研究背景: 遠程光體積變化描記圖法（rPPG）是一種非接觸式技術，用於從臉部影片中提取光體積變化描記圖法訊號以估算心率。rPPG 方法的準確度受影片資料品質的影響。在現實世界中，不同的光線、運動偽影和其他因素都會影響影片品質。雖然高品質影片能確保獲得更好的數據，但它需要更好的攝影機、更大的儲存空間、傳輸頻寬和運算能力。因此，為了優化 rPPG 性能，特別是在即時心率估算中，通常會對影片品質進行權衡。雖然影片品質對準確估算至關重要，但影片品質與估算準確性之間的關係仍需進一步研究。

研究目標: 本研究調查了 VIIDEO 演算法評估的影片品質與七種 rPPG 方法估計心率的表現之間的關係。

研究方法: 我們評估了各種顏色通道、攝影機角度、未壓縮 RAW 影像序列和令人驚嘆的 4k 影片的影片質量，以了解 VIIDEO 分數如何與人類對高品質視覺效果的感知相關聯。在以四種方式（高斯模糊、解析度減半、迭加高斯模糊和高斯雜訊）降級的影片上評估了七種 rPPG 方法，並透過 VIIDEO 分數進行量化。rPPG 方法使用的評估指標包括平均絕對誤差 (MAE)、成功率 (SR) 和訊號雜訊比 (SNR)。所使用的rPPG 資料集由 10 位受試者的臉部影片和生理訊號組成。這些記錄是在兩種不同的場景下進行的：一種場景是受試者靜坐，被稱為 “static test”；另一種場景是受試者在健身自行車上騎車，被稱為 “bike test”。我們研究了 MAE 和 VIIDEO 分數之間的相關性，以及 SR 和 VIIDEO 分數之間的相關性。為確保代表性的平衡，我們採用隨機抽樣的方法來確定 MAE 和 VIIDEO 分數之間的相關性。

研究結果: 我們在各種影片劣化條件下評估的 rPPG 估計結果表明，就 MAE 而言， POS 方法在靜態測試中優於其他方法，而 SPH 方法在自行車測試中領先。同時，在靜態和自行車測試中，CSC 的 SR 更好，而 POS 的 SNR 更好。觀察到的一個總體趨勢表明，隨著影片失真強度的增加，包括 MAE、SR 和 SNR 在內的 rPPG 性能指標也在下降。值得注意的是，對於原始影片和靜態測試中一定程度的影片衰減， CHROM 和POS 等方法的MAE 始終低於1 bpm。在這個靜態測試中，在被測對象正前方測量到的光照強度為 700 LUX，臉部區域的平均解析度為 498 × 562 像素。根據我們的觀察，同一 VIIDEO 分數的 MAE 值和SR 值差異很大。

研究結論: 我們的研究表明，超過一定的品質閾值後，影片品質的提高在 rPPG 性能提升方面的回報會遞減，無失真影片並不總是能明顯改善心率估計。影片品質會影響 rPPG 方法的準確性，使用者需要得到指導，了解哪種影片品質足以達到一定的準確性。然而，我們發現 VIIDEO 分數與rPPG 效能之間沒有明顯的關係。

Background: Remote photoplethysmography (rPPG) is a non-contact technique for extracting photoplethysmography signal from facial videos for heart rate estimation. The accuracy of rPPG methods is influenced by the quality of the video data. In real-world scenarios, varying lighting, motion artefacts, and other factors affect video quality. While high-quality video ensures better data, it requires a better camera, more storage space, transmission bandwidth, and computational effort. Consequently, a trade-off in video quality is often made to optimize rPPG performance, especially in real-time heart rate estimation. Although video quality is crucial for accurate estimation, the relationship between video quality and estimation accuracy still need further investigation.

Aim: This study investigate the relationship between video quality, as assessed by the VIIDEO algorithm, and the performance of seven rPPG methods in estimating heart rate.

Method: We assess video quality across various color channels, camera angles, sequences of RAW uncompressed images, and stunning 4k videos to understand how VIIDEO scores correlate with human perceptions of high-quality visuals. Seven rPPG methods are evaluated on videos degraded in four ways (Gaussian blur, resolution halving, iterative addition of Gaussian blur, and Gaussian noise) and quantified by the VIIDEO score. Evaluation metrics used for rPPG methods are mean absolute error (MAE), success rate (SR), and signal-to-noise ratio (SNR). The rPPG dataset used consists of facial videos and physiological signals from 10 human subjects. These recordings were conducted under two distinct scenarios: one where the subjects were seated still, referred to as the “static test”, and another where they were bicycling on an exercise bike, designated as the “bike test”. We examine the correlation between MAE and VIIDEO scores, as well as the correlation between SR and VIIDEO scores. To ensure a balanced representation, random sampling of data points was employed for correlation between MAE and VIIDEO scores.

