PCA-based magnification method for revealing small signals in video

Video magnification techniques are useful for visualizing small changes in videos. Current methods are mainly applied for two aspects: motion amplification and color amplification. For instance, Eulerian video magnification (EVM) has shown impressive results in the context of color of human face and subtle head motion caused by the influx of blood at each beat. Such visual results have possible applications in non-contact human physiological parameter measurement, such as heart rate estimation. Unfortunately, the linear EVM is sensitive to noise and frequencies of the changes should be customized, which generates a limitation of applications. This paper presents an advanced EVM for magnifying the signal amplitude in the presence of relatively high noise as well as unknown the frequencies of changes in video. Principal component analysis (PCA) is performed to decompose the frames and the component whose spatial variation best matches small changes to be magnified. The advantage of PCA-based method is that it can select the subtle signals with a denoising process like spatial filtering. Experimental results show that the PCA-based EVM can support larger amplification factors for small changes visualization as well as less noise and artifacts.