Quality of experience driven control of interactive media stream parameters

In recent years, cloud computing has led to many new kinds of services. One of these popular services is cloud gaming, which provides the entire game experience to the users remotely from a server, but also other applications are provided in a similar manner. In this paper we focus on the option to render the application in the cloud, thereby delivering the graphical output of the application to the user as a video stream. In more general terms, an interactive media stream is set up over the network between the user's device and the cloud server. The main issue with this approach is situated at the network, that currently gives little guarantees on the quality of service in terms of parameters such as available bandwidth, latency or packet loss. However, for interactive media stream cases, the user is merely interested in the perceived quality, regardless of the underlaying network situation. In this paper, we present an adaptive control mechanism that optimizes the quality of experience for the use case of a race game, by trading off visual quality against frame rate in function of the available bandwidth. Practical experiments verify that QoE driven adaptation leads to improved user experience compared to systems solely taking network characteristics into account

Iterative CT reconstruction using shearlet-based regularization

In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose. Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction. However, TV minimization methods show superior denoising performance for simple images (with little texture), but result in texture information loss when applied to more complex images. Since in medical imaging, we are often confronted with textured images, it might not be beneficial to use TV. Our objective is to find a regularization term outperforming TV for sparse-view reconstruction and image denoising in general. A recent efficient solver was developed for convex problems, based on a split-Bregman approach, able to incorporate regularization terms different from TV. In this work, a proof-of-concept study demonstrates the usage of the discrete shearlet transform as a sparsifying transform within this solver for CT reconstructions. In particular, the regularization term is the 1-norm of the shearlet coefficients. We compared our newly developed shearlet approach to traditional TV on both sparse-view and on low-count simulated and measured preclinical data. Shearlet-based regularization does not outperform TV-based regularization for all datasets. Reconstructed images exhibit small aliasing artifacts in sparse-view reconstruction problems, but show no staircasing effect. This results in a slightly higher resolution than with TV-based regularization

Replacing vascular corrosion casting by in-vivo micro-CT imaging for building 3D cardiovascular models in mice

The purpose of this study was to investigate if in vivo micro-computed tomography (CT) is a reliable alternative to micro-CT scanning of a vascular corrosion cast. This would allow one to study the early development of cardiovascular diseases. Datasets using both modalities were acquired, segmented, and used to generate a 3D geometrical model from nine mice. As blood pool contrast agent, Fenestra VC-131 was used. Batson's No. 17 was used as casting agent. Computational fluid dynamics simulations were performed on both datasets to quantify the difference in wall shear stress (WSS). Aortic arch diameters show 30% to 40% difference between the Fenestra VC-131 and the casted dataset. The aortic arch bifurcation angles show less than 20% difference between both datasets. Numerically computed WSS showed a 28% difference between both datasets. Our results indicate that in vivo micro-CT imaging can provide an excellent alternative for vascular corrosion casting. This enables follow-up studies

Automatic fine-grained area detection for thin client systems

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Black Hole Photon Rings Beyond General Relativity

We investigate whether photon ring observations in black hole imaging are able to distinguish between the Kerr black hole in general relativity and alternative black holes that deviate from Kerr. Certain aspects of photon rings have been argued to be robust observables in Very-Long-Baseline Interferometry (VLBI) black hole observations which carry imprints of the underlying spacetime. The photon ring shape, as well as its Lyapunov exponent (which encodes the narrowing of successive photon subrings), are detailed probes of the underlying geometry; measurements thereof have been argued to provide a strong null test of general relativity and the Kerr metric. However, a more complicated question is whether such observations of the photon ring properties can distinguish between Kerr and alternative black holes. We provide a first answer to this question by calculating photon rings of the Johannsen, Rasheed-Larsen, and Manko-Novikov black holes. We find that large deviations from Kerr and large observer inclinations are needed to obtain measurable differences in the photon ring shape. In other words, the Kerr photon ring shape appears to be the universal shape even for deviating black holes at low inclinations. On the other hand, the Lyapunov exponent shows more marked variations for deviations from the Kerr metric. Our analysis lays out the groundwork to determine deviations from the Kerr spacetime in photon rings that are potentially detectable by future observing missions.Comment: 31 pages + appendices; 20 figure

Split-Bregman-based sparse-view CT reconstruction

Total variation minimization has been extensively researched for image denoising and sparse view reconstruction. These methods show superior denoising performance for simple images with little texture, but result in texture information loss when applied to more complex images. It could thus be beneficial to use other regularizers within medical imaging. We propose a general regularization method, based on a split-Bregman approach. We show results for this framework combined with a total variation denoising operator, in comparison to ASD-POCS. We show that sparse-view reconstruction and noise regularization is possible. This general method will allow us to investigate other regularizers in the context of regularized CT reconstruction, and decrease the acquisition times in µCT