Video Frame Repair via Attention Transformation Network
Solution Overview
Problem
Existing video repair technologies struggle to effectively fuse information from adjacent frames, leading to unstable and inefficient video frame repair processes.
Innovation Solution
The proposed method involves processing each adjacent frame through a series of connected attention transformation networks, which accounts for attention between frames to improve fusion effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing video repair technologies are used, then the repair process can be completed, but the fusion effect is unstable and inefficient
Solution Approach 1:
The patent divides the video frame repair process into multiple stages by using a sequence of attention transformation modules. Each module processes the video frame group independently through attention mechanisms, allowing stable and efficient fusion of information from adjacent frames. This segmentation resolves the contradiction by making the repair process both reliable (through systematic processing) and productive (through modular efficiency).
Solution Approach 2:
The patent implements continuous attention transformation across multiple modules where each module builds upon the output of the previous one. This continuous processing ensures that information from adjacent frames is progressively fused in a stable manner, improving both the reliability of the fusion effect and the overall repair efficiency through uninterrupted processing.
2Measurement precision
If information from adjacent frames is effectively used, then more detailed information can be obtained, but the processing complexity increases
Solution Approach 1:
The patent introduces attention transformation modules as intermediary components that mediate between the input video frame group and the final repaired frame. These modules process and transform the information from adjacent frames, extracting detailed information while managing processing complexity through structured attention mechanisms rather than direct complex processing.
Solution Approach 2:
The patent changes the processing parameters by using multiple attention transformation modules in sequence, where each module operates with specific attention parameters. This allows the system to extract detailed information from adjacent frames by adjusting attention parameters across different modules, achieving high measurement precision while managing complexity through parameterized processing stages.
Data Source
AI summary
The embodiments of the present disclosure relate to a video frame repair method, apparatus, device, storage medium, and program product. The method comprises: acquiring a video frame group from a video, wherein the video frame group comprises the target video frame and video frames adjacent to the target video frame; inputting the video frame group into an attention transformation network to obtain a video frame group to be fused, wherein the attention transformation network comprises a set of attention transformation modules that are connected in series, an input of the attention transformation network is an input of a first attention transformation module in the set, and the video frame group comprises a video frame that is output by one or more-attention transformation modules and corresponds to the target video frame; and processing the video frame group to be fused to obtain a repaired target video frame.