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

arXiv: https://arxiv.org/abs/2207.10155 · HTML: https://mlsanity.com/paper/2207.10155/

## 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 (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Image Compression for Gigapixel Histopathology Image Analysis](https://mlsanity.com/paper/1811.02840/) (2018)
- [Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks](https://mlsanity.com/paper/1809.03355/) (2018)
- [CNN-based Density Estimation and Crowd Counting: A Survey](https://mlsanity.com/paper/2003.12783/) (2020)
- [Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead](https://mlsanity.com/paper/2105.09121/) (2021)
- [Rethinking FUN: Frequency-Domain Utilization Networks](https://mlsanity.com/paper/2012.03357/) (2020)
- [Multilinear Compressive Learning](https://mlsanity.com/paper/1905.07481/) (2019)
- [Remote Multilinear Compressive Learning with Adaptive Compression](https://mlsanity.com/paper/2109.01184/) (2021)

## Built on by (most cited first, 2 of 2)

- [PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks](https://mlsanity.com/paper/2301.12914/) (2023)
- [Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training](https://mlsanity.com/paper/2208.07075/) (2022)

## 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.