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<p>In this study, we evaluated and analyzed the effects of image compression on a neural network (NN)-based heart rate (HR) classification system. An NN-based HR-estimation system classifies facial images into groups of HR intervals. We evaluated the relationship between the image compression rates and accuracy of an NN-based HR estimation system. In our evaluation, the image of the face was compressed into lossless (PNG) and lossy (JPEG) formats to reduce the transmission bandwidth. The compressed images significantly reduce the required bandwidth and storage size. Furthermore, we analyzed the image classification accuracy of the DenseNet-121, VGG-16, and Inception V3 models. VGG-16 exhibited the highest performance, and the proposed system yielded an accuracy of 97.2% for correctly detecting the HR. Additionally, the results showed that lossy image compression quality slightly affected HR accuracy. This evaluation method can provide an effective solution under low computational complexity and low bitrate requirement for remote HR classification.</p>

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Evaluation and analysis of image compression effect on neural network-based heart rate classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

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  • Tianyu Dong,
  • Seongho Cook,
  • Jaiyoung Oh,
  • Euee S. Jang

摘要

In this study, we evaluated and analyzed the effects of image compression on a neural network (NN)-based heart rate (HR) classification system. An NN-based HR-estimation system classifies facial images into groups of HR intervals. We evaluated the relationship between the image compression rates and accuracy of an NN-based HR estimation system. In our evaluation, the image of the face was compressed into lossless (PNG) and lossy (JPEG) formats to reduce the transmission bandwidth. The compressed images significantly reduce the required bandwidth and storage size. Furthermore, we analyzed the image classification accuracy of the DenseNet-121, VGG-16, and Inception V3 models. VGG-16 exhibited the highest performance, and the proposed system yielded an accuracy of 97.2% for correctly detecting the HR. Additionally, the results showed that lossy image compression quality slightly affected HR accuracy. This evaluation method can provide an effective solution under low computational complexity and low bitrate requirement for remote HR classification.