Progressive Autoencoder Coding for Partial-Data Image Reconstruction
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Solution Overview
Problem
Existing autoencoder architectures require all compressed image data to be received for image reconstruction, failing to adapt to varying channel conditions such as bandwidth changes, leading to data loss and reconstruction issues.
Innovation Solution
Implement progressive encoding techniques for autoencoders by prioritizing and progressively transmitting tensor channels based on their contribution to image quality, allowing reconstruction at different quality levels even with partial data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If all compressed image data is transmitted to ensure complete image reconstruction, then image reconstruction quality is improved, but bandwidth consumption increases and data loss occurs under varying channel conditions
Solution Approach 1:
The patent segments the compressed image data into multiple priority levels (first priority, second priority, third priority channels). By dividing the data into segments with different importance levels, the system can transmit only essential segments during bandwidth-constrained conditions while maintaining acceptable reconstruction quality, thus resolving the contradiction between complete data transmission and bandwidth efficiency.
Solution Approach 2:
The patent applies local quality by differentiating the quality requirements of different data segments. High-priority channels contain essential information for basic image reconstruction, while lower-priority channels provide additional detail. This allows the system to maintain different quality levels for different parts of the data, optimizing the balance between reconstruction quality and transmission volume.
2Manufacturing precision
If all compressed image data must be received for reconstruction, then image reconstruction completeness is improved, but adaptability to varying channel conditions deteriorates
Solution Approach 1:
The patent implements dynamics by making the transmission strategy adaptive rather than static. The system dynamically adjusts the transmission quantity and priority based on real-time channel conditions (bandwidth availability, packet loss rates). Under good channel conditions, more data is transmitted; under poor conditions, only high-priority data is sent, ensuring the system adapts to varying conditions while maintaining reconstruction completeness.
Solution Approach 2:
The patent changes the transmission parameters (data quantity, priority levels) based on channel conditions. By monitoring channel quality and adjusting transmission parameters accordingly, the system maintains adaptability to varying conditions while ensuring complete or acceptable image reconstruction, resolving the contradiction between completeness and adaptability.
3Quantity of substance
If data compression is applied to reduce bandwidth, then bandwidth consumption is reduced, but image reconstruction quality deteriorates when data is dropped
Solution Approach 1:
The patent applies preliminary action by pre-ordering data channels according to their importance before transmission. The system预先 determines which channels are most critical for image reconstruction and transmits them first. This preliminary organization ensures that even if data is dropped during transmission, the most important information is already received, minimizing quality deterioration while maintaining low bandwidth consumption.
Data Source
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AI summary
This disclosure provides methods, devices, and systems for image encoding. The present implementations more specifically relate to progressive encoding techniques for autoencoders. In some aspects, an image encoder may encode an image as a tensor of latent attributes having multiple channels based on one or more first layers of a neural network model, and recombine the tensor channels, in a prioritized order, based on one or more second layers of the neural network model. The image encoder may progressively transmit the recombined tensor channels over a communication channel based on the prioritized order. In some implementations, the image encoder may transmit the recombined tensor channels, in order of priority, so that channels assigned higher priorities are transmitted before channels assigned lower priorities.