Results: Our rPPG estimation results, assessed under various video degradation conditions, reveal that in terms of MAE, the POS method outperforms others in static tests, while the SPH method leads in bike tests. Meanwhile, CSC have better SR while POS have better SNR in both static and bike tests. A general trend observed indicates a decline in rPPG performance metrics, including MAE, SR, and SNR, as the intensity of video distortion increases. Notably, for the original video and certain levels of video degradation in static test, methods like CHROM and POS consistently achieve a MAE of less than 1 bpm. In this static test, the lighting intensity measured directly in front of the subject was 700 LUX, and the average resolution of the facial area was 498×562 pixels. From our observation, there is a significant variation in the MAE and SR values for the same VIIDEO score.

Conclusion: Our study shows that beyond a certain quality threshold, improvements in video quality yield diminishing returns in rPPG performance enhancement, with distortion-free video not always leading to markedly better heart rate estimations. Video quality impacts the accuracy of rPPG methods and users need guidance on which video quality is sufficient for a certain accuracy. However, we found no clear relationship between the VIIDEO score and rPPG performance.

Agarla, M., Celona, L., and Schettini, R. (2020). No-reference quality assessment of incapture distorted videos. Journal of Imaging, 6(8):74.

Ahn, S. and Lee, S. (2018). Deep blind video quality assessment based on temporal human perception. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 619–623. IEEE.

Balakrishnan, G., Durand, F., and Guttag, J. (2013a). Detecting pulse from head motions in video. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3430–3437.

Balakrishnan, G., Durand, F., and Guttag, J. (2013b). Detecting Pulse from Head Motions in Video. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, pages 3430–3437. IEEE.

Bampis, C. G., Gupta, P., Soundararajan, R., and Bovik, A. C. (2017). Speed-qa: Spatial efficient entropic differencing for image and video quality. IEEE signal processing letters, 24(9):1333–1337.

Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., and Dubois, J. (2019). Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recognition Letters, 124:82–90.

Boccignone, G., Bursic, S., Cuculo, V., D＇Amelio, A., Grossi, G., Lanzarotti, R., and Patania, S. (2022). Deepfakes have no heart: A simple rppg-based method to reveal fake videos. In International Conference on Image Analysis and Processing, pages 186–195. Springer.

Bonmassar, G., Purdon, P. L., Jääskeläinen, I. P., Chiappa, K., Solo, V., Brown, E. N., and Belliveau, J. W. (2002). Motion and ballistocardiogram artifact removal for interleaved recording of eeg and eps during mri. Neuroimage, 16(4):1127–1141.

Botina-Monsalve, D., Benezeth, Y., and Miteran, J. (2022). Rtrppg: An ultra light 3dcnn for real-time remote photoplethysmography. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2146–2154.

Brandao, T. and Queluz, M. P. (2010). No-reference quality assessment of h. 264/avc encoded video. IEEE Transactions on Circuits and Systems for Video Technology, 20(11):1437–1447.

Cemiloglu, E. and Yilmaz, G. N. (2020). Blind video quality assessment via spatiotemporal statistical analysis of adaptive cube size 3d-dct coefficients. IET Image Processing, 14(5):845–852.

Cheang, P. Y. and Smith, P. R. (2003). An overview of non-contact photoplethysmography. Electronic systems and control division research, pages 57–59.

Chen, W. and McDuff, D. (2018). Deepphys: Video-based physiological measurement using convolutional attention networks. In Proceedings of the European Conference on Computer Vision (ECCV), pages 349–365.

Chikkerur, S., Sundaram, V., Reisslein, M., and Karam, L. J. (2011). Objective video quality assessment methods: A classification, review, and performance comparison. IEEE transactions on broadcasting, 57(2):165–182.

Cho, D., Kim, J., Lee, K. J., and Kim, S. (2021). Reduction of motion artifacts from remote photoplethysmography using adaptive noise cancellation and modified hsi model. IEEE Access, 9:122655–122667.

Cho, D. and Lee, B. (2016). Non-contact robust heart rate estimation using hsv color model and matrix-based iir filter in the face video imaging. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 3847–3850. IEEE.

Comaniciu, D., Ramesh, V., and Meer, P. (2003). Kernel-based object tracking. IEEE Transactions on pattern analysis and machine intelligence, 25(5):564–577.

Comas, J., Ruiz, A., and Sukno, F. (2022). Efficient remote photoplethysmography with temporal derivative modules and time-shift invariant loss. In Proceedings of the IEEE/ CVF Conference on Computer Vision and Pattern Recognition, pages 2182–2191.

Cootes, T. F., Edwards, G. J., and Taylor, C. J. (2001). Active appearance models. IEEE Transactions on pattern analysis and machine intelligence, 23(6):681–685.

