Video Frame Interpolation via Error-Region Segmentation
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Solution Overview
Problem
Existing video frame interpolation methods face challenges such as reliance on imperfect optical flow estimation, high computational costs, large memory requirements, and limited capability to capture large motions, particularly in vector-based approaches, and inefficient use of optical flow information.
Innovation Solution
A system and method that segment a candidate interpolated frame into regions with varying levels of estimated interpolation error, refine each region separately, and combine them to generate a final interpolated frame, using a motion-adaptive refinement neural network and optical flow maps to identify high-error and lower-error regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vector-based approaches with machine learning models are used to improve optical flow estimation accuracy, then the quality of interpolated intermediate frames is improved, but the computational cost and number of parameters increase significantly
Solution Approach 1:
The patent segments the frame refinement process into multiple stages (first stage and second stage), where each stage processes specific regions or aspects of the optical flow estimation. This segmentation allows the system to achieve high accuracy without requiring a single large complex model, thereby reducing overall parameter count while maintaining precision.
Solution Approach 2:
The patent applies different refinement strategies to different regions of the frame based on motion characteristics. High-motion regions receive different processing compared to low-motion regions, allowing the system to allocate computational resources efficiently and achieve high accuracy where needed without uniformly increasing complexity across the entire frame.
2Adaptability or versatility
If kernel-based approaches are used to synthesize pixels by convolving with predicted spatially adaptive kernels, then the capability to capture large motions is improved, but the memory requirements and computational cost increase
Solution Approach 1:
The patent extracts and utilizes optical flow information from the first stage to guide the refinement process in the second stage. By taking out and reusing this intermediate information, the system avoids the need to store and process large kernel matrices for the entire frame, thereby reducing memory requirements while maintaining the capability to handle large motions through targeted refinement.
3Productivity
If optical flow estimation is performed to generate intermediate frames, then the transition between consecutive frames is captured, but errors in optical flow estimation propagate to the interpolated frame quality
Solution Approach 1:
The patent performs preliminary optical flow estimation in the first stage to generate an initial interpolated frame. This preliminary action allows the system to establish a baseline that can then be refined in the second stage, ensuring that efficiency is maintained while reliability is improved through subsequent correction of estimation errors.
Solution Approach 2:
The patent implements a feedback mechanism where the output of the first stage (including optical flow estimates and initial interpolated frame) is fed into the second stage for refinement. This feedback loop allows errors from the initial estimation to be identified and corrected, thereby improving reliability while maintaining the productivity gained from the initial efficient estimation.
Data Source
AI summary
A system, device and method of video frame interpolation. In one aspect, a frame interpolation system receive candidate interpolated frame and difference data based on a first frame and second frame. It identifies a high-error region and one or more increasingly lower-error regions of the candidate interpolated frame based on the difference data and applies multiple successive stages of a refinement neural network to the regions of the candidate interpolated frame, from highest- to lowest-error. At each stage, the results of the previous stage are combined with the next lower-error region before being refined by the current stage. In some aspects, the system also generates the candidate interpolated frame and the difference data using a flow estimation neural network.