Video Compressive Sensing Using Gaussian Mixture Models

Jianbo Yang, Xin Yuan, Xuejun Liao,Patrick Llull, David J. Brady, Guillermo Sapiro, Lawrence Carin

Department of Electrical and Computer Engineering, Duke University

This website demonstrate the videos in the experiment described in the following paper:

Jianbo Yang, Xin Yuan, Xuejun Liao, Patrick Llull, David J. Brady, Guillermo Sapiro and Lawrence Carin. ”Video Compressive Sensing Using Gaussian Mixture Models,” accepted in IEEE Transactions on Image Processing 2014.

Results on Synthetic Data: Unrelated Training Data

1. Traffic (T=8)

2. Couple (T=8)

3.Train (T=8)

Results on Synthetic Data: Related Training Data

1. Traffic (T=36)

2. NBA Game (T=8)

Results on Real CACTI Data

1. Chopper wheel

2. Eyb blink

3. Hand lens

Adaptivity of Temporal Compression Ratio