Adaptive Time-Domain Filtering for Motion-Aware Video Encoding
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Solution Overview
Problem
Conventional time-domain filtering methods apply a uniform filtering magnitude to each image frame, leading to poor rate-distortion performance during video encoding.
Innovation Solution
Adaptive time-domain filtering based on relative motion complexity, where the filtering magnitude is adjusted according to the motion complexity of each image frame, reducing filtering for complex frames and enhancing it for simpler frames to retain or eliminate information effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a same preset filtering magnitude is applied to each image frame, then the filtering operation is simple and fast, but the rate-distortion performance of subsequent video encoding deteriorates
Solution Approach 1:
The patent applies dynamics by changing the filtering magnitude from a static preset value to a dynamic value that adapts to each image frame's motion complexity. The filtering magnitude is adjusted based on the relative motion feature extracted from each frame, allowing the system to optimize between filtering strength and information retention for each specific frame condition.
Solution Approach 2:
The patent applies local quality by differentiating the filtering magnitude across different image frames based on their individual motion characteristics. Instead of applying a uniform filtering magnitude to all frames, the system extracts relative motion features for each frame and applies appropriately tailored filtering magnitudes, treating each frame locally according to its specific needs.
2Device complexity
If a uniform filtering magnitude is used for all image frames, then the processing complexity is low, but valid information in complex frames is lost
Solution Approach 1:
The system dynamically adjusts the filtering magnitude based on the relative motion feature of each image frame. For frames with high motion complexity, a smaller filtering magnitude is applied to preserve valid information, while for frames with low motion complexity, a larger filtering magnitude can be applied to remove noise effectively.
Solution Approach 2:
The patent changes the filtering magnitude parameter adaptively for each image frame based on extracted relative motion features. This parameter change allows the system to optimize the balance between noise reduction and information retention for each specific frame condition, rather than using a fixed parameter for all frames.
3Manufacturing precision
If adaptive filtering magnitude based on motion complexity is applied, then the rate-distortion performance improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent segments the video processing into distinct stages: relative motion feature extraction, filtering magnitude determination, and time-domain filtering. This segmentation allows for optimized processing at each stage and facilitates parallel processing possibilities, helping to manage the computational complexity introduced by adaptive filtering.
Solution Approach 2:
The system performs preliminary action by extracting relative motion features and determining appropriate filtering magnitudes before performing the actual time-domain filtering. This preliminary preparation allows the filtering operation itself to be more efficient, as the optimal filtering parameters are already determined in advance for each frame.
Data Source
AI summary
A time-domain filtering method is provided, including: determining a to-be-filtered image frame in a group of pictures; extracting a relative motion feature of the to-be-filtered image frame, where the relative motion feature represents relative motion complexity between image contents of the to-be-filtered image frame and image contents of the remaining image frames in the group of pictures; and determining a target filtering magnitude corresponding to the relative motion feature, and performing time-domain filtering on the to-be-filtered image frame by using the target filtering magnitude.