Adaptive Sub-Pixel Motion Estimation for Video Artifacts
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Solution Overview
Problem
Existing motion estimation algorithms struggle to accurately handle slow-moving structures without compromising performance on scenes with fast and mixed motions, often resulting in artifacts such as flicker and reduced accuracy.
Innovation Solution
An adaptive motion estimation method that selectively updates candidate motion vectors, using a combination of integer and fractional updates, and employs a motion histogram analysis and frame-by-frame alternation to optimize processing for slow-moving objects without affecting fast-moving scenes, thereby reducing artifacts and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed mechanism is used to produce fractional motion vectors, then the processing is simple and fast, but the accuracy for slow-moving structures is insufficient and artifacts such as flicker occur
Solution Approach 1:
The system dynamically adjusts the motion estimation strategy based on detected motion characteristics. A motion detector identifies whether the scene contains slow-moving structures, and based on this detection, the system selectively applies refined motion estimation only when needed, transitioning from a fixed static approach to an adaptive dynamic approach that optimizes both accuracy and efficiency
Solution Approach 2:
The system changes the parameters of motion estimation processing based on scene characteristics. When slow-moving structures are detected, the system activates refined motion estimation with fractional pixel accuracy; otherwise, it uses the standard fixed mechanism, thereby adapting processing parameters to match the actual motion content of the video sequence
2Measurement precision
If refined motion estimation is applied to all scenes, then accuracy for slow-moving structures improves, but performance on fast-moving scenes deteriorates and processing complexity increases
Solution Approach 1:
The system applies different quality levels of motion estimation to different regions or temporal segments of the video based on local motion characteristics. Refined motion estimation with higher accuracy is applied locally only to frames or regions containing slow-moving structures, while standard processing is used elsewhere, ensuring high accuracy where needed without compromising overall processing efficiency
Solution Approach 2:
The video processing is segmented into detection and estimation phases, and further into standard and refined processing modes. The motion detector segments the video sequence to identify portions requiring refined estimation, allowing the system to process only the necessary segments with high accuracy while maintaining fast processing for the remainder
3Measurement precision
If refined motion estimation is applied to all scenes, then accuracy improves, but computational cost and processing time increase
Solution Approach 1:
The system applies refined motion estimation partially, only when and where it is actually needed based on motion detection results. Instead of applying the computationally expensive refined estimation to all scenes (excessive action), the system uses it selectively for portions containing slow-moving structures (partial action), thereby reducing overall computational energy consumption while maintaining necessary accuracy
Data Source
AI summary
Display 105 is capable of rendering, or otherwise displaying, one or more of a standard definition (SD) image, a two-dimensional (2D), a three-dimensional image (3D) and a high definition (HD) image 110.