Adaptive Temporal Filter for Video Coding Efficiency
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Solution Overview
Problem
Current video coding technologies face challenges in efficiently reducing redundancy in video signals, particularly in handling scene changes and noise levels, which affect the compression ratio and coding efficiency.
Innovation Solution
The implementation of temporal filtering in video processing, where parameters such as the number of reference frames, filter strength, and enabling/disabling decisions are adapted based on video content properties like scene change, noise level, and signal-to-noise ratio, to enhance coding efficiency by increasing similarity between frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If temporal filtering is applied to all frames with fixed parameters, then filtering consistency is improved, but coding efficiency deteriorates due to inability to adapt to scene changes and noise variations
Solution Approach 1:
The patent implements dynamic temporal filtering by adjusting filter parameters (strength, number of reference frames) based on detected scene changes and noise levels in different video segments. The filter transitions from static to adaptive, allowing optimal performance across varying content characteristics while maintaining coding efficiency.
Solution Approach 2:
The patent changes filter parameters (strength, reference frame count) based on video content analysis including scene change detection and noise level measurement. This parameter adaptation allows the filter to optimize coding efficiency for different content types while maintaining stability through controlled adjustments.
2Measurement precision
If the number of reference frames is increased to improve filtering accuracy, then temporal filtering performance is improved, but computational complexity and bandwidth requirements increase
Solution Approach 1:
The patent dynamically adjusts the number of reference frames based on scene change detection and noise level measurement. During stable low-noise periods, more reference frames are used for higher accuracy. During scene changes or high noise, fewer reference frames are used to reduce complexity, achieving optimal balance between accuracy and computational load.
Solution Approach 2:
The system transitions from using a fixed number of reference frames to a dynamic selection approach where the count adapts to content characteristics. This dynamic adjustment optimizes the trade-off between filtering accuracy and computational complexity based on actual video conditions.
3Productivity
If temporal filtering is applied with high filter strength to reduce residual information, then compression ratio is improved, but distortion in noisy regions increases
Solution Approach 1:
The patent adjusts filter strength based on noise level detection and scene change identification. In low-noise stable regions, high filter strength is applied to maximize compression. In noisy or scene change regions, filter strength is reduced to avoid excessive distortion, achieving optimal balance between compression ratio and quality.
Solution Approach 2:
The patent applies different filter strengths to different regions and time segments based on local content characteristics. Rather than uniform filtering, the strength is adapted locally according to noise levels and scene stability, preserving quality in sensitive regions while maximizing compression in suitable regions.
4Productivity
If adaptive parameter adjustment based on scene change detection is implemented, then coding efficiency is improved, but algorithm complexity increases
Solution Approach 1:
The patent implements parameter adjustment based on scene change detection and noise level measurement. While this adds algorithmic steps, the complexity is managed through efficient detection algorithms and conditional logic that only activates parameter changes when content characteristics warrant them, maintaining practical implementability.
Solution Approach 2:
The system uses feedback from scene change detection and noise level measurement to adjust filter parameters. This feedback mechanism improves coding efficiency by adapting to content characteristics, while the feedback processing is kept computationally efficient through targeted analysis rather than exhaustive processing.
Data Source
AI summary
Aspects of the disclosure provide methods and apparatuses for video processing. In some examples, an apparatus for video processing includes processing circuitry. The processing circuitry determines one or more parameters of a temporal filter based on video contents in an uncompressed video. The uncompressed video includes a sequence of frames. Then, the processing circuitry applies the temporal filter with the determined parameter on a first pixel in a first frame to determine a filtered value for the first pixel based on the first pixel in the first frame and second pixels in a group of reference frames for the first frame. Further, the processing circuitry encodes a filtered video that includes the filtered value for the first pixel in the first frame to generate a coded video bitstream that carries the filtered video.