Error-Aware Adaptive Video Interpolation for Motion Artifacts
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Solution Overview
Problem
Existing video frame interpolation methods struggle to robustly handle diverse video content, particularly scenes with significant motion and indistinguishable patterns, leading to artifacts, distortions, and hallucinations, and are limited by computational resources and time constraints.
Innovation Solution
A video interpolation pipeline that adaptively selects interpolation techniques based on estimated error levels, using machine-learning models and alternative algorithms to generate interpolated frames, and dynamically chooses between duplicating input frames or using more complex models depending on error estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a machine-learning model is used to generate interpolated frames, then the quality of interpolated frames is improved, but the computational resources and time required increase
Solution Approach 1:
The system dynamically adjusts the interpolation method based on real-time error estimates. When error is low, it uses machine-learning models for high quality. When error is high, it switches to alternative algorithms or frame duplication to reduce computational load and avoid artifacts.
Solution Approach 2:
The system changes the interpolation parameter (method selection) based on the estimated error level. It transitions between different interpolation strategies (machine-learning vs. alternative algorithms vs. duplication) depending on the complexity and motion characteristics of the video content.
2Adaptability or versatility
If a single interpolation module is used for all videos, then the system complexity is reduced, but the ability to handle diverse video content robustly deteriorates
Solution Approach 1:
The system implements a universal error estimation mechanism that works across all video types. This error estimate then guides the selection of appropriate interpolation methods, allowing a single system architecture to handle diverse video content robustly by adapting its behavior based on content analysis.
Solution Approach 2:
The system dynamically selects from multiple interpolation modules based on real-time error estimation. This allows it to adapt to different video characteristics (motion intensity, pattern distinguishability) without requiring a completely different system architecture for each case.
3Productivity
If interpolated frames are generated for scenes with significant motion, then the frame rate is increased, but artifacts and distortions increase
Solution Approach 1:
The system performs error estimation before generating interpolated frames. This preliminary assessment allows it to predict potential errors and adjust its strategy in advance, preventing artifacts before they occur rather than correcting them afterward.
Solution Approach 2:
The system takes preliminary anti-action by detecting high-error scenarios and preventing machine-learning-based interpolation in those cases. It proactively switches to alternative methods that are less prone to generating artifacts in challenging scenarios.
4Manufacturing precision
If the same interpolation method is used for all frames, then the processing time is reduced, but the quality consistency across different video scenes deteriorates
Solution Approach 1:
The system applies different interpolation methods to different frames based on local error estimates. Each frame is evaluated individually, and the appropriate method is selected for that specific frame, ensuring local quality optimization without uniformly processing all frames with the most complex method.
Data Source
AI summary
A device may generate, based on a first input frame and a second input frame of an input video, a first feature map and a second feature map. The device may generate a forward optical flow map and a backward optical flow map based on the first feature map and the second feature map. The device may generate an error estimate for the interpolated frame using the forward optical flow map and backward optical flow map. The device may select, based on a comparison of the error estimate and one or more criteria, an interpolation module from a plurality of interpolation modules. The device may generate the interpolated frame using the selected interpolation module. The device may combine the interpolated frame with the two input frames to generate at least a part of an output video.