Video Frame Interpolation Using Deep-Level Optical Flow
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Solution Overview
Problem
Current frame interpolation methods for videos rely on manual labeling, leading to poor efficiency, high computational costs, and picture deformation, especially when transitioning between frame rates of 24-30 fps and higher rates like 60 or 120 fps.
Innovation Solution
A method that uses deep-level features of adjacent video frames to obtain forward and inverse optical flow information, generating an interpolated frame without explicit manual labeling, thereby reducing reliance on costly manual processes and enhancing interpolation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used for frame interpolation, then interpolation accuracy can be maintained, but computational cost and time consumption increase significantly
Solution Approach 1:
The method performs preliminary extraction of deep-level features from video frames before interpolation. By pre-processing the frames to obtain essential feature representations, the system reduces the computational burden during the actual interpolation process, thereby decreasing time consumption while maintaining accuracy
Solution Approach 2:
The patent introduces optical flow information as an intermediary element between the input frames and the interpolated result. This optical flow mediator captures motion patterns and guides the interpolation process, enabling accurate frame generation without requiring extensive manual labeling or complex computations
2Measurement precision
If manual labeling is used for frame interpolation, then interpolation quality can be maintained, but computational resources and costs increase
Solution Approach 1:
The method extracts only the essential deep-level features from video frames that are necessary for interpolation, discarding redundant information. This selective extraction reduces computational resource requirements while preserving the quality needed for accurate frame generation
Solution Approach 2:
The patent replaces manual labeling processes with automated deep learning-based feature extraction and optical flow computation. This substitution eliminates the need for labor-intensive manual annotation while maintaining interpolation quality through algorithmic approaches
3Device complexity
If traditional frame interpolation methods are used, then process simplicity is maintained, but picture deformation and jitter occur
Solution Approach 1:
The method transitions from traditional 2D spatial interpolation to 4D spatiotemporal interpolation by incorporating optical flow information across time dimensions. This dimensional expansion enables the model to understand motion patterns and generate smoother interpolated frames without excessive complexity
Solution Approach 2:
The patent changes the parameters used for interpolation from simple pixel-based methods to deep-level feature representations combined with optical flow vectors. This parameter transformation improves picture quality by capturing semantic information and motion patterns while maintaining reasonable process complexity
Data Source
AI summary
The disclosure provides a method and an apparatus for interpolating a frame to a video. A first deep-level feature of a first frame is obtained and a second deep-level feature of a second frame is obtained. Forward optical flow information and inverse optical flow information between the first frame and the second frame are obtained based on first deep-level feature and the second deep-level feature. An interpolated frame between the first frame and the second frame is generated based on the forward optical flow information and the inverse optical flow information, and the interpolated frame is inserted between the first frame and the second frame.