Video Compressive Sensing Using Gaussian Mixture Models
Copyright policy )Video Compressive Sensing Using Gaussian Mixture Models
A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from temporally compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed inversion method with videos reconstructed from simulated compressive video measurements, and from a real compressive video camera. We also use the GMM as a tool to investigate adaptive video compressive sensing, i.e., adaptive rate of temporal compression.
- Duke University United States
Image processing (compression, reconstruction, etc.) in information and communication theory
Image processing (compression, reconstruction, etc.) in information and communication theory
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