Video Stabilization Using Characteristic Curves and Weighing Functions
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Solution Overview
Problem
Existing video stabilization techniques are computationally burdensome and ineffective in real-time applications, particularly sensitive to illumination changes, motion blur, new details, and moving objects, which limits their ability to accurately calculate global motion vectors and remove motion effects from video sequences.
Innovation Solution
A method that defines horizontal and vertical weighing functions to prioritize pixels within a zone of interest, computes characteristic curves, and calculates global displacements to minimize differences between images, using filters like High Pass and Butterworth filters to enhance alignment and reduce noise, thereby generating accurate motion vectors and stabilizing video sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature-based, block matching-based, or optical flow-based stabilization techniques are used, then motion compensation accuracy is improved, but computational complexity increases making real-time application difficult
Solution Approach 1:
The patent extracts only the essential horizontal and vertical characteristic curves from the image data, ignoring other complex features. This extraction approach captures the dominant motion information while discarding computationally expensive details, enabling real-time processing without sacrificing stabilization effectiveness
Solution Approach 2:
The patent replaces complex mechanical image processing algorithms (block matching, optical flow) with a simplified mathematical approach based on characteristic curves and their derivatives. This substitution transforms a computationally intensive mechanical search process into an efficient analytical calculation
2Productivity
If simple horizontal and vertical characteristics curve methods are used, then computational speed is improved, but sensitivity to illumination changes, motion blur, and moving objects increases
Solution Approach 1:
The patent performs preliminary high-pass filtering on the characteristic curves before computing motion vectors. This pre-processing step removes low-frequency components related to illumination changes and motion blur, allowing the subsequent simple curve matching to produce reliable results even in challenging conditions
Solution Approach 2:
The patent transforms the characteristic curves by applying high-pass filtering, which changes the frequency parameters of the signal. This transformation emphasizes high-frequency edge information while suppressing low-frequency illumination variations, making the motion estimation more robust without increasing computational complexity
3Measurement precision
If all pixels in the image are considered for motion estimation, then comprehensive motion analysis is achieved, but computational burden increases
Solution Approach 1:
The patent applies different processing priorities to different regions by emphasizing horizontal and vertical characteristic curves that pass through the center of the image. This local quality approach focuses computational energy on the most informative regions while reducing processing of less critical areas, achieving efficient real-time stabilization
Data Source
AI summary
A digital image processing system and method for removing motion effects from images of a video sequence, and generating corresponding motion compensated images.