Video Motion Detection by Adjusting Temporal Fields and Spatial Patterns
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Solution Overview
Problem
Current de-interlacing technologies struggle to efficiently separate motion and stationary video pixels in interlaced video signals, particularly in flat panel displays, where artifacts like sawtooth and judder become conspicuous, and increasing reference fields to improve separation increases memory bandwidth requirements.
Innovation Solution
A method that adjusts fields' length in the temporal domain and selects configurable patterns in the spatial domain for motion detection, using up to seven fields' temporal information while reducing bandwidth requirements by storing field differences, and enhancing detection accuracy through spatial patterns that adapt from areas to lines or pixels based on temporal field length.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If increasing reference fields is used to improve motion and stationary pixel separation, then detection accuracy is improved, but memory bandwidth requirement increases
Solution Approach 1:
The patent extracts and stores only the difference information between reference fields and current fields, rather than storing complete field data. This selective extraction of essential information (pixel differences) reduces the data volume requiring memory bandwidth while maintaining the capability to perform accurate motion detection across multiple reference fields
Solution Approach 2:
Instead of storing complete field information and computing differences during processing, the patent inverts the approach by pre-computing and storing only the difference values. This inversion transforms the data storage strategy from storing absolute values to storing relative changes, reducing memory bandwidth requirements
2Reliability
If using configurable spatial patterns (area, line, pixel) is implemented, then motion detection reliability is improved, but computational complexity increases
Solution Approach 1:
The patent implements dynamic spatial patterns that can adapt their configuration (pixel, line, or area) based on the specific video content and motion characteristics. This dynamic adaptability allows the system to optimize detection reliability for different scenarios while managing computational complexity through selective pattern application rather than exhaustive processing
Solution Approach 2:
The patent applies different spatial pattern configurations to different regions of the video image based on local motion characteristics. By tailoring the detection pattern (pixel-level for high-detail areas, line-level for intermediate areas, area-level for smooth regions) to local requirements, the system improves overall reliability while reducing unnecessary computational complexity in regions where simpler patterns suffice
Data Source
AI summary
A method for separating the motion and stationary video pixels during the video de-interlacing process by adjusting the fields' length in the temporal domain and selecting the configurable patterns in the spatial domain.