Machine Learning Compression for Hyperspectral Imagery Transmission
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Solution Overview
Problem
The increasing size of new media files such as ultra-high resolution audio, video, single or multiple-band images, and hyperspectral imagery outpaces the growth in available internet bandwidth, leading to slower transmission times and storage inefficiencies, as traditional compression methods like JPEG are insufficient to meet the growing bandwidth requirements.
Innovation Solution
A machine learning-based media compression and decompression system that employs neural networks to reduce the spatial dimensionality of images, creating bit strings for efficient compression and decompression, capable of handling single-band, multiple-band, and hyperspectral imagery, and executing on GPU hardware for enhanced speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression schemes like JPEG are used, then storage efficiency is improved, but transmission time increases and bandwidth requirements are not met
Solution Approach 1:
The patent transforms the compression approach by changing from traditional signal processing parameters to machine learning model parameters. The system learns optimal compression representations through training data, adapting parameters dynamically to achieve both high compression ratios and fast decoding speeds, thereby reducing transmission time while maintaining storage efficiency.
Solution Approach 2:
The patent replaces traditional mechanical signal processing methods with machine learning-based neural networks. The encoder and decoder networks substitute conventional compression algorithms, enabling the system to learn complex patterns in hyperspectral imagery that traditional methods cannot capture, achieving superior compression performance with reduced transmission time.
2Manufacturing precision
If file sizes increase to maintain quality, then visual fidelity is improved, but bandwidth requirements increase and transmission slows down
Solution Approach 1:
The patent performs preliminary learning and adaptation during the training phase, where the neural networks learn optimal compression representations from training data. This preliminary action enables the system to achieve high visual fidelity at lower bitrates during actual compression, eliminating the need to increase file sizes for quality maintenance, thereby preserving transmission speed.
3Device complexity
If conventional compression methods are used, then system complexity is reduced, but they cannot meet growing bandwidth requirements for new media types
Solution Approach 1:
The patent creates a universal compression framework that handles multiple media types including hyperspectral imagery, standard images, and video sequences. The neural network architecture is designed to be adaptable to different input formats and requirements, providing a single system that can meet varying bandwidth requirements across different applications without requiring separate specialized systems.
Data Source
AI summary
A process for reducing time of transmission for single-band, multiple-band or hyperspectral imagery using Machine Learning based compression is disclosed. The process uses Machine Learning to compress single-band, multiple-band and hyperspectral imagery, thereby decreasing the needed bandwidth and storage-capacity requirements for efficient transmission and data storage. The reduced file size for transmission accelerate the communications and reduces the transmission time. This enhances communications systems where there is a greater need for on or near real-time transmission, such as mission critical applications in national security, aerospace and natural resources.