Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications

TL;DR

This paper investigates how low-overhead lossy image compression affects the accuracy of crowd counting in smart city applications, balancing bandwidth savings against potential accuracy loss.

Contribution

It provides a detailed analysis of the trade-offs between compression-induced bandwidth reduction and crowd counting accuracy degradation.

Findings

Low-overhead lossy compression reduces bandwidth significantly.

Compression causes measurable accuracy degradation in crowd counting.

Optimal compression levels balance bandwidth savings and accuracy loss.

Abstract

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.