Da He, D., Winokur, E. S., and Sodini, C. G. (2011). A continuous, wearable, and wireless heart monitor using head ballistocardiogram (bcg) and head electrocardiogram (ecg). In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 4729–4732. IEEE.

Datcu, D., Cidota, M., Lukosch, S., and Rothkrantz, L. (2013). Noncontact automatic heart rate analysis in visible spectrum by specific face regions. In Proceedings of the 14th International Conference on Computer Systems and Technologies, pages 120–127.

De Haan, G. and Jeanne, V. (2013). Robust pulse rate from chrominance-based rppg. IEEE Transactions on Biomedical Engineering, 60(10):2878–2886.

De Haan, G. and Van Leest, A. (2014). Improved motion robustness of remote-ppg by using the blood volume pulse signature. Physiological measurement, 35(9):1913.

Dong, C., Loy, C. C., He, K., and Tang, X. (2015). Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2):295–307.

Farnebäck, G. (2003). Two-frame motion estimation based on polynomial expansion. In Scandinavian conference on Image analysis, pages 363–370. Springer.

Feng, L., Po, L.-M., Xu, X., and Li, Y. (2014). Motion artifacts suppression for remote imaging photoplethysmography. In 2014 19th International Conference on Digital Signal Processing, pages 18–23. IEEE.

Feng, L., Po, L.-M., Xu, X., Li, Y., and Ma, R. (2015). Motion-resistant remote imaging photoplethysmography based on the optical properties of skin. IEEE Transactions on Circuits and Systems for Video Technology, 25(5):879–891.

Feng, Y., Wu, F., Shao, X., Wang, Y., and Zhou, X. (2018). Joint 3d face reconstruction and dense alignment with position map regression network. In Proceedings of the European Conference on Computer Vision (ECCV), pages 534–551.

Fitzpatrick, T. B. (1988). The validity and practicality of sun-reactive skin types i through vi. Archives of dermatology, 124(6):869–871.

Gao, H., Wu, X., Geng, J., and Lv, Y. (2022). Remote heart rate estimation by signal quality attention network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2122–2129.

Gideon, J. and Stent, S. (2021). The way to my heart is through contrastive learning: Remote photoplethysmography from unlabelled video. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3995–4004.

Gunawan, I. P. and Ghanbari, M. (2008). Reduced-reference video quality assessment using discriminative local harmonic strength with motion consideration. IEEE Transactions on Circuits and Systems for Video Technology, 18(1):71–83.

Gupta, P. (2021). Merastc: Micro-expression recognition using effective feature encodings and 2d convolutional neural network. IEEE Transactions on Affective Computing.

Gupta, P., Bhowmick, B., and Pal, A. (2018). Exploring the feasibility of face video based instantaneous heart-rate for micro-expression spotting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 1316–1323.

Gupta, P., Bhowmick, B., and Pal, A. (2020). Mombat: Heart rate monitoring from face video using pulse modeling and bayesian tracking. Computers in biology and medicine, 121:103813.

Hanfland, S. and Paul, M. (2016). Video format dependency of ppgi signals. In Proceedings of the International Conference on Electrical Engineering, volume 1, page 2. Haque, M. A., Irani, R., Nasrollahi, K., and Moeslund, T. B. (2016). Heartbeat Rate Measurement from Facial Video. IEEE Intelligent Systems, 31(3):40–48.

Hertzman, A. B. (1937). Photoelectric plethysmography of the fingers and toes in man. Proceedings of the Society for Experimental Biology and Medicine, 37(3):529–534.

Heusch, G., Anjos, A., and Marcel, S. (2017). A reproducible study on remote heart rate measurement. arXiv preprint arXiv:1709.00962.

Horn, B. K. and Schunck, B. G. (1981). Determining optical flow. In Techniques and Applications of Image Understanding, volume 281, pages 319–331. International Society for Optics and Photonics.

Hsu, G.-S., Ambikapathi, A., and Chen, M.-S. (2017). Deep learning with time-frequency representation for pulse estimation from facial videos. In 2017 IEEE International Joint Conference on Biometrics (IJCB), pages 383–389. IEEE.

Hsu, Y., Lin, Y.-L., and Hsu, W. (2014). Learning-based heart rate detection from remote photoplethysmography features. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4433–4437. IEEE.

Hu, M., Qian, F., Guo, D., Wang, X., He, L., and Ren, F. (2021). Eta-rppgnet: Effective time-domain attention network for remote heart rate measurement. IEEE Transactions on Instrumentation and Measurement, 70:1–12.

Hülsbusch, M. and Rembold, B. (2008). Ein bildgestütztes, funktionelles verfahren zur optoelektronischen erfassung der hautperfusion. Technical report, Lehrstuhl und Institut für Hochfrequenztechnik.

Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., and Brox, T. (2017). Flownet 2.0: Evolution of optical flow estimation with deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2462–2470.

