Reconstructed Video Correction Using Asymmetric Pixel Thresholds
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Solution Overview
Problem
Current video processing technologies fail to address the issue of low-quality reconstruction due to the inability to effectively filter the video data, the inability to effectively filter the video data, resulting in low-quality reconstructed video data.
Innovation Solution
A method and apparatus for correcting the reconstructed video data by using the method and apparatus for correcting the reconstructed video data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If filtering operations are performed on reconstructed video to reduce distortion, then distortion degree is reduced, but correction accuracy remains low resulting in low-quality reconstructed video
Solution Approach 1:
The patent changes the parameter of pixel threshold from symmetric to asymmetric relationships. Specifically, it uses different threshold values for different pixel value ranges (e.g., first threshold for pixels below mid-value, second threshold for pixels above mid-value), allowing independent optimization of correction accuracy for different regions of the video signal, thereby resolving the contradiction between distortion reduction and correction accuracy.
Solution Approach 2:
The patent applies different correction strategies to different parts of the video signal based on local characteristics. By dividing the pixel value range into different segments and applying asymmetric threshold pairs specific to each segment, the method achieves locally optimized correction that improves overall accuracy while reducing distortion in specific regions where it matters most.
2Manufacturing precision
If asymmetric pixel threshold pairs are used for correction, then correction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the pixel value range into distinct intervals and assigns specific asymmetric threshold pairs to each interval. This segmentation approach allows the system to use simple, pre-determined threshold values for each segment rather than performing complex real-time calculations, thereby maintaining correction accuracy while controlling computational complexity through lookup-table-based implementation.
Data Source
AI summary
A video data processing method, performed by a computer device, includes: obtaining reconstructed video data; obtaining one or more candidate pixel threshold pairs, wherein at least one of the one or more candidate pixel threshold pairs include two candidate pixel thresholds having first relationship that is asymmetric; determining one or more distortion costs associated with correcting a target reconstructed video frame of the reconstructed video data in a target correction mode according to the one or more candidate pixel threshold pairs; screening, according to the one or more distortion costs, a target pixel threshold pair of the reconstructed video data, in the target correction mode, from the one or more candidate pixel threshold pairs; and obtaining a target correction result of the reconstructed video data by correcting, in the target correction mode according to the target pixel threshold pair, one or more reconstructed video frames of the reconstructed video data.