Video Frame Rotation Correction via Scene Segmentation
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Solution Overview
Problem
Videos recorded with rotating cameras or unsteady hands often result in frames that are difficult to view or edit due to unwanted rotation, requiring tedious manual correction.
Innovation Solution
An automated system that includes a rotation detection unit, scene change detection unit, and change integrator to automatically correct the rotation of video frames, distinguishing between frame rotation and scene changes to prevent misidentification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correction of rotated video frames is performed, then rotation accuracy can be ensured, but the editing process becomes tedious and time-consuming
Solution Approach 1:
The system performs automatic rotation correction by detecting the rotation angle of each frame and applying the necessary correction without requiring manual user intervention. The video processing system serves itself by autonomously identifying and correcting rotated frames based on scene change detection and rotation analysis, eliminating the tedious manual adjustment process while maintaining correction accuracy
Solution Approach 2:
The system performs preliminary detection of rotation angles and scene changes before applying corrections. By pre-analyzing each frame to determine its rotation state and identifying scene boundaries, the system prepares correction data in advance, enabling efficient batch processing that reduces overall editing time while ensuring accurate correction application
2Productivity
If automatic rotation correction is applied to all frames, then editing efficiency improves, but scene changes may be misidentified leading to incorrect corrections
Solution Approach 1:
The video sequence is segmented into distinct scenes by detecting scene changes between frames. The system divides the video into scene segments and applies rotation correction independently within each scene, preventing misidentification across scene boundaries. This segmentation approach allows the system to maintain high productivity while improving reliability by treating each scene as a separate processing unit with its own rotation characteristics
Solution Approach 2:
The system uses feedback mechanisms to verify scene change detections and adjust rotation correction decisions. By continuously monitoring frame similarities and comparing detected changes against expected patterns, the system can confirm or reject potential scene changes, reducing false positives and ensuring accurate correction application only where appropriate
3Measurement precision
If frame by frame rotation detection is performed, then correction precision is improved, but processing complexity and computational load increase
Solution Approach 1:
The system extracts only the essential information needed for rotation detection from each frame, such as key feature points or dominant orientation indicators, rather than analyzing the entire frame content. This extraction approach maintains high detection precision by focusing on critical rotational cues while reducing processing complexity and computational requirements by eliminating unnecessary analysis of irrelevant frame elements
Data Source
AI summary
A method and apparatus for correcting a rotation of a video frame are described. According to a method, an amount of the rotation of the video frame with respect to a reference is determined. The rotation of the video frame is corrected based at least in part on the detected amount of the rotation of the video frame.