|Title||Video compressive sensing using Gaussian mixture models.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||J Yang, X Yuan, X Liao, P Llull, DJ Brady, G Sapiro, and L Carin|
|Journal||Ieee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society|
|Pagination||4863 - 4878|
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.
|Short Title||Ieee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society|