Autoencoder Progressive Coding for Bandwidth-Adaptive 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 tensor channels, each representing different aspects of image information. By dividing the data into separable channels, the system can selectively transmit only the essential channels needed for acceptable image reconstruction, reducing overall bandwidth consumption while maintaining reconstruction quality under varying channel conditions
Solution Approach 2:
The patent applies local quality by assigning different priorities to different tensor channels based on their importance for image reconstruction. Not all channels are treated equally; instead, channels are prioritized based on their contribution to image quality, allowing the system to transmit high-priority channels first and achieve acceptable reconstruction even when lower-priority channels are dropped due to bandwidth constraints
2Manufacturing precision
If all tensor channels are transmitted to maintain complete image information, then image reconstruction accuracy is improved, but adaptability to varying bandwidth conditions deteriorates
Solution Approach 1:
The patent implements dynamics by making the transmission strategy adaptive rather than static. The system dynamically adjusts which channels to transmit based on available bandwidth conditions. When bandwidth is sufficient, all channels can be transmitted for maximum accuracy. When bandwidth is constrained, the system dynamically selects and transmits only the highest-priority channels, maintaining adaptability to varying network conditions
Solution Approach 2:
The patent changes the parameter of channel prioritization based on bandwidth conditions. By modifying which channels are transmitted and in what order based on network availability, the system maintains image reconstruction accuracy when possible while adapting to bandwidth constraints when necessary, achieving both accuracy and adaptability
3Ease of operation
If tensor channels are transmitted in traditional order, then transmission simplicity is maintained, but image quality under bandwidth constraints deteriorates
Solution Approach 1:
The patent applies local quality by assigning different priorities to different tensor channels based on their importance for image reconstruction. Instead of treating all channels equally, the system identifies and prioritizes channels that contribute most to image quality, ensuring that under bandwidth constraints, the most important channels are transmitted first to maintain acceptable image quality
Solution Approach 2:
The patent implements preliminary action by pre-determining the priority order of tensor channels before transmission begins. The system analyzes which channels are most important for image reconstruction and establishes a prioritization scheme in advance, so that when bandwidth constraints arise, the system can immediately begin transmitting channels in the correct priority order without complex real-time decision-making
Data Source
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.