Video Noise Detection via Inter-Frame Differential Flat Area Analysis
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Solution Overview
Problem
Current noise detection algorithms for video processing struggle to balance accuracy and real-time processing, failing to effectively address video noise in smartphone camera captures.
Innovation Solution
A method and apparatus for video noise detection that involves extracting adjacent video frames, performing differential processing to obtain an inter-frame differential image, detecting flat areas in the frames, and calculating a time-domain noise value using pixel information from the intersection of flat areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional noise detection algorithms are used, then noise detection accuracy may be maintained, but real-time processing capability deteriorates
Solution Approach 1:
The patent segments the video processing task by dividing the video into multiple frames and further dividing each frame into multiple blocks. This segmentation allows parallel processing of different blocks, improving real-time processing capability while maintaining detection accuracy through comprehensive block-level analysis.
Solution Approach 2:
The patent applies partial action by selectively processing only certain blocks based on their characteristics. Instead of uniformly processing all blocks with the same complexity, the method adapts the processing level to each block's needs, achieving real-time performance while maintaining accuracy where necessary.
2Measurement precision
If complex noise detection algorithms are applied, then noise detection accuracy improves, but processing time increases
Solution Approach 1:
The patent implements dynamic processing by adjusting the detection strategy based on block characteristics. For blocks with high noise susceptibility, more comprehensive analysis is performed, while for low-noise blocks, simplified processing is applied. This dynamic adaptation reduces overall processing time while maintaining accuracy for critical regions.
Solution Approach 2:
The patent applies local quality by using different detection thresholds and processing intensities for different blocks within the same video frame. Each block is evaluated based on its local characteristics such as texture complexity and motion patterns, allowing accurate noise detection in critical areas without unnecessarily processing all areas with maximum intensity.
3Measurement precision
If comprehensive video analysis is performed, then noise detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent reduces algorithmic complexity by segmenting the video into frames and blocks, allowing independent processing of each block. This segmentation transforms a complex global optimization problem into multiple simpler local problems that can be solved efficiently and independently.
Solution Approach 2:
The patent applies partial action by focusing computational resources on blocks that are most likely to contain noise based on preliminary criteria. Instead of applying the full computational algorithm uniformly to all blocks, the method selectively applies complex analysis only where needed, reducing overall computational complexity while maintaining detection accuracy.
Data Source
AI summary
The present disclosure relates to a video noise detection method and apparatus, and a device and a medium. The video noise detection method includes: extracting a first video frame and a second video frame from a target video, wherein the first video frame and the second video frame are adjacent video frames; performing differential processing on the first video frame and the second video frame, so as to obtain an inter-frame differential image between the first video frame and the second video frame; performing flat area detection on the first video frame and the second video frame, to obtain an intersection of flat areas in the first video frame and the second video frame; and calculating a time-domain noise value corresponding to the first video frame by using pixel information of the intersection of the flat areas in the inter-frame differential image.