Irani, R., Nasrollahi, K., and Moeslund, T. B. (2014). Improved pulse detection from head motions using dct. In 2014 international conference on computer vision theory and applications (VISAPP), volume 3, pages 118–124. IEEE.

Jansen, B. H., Larson, B. H., and Shankar, K. (1991). Monitoring of the ballistocardiogram with the static charge sensitive bed. IEEE Transactions on Biomedical Engineering, 38(8):748–751.

Katwal, R. (2021). Liveness detection for 3d face mask attacks. Master's thesis, NTNU.

Kessler, V., Thiam, P., Amirian, M., and Schwenker, F. (2017). Pain recognition with camera photoplethysmography. In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pages 1–5. IEEE.

Koivistoinen, T., Junnila, S., Varri, A., and Koobi, T. (2004). A new method for measuring the ballistocardiogram using emfi sensors in a normal chair. In The 26th annual international conference of the IEEE engineering in medicine and biology society, volume 1, pages 2026–2029. IEEE.

Kossack, B., Wisotzky, E., Hilsmann, A., and Eisert, P. (2021). Automatic region-based heart rate measurement using remote photoplethysmography. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2755–2759.

Kumar, M., Veeraraghavan, A., and Sabharwal, A. (2015). Distanceppg: Robust non-contact vital signs monitoring using a camera. Biomedical optics express, 6(5):1565–1588.

Lasmar, N.-E., Stitou, Y., and Berthoumieu, Y. (2009). Multiscale skewed heavy tailed model for texture analysis. In 2009 16th IEEE International Conference on Image Processing (ICIP), pages 2281–2284. IEEE.

Lee, H., Cho, A., Lee, S., and Whang, M. (2019). Vision-based measurement of heart rate from ballistocardiographic head movements using unsupervised clustering. Sensors, 19(15):3263.

Lee, H., Lee, J., Kwon, Y., Kwon, J., Park, S., Sohn, R., and Park, C. (2022). Multitask siamese network for remote photoplethysmography and respiration estimation. Sensors, 22(14):5101.

Lee, K.-Z., Hung, P.-C., and Tsai, L.-W. (2012). Contact-free heart rate measurement using a camera. In 2012 Ninth Conference on Computer and Robot Vision, pages 147–152. IEEE.

Lewandowska, M., Rumiński, J., Kocejko, T., and Nowak, J. (2011). Measuring pulse rate with a webcam—a non-contact method for evaluating cardiac activity. In 2011 federated conference on computer science and information systems (FedCSIS), pages 405–410. IEEE.

Li, D., JIANG, T., and JIANG, M. (2019a). Recent advances and challenges in video quality assessment. ZTE Communications, 17(1):3–11.

Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., and Huang, F. (2019b). Dsfd: dual shot face detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5060–5069.

Li, J., Yu, Z., and Shi, J. (2022). Learning motion-robust remote photoplethysmography through arbitrary resolution videos. arXiv preprint arXiv:2211.16922.

Li, X., Chen, J., Zhao, G., and Pietikainen, M. (2014). Remote heart rate measurement from face videos under realistic situations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4264–4271.

Li, X., Guo, Q., and Lu, X. (2016). Spatiotemporal statistics for video quality assessment. IEEE Transactions on Image Processing, 25(7):3329–3342.

Li, Y., Po, L.-M., Cheung, C.-H., Xu, X., Feng, L., Yuan, F., and Cheung, K.-W. (2015). Noreference video quality assessment with 3d shearlet transform and convolutional neural networks. IEEE Transactions on Circuits and Systems for Video Technology, 26(6):1044– 1057.

Lin, X. and de Haan, G. (2014). Using blood volume pulse vector to extract rppg signal in infrared spectrum. Master's thesis, Technische Universiteit Eindhoven.

Liu, S.-Q. and Yuen, P. C. (2020). A general remote photoplethysmography estimator with spatiotemporal convolutional network. In 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pages 481–488. IEEE.

Liu, X., Fromm, J., Patel, S., and McDuff, D. (2020). Multi-task temporal shift attention networks for on-device contactless vitals measurement. Advances in Neural Information Processing Systems, 33:19400–19411.

Liu, X., Hill, B., Jiang, Z., Patel, S., and McDuff, D. (2023). Efficientphys: Enabling simple, fast and accurate camera-based cardiac measurement. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 5008–5017.

Lu, H., Han, H., and Zhou, S. K. (2021). Dual-gan: Joint bvp and noise modeling for remote physiological measurement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12404–12413.

Lucas, B. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In IJCAI, volume 81.

Ma, L., Li, S., and Ngan, K. N. (2012). Reduced-reference video quality assessment of compressed video sequences. IEEE Transactions on circuits and systems for video technology, 22(10):1441–1456.

Macwan, R., Benezeth, Y., and Mansouri, A. (2018a). Remote photoplethysmography with constrained ica using periodicity and chrominance constraints. Biomedical engineering online, 17(1):22.

