Video Stabilization Constraint Tree for Multi-Parameter Window Placement
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Solution Overview
Problem
The increase in dimensions of the constraint model for video stabilization due to multiple stabilization parameters makes it costly or infeasible, especially when using array structures for querying.
Innovation Solution
Utilizing a constraint model space partition tree, such as a k-D tree or binary space partitioning tree, to split the constraint model into smaller zones, allowing efficient access and determination of feasible viewing window placements within the source field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If array structures are used for querying the constraint model, then complete coverage of feasible viewing window placements is achieved, but computational cost and memory requirements become prohibitive
Solution Approach 1:
The constraint model space is segmented into multiple hyper-rectangular zones using a space partitioning tree structure. Each zone represents a subset of feasible viewing window placements, allowing the model to be stored and queried in a distributed manner rather than as a single large array. This segmentation reduces memory consumption while maintaining complete coverage of the constraint space.
Solution Approach 2:
The patent transitions from a flat array structure to a hierarchical tree structure with multiple dimensions (spatial zones and parameter spaces). The space partitioning tree introduces a hierarchical dimension that organizes constraint data across multiple levels, enabling efficient storage and querying without requiring a dense array representation of the entire constraint space.
2Manufacturing precision
If the number of stabilization parameters is increased, then video stabilization quality is improved, but the dimensions of the constraint model increase making it too costly to use
Solution Approach 1:
The space partitioning tree structure is dynamically adapted to the number of stabilization parameters. As parameters are added, the tree structure automatically adjusts its dimensionality and zone divisions, allowing the system to handle varying levels of complexity without requiring a complete redesign of the constraint model storage approach. This dynamic adaptation enables high precision stabilization while managing computational complexity.
Solution Approach 2:
The patent changes the fundamental parameters of how the constraint model is represented, transitioning from fixed array dimensions to flexible tree-based spatial partitioning. This parameter change allows the system to accommodate any number of stabilization parameters by dynamically creating appropriate zones and hyper-rectangles in the partitioned space, rather than being constrained by pre-defined array dimensions.
3Ease of operation
If a dense array structure is used to represent the constraint model, then all feasible placements can be queried, but the computational burden becomes too high
Solution Approach 1:
The constraint model is segmented into discrete hyper-rectangular zones within the space partitioning tree. Each zone can be independently queried and processed, allowing the system to efficiently search only relevant portions of the constraint space rather than iterating through a dense array. This segmentation maintains complete query capability while dramatically improving processing efficiency by eliminating unnecessary computations in unrelated zones.
Solution Approach 2:
The space partitioning tree performs preliminary organization of the constraint model space into zones and hyper-rectangles before querying is needed. This preliminary action pre-computes the spatial relationships and feasible placement regions, so that during actual stabilization operations, the system can quickly navigate the pre-organized structure rather than computing constraints in real-time from a dense array representation.
Data Source
AI summary
A constraint model may include a representation of feasible viewing window placement within the field of view of a video for stabilization. When the viewing window extends beyond the field of view of the video, the placement of the viewing window may be modified using a constraint model space partition tree. The constraint model space partition tree may include branches that split the constraint model and enable use of greater number of stabilization parameters to stabilize the video.