Learning to Predict Image-based Rendering Artifacts with Respect to a Hidden Reference Image

Image metrics predict the perceived per-pixel difference between a reference image and its degraded (e. g., re-rendered) version. In several important applications, the reference image is not available and image metrics cannot be applied. We devise a neural network architecture and training procedure that allows predicting the MSE, SSIM or VGG16 image difference from the distorted image alone while the reference is not observed. This is enabled by two insights: The first is to inject sufficiently many un-distorted natural image patches, which can be found in arbitrary amounts and are known to have no perceivable difference to themselves. This avoids false positives. The second is to balance the learning, where it is carefully made sure that all image errors are equally likely, avoiding false negatives. Surprisingly, we observe, that the resulting no-reference metric, subjectively, can even perform better than the reference-based one, as it had to become robust against mis-alignments. We evaluate the effectiveness of our approach in an image-based rendering context, both quantitatively and qualitatively. Finally, we demonstrate two applications which reduce light field capture time and provide guidance for interactive depth adjustment.Comment: 13 pages, 11 figure

Haar wavelet-based adaptive finite volume shallow water solver

This paper presents the formulation of an adaptive finite volume (FV) model for the shallow water equations. A Godunov-type reformulation combining the Haar wavelet is achieved to enable solutiondriven resolution adaptivity (both coarsening and refinement) by depending on the wavelet’s threshold value. The ability to properly model irregular topographies and wetting/drying are transferred from the (baseline) FV uniform mesh model, with no extra notable efforts. Selected hydraulic tests are employed to analyse the performance of the Haar wavelet FV shallow water solver considering adaptivity and practical issues including choice for the threshold value driving the adaptivity, mesh convergence study, shock and wet/dry front capturing abilities. Our findings show that Haar wavelet-based adaptive FV solutions offer great potential to improve the reliability of multiscale shallow water models

What constitutes a local public Ssphere?: Building a monitoring framework for comparative analysis

Despite the research tradition in analyzing public communication, local public spheres have been rather neglected by communication science, although they are crucial for social cohesion and democracy. Existing empirical studies about local public spheres are mostly case studies which implicitly assume that cities are alike. Based on a participatory-liberal understanding of democracy, we develop a theoretical framework, from which we derive a monitor covering structural, social, and spatial aspects of local communication to empirically compare local public spheres along four dimensions: (1) information, (2) participation, (3) inclusion, and (4) diversity. In a pilot study, we then apply our monitor to four German cities that are comparable in size and regional function (‘regiopolises’). The monitoring framework is built on local statistical data, some of which was provided by the cities, while some came from our own research. We show that the social structures and the normative assessment of the quality of local public spheres can vary among similar cities and between the four dimensions. We hope the innovative monitor prototype enables scholars and local actors to compare local public spheres across spaces, places, and time, and to investigate the impact of social change and digitalization on local public spheres