对基于神经网络的心率分类图像压缩效应的评估和分析
Tianyu Dong1, Seongho Cook2, Jaiyoung Oh1
1Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
Scientific reports
|July 2, 2025
概括
图像压缩对神经网络心率 (HR) 分类准确度的影响最小. VGG-16实现了97.2%的准确性,证明了高效的远程HR监控,即使有损耗压缩.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 基于神经网络 (NN) 的系统越来越多地用于从面部图像中估计心率 (HR).
- 图像压缩对于远程健康监测系统中高效的数据传输和存储至关重要.
研究的目的:
- 评估图像压缩 (无损PNG,有损JPEG) 对基于NN的HR分类准确性的影响.
- 为了比较不同压缩率下的DenseNet-121,VGG-16和Inception V3模型的性能.
主要方法:
- 面部图像使用无损 (PNG) 和有损 (JPEG) 格式进行压缩.
- 用压缩图像对DenseNet-121,VGG-16和Inception V3模型进行了HR分类的准确性评估.
- 分析了压缩速率与分类准确性之间的关系.
主要成果:
- VGG-16表现出最高的性能,在HR检测中达到97.2%的准确性.
- 丢失的图像压缩 (JPEG) 仅对HR分类准确性产生轻微影响.
- 压缩图像显著降低了带宽和存储需求.
结论:
- 图像压缩技术,包括丢失方法,对于基于NN的远程HR分类系统是可行的.
- 拟议的系统为低复杂性,低比特率的HR监控提供了有效的解决方案.
- VGG-16是一个适合在图像压缩约束下准确估计HR的模型.
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