Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications
This addresses bandwidth congestion issues for smart city applications, but it is incremental as it focuses on analyzing existing compression methods rather than introducing new ones.
The paper analyzes how low-overhead lossy image compression affects the accuracy of visual crowd counting in smart cities, measuring the trade-off between bandwidth reduction and accuracy degradation.
Images and video frames captured by cameras placed throughout smart cities are often transmitted over the network to a server to be processed by deep neural networks for various tasks. Transmission of raw images, i.e., without any form of compression, requires high bandwidth and can lead to congestion issues and delays in transmission. The use of lossy image compression techniques can reduce the quality of the images, leading to accuracy degradation. In this paper, we analyze the effect of applying low-overhead lossy image compression methods on the accuracy of visual crowd counting, and measure the trade-off between bandwidth reduction and the obtained accuracy.