Student: Connor Ennis (Clemson University)
Supervisor: Jon Calhoun (Clemson University)
Abstract: When transmitting image data from a deployed edge device, a high-bandwidth connection to a cloud system cannot be guaranteed. An early-warning system for an intersection crosswalk, for instance, would have to be able to compress and transmit data with enough quality to ensure prompt detection of danger through remote image processing. Adaptive lossy compression provides a potential solution for this, although it is yet to be evaluated on actual edge hardware. By separating the compression and detection pipelines between client and server processes, improving compression ratios by up to 4.95% via a unified lossless stage, demonstrating compression performance on an Arm-powered edge device, and benchmarking network performance under a range of realistic bandwidth conditions, we attempt to evaluate the viability of this method under realistic conditions. This poster discusses our revised architecture and its performance, along with the relevance of our results towards method refinement.
ACM-SRC Semi-Finalist: no
Poster Summary: PDF
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