Video Stabilization Using Reliable Motion Vector Blocks
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Solution Overview
Problem
Existing digital image stabilization methods face inefficiencies in motion vector computation and global motion estimation due to unreliable block sizes, leading to wasted computation and inferior accuracy, especially when dealing with large moving objects and noise.
Innovation Solution
The method involves segmenting frames into valid and invalid blocks based on motion vector reliability, using a hierarchical image representation to compute a single global motion vector, and refining it for higher resolution, thereby reducing unnecessary computation and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital image stabilization computes motion vectors for all blocks in a frame, then complete motion information is obtained, but computational complexity increases and processing time is wasted on unreliable blocks
Solution Approach 1:
The patent divides the image frame into multiple blocks and further segments these blocks into reliable and unreliable categories based on motion characteristics. By segmenting the computation domain, the system processes only reliable blocks for stabilization, eliminating wasted computation on unreliable blocks while maintaining complete motion information where available.
Solution Approach 2:
The patent performs preliminary classification of blocks as reliable or unreliable before performing full motion compensation. This preliminary action identifies which blocks warrant detailed processing and which can be excluded, thereby reducing overall computational time while ensuring reliable blocks receive appropriate attention.
2Device complexity
If digital image stabilization uses fixed block sizes for motion estimation, then processing is simplified, but accuracy deteriorates when dealing with large moving objects and noise
Solution Approach 1:
The patent implements dynamic block processing where the treatment of each block adapts based on its reliability characteristics. Rather than applying a fixed processing approach to all blocks, the system dynamically adjusts computation based on whether each block is classified as reliable or unreliable, thereby improving accuracy without excessive complexity.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the image based on local characteristics. Reliable blocks receive full motion estimation and compensation processing, while unreliable blocks receive reduced or alternative processing. This local differentiation improves overall accuracy by matching processing intensity to actual block quality.
3Stability of the object's composition
If digital image stabilization processes all blocks with equal detail, then uniform processing is achieved, but computational efficiency decreases due to processing unreliable blocks
Solution Approach 1:
The patent segments the set of blocks into two distinct groups: reliable and unreliable. This segmentation allows the system to apply uniform detailed processing only to reliable blocks while using simplified or alternative processing for unreliable blocks, thereby maintaining processing consistency where appropriate while dramatically improving overall efficiency.
Solution Approach 2:
The patent applies full processing action only to the extent necessary - that is, only to reliable blocks that can benefit from detailed motion estimation. By applying partial processing (full detail) to some blocks and reduced processing to others, the system achieves optimal efficiency without sacrificing necessary processing quality.
Data Source
AI summary
Stabilization for devices such as hand-held camcorders segments a low-resolution frame into a region of reliable estimation, finds a global motion vector for the region at high resolution, and uses the global motion vector to compensate for jitter.