Image Quality Metrics for Stochastic Rasterization

We develop a simple perceptual image quality metric for images resulting from stochastic rasterization. The new metric is based on the frequency selectivity of cortical cells, using ideas derived from existing perceptual metrics and research of the human visual system. Masking is not taken into account in the metric, since it does not have a significant effect in this specific application. The new metric achieves high correlation with results from HDR-VDP2 while being conceptually simple and accurately reflecting smaller quality differences than the existing metrics. In addition to HDR-VDP2, measurement results are compared against MS-SSIM results. The new metric is applied to a set of images produced with different sampling schemes to provide quantitative information about the relative quality, strengths, and weaknesses of the different sampling schemes. Several purpose-built three-dimensional test scenes are used for this quality analysis in addition to a few widely used natural scenes. The star discrepancy of sampling patterns is found to be correlated to the average perceptual quality, even though discrepancy can not be recommended as the sole method for estimating perceptual quality. A hardware-friendly low-discrepancy sampling scheme achieves generally good results, but the quality difference to simpler per-pixel stratified sampling decreases as the sample count increases. A comprehensive mathematical model of rendering discrete frames from dynamic 3D scenes is provided as background to the quality analysis

Fortgeschrittene Entrauschungs-Verfahren und speicherlose Beschleunigungstechniken für realistische Bildsynthese

Stochastic ray tracing methods have become the industry's standard for today's realistic image synthesis thanks to their ability to achieve a supreme degree of realism by physically simulating various natural phenomena of light and cameras (e.g. global illumination, depth-of-field, or motion blur). Unfortunately, high computational cost for more complex scenes and image noise from insufficient simulations are major issues of these methods and, hence, acceleration and denoising are key components in stochastic ray tracing systems. In this thesis, we introduce two new filtering methods for advanced lighting and camera effects, as well as a novel approach for memoryless acceleration. In particular, we present an interactive filter for global illumination in the presence of depth-of-field, and a general and robust adaptive reconstruction framework for high-quality images with a wide range of rendering effects. To address complex scene geometry, we propose a novel concept which models the acceleration structure completely implicit, i.e. without any additional memory cost at all, while still allowing for interactive performance. Our contributions advance the state-of-the-art of denoising techniques for realistic image synthesis as well as the field of memoryless acceleration for ray tracing systems.Stochastische Ray-Tracing Methoden sind heutzutage der Industriestandard für realistische Bildsynthese, da sie einen hohen Grad an Realismus erzeugen können, indem sie verschiedene natürliche Phänomene (z.B. globale Beleuchtung, Tiefenunschärfe oder Bewegungsunschärfe) physikalisch korrekt simulieren. Offene Probleme dieser Verfahren sind hohe Rechenzeit für komplexere Szenen sowie Bildrauschen durch unzulängliche Simulationen. Demzufolge sind Beschleunigungstechniken und Entrauschungsverfahren essentielle Komponenten in stochastischen Ray-Tracing-Systemen. In dieser Arbeit stellen wir zwei neue Filter-Methoden für erweiterte Beleuchungs- und Kamera-Effekte sowie ein neuartiges Verfahren für eine speicherlose Beschleunigungsstruktur vor. Im Detail präsentieren wir einen interaktiven Filter für globale Beleuchtung in Kombination mit Tiefenunschärfe und einen generischen, robusten Ansatz für die adaptive Rekonstruktion von hoch-qualitativen Bildern mit einer großen Auswahl an Rendering-Effekten. Für das Problem hoher geometrischer Szenen-Komplexität demonstrieren wir ein neuartiges Konzept für die implizierte Modellierung der Beschleunigungsstruktur, welches keinen zusätzlichen Speicher verbraucht, aber weiterhin interaktive Laufzeiten ermöglicht. Unsere Beiträge verbessern sowohl den aktuellen Stand von Entrauschungs-Verfahren in der realistischen Bildsynthese als auch das Feld der speicherlosen Beschleunigungsstrukturen für Ray-Tracing-Systeme