Selecting texture resolution using a task-specific visibility metric

In real-time rendering, the appearance of scenes is greatly affected by the quality and resolution of the textures used for image synthesis. At the same time, the size of textures determines the performance and the memory requirements of rendering. As a result, finding the optimal texture resolution is critical, but also a non-trivial task since the visibility of texture imperfections depends on underlying geometry, illumination, interactions between several texture maps, and viewing positions. Ideally, we would like to automate the task with a visibility metric, which could predict the optimal texture resolution. To maximize the performance of such a metric, it should be trained on a given task. This, however, requires sufficient user data which is often difficult to obtain. To address this problem, we develop a procedure for training an image visibility metric for a specific task while reducing the effort required to collect new data. The procedure involves generating a large dataset using an existing visibility metric followed by refining that dataset with the help of an efficient perceptual experiment. Then, such a refined dataset is used to retune the metric. This way, we augment sparse perceptual data to a large number of per-pixel annotated visibility maps which serve as the training data for application-specific visibility metrics. While our approach is general and can be potentially applied for different image distortions, we demonstrate an application in a game-engine where we optimize the resolution of various textures, such as albedo and normal maps

Render2MPEG: A Perception-based Framework Towards Integrating Rendering and Video Compression

Currently 3D animation rendering and video compression are completely independent processes even if rendered frames are streamed on-the-fly within a client-server platform. In such scenario, which may involve time-varying transmission bandwidths and different display characteristics at the client side, dynamic adjustment of the rendering quality to such requirements can lead to a better use of server resources. In this work, we present a framework where the renderer and MPEG codec are coupled through a straightforward interface that provides precise motion vectors from the rendering side to the codec and perceptual error thresholds for each pixel in the opposite direction. The perceptual error thresholds take into account bandwidth-dependent quantization errors resulting from the lossy compression as well as image content-dependent luminance and spatial contrast masking. The availability of the discrete cosine transform (DCT) coefficients at the codec side enables to use advanced models of the human visual system (HVS) in the perceptual error threshold derivation without incurring any significant cost. Those error thresholds are then used to control the rendering quality and make it well aligned with the compressed stream quality. In our prototype system we use the lightcuts technique developed by Walter et al., which we enhance to handle dynamic image sequences, and an MPEG-2 implementation. Our results clearly demonstrate many advantages of coupling the rendering with video compression in terms of faster rendering. Furthermore, temporally coherent rendering leads to a reduction of temporal artifacts

Global Illumination using Photon Ray Splatting

We present a novel framework for efficiently computing the indirect illumination in diffuse and moderately glossy scenes using density estimation techniques. Many existing global illumination approaches either quickly compute an overly approximate solution or perform an orders of magnitude slower computation to obtain high-quality results for the indirect illumination. The proposed method improves photon density estimation and leads to significantly better visual quality in particular for complex geometry, while only slightly increasing the computation time. We perform direct splatting of photon rays, which allows us to use simpler search data structures. Since our density estimation is carried out in ray space rather than on surfaces, as in the commonly used photon mapping algorithm, the results are more robust against geometrically incurred sources of bias. This holds also in combination with final gathering where photon mapping often overestimates the illumination near concave geometric features. In addition, we show that our photon splatting technique can be extended to handle moderately glossy surfaces and can be combined with traditional irradiance caching for sparse sampling and filtering in image space