Motion Adaptive Deinterlacer Reducing Video Artifacts
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Solution Overview
Problem
Conventional video deinterlacing algorithms produce undesirable artifacts, particularly in the presence of motion, which degrade picture quality in video processing devices.
Innovation Solution
A motion-adaptive filter and deinterlacer system that uses a DDS module with a denoising filter and deinterlacer, which generates motion data to adapt filtering and interpolation processes, reducing artifacts by selectively enabling or disabling filters and interpolation based on detected motion and noise levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional deinterlacing algorithms are used, then the video signal can be processed, but undesirable artifacts are produced that degrade picture quality
Solution Approach 1:
The patent implements motion-adaptive deinterlacing where the processing mode dynamically changes based on detected motion in the video signal. The system switches between different deinterlacing algorithms (e.g., field repeat, linear interpolation, motion compensation) depending on the motion characteristics of different regions, thereby reducing artifacts while maintaining picture quality.
Solution Approach 2:
The patent applies different deinterlacing strategies to different regions of the video image based on local motion characteristics. By analyzing motion vectors and motion magnitude in specific blocks or regions, the system selectively applies appropriate filtering and interpolation methods to each region, minimizing artifacts locally while preserving overall picture quality.
2Reliability
If video filters are applied to improve picture quality, then noise and artifacts are reduced, but motion in the video image produces undesirable picture degradation
Solution Approach 1:
The patent employs motion-adaptive filtering where the filter strength and type are dynamically adjusted based on detected motion. When motion is detected in a region, the filter is weakened or disabled to prevent motion degradation; when motion is absent or minimal, stronger filtering is applied to reduce noise and improve picture quality.
Solution Approach 2:
The patent applies filtering selectively to different regions of the video image based on local motion characteristics. Regions with high motion content receive minimal or no filtering, while regions with low motion content receive stronger filtering to improve picture quality. This localized approach prevents motion degradation while maintaining noise reduction benefits.
3Adaptability or versatility
If deinterlacing is performed to convert interlaced signal to progressive scan, then the video format is improved for modern displays, but artifacts are introduced that degrade the output quality
Solution Approach 1:
The patent implements adaptive deinterlacing algorithms that dynamically select the appropriate reconstruction method based on motion detection. For stationary or low-motion regions, simple interpolation methods are used; for high-motion regions, motion-compensated methods or field repetition is employed. This dynamic adaptation reduces artifacts while maintaining format compatibility.
Solution Approach 2:
The patent applies different deinterlacing reconstruction methods to different regions of the image based on local motion characteristics. By analyzing motion vectors in each block, the system selectively applies motion-compensated interpolation where needed and simpler methods where sufficient, thereby minimizing artifacts while achieving progressive scan output.
Data Source
AI summary
A device for use in conjunction with a video processing device includes a deinterlacer that selectively interpolates a plurality of pictures into a plurality of selectively deinterlaced pictures, based on deinterlace motion data. A motion detector generates the deinterlace motion data for a picture of the plurality of pictures. The deinterlace motion data is generated based on instantaneous deinterlace motion data generated by comparing an amount of motion for individual pixels of the picture of the plurality of pictures to a motion detection threshold, and also based on historic motion data that considers motion for at least three adjacent pictures of the plurality of pictures having the same odd/even polarity.