Video Stabilization Using Modified Motion Transformations
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Solution Overview
Problem
Handheld video recording devices often suffer from video shake due to user movement, which existing optical video stabilization mechanisms may not effectively address without increasing device size and cost.
Innovation Solution
A computer-implemented method that stabilizes video by identifying camera movement between frames, modifying the transformation to emphasize long-term movements, and applying a compensation transform to reduce distortion, allowing for real-time stabilization without storing multiple unstabilized frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If optical video stabilization devices are used to compensate for camera shake, then video stability is improved, but device size and manufacturing cost increase
Solution Approach 1:
The patent replaces the mechanical optical stabilization system with a computational approach. The computing system analyzes consecutive video frames, identifies camera movement through feature point tracking, and applies digital transformations to stabilize the video. This substitution eliminates the need for mechanical components like gyroscopes or movable lenses, thereby reducing device size while maintaining stabilization effectiveness.
Solution Approach 2:
The patent creates a digital copy of the video content and applies stabilization transformations to the copied frames rather than physically moving the original captured images. By generating stabilized versions through computational transformation of frame data, the system achieves stabilization without requiring physical optical components that would increase device volume.
2Stability of the object's composition
If optical video stabilization devices are used to compensate for camera shake, then video stability is improved, but manufacturing cost increases
Solution Approach 1:
The patent replaces expensive mechanical optical stabilization hardware with computational algorithms that can be implemented in software or firmware. This substitution dramatically reduces manufacturing costs as the computational approach requires only processing power already present in modern recording devices, eliminating the need for costly specialized components.
Solution Approach 2:
The computing system within the recording device performs the stabilization processing using its own computational resources. The device analyzes its captured video frames and applies corrections independently, eliminating the need for additional external stabilization hardware and reducing overall system cost.
3Stability of the object's composition
If computational video stabilization is applied to each frame, then video stability is improved, but processing time increases
Solution Approach 1:
The patent performs stabilization processing on video frames as they are captured or immediately afterward, rather than waiting until the entire video sequence is recorded. By processing frames in real-time or near-real-time order, the system minimizes processing delays and enables potential live stabilization applications.
Solution Approach 2:
The patent processes video stabilization on a frame-by-frame basis rather than attempting to process the entire video sequence simultaneously. This segmentation allows the computational workload to be divided into manageable units that can be processed sequentially with minimal memory requirements and reduced overall processing time.
Data Source
AI summary
In general, the subject matter can be embodied in methods, systems, and program products for identifying, by a computing system and using first and second frames of a video, a transformation that indicates movement of a camera with respect to the frames. The computing system generates a modified transformation so that the transformation is less representative of recent movement. The computing system uses the transformation and the modified transformation to generate a second transformation. The computing system identifies an anticipated distortion that would be present in a stabilized version of the second frame. The computing system determines an amount by which to reduce a stabilizing effect. The computing system applies the second transformation to the second frame to stabilize the second frame, where the stabilizing effect has been reduced based on the determined amount by which to reduce the stabilizing effect.