JPEG image compression algorithm is a widely used technique for image size reduction in edge and cloud computing settings. However, applying such lossy compression on images processed by deep neural networks can lead to significant accuracy degradation. Inspired by the curriculum learning paradigm, we propose a training approach called curriculum pre-training (CPT) for crowd counting on compressed images, which alleviates the drop in accuracy resulting from lossy compression. We verify the effectiveness of our approach by extensive experiments on three crowd counting datasets, two crowd counting DNN models and various levels of compression. The proposed training method is not overly sensitive to hyper-parameters, and reduces the error, particularly for heavily compressed images, by up to 19.70%.

翻译：JPEG图像压缩算法是一种在边缘计算和云计算环境中广泛使用的图像尺寸缩减技术。然而，将这种有损压缩应用于深度神经网络处理的图像时，会导致显著的精度下降。受课程学习范式的启发，我们提出了一种名为课程预训练（CPT）的训练方法，用于处理压缩图像的人群计数任务，该方法能够缓解有损压缩导致的精度下降问题。通过在三个群计数数据集、两种群计数深度神经网络模型以及不同压缩级别上的大量实验，我们验证了该方法的有效性。所提出的训练方法对超参数不敏感，并且能够将误差降低最多19.70%，尤其是在处理强压缩图像时效果显著。