Feature Channel Quantization for AI-Oriented Bitstream Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image compression technologies are not optimized for machine learning applications and result in inefficiencies due to their focus on high-resolution, high-quality image processing for human vision, making them unsuitable for artificial intelligence services.
Innovation Solution
A feature encoding/decoding method and apparatus that adaptively quantizes and dequantizes feature channels based on their importance or information amount, allowing for improved encoding/decoding efficiency and reduced loss during compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image compression technology is used, then high-resolution, high-quality image processing is achieved, but it is not suitable for artificial intelligence services
Solution Approach 1:
The patent applies local quality by differentiating the compression treatment for different feature channels based on their importance. Important feature channels maintain higher quality with lower quantization parameters, while less important channels undergo stronger compression. This localized differentiation resolves the contradiction by optimizing for AI service requirements rather than uniform human vision quality.
Solution Approach 2:
The patent changes the quantization parameter (QP) values dynamically based on feature channel importance. By adjusting QP parameters according to the amount of information in each channel, the system achieves compression optimized for AI services while maintaining necessary processing quality for machine learning tasks.
2Device complexity
If uniform quantization is applied to all feature channels, then the compression process is simple, but information loss increases for important channels
Solution Approach 1:
Instead of uniform quantization, the patent applies local quality by assigning different quantization parameters to different feature channels. Important channels with more information receive lower QP values (less compression), while less important channels receive higher QP values (more compression). This resolves the contradiction by reducing information loss in critical channels while maintaining manageable process complexity through automated importance assessment.
Solution Approach 2:
The patent introduces dynamic quantization where QP values are not fixed but adapt based on the amount of information in each feature channel. The system dynamically adjusts quantization parameters during the compression process, allowing optimal balance between compression efficiency and information preservation for each channel's specific characteristics.
3Productivity
If higher compression is applied to reduce data size, then encoding efficiency improves, but loss during compression increases
Solution Approach 1:
The patent applies local quality by differentiating compression strength across feature channels. Channels with more information receive milder compression (lower QP), while channels with less information undergo stronger compression (higher QP). This resolves the contradiction by optimizing encoding efficiency through aggressive compression where acceptable while preserving critical information in important channels.
Solution Approach 2:
The patent changes quantization parameters dynamically based on feature channel characteristics. By adjusting QP values according to the amount of information in each channel, the system achieves optimal compression efficiency while minimizing information loss in channels that are most important for AI service performance.
Data Source
AI summary
Provided are a feature encoding/decoding method and device, and a computer-readable recording medium generated by the feature encoding method. The feature decoding method according to the present disclosure may comprise the steps of: determining, on the basis of first information, whether quantization parameter (QP) information on feature channels is encoded in units of feature channels; acquiring the QP information from a bitstream on the basis that the QP information is encoded in units of feature channels; and setting QP values for the feature channels on the basis of the QP information.