Adaptive Video Equalizer for Noise and Distortion Reduction
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Solution Overview
Problem
Video processing systems face challenges in effectively reducing noise and distortion, particularly in analog video channels and compressed digital sources, where dynamic changes in channel and compression characteristics complicate proper equalization, leading to increased distortion and inefficient encoding processes.
Innovation Solution
A multi-band equalizer system that dynamically adjusts gain coefficients based on per-pixel noise and distortion detection, utilizing a luminance detector and edge detector to differentiate between actual edges and noise artifacts, and further informed by video motion classification, to reduce noise and distortion in video data with low processing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed equalization filter is applied to video data, then the processing is simple and fast, but the filter cannot adapt to dynamic channel and compression characteristics resulting in increased distortion
Solution Approach 1:
The patent implements dynamic equalization by adjusting filter coefficients in real-time based on detected noise and distortion characteristics. The system transitions from fixed filtering to adaptive filtering where the equalizer parameters are continuously updated according to the video signal's changing properties, allowing the system to adapt to dynamic channel characteristics and compression artifacts.
Solution Approach 2:
The patent employs feedback mechanisms where the output of the equalizer is fed back to the noise and distortion detector, which then adjusts the equalizer coefficients accordingly. This closed-loop feedback system allows the equalizer to learn from its own performance and automatically adapt to changing conditions without requiring manual intervention.
2Object-affected harmful factors
If aggressive filtering is applied to remove noise and distortion, then noise reduction is improved, but video quality deteriorates due to loss of legitimate edge information
Solution Approach 1:
The patent applies different filtering strengths to different regions of the video image based on local characteristics. Edge regions are identified and protected from aggressive filtering, while flat regions undergo more aggressive noise reduction. This spatially adaptive approach allows the system to remove noise where appropriate while preserving edges and fine details where needed.
Solution Approach 2:
The patent replaces traditional mechanical filtering approaches with a more intelligent system that uses pattern recognition and machine learning algorithms to distinguish between noise and legitimate image content. Instead of applying uniform filtering, the system uses computational methods to make intelligent decisions about which pixels to filter and which to preserve.
3Measurement precision
If per-pixel noise detection is performed to enable adaptive filtering, then filtering accuracy is improved, but processing overhead increases
Solution Approach 1:
The patent divides the video processing task into segments, processing the video frame by frame and potentially dividing frames into smaller blocks. The noise and distortion detection is performed on segmented regions rather than the entire video stream at once, allowing for more accurate per-pixel analysis while managing computational load through progressive processing.
Solution Approach 2:
The patent implements periodic noise and distortion detection rather than continuous analysis. The system performs detection at key intervals and updates filter coefficients periodically, rather than continuously adjusting for every pixel change. This periodic approach reduces processing overhead while maintaining effective noise reduction performance.
4Productivity
If compression artifacts are present in video data, then encoding efficiency is reduced due to increased data stream output, but removing artifacts requires complex processing
Solution Approach 1:
The patent applies preliminary equalization and noise reduction processing before the video data is encoded. By pre-processing the video signal to remove compression artifacts and reduce noise, the subsequent encoding process works with cleaner data, improving encoding efficiency and reducing the final data stream size without requiring complex artifact removal during encoding.
Data Source
AI summary
An adaptive equalization method and system for reducing noise and distortion in a sampled video signal improves the quality of output video information in both consumer and professional applications. A multi-band equalizer applied to the sampled video information reduces noise and distortion artifacts in the sampled video information. The gains of each of the equalizer bands is adjusted dynamically by control outputs provided by a noise and distortion detector. The noise and distortion detector incorporates circuits for comparing the luminance of each plane on a per-pixel basis with neighboring pixels and also includes a pattern matching comparator that detects edge features by comparing each pixel and its neighbors with a set of predetermined patterns. The noise and distortion detection can be further confirmed by comparing the detection results across multiple frames and the equalizer gain values may be further selected by a result of a video motion type classification.