The accurate estimation of peripheral capillary oxygen saturation (SpO 2 ) is vital for monitoring respiratory health, with applications spanning medical diagnostics and fitness tracking. Remote photoplethysmography (rPPG) offers a convenient and non-contact approach for SpO 2 estimation. However, existing methods predominantly rely on data within the normal SpO 2 range, hindering their effectiveness during hypoxemia. Moreover, cross-user variations poses significant challenges for practicality. To address these limitations, we propose a simple yet effective normalization-based SpO 2 estimation algorithm. By aligning individual Ratio-of-Ratios (RoR) data with a standard model at the matching SpO 2 level, we mitigate cross-user variation, accommodate different camera configurations, and account for lighting changes. Our experiments demonstrate that the proposed method achieves an rMSE of 2.8% with leave-one-subject-out cross-validation across the full SpO 2 range (70%-100%), significantly outperforming existing RoR-based and CNN-based SpO 2 estimation approaches. Notably, our methods excel in accurately identifying hypoxemia, a critical clinical requirement. We anticipate broader applicability of our approach in rPPG-based vital sign monitoring, underlining the potential for enhancing robustness and reliability in various domains.