Motion Vector Fusion via Phase Plane Correlation and 3D Recursive Processing
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Solution Overview
Problem
Current video frame interpolation techniques face challenges in accurately determining motion vectors, especially for complex motions like object rotation or revolution, and are affected by repetitive patterns and luminance variations.
Innovation Solution
Combining phase plane correlation (PPC) and three-dimensional (3D) recursive processing techniques to generate and refine motion vectors, using PPC to generate candidates and 3D recursive methods to adjust penalties and select optimal motion vectors, while addressing issues with repetitive patterns and luminance variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If phase plane correlation (PPC) methods are used to generate motion vectors, then motion vectors for translational motion and brightness changes are improved, but repetitive patterns in the generated motion vectors cause deterioration
Solution Approach 1:
The patent combines PPC methods with 3D recursive filtering to merge the strengths of both approaches. PPC provides accurate initial motion vectors for translational motion and brightness changes, while 3D recursive filtering removes repetitive patterns and improves temporal consistency across frames
Solution Approach 2:
The 3D recursive filtering process uses feedback from previously processed frames to refine motion vectors. By comparing current frame motion vectors with historical data and applying recursive updates, the system eliminates repetitive patterns while maintaining accuracy
2Stability of the object's composition
If 3D recursive methods are used to provide motion vectors for non-translational motion, then spatial and temporal smoothness are improved, but convergence varies drastically depending on time and situation
Solution Approach 1:
The patent applies preliminary PPC processing to generate accurate initial motion vectors before applying 3D recursive filtering. This preliminary action provides a reliable starting point that improves convergence stability, especially for non-translational motions like rotation and revolution
Solution Approach 2:
The system dynamically adjusts filtering parameters based on motion characteristics. By detecting the type of motion (translational vs. non-translational) and adapting the 3D recursive filtering parameters accordingly, the patent maintains consistent convergence across different situations and time periods
3Adaptability or versatility
If motion vectors are determined for complex motions like object rotation or revolution, then coverage of motion types is improved, but accuracy of motion vector determination deteriorates
Solution Approach 1:
The patent employs dynamic motion estimation that adapts to different motion types. The system switches between PPC-based methods for translational motion and 3D recursive filtering for non-translational motion, maintaining high accuracy across diverse motion scenarios including rotation and revolution
Data Source
AI summary
A system comprises a phase plane correlation (PPC) processing module configured to receive video data from a video source and generate a motion vector (MV) candidate, and a three-dimensional (3D) recursive processing module configured to receive the MV candidate from the PPC processing module, perform 3D recursive processing on a number of MV candidates including the MV candidate received from the PPC processing module, and select one of the MV candidates based on the 3D recursive processing.