Macwan, R., Benezeth, Y., Nakamura, K., Gomez, R., Wu, Y., and Mansouri, A. (2018b). Parameter-free adaptive step-size multiobjective optimization applied to remote photoplethysmography. In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pages 267–270. IEEE.

Maity, A. K., Wang, J., Sabharwal, A., and Nayar, S. K. (2022). Robustppg: camerabased robust heart rate estimation using motion cancellation. Biomedical Optics Express, 13(10):5447–5467.

Manasa, K. and Channappayya, S. S. (2016). An optical flow-based no-reference video quality assessment algorithm. In 2016 IEEE International Conference on Image Processing (ICIP), pages 2400–2404. IEEE.

Martinez, B., Valstar, M. F., Binefa, X., and Pantic, M. (2012). Local evidence aggregation for regression-based facial point detection. IEEE transactions on pattern analysis and machine intelligence, 35(5):1149–1163.

Martinez, L. F. C., Paez, G., and Strojnik, M. (2011). Optimal wavelength selection for noncontact reflection photoplethysmography. In 22nd Congress of the International Commission for Optics: Light for the Development of the World, volume 8011, page 801191. International Society for Optics and Photonics.

McDuff, D., Gontarek, S., and Picard, R. W. (2014a). Improvements in remote cardiopulmonary measurement using a five band digital camera. IEEE Transactions on Biomedical Engineering, 61(10):2593–2601.

McDuff, D., Gontarek, S., and Picard, R. W. (2014b). Remote detection of photoplethysmographic systolic and diastolic peaks using a digital camera. IEEE Transactions on Biomedical Engineering, 61(12):2948–2954.

McDuff, D., Nishidate, I., Nakano, K., Haneishi, H., Aoki, Y., Tanabe, C., Niizeki, K., and Aizu, Y. (2020). Non-contact imaging of peripheral hemodynamics during cognitive and psychological stressors. Scientific Reports, 10(1):1–13.

McDuff, D. J., Blackford, E. B., and Estepp, J. R. (2017). The impact of video compression on remote cardiac pulse measurement using imaging photoplethysmography. In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pages 63–70. IEEE.

Men, H., Lin, H., and Saupe, D. (2018). Spatiotemporal feature combination model for noreference video quality assessment. In 2018 Tenth international conference on quality of multimedia experience (QoMEX), pages 1–3. IEEE.

Mittal, A., Moorthy, A. K., and Bovik, A. C. (2011). Blind/referenceless image spatial quality evaluator. In 2011 conference record of the forty fifth asilomar conference on signals, systems and computers (ASILOMAR), pages 723–727. IEEE.

Mittal, A., Moorthy, A. K., and Bovik, A. C. (2012). No-reference image quality assessment in the spatial domain. IEEE Transactions on image processing, 21(12):4695–4708.

Mittal, A., Saad, M. A., and Bovik, A. C. (2015). A completely blind video integrity oracle. IEEE Transactions on Image Processing, 25(1):289–300.

Monitors, C. (2002). Heart rate meters, and alarms. ANSI/AAMI Standard EC13.

Moorthy, A. K. and Bovik, A. C. (2010). Efficient video quality assessment along temporal trajectories. IEEE transactions on circuits and systems for video technology, 20(11):1653–1658.

Morbiducci, U., Scalise, L., De Melis, M., and Grigioni, M. (2007). Optical vibrocardiography: A novel tool for the optical monitoring of cardiac activity. Annals of biomedical engineering, 35(1):45–58.

Mounsey, P. (1957). Praecordial ballistocardiography. British heart journal, 19(2):259.

Mukherjee, D., Bankoski, J., Grange, A., Han, J., Koleszar, J., Wilkins, P., Xu, Y., and Bultje, R. (2013). The latest open-source video codec vp9-an overview and preliminary results. In 2013 Picture Coding Symposium (PCS), pages 390–393. IEEE.

Nakajima, K., Maekawa, T., and Miike, H. (1997). Detection of apparent skin motion using optical flow analysis: Blood pulsation signal obtained from optical flow sequence. Review of scientific instruments, 68(2):1331–1336.

Niu, X., Han, H., Shan, S., and Chen, X. (2018a). Synrhythm: Learning a deep heart rate estimator from general to specific. In 2018 24th International Conference on Pattern Recognition (ICPR), pages 3580–3585. IEEE.

Niu, X., Han, H., Shan, S., and Chen, X. (2018b). Vipl-hr: A multi-modal database for pulse estimation from less-constrained face video. In Asian Conference on Computer Vision, pages 562–576. Springer.

Niu, X., Shan, S., Han, H., and Chen, X. (2019). Rhythmnet: End-to-end heart rate estimation from face via spatial-temporal representation. IEEE Transactions on Image Processing.

