Recursive Motion Video De-interlacing Artifact Reduction
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Solution Overview
Problem
Conventional de-interlacing technologies suffer from motion-caused artifacts such as 'feathering' and 'halo' effects, particularly in fast-moving content and high-frequency backgrounds, and require long stabilization times during scene changes, while also incurring high costs for maintaining and reloading motion history data.
Innovation Solution
The implementation of a recursive motion processing system that detects still motion conditions and employs adaptive control over three types of motion detection: local still detection, large area motion detection, and feathering pattern detection, to generate accurate motion maps and crop out improper motion effects, thereby reducing or eliminating artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional motion detection algorithms are used for de-interlacing, then motion compensation can be performed, but motion-caused artifacts such as feathering and halo effects occur in fast-moving content and high-frequency backgrounds
Solution Approach 1:
The patent applies different motion detection and processing strategies to different regions of the video frame. By analyzing local motion characteristics in specific blocks or regions, the system can identify areas with high-frequency content or fast motion and apply appropriate de-interlacing methods locally, thereby reducing feathering and halo artifacts while maintaining overall de-interlacing accuracy.
Solution Approach 2:
The system dynamically adjusts motion detection parameters and processing intensity based on the actual motion content in each frame or region. By detecting motion magnitude, direction, and complexity in real-time, the algorithm can adaptively switch between different de-interlacing modes (e.g., spatial interpolation, temporal interpolation, or motion-compensated methods) to minimize artifacts while handling fast-moving content effectively.
2Reliability
If motion history data is maintained for accurate motion detection, then motion compensation improves, but storage and processing costs increase
Solution Approach 1:
The patent implements a selective retention strategy for motion history data, discarding redundant or less useful motion information while preserving critical motion patterns needed for accurate de-interlacing. By identifying and removing unnecessary historical data, the system reduces storage requirements and processing overhead while maintaining the essential motion context needed for artifact reduction.
Solution Approach 2:
The system extracts only the most relevant motion features from historical data, such as dominant motion vectors or significant motion regions, rather than storing and processing complete motion histories. This selective extraction reduces the quantity of motion history data that must be maintained while preserving the key information needed for accurate motion compensation and artifact suppression.
3Reliability
If complex motion detection algorithms are used to handle all motion types, then motion compensation is thorough, but processing time and computational resources increase
Solution Approach 1:
The patent applies a tiered motion detection approach where a simplified algorithm processes the entire frame first to identify regions of interest, and then more complex algorithms are applied only to specific areas requiring detailed motion analysis. This partial application of complex processing reduces overall computational load and processing time while maintaining thorough motion compensation where most needed.
Solution Approach 2:
The video frame is divided into multiple blocks or regions, and different levels of motion detection complexity are applied to different segments based on their motion characteristics. High-motion or artifact-prone regions receive more sophisticated processing, while low-motion areas use simpler algorithms, thereby optimizing the balance between motion compensation completeness and processing efficiency.
Data Source
AI summary
A video de-interlacing device includes a video data input interface to receive a stream of interlaced video data, a motion detector coupled to the video data input interface to produce motion detection video data, and a video data output interface to pass de-interlaced video data generated from the motion detection video data. The video de-interlacing device also includes a recursive motion processor to detect a still motion condition in at least one area of the motion detection video data. The recursive motion processor includes a local still detector, a large area motion detector, and a feathering detector. The recursive motion processor is arranged to produce motion information used to crop the motion detection video data based on the detected still motion condition.