Spatio-Temporal Transformer for Video Compression
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Solution Overview
Problem
Existing video compression methods only partially exploit spatial-temporal redundancies, leading to inefficient data reduction and increased storage and transmission demands.
Innovation Solution
A neural video compression system utilizing a spatio-temporal-aware cross-covariance transformer to aggregate and fuse spatial and temporal features, enabling efficient exploitation of spatial-temporal redundancies through attention mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple concatenation or subtraction operations are used to combine spatial and temporal information, then the method is computationally simple, but spatial-temporal redundancies are only partially exploited leading to inefficient compression
Solution Approach 1:
The patent replaces simple mechanical operations (concatenation, subtraction) with a neural network-based attention mechanism that automatically learns and exploits spatial-temporal correlations. The attention mechanism computes weighted relationships between spatial and temporal features, enabling more effective redundancy exploitation while maintaining reasonable computational complexity through parallel processing.
Solution Approach 2:
The invention changes the operational parameters from fixed simple operations to adaptive learned operations. The attention mechanism dynamically adjusts weights based on input content, allowing the system to adaptively exploit spatial-temporal redundancies rather than applying uniform simple operations, thereby improving compression efficiency.
2Productivity
If advanced spatial-temporal feature fusion is implemented, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex feature fusion process into distinct functional modules: spatial feature extraction, temporal feature extraction, and attention-based fusion. This modular segmentation allows each component to be optimized independently and enables parallel processing, reducing overall computational complexity while maintaining high compression efficiency.
Solution Approach 2:
The invention introduces a new dimensional perspective by treating temporal features as an additional dimension alongside spatial features. The attention mechanism operates in this extended spatio-temporal dimension, efficiently fusing features across both dimensions without requiring exhaustive computation, thereby achieving high compression efficiency with manageable complexity.
3Manufacturing precision
If more spatial and temporal information is retained in compressed representation, then reconstruction quality is improved, but data volume increases reducing compression effectiveness
Solution Approach 1:
The patent extracts only the most relevant spatial-temporal features using the attention mechanism, which identifies and selects key correlations between frames and regions. By extracting only essential information rather than retaining all spatial-temporal data, the system achieves high reconstruction quality while minimizing data volume, thus improving compression effectiveness.
Solution Approach 2:
The invention applies local quality by using attention weights to selectively retain different amounts of information for different spatial regions and temporal positions. Important regions and critical temporal relationships retain more information for high reconstruction quality, while less important areas use fewer bits, optimizing the balance between quality and compression ratio.
Data Source
AI summary
In some embodiments, a system includes a first component to extract temporal features from a current frame being coded and a previous frame of a video. A second component uses a first transformer to fuse spatial features from the current frame with the temporal features to generate spatio-temporal features as first output. A third component uses a second transformer to perform entropy coding using the first output and at least a portion of the temporal features to generate a second output. A fourth component uses a third transformer to reconstruct the current frame based on the first output that is processed using the second output and the temporal features.