Recent advancements in remote heart rate measurement (rPPG), motivated by
data-driven approaches, have significantly improved accuracy. However, certain
challenges, such as video compression, still remain: recovering the rPPG signal
from highly compressed videos is particularly complex. Although several studies
have highlighted the difficulties and impact of video compression for this,
effective solutions remain limited. 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.

通过脉搏信号放大变换来适应压缩视频，解决视频压缩对无创心率测量的影响并取得出色的心率估计结果。