Self-Attention Video Frame Alignment Model
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Solution Overview
Problem
Existing video processing technologies face challenges in efficiently aligning frames due to misalignment issues, leading to significant computational overhead, especially in resource-limited devices like smartphones.
Innovation Solution
The proposed solution involves an attention-based alignment model that uses self-attention mechanisms to align frames by leveraging the locality of motion among neighboring support frames, with a gating module to control alignment operations based on pixel displacements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video processing aligns all frames using full computational operations, then alignment accuracy is improved, but computational overhead increases significantly
Solution Approach 1:
The patent segments the video processing into distinct feature extraction stages and applies selective alignment only to specific feature types (e.g., motion-compensated features versus static features). This segmentation allows the system to avoid full computational alignment on all frames while maintaining accuracy where needed.
Solution Approach 2:
The patent applies different processing qualities to different regions or features within the video data. Motion-prone regions receive full alignment processing while static regions use simplified processing, optimizing the balance between alignment accuracy and computational cost based on local requirements.
2Manufacturing precision
If deep neural networks process more temporal information from neighboring frames, then video reconstruction quality is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction and motion estimation on neighboring frames before the main reconstruction process. By pre-processing temporal information and organizing it into reusable feature representations, the system reduces the computational burden during actual video reconstruction while maintaining quality.
Solution Approach 2:
The patent applies partial temporal processing by selectively using information from certain neighboring frames based on motion complexity. For low-motion regions, fewer temporal frames are processed, while high-motion regions receive more extensive temporal analysis, optimizing the trade-off between quality and processing time.
3Measurement precision
If video processing performs comprehensive feature alignment operations, then frame alignment accuracy is improved, but energy consumption increases on mobile devices
Solution Approach 1:
The patent implements dynamic processing that adapts to the content being analyzed. The system adjusts the level of alignment operations based on detected motion characteristics, applying intensive processing only when and where needed, thereby reducing overall energy consumption while maintaining alignment accuracy for dynamic regions.
Solution Approach 2:
The patent changes processing parameters such as feature extraction depth and alignment complexity based on video content characteristics. By dynamically adjusting these parameters according to motion intensity and scene complexity, the system optimizes energy efficiency without sacrificing alignment precision when required.
Data Source
AI summary
A processor-implemented method for processing a video includes receiving the video as an input at an artificial neural network (ANN). The video includes a sequence of frames. A set of features of a current frame of the video and a prior frame of the video are extracted. The set of features including a set of support features for a set of pixels of the prior frame to be aligned with a set of reference features of the current frame. A similarity between a support feature for each pixel in the set of pixels of the set of support features of the prior frame and a corresponding reference feature of the current frame is computed. An attention map is generated based on the similarity. An output including a reconstruction of the current frame is generated based on the attention map.