Video Inpainting via Key Frame Segmentation and Temporal Memory
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Solution Overview
Problem
Existing video processing technologies face inefficiencies in removing objects from video frames and complementing missing areas, particularly in mobile terminal applications, due to low processing speed, resource wastage, and unstable results.
Innovation Solution
A method involving an electronic apparatus that extracts key frames and non-key frames from a video, inpaints key frames using corresponding masks, and then inpaints non-key frames based on the inpainted key frames, utilizing a spatial-temporal memory Transformer module and a fast matching convolution neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video inpainting is performed on all frames, then inpainting quality is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The video frames are segmented into key frames and non-key frames based on motion characteristics. Key frames are selected for comprehensive inpainting processing, while non-key frames are processed differently or skipped, dividing the processing workload into distinct segments based on frame importance and motion content.
Solution Approach 2:
Instead of applying inpainting to all frames (excessive action), the method applies inpainting selectively only to key frames (partial action). This partial processing approach maintains sufficient inpainting quality for the most important frames while significantly reducing overall computational cost and processing time.
2Manufacturing precision
If video inpainting is performed on all frames, then inpainting completeness is improved, but computational resources are wasted
Solution Approach 1:
Frames are segmented into key frames and non-key frames based on motion analysis. The segmentation allows the system to allocate computational resources differently: intensive inpainting processing is applied to key frames while non-key frames receive reduced or no processing, optimizing resource utilization.
Solution Approach 2:
The method applies partial action by performing comprehensive inpainting only on key frames rather than all frames. This partial processing strategy ensures complete inpainting for the most critical frames while avoiding wasteful computation on non-key frames, thereby reducing overall energy consumption.
3Device complexity
If conventional inpainting methods are used, then processing simplicity is maintained, but processing speed is slow
Solution Approach 1:
The system dynamically adjusts the inpainting processing strategy based on frame characteristics. By analyzing motion information to identify key frames, the system dynamically selects which frames require comprehensive inpainting and which can be processed more lightly or skipped, optimizing processing speed without sacrificing quality.
Solution Approach 2:
The method changes the processing parameters and approach based on frame type. Key frames undergo comprehensive inpainting with standard parameters, while non-key frames receive different processing parameters or are excluded from processing entirely, thereby increasing overall processing speed while maintaining quality where needed.
Data Source
AI summary
According to an embodiment of the disclosure, a method performed by an electronic apparatus may include extracting at least one key frame and at least one non-key frame from a video. According to an embodiment of the disclosure, a method performed by an electronic apparatus may include inpainting the at least one key frame based on at least one mask corresponding to the at least one key frame. According to an embodiment of the disclosure, a method performed by an electronic apparatus may include inpainting the at least one non-key frame based on the at least one inpainted key frame.