Niu, X., Yu, Z., Han, H., Li, X., Shan, S., and Zhao, G. (2020). Video-based remote physiological measurement via cross-verified feature disentangling. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16, pages 295–310. Springer.

Oh, T.-H., Jaroensri, R., Kim, C., Elgharib, M., Durand, F., Freeman, W. T., and Matusik, W. (2018). Learning-based video motion magnification. In Proceedings of the European Conference on Computer Vision (ECCV), pages 633–648.

Philips (2021). Measurements to guide your patient care. Philips Efficia CM12 Patient Monitor Manual.

Pilz, C. S., Blazek, V., and Leonhardt, S. (2019). On the vector space in photoplethysmography imaging. arXiv preprint arXiv:1906.04431.

Pilz, C. S., Krajewski, J., and Blazek, V. (2017). On the diffusion process for heart rate
estimation from face videos under realistic conditions. In German Conference on Pattern Recognition, pages 361–373. Springer.

Pilz, C. S., Zaunseder, S., Krajewski, J., and Blazek, V. (2018). Local group invariance for heart rate estimation from face videos in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 1254–1262.

Pinson, M. H. and Wolf, S. (2004). A new standardized method for objectively measuring video quality. IEEE Transactions on broadcasting, 50(3):312–322.

Poh, M.-Z., McDuff, D. J., and Picard, R. W. (2010a). Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE transactions on biomedical engineering, 58(1):7–11.

Poh, M.-Z., McDuff, D. J., and Picard, R. W. (2010b). Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics express, 18(10):10762–10774.

Premkumar, K. S., Angelopoulou, A., Kapetanios, E., Chaussalet, T., and Hemanth, D. J. (2022). Super-resolution convolutional network for image quality enhancement in remote photoplethysmography based heart rate estimation. In Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V. V., Dongarra, J. J., and Sloot, P. M. A., editors, Computational Science – ICCS 2022, pages 157–163, Cham. Springer International Publishing.

Qiu, Y., Liu, Y., Arteaga-Falconi, J., Dong, H., and El Saddik, A. (2018). Evm-cnn: Realtime contactless heart rate estimation from facial video. IEEE transactions on multimedia, 21(7):1778–1787.

Ruderman, D. L. (1994). The statistics of natural images. Network: computation in neural systems, 5(4):517.

Saad, M. A., Bovik, A. C., and Charrier, C. (2014). Blind prediction of natural video quality. IEEE Transactions on Image Processing, 23(3):1352–1365.

Saragih, J. M., Lucey, S., and Cohn, J. F. (2011). Deformable model fitting by regularized landmark mean-shift. International journal of computer vision, 91:200–215.

Seshadrinathan, K. and Bovik, A. C. (2009). Motion tuned spatio-temporal quality assessment of natural videos. IEEE transactions on image processing, 19(2):335–350.

Seshadrinathan, K., Soundararajan, R., Bovik, A. C., and Cormack, L. K. (2010). Study of subjective and objective quality assessment of video. IEEE transactions on Image Processing, 19(6):1427–1441.

Shan, L. and Yu, M. (2013). Video-based heart rate measurement using head motion tracking and ica. In 2013 6th International Congress on Image and Signal Processing (CISP), volume 1, pages 160–164. IEEE.

Shi, J. et al. (1994). Good features to track. In 1994 Proceedings of IEEE conference on computer vision and pattern recognition, pages 593–600. IEEE.

Soleymani, M., Lichtenauer, J., Pun, T., and Pantic, M. (2011). A multimodal database for affect recognition and implicit tagging. IEEE Transactions on Affective Computing, 3(1):42–55.

Song, R., Li, J., Wang, M., Cheng, J., Li, C., and Chen, X. (2021). Remote photoplethysmography with an eemd-mcca method robust against spatially uneven illuminations. IEEE Sensors Journal, 21(12):13484–13494.

Soundararajan, R. and Bovik, A. C. (2012). Video quality assessment by reduced reference spatio-temporal entropic differencing. IEEE Transactions on Circuits and Systems for Video Technology, 23(4):684–694.

Spetlík, R., Cech, J., and Matas, J. (2018). Non-contact reflectance photoplethysmography: Progress, limitations, and myths. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 702–709. IEEE.

Špetlík, R., Franc, V., and Matas, J. (2018). Visual heart rate estimation with convolutional neural network. In Proceedings of the British Machine Vision Conference, Newcastle, UK, pages 3–6.

Stricker, R., Müller, S., and Gross, H.-M. (2014). Non-contact video-based pulse rate measurement on a mobile service robot. In The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pages 1056–1062. IEEE.

Takano, C. and Ohta, Y. (2007). Heart rate measurement based on a time-lapse image. Medical engineering & physics, 29(8):853–857.

Tasli, H. E., Gudi, A., and den Uyl, M. (2014). Remote ppg based vital sign measurement using adaptive facial regions. In 2014 IEEE International Conference on Image Processing (ICIP), pages 1410–1414. IEEE.

