Feature Map Encoding with ROI-Based Compression for AI Tasks
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Solution Overview
Problem
Existing image compression technologies are not optimized for machine tasks and lack efficiency in processing large amounts of image data required for artificial intelligence services.
Innovation Solution
A feature encoding/decoding method and apparatus that distinguishes regions of interest (ROI) and non-ROI in feature maps, using representative values and difference values to enhance encoding/decoding efficiency, generating a compressed bitstream for machine tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image compression technologies are used for machine tasks, then high-resolution and high-quality image processing is achieved, but encoding/decoding efficiency is insufficient for processing large amounts of image data required for artificial intelligence services
Solution Approach 1:
The feature map is divided into multiple channels, and each channel is further divided into multiple regions. This segmentation allows selective encoding of different regions based on their importance to machine tasks, thereby improving encoding efficiency while maintaining necessary quality for AI processing.
Solution Approach 2:
Different encoding strategies are applied to different regions within the same feature map. Regions of interest (ROIs) that are important for machine tasks are encoded with higher quality, while non-ROI regions use lower quality encoding, optimizing the balance between overall quality and encoding efficiency.
2Loss of information
If all regions of a feature map are encoded with equal quality, then comprehensive image information is preserved, but encoding complexity and processing time increase
Solution Approach 1:
Instead of encoding all regions with equal high quality, the method applies partial encoding action only to regions that are important for machine tasks. This reduces the overall encoding complexity while preserving the critical feature information needed for AI processing.
Solution Approach 2:
The encoding quality parameter is changed dynamically based on region importance. ROIs are assigned higher quality parameters while non-ROIs use lower quality parameters, reducing overall encoding complexity without losing critical information for machine tasks.
3Productivity
If region-wise selective encoding is applied to improve efficiency, then encoding/decoding speed increases, but complexity of determining ROI and non-ROI regions increases
Solution Approach 1:
The regions are classified into ROIs and non-ROIs before the encoding process begins. This preliminary classification based on importance metrics allows the subsequent encoding to proceed efficiently without complex real-time decisions, improving encoding speed while managing classification complexity in advance.
Data Source
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AI summary
The present disclosure relates to a feature encoding/decoding method, a recording medium storing a bitstream, and a method for transmitting a bitstream. A feature decoding method performed by a feature decoding device, according to one embodiment of the present disclosure, comprises the steps of: obtaining information on a channel within a feature from a bitstream; and reconstructing the channel on the basis of the information on the channel, wherein the information on the channel may include information on whether coding has been performed according to an importance level of each region of the channel.