Feature Tensor Channel Ordering for Accurate Machine Decoding
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Solution Overview
Problem
Existing image compression technologies are not optimized for machine learning applications, lacking efficiency and suitability for processing large volumes of image data efficiently.
Innovation Solution
A feature encoding/decoding method and apparatus that manages and stores feature tensors by considering the actual order of feature channels, improving encoding/decoding efficiency and accuracy through the generation and transmission of bitstreams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image compression technology is used for machine learning applications, then human vision image quality is maintained, but encoding/decoding efficiency for machine tasks deteriorates
Solution Approach 1:
The patent changes the fundamental parameters of image compression by optimizing for machine task performance rather than human vision quality. This involves modifying compression algorithms to preserve features relevant to machine learning tasks (such as edge detection, object recognition) while allowing degradation in aspects not important for machine tasks, thereby improving encoding/decoding efficiency without sacrificing machine task accuracy
Solution Approach 2:
The patent applies different compression strategies to different regions or features within an image based on their importance for machine learning tasks. Critical features that impact machine task performance are preserved with higher fidelity, while less important regions are compressed more aggressively, achieving a balance between efficiency and task-specific accuracy
2Measurement precision
If feature channels are not managed according to actual order in feature tensor, then buffer management is simplified, but feature reconstruction accuracy deteriorates
Solution Approach 1:
The patent applies preliminary sorting or reordering operations to feature channels before they are stored in buffers or processed further. By pre-organizing feature channels in their actual order within the feature tensor, the system ensures that subsequent processing steps receive data in the correct sequence, maintaining reconstruction accuracy without requiring complex real-time reordering operations during decoding
3Loss of information
If information about filter order is not transmitted in bitstream, then transmission data volume is reduced, but decoding accuracy deteriorates
Solution Approach 1:
The patent extracts and transmits only the essential information about filter order and feature channel arrangement in the bitstream, rather than transmitting complete filter configurations. By identifying and transmitting only the critical ordering information needed for accurate decoding, the system minimizes information loss while maintaining decoding accuracy and keeping the bitstream compact
Data Source
AI summary
A feature encoding/decoding method and device, and a computer-readable recording medium generated by the feature encoding method are provided. The feature decoding method according to the present disclosure relates to a feature decoding method performed by a feature decoding device, and may comprise the steps of: acquiring information on a sequence of feature channels in a feature tensor from a bitstream; and determining the sequence of the feature channels on the basis of the information on the sequence of the feature channels.