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Intrinsic Motion Stability Assessment for Video Stabilization
IEEE Transactions on Visualization and Computer Graphics
|July 12, 2018
Summary
This study introduces a new algorithm to measure video motion stability without a reference. It analyzes the intrinsic smoothness of video motion paths, correlating well with human judgment.
Area of Science:
- Computer Vision
- Video Processing
- Image Analysis
Background:
- Video stabilization aims to reduce unwanted motion.
- Assessing the quality of stabilization, especially motion smoothness, is crucial.
- Existing methods often rely on reference frames, limiting their applicability.
Purpose of the Study:
- To develop a novel, non-reference algorithm for assessing video motion stability after stabilization.
- To quantify the intrinsic smoothness of video motion paths.
- To provide a reliable metric that correlates with human subjective judgments of stability.
Main Methods:
- Representing the motion path as a curve within the Lie group of homographies.
- Characterizing motion path smoothness using intrinsic geodesic curvature.
- Employing a bundle of paths to manage spatially variant motions.
- Computing weighted curvature for a comprehensive stability assessment.
- Investigating distortion and cropping as supplementary factors.
Main Results:
- The proposed algorithm effectively assesses video motion stability in a non-reference manner.
- The method demonstrates strong correlation with human subjective evaluations.
- Experimental validation on 160 video clips confirms the algorithm's effectiveness.
Conclusions:
- The novel algorithm provides an accurate and objective measure of video stabilization quality.
- This non-reference approach offers a valuable tool for video processing and analysis.
- The findings contribute to improving automated video stabilization assessment.
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