Detailed image structures and visual textures (of stochastic nature) in digital video are difficult to compress
efficiently. At medium to low bit rates, texture flattening and blocking artifacts appear, even when using advanced
video coding standards such as H.264/MPEG-4 AVC. In this paper, we propose video compression systems to
compress stochastic textures by exploiting rank-reduction techniques. In this work, rank reduction is implemented
by applying a singular value decomposition and selective transmission of the primary signal components as in
principal component analysis. In the low bit-rate range, our implementation shows encouraging results compared
to H.264/MPEG-4 AVC, not only in rate-distortion performance, but also in the improved visual quality of the
Most adaptive optics systems (AO) are based on a simple control law that is unable to account for the temporal evolution of the wavefront. In this paper, a recently proposed data-driven <i>Η</i><sub>2</sub>-optimal control approach is demonstrated on an AO laboratory setup. The proposed control approach does not assume any form of decoupling and can therefore exploit the spatio-temporal correlation in the wavefront. The performance of the optimal control approach is compared with a conventional method. An analysis of the dominant error sources shows that the optimal control approach leads to a significant reduction in the temporal error. Since the temporal error grows with the Greenwood to sampling frequency ratio, the performance gain is especially large at large ratios.
The problem of finding the closed-loop optimal controller is formulated in an <i>Η</i><sub>2</sub>-optimal control framework. This provides a natural way to account for the fact that in many AO systems the wavefront phase cannot be measured directly. Given a multi-variable disturbance model of both wavefront slopes and wavefront phases, this provides a general procedure to compute the closed-loop controller. If the wavefront sensor and deformable mirror are static and the only dynamics in the system is a unit-sample delay between measurement and correction, an analytical expression for the optimal controller can be derived. This results in a control approach, in which both identification and computation of the optimal controller are exclusively based on standard matrix operations. No Riccati equation needs to be solved to compute the optimal controller. The proposed <i>Η</i><sub>2</sub>-control approach is numerically validated on open-loop wavefront sensor data and its performance is compared with the common approach. Also the sensitivity to measurement noise is considered.
Even though the wavefront distortion introduced by atmospheric turbulence is a dynamic process, its temporal evolution is usually neglected in the adaptive optics (AO) control design. Most AO control systems consider only the spatial correlation in a separate wavefront reconstruction step. By accounting for the temporal evolution of the wavefront it should be possible to further reduce the residual phase error and enable the use of fainter guide stars. Designing a controller that takes full advantage of the spatio-temporal correlation in the wavefront requires a detailed model of the wavefront distortion. In this paper we present a dedicated subspace identification algorithm that is able to provide the required prior knowledge. On the basis of open-loop wavefront slope data it estimates a multi-variable state-space model of the wavefront disturbance. The model provides a full description of the spatio-temporal statistics in a form that is suitable for control. The algorithm is demonstrated on open-loop wavefront data.
An Adaptive Optics (AO) system for astronomy is analysed from a control point of view. The focus is put on the temporal error. The AO controller is identified as a feedback regulator system, operating in closed-loop with the aim of rejecting wavefront disturbances. Limitations on the performance of feedback regulator systems are discussed. The concept of optimal control is proposed to minimise the temporal error. The issue of closed-loop feedback controller design is made transparent by using the principle of Internal Model Control. The central issue in reducing the temporal wavefront is the design of a feedforward prediction filter. In three separate tests - a numerical simulation example, measured data from an AO test bench and open-loop telescope data - the advantage of optimal control over the common approach of integral control is demonstrated. Optimal control of the temporal error yields a smaller temporal error, enables a longer integration time in the wavefront sensor, or the use of fainter natural guide stars.