LoRaWAN Image Compression via Contour Extraction
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Solution Overview
Problem
Existing wireless imaging systems, such as those using LoRaWAN, face significant challenges in transmitting image data over long distances without incurring high data costs or requiring high-bandwidth connections, due to the limitations of current image compression algorithms.
Innovation Solution
The system employs an image acquisition device with onboard digital image processing software that automatically identifies objects of interest and reduces full images to smaller portions made up of outlines, contours, or measurement vectors, which can be transmitted using less bandwidth. This process includes background subtraction, thresholding, contouring, and the use of pre-defined shape dictionaries to minimize file size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If full images are transmitted over LoRaWAN, then image completeness is improved, but transmission time increases to days
Solution Approach 1:
The patent extracts only the essential features from full images - specifically contours, outlines, and measurements of targeted objects. This extraction process removes redundant pixel data while retaining the critical information needed for monitoring purposes, enabling transmission within LoRaWAN's bandwidth constraints without requiring days to transmit complete images.
Solution Approach 2:
The patent segments the image processing into two stages: edge processing at the remote device that extracts contours and measurements, and optional cloud processing that can request additional detail only when necessary. This segmentation allows the system to transmit minimal data routinely while maintaining the option to obtain complete images on demand.
2Loss of time
If JPEG compression is used, then transmission time is reduced from days to hours, but image quality is degraded
Solution Approach 1:
The patent replaces the traditional mechanical JPEG compression approach with a fundamentally different method - extracting geometric contours and measurements directly from images. Instead of compressing pixel data through algorithms that introduce artifacts, the system substitutes a vector-based representation that preserves measurement accuracy while achieving extreme compression ratios suitable for LoRaWAN transmission.
3Quantity of substance
If vector conversion is used, then file size is reduced, but existing systems lack digital dictionaries for further compression
Solution Approach 1:
The patent creates digital dictionaries that contain pre-defined contour patterns and measurements for common objects of interest in agricultural and wildlife monitoring. Instead of transmitting every contour detail, the system copies references from these dictionaries and transmits only the identifiers and transformations needed, achieving further compression beyond standard vector conversion.
Data Source
AI summary
A system and method for creating small data size representations of images, created on the image detection device, over wireless connections when there is insufficient bandwidth to support detailed images is disclosed. Image data size is often too large to send over low-powered long-distance wireless connections. Image data must be dramatically reduced to enable use of the lowest-power longest-distance wireless platforms including LoRa with LoRaWAN. Common image compression algorithms include jpeg and mpeg that provide only moderate reductions in data size. The described invention reduces data size beyond jpeg compression by reducing targeted image objects to simple outlines, contours or vectors. Monitoring security, wildlife, agricultural and other natural events require images of objects including insects, crops, livestock, wildlife or intruders. Contours, outlines or vectors of targeted objects are often sufficiently recognizable to provide useful information.