Motion Blur Simulation via Stabilized Frame Combination
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Solution Overview
Problem
Capturing video frames with motion blur results in undesired artifacts when stabilized, and measuring motion is computationally intensive and power-consuming.
Innovation Solution
A system that stabilizes video frames and combines multiple frames into single motion-blurred frames, reducing the frame rate and simulating motion blur, using a processor to configure machine-readable instructions for visual information, stabilization, and motion blur components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If motion blur is inserted into video frames by measuring pixel motion, then motion blur is achieved, but computational intensity and power consumption increase
Solution Approach 1:
The system performs preliminary stabilization of video frames before combining them. By stabilizing frames in advance and removing unintended motion beforehand, the system avoids the need for computationally intensive motion measurement during the motion blur generation phase, thus reducing real-time computational load and power consumption while maintaining motion blur quality
Solution Approach 2:
Instead of measuring actual pixel motion which requires intensive computation, the system creates a simplified model by combining multiple stabilized frames with different exposure times. This copying approach simulates motion blur effects through frame combination rather than direct motion measurement, significantly reducing computational requirements
2Reliability
If video frames are stabilized to remove unintended motion, then artifact reduction is achieved, but processing time increases
Solution Approach 1:
The system performs stabilization as a preliminary step before motion blur generation. By completing stabilization upfront and caching the stabilized frames, the system eliminates the need to repeat stabilization during motion blur generation, thus reducing overall processing time while maintaining video quality
Solution Approach 2:
The system combines multiple stabilized frames with different exposure times into a single motion blurred frame. This merging process achieves both artifact reduction (through stabilization) and motion blur simulation simultaneously, eliminating the need for separate processing steps and reducing total processing time
3Manufacturing precision
If multiple stabilized video frames are combined into single motion blurred frames, then motion blur is simulated and frame rate is reduced, but computational intensity remains high
Solution Approach 1:
The system uses a simplified mathematical model to combine stabilized frames, copying the essential motion blur effect through weighted averaging of frames with different exposure times. This approach avoids complex motion measurement and optical flow calculations, reducing processing complexity while maintaining motion blur realism
Solution Approach 2:
The system changes the exposure time parameter across multiple captured frames and combines them with appropriate weighting. By varying this single parameter (exposure time) rather than performing complex motion analysis, the system achieves realistic motion blur with reduced computational complexity
Data Source
AI summary
Video frames are captured by an image capture device and stabilized to generate stabilized video frames. Multiple stabilized video frames are combined into single motion blurred video frames. Combination of multiple stabilized video frames into single motion blurred video frames produces motion blur within the single motion blurred video frames that is both physical and real.