Image Encoding Attention Region Residual Data
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Solution Overview
Problem
Existing image encoding and decoding methods do not effectively maintain or improve performance when applied to machine learning tasks, particularly in maintaining image quality for attention regions.
Innovation Solution
The method involves detecting attention regions in an original image, generating residual data between the original and reconstructed images, and encoding this data to minimize restoration errors, using a bitstream generator and decoder to correct and scale the attention regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If image encoding is performed to reduce resolution, then data size is reduced and transmission/storage efficiency is improved, but restoration error increases and image quality deteriorates
Solution Approach 1:
The image is divided into attention regions and non-attention regions. Different encoding strategies are applied to each region: high-quality encoding for attention regions and compressed encoding for non-attention regions. This segmentation allows the system to maintain image quality where needed while reducing overall data size.
Solution Approach 2:
The patent applies different quality levels to different regions of the image. Attention regions receive higher quality encoding to minimize restoration error, while non-attention regions can tolerate lower quality. This local differentiation resolves the contradiction between overall data reduction and local quality preservation.
2Device complexity
If conventional encoding is used, then encoding complexity is low, but machine learning task performance deteriorates due to poor attention region quality
Solution Approach 1:
The system performs preliminary detection of attention regions using a detector before encoding. This preliminary action identifies which regions require special handling, allowing the encoding process to prioritize computational resources accordingly. The preliminary detection enables subsequent selective encoding that improves ML performance without excessive complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the attention region detection results feed back into the encoding process. The encoder uses the detected attention regions to adjust encoding parameters and allocate resources dynamically. This feedback loop ensures that encoding complexity is optimized based on actual image content requirements.
Data Source
AI summary
An image encoding method includes detecting at least one attention region from an original image, acquiring a reconstructed picture for the original image, generating residual data between a first attention region in the original image and a second attention region corresponding to the first attention region in the reconstructed picture, and encoding the residual data.