A New Compressive Video Sensing Framework for Mobile Broadcast

A new video coding method based on compressive sampling is proposed. In this method, a video is coded using compressive measurements on video cubes. Video reconstruction is performed by minimization of total variation (TV) of the pixelwise discrete cosine transform coefficients along the temporal direction. A new reconstruction algorithm is developed from TVAL3, an efficient TV minimization algorithm based on the alternating minimization and augmented Lagrangian methods. Video coding with this method is inherently scalable, and has applications in mobile broadcast

Linear and Near-Linear Bounds for Stochastic Dispersion

. It has been suggested that stochastic flows might be used to model the spread of a passive substance on the surface of a body of water. We define a stochastic flow by the equations OE 0 (x) = x; dOE t (x) = F (dt; OE t (x)); where F (t; x) is a field of semimartingales on x 2 R d for d 2 whose local characteristics are bounded and Lipschitz. The particles are points in a bounded set X, and we ask how far the substance has spread in a time T. That is, we define \Phi T = sup x2X sup 0tT fl fl OE t (x) fl fl ; and seek to bound P \Phi \Phi T ? z \Psi . If the field F has a directed drift, of course, kOE t (x)k will grow linearly with time. Taking the maximum does not increase the growth rate very much: we show that for any ff ? 1, limsup T!1 \Phi T T (log T ) ff = 0 a.s. Without drift, when F (\Delta; x) are required to be martingales, although single points move on the order of p T , it is easy to construct examples in which the supremum \Phi T still g..

Linear And Near-Linear Bounds For Stochastic Dispersion

. It has been suggested that stochastic flows might be used to model the spread of a passive substance on the surface of a body of water. We define a stochastic flow by the equations OE 0 (x) = x; dOE t (x) = F (dt; OE t (x)); where F (t; x) is a field of semimartingales on x 2 R d for d 2 whose local characteristics are bounded and Lipschitz. The particles are points in a bounded set X, and we ask how far the substance has spread in a time T. That is, we define \Phi T = sup x2X sup 0tT fl fl OE t (x) fl fl ; and seek to bound P \Phi \Phi T ? z \Psi . If the field F has a directed drift, of course, kOE t (x)k will grow linearly with time. Taking the maximum does not increase the growth rate very much: we show that for any ff ? 1, limsup T!1 \Phi T T (logT ) ff = 0 a.s. Without drift, when F (\Delta; x) are required to be martingales, although single points move on the order of p T , it is easy to construct examples in which the supremum \Phi T still grow..