Uncertainty-Guided Frame Interpolation for Artifact-Prone Video
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Solution Overview
Problem
Conventional deep learning video frame interpolation methods struggle to generalize their results to animated data due to complex lighting effects and large motion, leading to severe artifacts, and retraining for each specific use case is not viable.
Innovation Solution
A deep learning-based uncertainty-guided video frame interpolation method that incorporates known regions of intermediate frames, estimates expected error, and utilizes a training procedure to handle uncertainties, enabling improved interpolation quality through a partial rendering pass.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional deep learning video frame interpolation methods are used, then interpolation can be performed, but the results fail to generalize to animated data and produce severe artifacts
Solution Approach 1:
The patent changes the parameter of using uncertainty estimation to guide the interpolation process. By estimating uncertainty in the interpolation results and using this information to adjust the rendering process, the system adapts to different video content types including animated data, thereby improving generalization capability while maintaining interpolation quality.
Solution Approach 2:
The patent implements a feedback mechanism where the uncertainty estimation of interpolation results is fed back into the rendering process. This feedback loop allows the system to continuously improve the interpolation quality by adjusting the rendering based on the estimated uncertainty, enabling better generalization to various content types.
2Manufacturing precision
If retraining is performed for each specific use case, then interpolation quality can be optimized, but the process is not viable due to varying data statistics
Solution Approach 1:
The patent creates a universal interpolation method that can handle multiple video content types without requiring separate training for each use case. By using uncertainty estimation and adaptive rendering, the single trained model can generalize to different data statistics and content types, eliminating the need for retraining while maintaining high interpolation quality.
Solution Approach 2:
The patent uses parameter changes in the uncertainty estimation to adapt the model to different use cases without retraining. By adjusting the uncertainty threshold and rendering parameters based on the estimated uncertainty, the system can optimize interpolation quality for different content types using the same base model.
3Measurement precision
If complex lighting effects and large motion are handled, then interpolation accuracy improves, but existing methods introduce severe artifacts
Solution Approach 1:
The patent uses uncertainty estimation as feedback to identify regions where interpolation accuracy is compromised due to complex lighting effects or large motion. The uncertainty information is then used to guide the rendering process, allowing the system to focus computational resources on problematic regions and adjust rendering parameters to eliminate artifacts while maintaining accuracy.
Solution Approach 2:
The patent applies local quality by using uncertainty estimation to identify specific regions in the video frame that require special handling. Instead of uniformly processing the entire frame, the system focuses on regions with high uncertainty (such as areas with complex lighting or large motion), applying enhanced rendering only where needed to maintain accuracy while avoiding artifacts in stable regions.
Data Source
AI summary
A system includes a hardware processor, a memory storing software code, and a machine learning (ML) model-based video frame interpolator. The hardware processor executes the software code to provide first and second frames of a video sequence including a plurality of frames, respective binary masks for the first and second frames, and optionally an intermediate frame of the video sequence between the first and second frames and a binary mask for the intermediate frame, as interpolation inputs to the ML model-based video frame interpolator. The hardware processor further executes the software code to generate, using the ML model-based video frame interpolator and the interpolation inputs, an interpolated frame and an error map for the interpolated frame, wherein generating the interpolated frame and the error map includes a cross-backward warping of respective latent feature representations of each of the plurality of frames.