Transformer Attention Mechanism for Entropy Coding Efficiency
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Solution Overview
Problem
Current image/video compression techniques, particularly neural-network-based methods, face limitations in capturing long-range correlations among quantized latents, which hampers their ability to achieve optimal coding efficiency due to the restricted receptive field of convolutional layers.
Innovation Solution
Integration of a transformer model with an attention mechanism into the entropy coding process to capture both long-range and short-range correlations among quantized latents, enhancing the coding efficiency by determining probability distributions for entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolution-based neural networks are used for entropy coding, then the model can capture short-range correlations among quantized latents, but it fails to capture long-range correlations due to restricted receptive field
Solution Approach 1:
The patent replaces the convolution-based mechanical processing system with a transformer-based attention mechanism. The attention mechanism computes relationships between all pairs of quantized latents directly, substituting the iterative local filtering of convolution with a global contextualization approach that naturally captures long-range dependencies without being constrained by receptive field size.
Solution Approach 2:
The patent introduces a new computational dimension through the attention mechanism's query-key-value framework. Instead of processing information through sequential convolutional layers that accumulate receptive field gradually, the attention mechanism operates in a transformed space where all positions can directly interact, effectively adding a global contextualization dimension to the processing architecture.
2Measurement precision
If transformer model with attention mechanism is used, then long-range correlations can be captured, but computational complexity increases
Solution Approach 1:
The patent segments the attention computation into distinct functional components: query generation, key generation, value generation, and attention weighting. This segmentation allows for modular implementation and optimization of each component separately, managing the overall computational complexity by breaking down the transformer model into manageable functional blocks that can be processed and optimized independently.
3Productivity
If conventional entropy coding methods are used, then the coding process is simple and fast, but coding efficiency is limited due to inability to model long-range dependencies
Solution Approach 1:
The patent substitutes traditional statistical modeling methods (such as context models and arithmetic coding) with a neural network-based transformer model. This replacement enables the system to learn and exploit statistical dependencies automatically through attention mechanisms, achieving better modeling of long-range correlations while maintaining coding efficiency through end-to-end optimization of the entire compression pipeline.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for data processing. A method for data processing is proposed. The method comprises: determining, by using a first model with an attention mechanism during a conversion between data and a bitstream of the data, a probability distribution for entropy coding associated with the bitstream; and performing the conversion based on the probability distribution.