Edge Image Data Optimization for Computer Vision Latency
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Solution Overview
Problem
Current computer vision systems face challenges in efficiently offloading image processing tasks to remote computing devices, balancing latency and bandwidth requirements, while maintaining high computing capabilities.
Innovation Solution
Implementing data optimization techniques on edge-based computing devices to determine and apply optimal data processing methods for image communication, with feedback loops to adjust techniques based on the remote computing device's needs, enabling efficient data transmission and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is transmitted in full resolution to remote computing device, then computer vision analysis accuracy is improved, but bandwidth consumption increases
Solution Approach 1:
The patent segments image data into different resolution components and transmits only necessary segments to the remote computing device. Full-resolution images are processed locally, and only essential data portions are transmitted over the network, reducing bandwidth consumption while preserving analysis accuracy.
Solution Approach 2:
The system extracts and transmits only the essential or relevant portions of image data to the remote computing device, rather than transmitting complete high-resolution images. This extraction approach reduces the quantity of transmitted data while maintaining the sufficiency for computer vision analysis.
2Loss of time
If image processing is performed locally on edge device, then latency is reduced, but computing resource consumption increases
Solution Approach 1:
The patent implements dynamic processing where the edge device performs real-time preprocessing and filtering of image data, adapting the level of local processing based on computational requirements. This dynamic approach reduces latency by handling time-critical operations locally while managing computing resource consumption through adaptive processing intensity.
Solution Approach 2:
The system performs partial processing of image data at the edge device rather than complete processing. Essential preprocessing operations are executed locally to reduce latency, while more computationally intensive analysis is performed remotely, balancing latency reduction with computing resource management.
3Quantity of substance
If data optimization is applied to images, then bandwidth usage is reduced, but data quality may deteriorate
Solution Approach 1:
The patent applies different quality levels to different regions or portions of image data based on their importance. Critical regions maintain higher quality while less important areas are compressed more aggressively, reducing overall bandwidth usage while preserving essential information quality for computer vision tasks.
Solution Approach 2:
The system dynamically changes data transmission parameters such as resolution, compression ratio, and format based on network conditions and analysis requirements. This allows bandwidth usage to be reduced through parameter optimization while maintaining sufficient data quality by adjusting parameters adaptively rather than using fixed low-quality settings.
Data Source
AI summary
Systems and methods for applying data optimization to images to meet a computer vision requirement are disclosed. According to an aspect, a method includes determining a computer vision requirement of a remote computing device. The method also includes determining, based on the computer vision requirement, a data optimization technique to apply to multiple images. Further, the method includes applying the data optimization technique to one or more of the images to generate data for communication. The method also includes communicating the data to the remote computing device.