Temporal Compositional Denoising for Video Flickering Reduction
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Solution Overview
Problem
Conventional video denoising techniques fail to effectively leverage temporal information across frames, leading to flickering and artifacts in denoised video content, and are resource-intensive.
Innovation Solution
The technique involves converting frames into learned components and using a machine learning model to generate denoised frames by combining these components, while optimizing resource consumption through caching and quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video denoising techniques are used, then resource consumption is high, but denoising quality is insufficient and flickering occurs
Solution Approach 1:
The patent segments the video frames into multiple temporal layers or components, allowing different parts of the video to be processed independently. This segmentation enables selective denoising of only the necessary temporal components, reducing overall resource consumption while maintaining high denoising quality through targeted processing of specific frame groups rather than treating all frames uniformly.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and organizing frames into temporal groups before the main denoising operation. Frames are pre-sorted and structured according to their temporal relationships, allowing the denoising algorithm to operate more efficiently on organized data structures. This preliminary organization reduces computational overhead during the actual denoising process while preserving temporal consistency.
2Productivity
If conventional video denoising techniques are used, then processing time is long, but temporal consistency is poor
Solution Approach 1:
By segmenting the video into temporal layers and processing groups of frames together, the patent maintains temporal consistency through coordinated processing of related frames. The segmentation allows the algorithm to preserve temporal relationships within each group while processing multiple frames in parallel, thereby increasing processing speed without sacrificing temporal stability.
Solution Approach 2:
The patent merges multiple temporal layers or frame groups into a unified denoised output, combining the results of processed frame groups while preserving their temporal relationships. This merging operation restores temporal consistency by integrating the denoised frames in their proper temporal sequence, ensuring that the final output maintains coherence across time while benefiting from the speedups achieved during parallel processing of individual groups.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for denoising video content. The technique includes converting a first frame into a first set of learned components. The technique also includes converting one or more frames that are temporally related to the first frame into one or more additional sets of learned components. The technique further includes generating, via a first machine learning model, a denoised frame corresponding to the first frame based on the first set of learned components and the one or more additional sets of learned components.