Jointly Trained Neural Networks for Image Compression Latency
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Solution Overview
Problem
The high computation complexity of neural networks and the large volume of images to be processed lead to latency issues during image transmission from local to remote systems, making real-time image processing challenging, especially when existing image compression techniques are not optimized for neural network-based processing.
Innovation Solution
A method involving jointly trained compression and processing neural networks that compress images into a compressed representation, allowing for efficient processing and transmission, while maintaining or enhancing processing accuracy through a neural network accelerator architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If images are transmitted from local to remote systems for processing, then machine learning tasks can be performed on high-resolution images, but transmission latency increases significantly
Solution Approach 1:
The patent extracts only the essential features and semantic information from images using a compression neural network, transforming full-resolution images into compressed representations that retain processing-relevant data while removing redundant visual details. This extraction approach enables remote processing of extracted features rather than complete images, significantly reducing transmission requirements.
Solution Approach 2:
The patent segments the image processing pipeline into two distinct neural network components: a compression neural network that processes images locally and extracts features, and a processing neural network that performs machine learning tasks remotely on compressed representations. This segmentation allows independent optimization of each component and enables asynchronous operation where compression occurs locally while processing occurs remotely.
2Measurement precision
If high-resolution images are processed remotely, then processing accuracy is maintained, but transmission bandwidth requirements increase
Solution Approach 1:
The patent introduces a compressed representation as an intermediary data structure between the original image and the processing neural network. This intermediate form retains the essential semantic information needed for accurate processing while using significantly fewer bytes than original high-resolution images, acting as a efficient mediator that preserves accuracy while reducing data volume.
Solution Approach 2:
The compression neural network transforms images by changing key parameters including resolution, color depth, and feature representation dimensions. By adjusting these parameters to retain only processing-relevant information rather than all visual details, the system achieves efficient compression that maintains processing accuracy while dramatically reducing data volume for transmission.
3Quantity of substance
If existing image compression techniques are used, then data transmission is reduced, but processing accuracy deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/image processing compression algorithms with neural network-based compression. The compression neural network learns optimal compression strategies by training on paired examples of original images and their corresponding processing results, automatically discovering which visual features are most important for processing accuracy. This substitution of traditional compression mechanics with learned neural compression preserves accuracy while achieving better compression ratios.
Solution Approach 2:
The system implements feedback loops during training where the processing neural network's performance on compressed representations feeds back to guide the compression neural network's optimization. This feedback mechanism ensures that compression is specifically optimized to preserve the features most relevant for processing tasks, rather than using generic compression that may lose processing-critical information.
4Measurement precision
If joint training of compression and processing networks is implemented, then processing accuracy is enhanced, but training complexity increases
Solution Approach 1:
The patent merges the training processes of the compression neural network and processing neural network into a unified joint training framework. During joint training, both networks are trained simultaneously with shared loss functions that optimize both compression efficiency and processing accuracy together. This merging eliminates the need for separate training phases and allows the networks to co-adapt, where the compression network learns to preserve features that the processing network needs while the processing network learns to extract meaningful information from compressed representations.
Data Source
AI summary
The present disclosure relates to a system and method for image processing. In some embodiments, an exemplary image processing method includes: receiving an image; compressing, with a compression neural network, the image into a compressed representation; and performing, with a processing neural network, a machine learning task on the compressed representation to generate a learning result. The compression neural network and the processing neural network are jointly trained.