Video Frame Interpolation via Semantic and Pixel Optical Flow
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Solution Overview
Problem
Existing video frame interpolation methods often suffer from low computational efficiency and serious image distortion, leading to poor frame interpolation effects and low efficiency.
Innovation Solution
The method involves obtaining visual semantic features and pixel information from adjacent frames, generating semantic optical flow and pixel optical flow information, and using these to create an interpolation frame, thereby improving the frame interpolation process by integrating semantic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional video frame interpolation methods are used, then the frame rate can be increased, but image distortion and computational inefficiency occur
Solution Approach 1:
The patent segments the optical flow computation into two distinct components: semantic optical flow (dealing with object-level motion) and pixel optical flow (dealing with fine-grained pixel-level motion). This segmentation allows each component to be optimized independently, improving both computational efficiency and interpolation accuracy while maintaining high frame rates
Solution Approach 2:
The patent introduces semantic features as an intermediary layer between the input frames and the interpolation process. By first extracting semantic features from the input frames and computing semantic optical flow, the system creates a meaningful intermediate representation that guides the pixel-level interpolation, reducing distortion and improving image quality
2Productivity
If computational complexity is reduced for efficiency, then processing speed improves, but interpolation accuracy deteriorates
Solution Approach 1:
The patent divides the complex optical flow computation into semantic and pixel levels, allowing the semantic level to handle coarse motion at lower computational cost while the pixel level refines details. This segmented approach maintains high interpolation accuracy without requiring excessive computational resources
Solution Approach 2:
The patent applies partial action by focusing computational resources on the most critical aspects: semantic feature extraction and semantic optical flow computation. The pixel optical flow is then computed based on these semantic guides, reducing the overall computational burden while maintaining accuracy where it matters most
Data Source
AI summary
The disclosure discloses a method for video frame interpolation. The method includes: obtaining a first visual semantic feature and first pixel information of a first frame, and obtaining a second visual semantic feature and second pixel information of a second frame; generating semantic optical flow information based on the first visual semantic feature and the second visual semantic feature; generating pixel optical flow information based on the first pixel information and the second pixel information; and generating an interpolation frame between the first frame and the second frame based on the semantic optical flow information and the pixel optical flow information, and interpolating the interpolation frame between the first frame and the second frame.