Tran, D. N., Lee, H., and Kim, C. (2015). A robust real time system for remote heart rate measurement via camera. In 2015 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE.

Tsou, Y.-Y., Lee, Y.-A., and Hsu, C.-T. (2020a). Multi-task learning for simultaneous video generation and remote photoplethysmography estimation. In Proceedings of the Asian Conference on Computer Vision.

Tsou, Y.-Y., Lee, Y.-A., Hsu, C.-T., and Chang, S.-H. (2020b). Siamese-rppg network: remote photoplethysmography signal estimation from face videos. In Proceedings of the 35th Annual ACM Symposium on Applied Computing, pages 2066–2073.

Tsouri, G. R., Kyal, S., Dianat, S., and Mestha, L. (2012). Constrained independent component analysis approach to nonobtrusive pulse rate measurements. Journal of biomedical optics, 17:077011.

Tu, Z., Wang, Y., Birkbeck, N., Adsumilli, B., and Bovik, A. C. (2021). Ugc-vqa: Benchmarking blind video quality assessment for user generated content. IEEE Transactions on Image Processing, 30:4449–4464.

Tulyakov, S., Alameda-Pineda, X., Ricci, E., Yin, L., Cohn, J. F., and Sebe, N. (2016). Selfadaptive matrix completion for heart rate estimation from face videos under realistic conditions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2396–2404.

van Gastel, M., Stuijk, S., and de Haan, G. (2015). Motion robust remote-ppg in infrared. IEEE Transactions on Biomedical Engineering, 62(5):1425–1433.

Varga, D. (2022). No-reference video quality assessment using multi-pooled, saliency weighted deep features and decision fusion. Sensors, 22(6):2209.

Verkruysse, W., Svaasand, L. O., and Nelson, J. S. (2008). Remote plethysmographic imaging using ambient light. Optics express, 16(26):21434–21445.

Viola, P., Jones, M., et al. (2001). Rapid object detection using a boosted cascade of simple features. CVPR (1), 1(511-518):3.

Wadhwa, N., Rubinstein, M., Durand, F., and Freeman, W. T. (2013). Phase-based video motion processing. ACM Transactions on Graphics (TOG), 32(4):80.

Wadhwa, N., Rubinstein, M., Durand, F., and Freeman, W. T. (2014). Riesz pyramids for fast phase-based video magnification. In 2014 IEEE International Conference on Computational Photography (ICCP), pages 1–10. IEEE.

Wang, C.-C. (2020). Non-contact heart rate measurement based on facial videos. Master's thesis, National Cheng Kung University, No. 1, Dasyue Rd, East District, Tainan City, 701.

Wang, W., Den Brinker, A. C., and De Haan, G. (2018). Single element remote-ppg. IEEE Transactions on Biomedical Engineering.

Wang, W., Den Brinker, A. C., and De Haan, G. (2019). Discriminative signatures for remoteppg. IEEE Transactions on Biomedical Engineering.

Wang, W., Den Brinker, A. C., Stuijk, S., and De Haan, G. (2016). Algorithmic principles of remote ppg. IEEE Transactions on Biomedical Engineering, 64(7):1479–1491.

Wang, W., Stuijk, S., and De Haan, G. (2015a). Exploiting spatial redundancy of image sensor for motion robust rPPG. IEEE Transactions on Biomedical Engineering, 62(2):415–425.

Wang, W., Stuijk, S., and De Haan, G. (2015b). A novel algorithm for remote photoplethysmography: Spatial subspace rotation. IEEE transactions on biomedical engineering, 63(9):1974–1984.

Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. (2004a). Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612.

Wang, Z. and Li, Q. (2007). Video quality assessment using a statistical model of human visual speed perception. JOSA A, 24(12):B61–B69.

Wang, Z., Lu, L., and Bovik, A. C. (2004b). Video quality assessment based on structural distortion measurement. Signal processing: Image communication, 19(2):121–132.

Wiens, A. D., Etemadi, M., Roy, S., Klein, L., and Inan, O. T. (2014). Toward continuous, noninvasive assessment of ventricular function and hemodynamics: Wearable ballistocardiography. IEEE journal of biomedical and health informatics, 19(4):1435–1442.

Wieringa, F. P., Mastik, F., and van der Steen, A. F. (2005). Contactless multiple wavelength photoplethysmographic imaging: a first step toward “spo 2 camera＂technology. Annals of biomedical engineering, 33(8):1034–1041.

Wu, B.-F., Chu, Y.-W., Huang, P.-W., and Chung, M.-L. (2019a). Neural network based luminance variation resistant remote-photoplethysmography for driver＇s heart rate monitoring. IEEE Access, 7:57210–57225.

