Semantic Learned Image Compression With Latent Channel Reordering
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Solution Overview
Problem
Conventional learned image compression frameworks fail to fully exploit coding efficiency due to poor utilization of latent channel correlations, fixed mask convolutions, and lack of integration with semantic information, leading to suboptimal prediction accuracy and bit rate reduction.
Innovation Solution
Implement latent channel reordering, channel-shuffling context models, semantic variational autoencoders, guided masked autoencoders, and representation U-Net frameworks to enhance coding efficiency by reordering latent channels, utilizing semantic information, and improving context modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional learned image compression frameworks are used, then implementation is simpler, but coding efficiency is poor due to failure to exploit latent channel correlations
Solution Approach 1:
The patent applies dynamic channel reordering that adapts to the specific image content being compressed. The system dynamically determines optimal channel permutations based on the input image characteristics, allowing the compression framework to adapt its structure rather than using a fixed ordering. This dynamic adaptation enables better exploitation of latent channel correlations for improved coding efficiency.
Solution Approach 2:
The patent changes the parameter of channel ordering by testing multiple permutations and selecting the optimal one. By varying the channel arrangement parameters and selecting the configuration that maximizes coding efficiency, the system transforms a static compression framework into one that optimizes its internal parameters based on content characteristics.
2Device complexity
If fixed mask convolutions are used, then computational complexity is reduced, but prediction accuracy becomes suboptimal
Solution Approach 1:
The patent segments the context modeling process into multiple stages: initial fixed mask convolution for baseline prediction, followed by residual modeling that addresses prediction errors. This segmentation allows the system to use simple fixed masks for coarse prediction while dedicating additional computational resources to refining accuracy through residual processing.
Solution Approach 2:
The patent introduces residual information as an intermediary between the fixed mask convolution output and the final prediction. The residual modeling component acts as a mediator that captures the differences between the simple fixed mask prediction and the actual target, thereby improving overall prediction accuracy without requiring the primary convolution to be complex.
3Speed
If semantic information is not integrated, then processing speed is maintained, but bit rate reduction is limited
Solution Approach 1:
The patent performs preliminary semantic analysis and channel reordering before the main compression process. By pre-organizing channels based on semantic relationships and correlation analysis beforehand, the system reduces the information content that needs to be encoded later, achieving bit rate reduction without significantly impacting processing speed during the main compression pass.
Data Source
AI summary
A method of image compression implemented by a coding device. The method comprises receiving an input latent image comprising latent image patches containing latent image data, selecting a subset of the latent image patches; applying the latent image patches to the input of a first encoder in the coding device, receiving conditioning side information, encoding, by the first encoder, the subset of latent image patches based on the conditioning side information to generate encoded latent image patches. The method further includes combining the encoded latent image patches with a plurality of mask tokens, applying the combined encoded latent image patches and plurality of mask tokens to the input of a decoder in the coding device, decoding the combined encoded latent image patches and plurality of mask tokens based on the conditioning side information to generate a reconstructed latent feature map, and rearranging the reconstructed latent feature map to produce an output latent image.