Video Noise Detection Using Statistical Processing
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Solution Overview
Problem
Existing noise reduction apparatuses struggle to accurately detect noise in video signals with noise present on images only, during non-blanking intervals, and fail to effectively reduce noise in moving images while maintaining high noise reduction performance across varying noise levels.
Innovation Solution
A noise detection and reduction system that employs multiple high-frequency component extractors and statistical processing to generate noise detection signals, combined with line and frame recursive filters to adaptively weight noise reduction coefficients based on noise levels, ensuring accurate noise detection and reduction across still and moving images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If noise detection is performed only during vertical or horizontal blanking intervals, then noise detection accuracy is improved, but noise detection capability is lost when noise is present only on images during non-blanking intervals
Solution Approach 1:
The patent segments noise detection into two distinct paths: one for blanking intervals (using high-pass filter extraction) and one for image-carrying periods (using statistical processing of high-frequency components). This segmentation allows each path to be optimized for its specific condition while maintaining overall detection capability across all scenarios.
Solution Approach 2:
The patent dynamically switches between different noise detection methods based on the signal condition. During blanking intervals, it uses high-pass filter-based detection; during image-carrying periods, it uses statistical processing. This dynamic adaptation ensures accurate noise detection regardless of when noise occurs in the video signal.
2Object-affected harmful factors
If coring unit increases threshold level for higher noise reduction performance, then noise reduction performance is improved, but lower-level video signal components are removed along with noise
Solution Approach 1:
The patent applies different processing qualities to different frequency components. The coring unit selectively removes only the high-frequency noise components while preserving lower-frequency video signal components. This local quality approach ensures noise reduction without losing important image information.
Solution Approach 2:
The patent segments the video signal into high-frequency components (containing noise) and low-frequency components (containing image information). By processing only the high-frequency portion through coring and then recombining, it achieves noise reduction while preserving the essential video signal content.
3Object-affected harmful factors
If recursive filtering is applied to reduce noise in still images, then noise reduction performance is improved, but effectiveness decreases for moving images
Solution Approach 1:
The patent dynamically adjusts the attenuation coefficient in the recursive filter based on the detected noise level. When noise is high, the coefficient increases noise reduction; when noise is low or in moving images, it reduces the effect. This dynamic adjustment maintains effectiveness across both still and moving images.
Solution Approach 2:
The patent changes the attenuation coefficient parameter of the recursive filter based on noise detection results. This parameter adjustment allows the same recursive filtering mechanism to effectively handle both still images (high noise reduction) and moving images (lower noise reduction) without requiring different algorithms.
Data Source
AI summary
Four high-frequency components are extracted from video signals: the first from a blanking interval; the second from an image-carrying period between blanking intervals; and the third and fourth from the video and one-line and -frame delayed signals, respectively. Statistical processing is performed to obtain absolute values of the second to fourth component levels per pixel and the number of pixels of the components per image per absolute level. A noise detecting signal is generated based on the first component level irrespective of the processing when the level is higher than a predetermined level, if not, first to third levels are obtained for the first to third components, respectively, each having the smallest number of pixels among levels other than zero each having a larger number of pixels than zero for the components. The detecting signal is generated based upon the lowest or second lowest level among the first to third levels.