Multi-Scale Attention Resampling for Efficient Machine Vision Coding
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Solution Overview
Problem
Existing video coding standards face challenges in achieving high compression efficiency while maintaining subjective quality, particularly with the development of advanced standards like VVC/H.266, which require improved methods for reducing storage and transmission bandwidth.
Innovation Solution
Implementing a video processing method that includes generating a multi-scale attention mask and training a resampling module to enhance video compression and decompression processes, utilizing techniques such as deep learning and neural networks for adaptive spatial resampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video compression is increased to reduce storage and transmission bandwidth, then compression efficiency is improved, but subjective quality deteriorates
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different spatial regions through attention mechanisms. The model identifies important regions (such as objects or areas with high information content) and applies different compression strengths to them compared to less important regions. This allows aggressive compression in low-importance areas while preserving quality in high-importance areas, thereby achieving high overall compression efficiency without significant subjective quality loss.
Solution Approach 2:
The patent employs dynamics by using adaptive attention masks that dynamically adjust based on the input video content. The attention mechanism continuously learns which spatial regions are most important for machine vision tasks and adjusts compression parameters accordingly in real-time. This dynamic adaptation enables the system to optimize the balance between compression efficiency and quality preservation for each specific video frame, resolving the contradiction between these two parameters.
2Productivity
If compression efficiency is improved through advanced coding standards, then storage and transmission requirements are reduced, but coding complexity increases
Solution Approach 1:
The patent replaces traditional mechanical video coding operations with a deep learning-based neural network model. Instead of using complex hand-crafted algorithms and multiple processing stages inherent in advanced coding standards, the system uses a trained neural network that directly processes video frames. This substitution simplifies the overall coding process while achieving high compression efficiency, as the neural network learns optimal compression characteristics from data rather than relying on complex predetermined rules.
Solution Approach 2:
The patent applies parameter changes by transforming the compression process from a fixed algorithmic approach to a data-driven parameter learning approach. The neural network model learns compression parameters automatically from training data, replacing the need for complex manual configuration and adjustment of coding parameters. This enables the system to adapt to different video content and compression requirements through parameter learning rather than through increased algorithmic complexity.
3Ease of manufacture
If traditional video coding is used, then implementation is simpler, but compression efficiency and machine vision performance are insufficient
Solution Approach 1:
The patent applies copying by using a pre-trained neural network model that has learned optimal compression characteristics from extensive training data. Instead of implementing complex coding algorithms from scratch, the system copies the learned patterns and compression strategies that were discovered during the training phase. This allows the system to achieve high compression efficiency comparable to or better than traditional advanced coding standards, while maintaining relative implementation simplicity by leveraging the pre-learned model rather than developing new complex algorithms.
Data Source
AI summary
A video processing method includes receiving an input picture; generating a multi-scale attention mask for the input picture; and training a resampling module using the multi-scale attention mask, wherein the trained resampling module is used to process the input picture.