Video Motion Vector Filtering for Spurious Effect Removal
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Solution Overview
Problem
Motion estimation algorithms in video sequences struggle to effectively filter out spurious motion effects, such as camera shakes and object movements, leading to inaccurate representation of video frames.
Innovation Solution
A method that utilizes block matching algorithms to calculate motion vectors for selected blocks of pixels, applying filters to discard vectors that are significantly different from surrounding blocks or located in homogeneous areas, and using recursive procedures to calculate roto-translational and zooming parameters, thereby isolating and removing spurious motion effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If block matching algorithms are used to calculate motion vectors for all blocks, then motion estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The image is divided into blocks, and motion vectors are calculated selectively for certain blocks rather than all blocks. The method segments the computational workload by identifying representative blocks that capture the essential motion characteristics of larger regions, reducing the total number of motion vector calculations while maintaining accuracy.
Solution Approach 2:
The patent applies partial action by calculating motion vectors for only a subset of blocks rather than all blocks. It uses a threshold-based approach where motion vectors are computed only for blocks that meet certain criteria (e.g., sufficient motion magnitude), avoiding unnecessary computations for blocks with minimal or spurious motion.
2Loss of information
If motion vectors from all blocks are used for spurious motion estimation, then estimation completeness is improved, but accuracy deteriorates due to inclusion of erroneous vectors
Solution Approach 1:
The method extracts and removes spurious motion vectors from the set of all motion vectors. It identifies erroneous vectors through criteria such as high disagreement with neighboring blocks, location in homogeneous regions, or excessive magnitude, and excludes them from the spurious motion estimation calculation to improve accuracy.
Solution Approach 2:
The patent applies different quality criteria to different blocks based on their local characteristics. Blocks in homogeneous regions or with low motion activity are treated differently from blocks with significant motion, allowing the system to filter out spurious vectors while preserving genuine motion information.
3Reliability
If filtering criteria are applied to discard motion vectors, then spurious motion filtering is improved, but motion information loss increases
Solution Approach 1:
The method adjusts filtering parameters dynamically based on local image characteristics. Thresholds for discarding motion vectors are adapted according to the specific block's properties (e.g., motion magnitude, neighborhood agreement), allowing aggressive filtering of spurious vectors while preserving genuine motion in regions where it is significant.
Data Source
AI summary
According to the novel method, roto-translational and zooming parameters describing spurious motion effects are determined by exploiting any of the many block matching algorithms commonly used for motion estimation for calculating a motion vector for all or for a selected number of blocks of pixels of the current frame that is processed. Some of the so calculated motion vectors are not taken into account for estimating spurious motion effects. The roto-translational and zooming parameters describing what is considered to be spurious global motion between a current frame and the precedent frame of the sequence, are calculated by processing the selected motion vectors of blocks of pixels of the frame through a recursive procedure that includes computing error values and readjusting the roto-translational and zooming parameters based on the error values.