Video Frame Interpolation for Repetitive Pattern Motion Estimation
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Solution Overview
Problem
Existing frame rate conversion methods, both classical and neural network-based, struggle with accurately estimating motion in regions with repetitive patterns, leading to artifacts such as ghosting and blurring in interpolated video frames, particularly on devices with limited resources.
Innovation Solution
A method that combines classical algorithms with a trained motion estimation neural network, utilizing a loss function that accounts for the degree of similarity and self-similarity of motion vectors in repetitive pattern regions, and applies regularization to improve motion estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If classical motion estimation algorithms are used, then computational complexity is low and devices with limited resources can be used, but motion estimation accuracy deteriorates in repetitive pattern regions leading to artifacts
Solution Approach 1:
The patent applies preliminary action by detecting repetitive pattern regions in advance before performing motion estimation. The system identifies regions with repetitive patterns (such as fences, windows, or textured surfaces) and prepares special handling for these areas, preventing the classic mismatch problem from occurring in the first place.
Solution Approach 2:
The patent implements local quality by applying different motion estimation strategies to different regions of the image. For repetitive pattern regions, the system uses constrained motion estimation that enforces similarity constraints across multiple candidate blocks, while non-repetitive regions can use standard motion estimation algorithms.
2Measurement precision
If neural networks are used for motion estimation, then motion estimation accuracy improves, but computational complexity increases significantly
Solution Approach 1:
The patent applies partial action by using a simplified neural network approach that only processes repetitive pattern regions rather than the entire frame. The system detects repetitive regions and applies the neural network motion estimation only to these specific areas, reducing the overall computational burden while maintaining accuracy where it matters most.
3Manufacturing precision
If complex neural networks with many parameters are used, then frame interpolation quality improves, but training requirements and resource consumption increase
Solution Approach 1:
The patent applies segmentation by dividing the frame interpolation task into distinct components: repetitive pattern detection, motion estimation for repetitive regions, and standard interpolation for non-repetitive regions. This segmentation allows the use of simpler, more efficient algorithms for specific tasks rather than relying on a single complex neural network to handle all cases.
Data Source
AI summary
A method for interpolating video frames, includes: obtaining at least two key frames of a video, for which a motion estimation is to be performed, detecting repetitive pattern regions on the at least one key frame of the at least two key frames, estimating motion between the at least one key frame of the at least two key frames and the interpolated frame being interpolated by feeding the at least two key frames and the repetitive pattern regions to a trained motion estimation neural network.