Recent advancements in data-driven approaches for remote photoplethysmography (rPPG) have significantly improved the accuracy of remote heart rate estimation. However, the performance of such approaches worsens considerably under video compression, which is nevertheless necessary to store and transmit video data efficiently. In this paper, we present a novel approach to address the impact of video compression on rPPG estimation, which leverages a pulse-signal magnification transformation to adapt compressed videos to an uncompressed data domain in which the rPPG signal is magnified. We validate the effectiveness of our model by exhaustive evaluations on two publicly available datasets, UCLA-rPPG and UBFC-rPPG, employing both intra- and cross-database performance at several compression rates. Additionally, we assess the robustness of our approach on two additional highly compressed and widely-used datasets, MAHNOB-HCI and COHFACE, which reveal outstanding heart rate estimation results.

翻译：近年来，数据驱动的远程光电容积描记术（rPPG）方法显著提升了远程心率估计的准确性。然而，此类方法在视频压缩条件下的性能会大幅下降，而视频压缩对于高效存储和传输视频数据又是必不可少的。本文提出了一种新颖的方法来应对视频压缩对rPPG估计的影响，该方法利用脉冲信号放大变换，将压缩视频适配到未压缩的数据域中，从而放大rPPG信号。我们在两个公开数据集UCLA-rPPG和UBFC-rPPG上进行了详尽的评估，采用多种压缩率下的库内与跨库性能测试，验证了模型的有效性。此外，我们在另外两个高度压缩且广泛使用的数据集MAHNOB-HCI和COHFACE上评估了所提方法的鲁棒性，结果显示其具有出色的心率估计性能。