Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 20 Jul 2022.
Builds on
8 of 16 references.
References · 8
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2018Neural Image Compression for Gigapixel Histopathology Image Analysiscited by 65
- 2018Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networkscited by 56
- 2020CNN-based Density Estimation and Crowd Counting: A Surveycited by 43
- 2021Single-Layer Vision Transformers for More Accurate Early Exits with Less Overheadcited by 26
- 2020Rethinking FUN: Frequency-Domain Utilization Networkscited by 6
- 2019Multilinear Compressive Learningcited by 5
- 2021Remote Multilinear Compressive Learning with Adaptive Compressioncited by 1
Built on by
Citing papers per year: 2022: 1; 2023: 1.
2 papers.