Wu, B.-F., Huang, P.-W., He, D.-H., Lin, C.-H., and Chen, K.-H. (2019b). Remote photoplethysmography enhancement with machine leaning methods. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pages 2466–2471. IEEE.

Wu, B.-F., Huang, P.-W., Lin, C.-H., Chung, M.-L., Tsou, T.-Y., and Wu, Y.-L. (2018). Motion resistant image-photoplethysmography based on spectral peak tracking algorithm. IEEE Access, 6:21621–21634.

Wu, B.-F., Yang, Y.-Y., Tsai, B.-R., Huang, P.-W., Tsai, Y.-C., and Chen, K.-H. (2019c). Remote heartrate measurement based on signal feature detection in time domain. In 2019 International Conference on System Science and Engineering (ICSSE), pages 88–93. IEEE.

Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., and Freeman, W. (2012). Eulerian video magnification for revealing subtle changes in the world. ACM transactions on graphics (TOG), 31(4):1–8.

Xi, L., Wu, X., Chen, W., Wang, J., and Zhao, C. (2022). Weighted combination and singular spectrum analysis based remote photoplethysmography pulse extraction in low-light environments. Medical Engineering & Physics, 105:103822.

Xu, Q., Wu, Z., Su, L., Qin, L., Jiang, S., and Huang, Q. (2010). Bridging the gap between objective score and subjective preference in video quality assessment. In 2010 IEEE International Conference on Multimedia and Expo, pages 908–913. IEEE.

Xue, W., Zhang, L., Mou, X., and Bovik, A. C. (2013). Gradient magnitude similarity deviation: A highly efficient perceptual image quality index. IEEE transactions on image processing, 23(2):684–695.

Yan, P. and Mou, X. (2018). Video quality assessment based on motion structure partition similarity of spatiotemporal slice images. Journal of Electronic Imaging, 27(3):033019.

Yang, Z., Wang, H., and Lu, F. (2022). Assessment of deep learning-based heart rate estimation using remote photoplethysmography under different illuminations. IEEE Transactions on Human-Machine Systems, 52(6):1236–1246.

Yao, C., Wang, S., Zhang, J., He, W., Du, H., Ren, J., Bai, R., and Liu, J. (2021). rppg-based spoofing detection for face mask attack using efficientnet on weighted spatial-temporal representation. In 2021 IEEE International Conference on Image Processing (ICIP), pages 3872–3876. IEEE.

Yu, Z., Li, X., Niu, X., Shi, J., and Zhao, G. (2020). Autohr: A strong end-to-end baseline for remote heart rate measurement with neural searching. IEEE Signal Processing Letters, 27:1245–1249.

Yu, Z., Li, X., and Zhao, G. (2019a). Remote photoplethysmograph signal measurement from facial videos using spatio-temporal networks. In Proc. BMVC.

Yu, Z., Peng, W., Li, X., Hong, X., and Zhao, G. (2019b). Remote heart rate measurement from highly compressed facial videos: an end-to-end deep learning solution with video enhancement. In Proceedings of the IEEE International Conference on Computer Vision, pages 151–160.

Yu, Z., Shen, Y., Shi, J., Zhao, H., Torr, P. H., and Zhao, G. (2022). Physformer: facial videobased physiological measurement with temporal difference transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4186–4196.

Zhang, B., Li, J., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., Xia, Y., Pei, W., and ASFD, R. J. (2020). Automatic and scalable face detector. arXiv preprint arXiv:2003.11228.

Zhang, Y., Pintea, S. L., and Van Gemert, J. C. (2017). Video acceleration magnification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 529–537.

Zhao, C., Lin, C.-L., Chen, W., and Li, Z. (2018). A novel framework for remote photoplethysmography pulse extraction on compressed videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 1299–1308.

Zhao, Y., Zou, B., Yang, F., Lu, L., Belkacem, A. N., and Chen, C. (2021). Video-based physiological measurement using 3d central difference convolution attention network. In 2021 IEEE International Joint Conference on Biometrics (IJCB), pages 1–6. IEEE.

Zheng, J., Hu, S., Chouliaras, V., and Summers, R. (2008). Feasibility of imaging photoplethysmography. In 2008 International Conference on BioMedical Engineering and Informatics, volume 2, pages 72–75. IEEE.

Zhou, K., Krause, S., Blocher, T., and Stork, W. (2020). Enhancing remote-ppg pulse extraction in disturbance scenarios utilizing spectral characteristics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 280–281.

Zhu, K. and Saupe, D. (2013). Performance evaluation of hd camcorders: measuring texture distortions using gabor filters and spatio-velocity csf. In Image Quality and System Performance X, volume 8653, pages 68–80. SPIE.

Zhu, Y., Cai, H., Zhang, S., Wang, C., and Xiong, Y. (2020). Tinaface: Strong but simple baseline for face detection. arXiv preprint arXiv:2011.13183.