论文标题
分析低空损失图像压缩对智能城市应用的视觉人群计数的影响
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications
论文作者
论文摘要
在整个智能城市中放置的相机捕获的相机捕获的图像和视频帧通常会通过网络传输到服务器，以通过深层神经网络处理各种任务。原始图像的传输，即没有任何形式的压缩，需要高带宽，并且可能导致交通拥堵问题和传输延迟。使用有损图像压缩技术的使用可以降低图像的质量，从而导致精确降解。在本文中，我们分析了应用低空损耗的图像压缩方法对视觉人群计数准确性的效果，并测量带宽降低和获得的准确性之间的权衡。
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.
