, then stabilization adapts to changing motion conditions over time, but processing complexity and computational load increase
Solution Approach 1:
The capture duration is segmented into distinct motion phases or time intervals, with stabilization parameters adjusted for each segment based on the dominant motion characteristics of that period. This segmentation approach allows the system to handle complex temporal variations in motion by breaking them into manageable segments, each processed with appropriate stabilization settings rather than attempting to handle all variations simultaneously
Data Source
AI summary
An image capture device may capture visual content during a capture duration. The context of capture of the visual content by the image capture device may be assessed. The context of capture of the visual content by the image capture device may be used to determine values of stabilization parameters for the visual content.