Truncated Filter for Reduced Latency Video Stabilization
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Solution Overview
Problem
Digital video image stabilization techniques are computationally intensive, making them unsuitable for real-time processing on devices with limited resources, such as multi-purpose handheld devices, which often lack the necessary processing power to efficiently correct jittery video motion without significant delays.
Innovation Solution
The implementation of a truncated filter with a reduced number of taps for temporal smoothing of global motion transforms, allowing for reduced latency and memory usage by utilizing information from fewer future and past frames, thereby optimizing processing on devices with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If digital video image stabilization is applied using traditional filtering methods, then video quality is improved, but processing latency increases and memory usage increases
Solution Approach 1:
The patent extracts only the essential information needed for stabilization by using a truncated filter that processes fewer future and past frames. Instead of using a large number of frames for filtering, the invention selectively uses a reduced set of frames (e.g., 1-3 future frames and 1-3 past frames), removing the unnecessary computational burden while retaining the core stabilization function.
Solution Approach 2:
The patent applies partial action by using a truncated filter that processes only a portion of the available frame data. Rather than filtering through all possible future and past frames, the invention uses a limited window of frames (truncated sequence), achieving sufficient stabilization quality with reduced computational effort and lower latency.
2Manufacturing precision
If digital video image stabilization is applied using traditional filtering methods, then video quality is improved, but device resource consumption increases
Solution Approach 1:
The patent removes the excessive computational requirements by truncating the filter to use only essential frames. This extraction of necessary information reduces the processing burden on mobile devices while maintaining adequate stabilization quality, making the technique feasible for resource-constrained environments.
Solution Approach 2:
The patent changes the filter parameters by reducing the number of taps (frames) used in the filtering process. By adjusting the temporal window size to a smaller truncated sequence, the invention reduces computational complexity and memory requirements while preserving the core stabilization functionality for mobile device deployment.
3Productivity
If a truncated filter with fewer taps is used, then processing speed is improved, but video stabilization quality may deteriorate
Solution Approach 1:
The patent achieves an optimal balance by applying partial action - using just enough frames (truncated sequence) to maintain acceptable stabilization quality without over-processing. The truncated filter uses a carefully selected subset of frames that provides sufficient smoothing while enabling real-time processing on mobile devices.
Solution Approach 2:
The patent introduces dynamic adaptability by allowing the truncated filter parameters to be adjusted based on video content characteristics and device capabilities. The filter can dynamically modify its window size and composition to maintain quality across varying scenarios, adapting to different motion patterns and resource constraints.
Data Source
AI summary
Reduced latency video stabilization methods and tools generate truncated filters for use in the temporal smoothing of global motion transforms representing jittery motion in captured video. The truncated filters comprise future and past tap counts that can be different from each other and are typically less than those of a baseline filter providing a baseline of video stabilization quality. The truncated filter future tap count can be determined experimentally by comparing a smoothed global motion transform set generated by applying a baseline filter to a video segment to those generated by multiple test filter with varying future tap counts, then settings the truncated filter future tap count based on an inflection point on an error-future tap count curve. A similar approach can be used to determine the truncated filter past tap count.