Deep pulse-signal magnification for remote heart rate estimation in compressed videos

• A deep learning framework to reduce video compression effects on rPPG signals. • Selectively enhances rPPG-relevant info, unlike standard video quality methods. • Combines an rPPG Estimator and a magnifier for pulse signals in compressed video. • Validated on four public datasets, showing strong robustness and generalization. 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 investigate the impact of video compression on the recovery of physiological signals from camera-based recordings. To mitigate the negative effects of compression on rPPG estimation, we propose a novel two-stage training strategy. This approach incorporates a pulse-signal magnification transformation, which adapts compressed video data into an uncompressed domain, where the rPPG signal is amplified. We validate the effectiveness of our model through comprehensive evaluations on two publicly available datasets, UCLA-rPPG and UBFC-rPPG, assessing both intra- and cross-database performance across various 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.