FOVQA: Blind Foveated Video Quality Assessment

Previous blind or No Reference (NR) video quality assessment (VQA) models largely rely on features drawn from natural scene statistics (NSS), but under the assumption that the image statistics are stationary in the spatial domain. Several of these models are quite successful on standard pictures. However, in Virtual Reality (VR) applications, foveated video compression is regaining attention, and the concept of space-variant quality assessment is of interest, given the availability of increasingly high spatial and temporal resolution contents and practical ways of measuring gaze direction. Distortions from foveated video compression increase with increased eccentricity, implying that the natural scene statistics are space-variant. Towards advancing the development of foveated compression / streaming algorithms, we have devised a no-reference (NR) foveated video quality assessment model, called FOVQA, which is based on new models of space-variant natural scene statistics (NSS) and natural video statistics (NVS). Specifically, we deploy a space-variant generalized Gaussian distribution (SV-GGD) model and a space-variant asynchronous generalized Gaussian distribution (SV-AGGD) model of mean subtracted contrast normalized (MSCN) coefficients and products of neighboring MSCN coefficients, respectively. We devise a foveated video quality predictor that extracts radial basis features, and other features that capture perceptually annoying rapid quality fall-offs. We find that FOVQA achieves state-of-the-art (SOTA) performance on the new 2D LIVE-FBT-FCVR database, as compared with other leading FIQA / VQA models. we have made our implementation of FOVQA available at: http://live.ece.utexas.edu/research/Quality/FOVQA.zip

Real-Time Reyes-Style Adaptive Surface Subdivision

We present a GPU based implementation of Reyes-style adaptive surface subdivision, known in Reyes terminology as the Bound/Split and Dice stages. The performance of this task is important for the Reyes pipeline to map efficiently to graphics hardware, but its recursive nature and irregular and unbounded memory requirements present a challenge to an efficient implementation. Our solution begins by characterizing Reyes subdivision as a work queue with irregular computation, targeted to a massively parallel GPU. We propose efficient solutions to these general problems by casting our solution in terms of the fundamental primitives of prefix-sum and reduction, often encountered in parallel and GPGPU environments. Our results indicate that real-time Reyes subdivision can indeed be obtained on today's GPUs. We are able to subdivide a complex model to subpixel accuracy within 15 ms. Our measured performance is several times better than that of Pixar's RenderMan. Our implementation scales well with the input size and depth of subdivision. We also address concerns of memory size and bandwidth, and analyze the feasibility of conventional ideas on screen-space buckets

Task Management for Irregular-Parallel Workloads on the GPU

We explore software mechanisms for managing irregular tasks on graphics processing units (GPUs). We demonstrate that dynamic scheduling and efficient memory management are critical problems in achieving high efficiency on irregular workloads. We experiment with several task-management techniques, ranging from the use of a single monolithic task queue to distributed queuing with task stealing and donation. On irregular workloads, we show that both centralized and distributed queues have more than 100 times as much idle times as our task-stealing and -donation queues. Our preferred choice is task-donation because of comparable performance to task-stealing while using less memory overhead. To help in this analysis, we use an artificial task-management system that monitors performance and memory usage to quantify the impact of these different techniques. We validate our results by implementing a Reyes renderer with its irregular split-and-dice workload that is able to achieve real-time framerates on a single GPU

Parallel View-Dependent Tessellation of Catmull-Clark Subdivision Surfaces

We present a strategy for performing view-adaptive, crack-free tessellation of Catmull-Clark subdivision surfaces entirely on programmable graphics hardware. Our scheme extends the concept of breadth-first subdivision, which up to this point has only been applied to parametric patches. While mesh representations designed for a CPU often involve pointer-based structures and irregular per-element storage, neither of these is well-suited to GPU execution. To solve this problem, we use a simple yet effective data structure for representing a subdivision mesh, and design a careful algorithm to update the mesh in a completely parallel manner. We demonstrate that in spite of the complexities of the subdivision procedure, real-time tessellation to pixel-sized primitives can be done. Our implementation does not rely on any approximation of the limit surface, and avoids both subdivision cracks and T-junctions in the subdivided mesh. Using the approach in this paper, we are able to perform real-time subdivision for several static as well as animated models. Rendering performance is scalable for increasingly complex models

Parallel view-dependent tessellation of Catmull-Clark subdivision surfaces

Figure 1: We adaptively subdivide faces of a Catmull-Clark subdivision mesh until the screen-space geometric criterion is met. Using a parallel approach to the subdivision procedure, we are able to obtain interactive performance for complex real-life models. Moreover, we ensure that the subdivided mesh is free of cracks and T-junctions. We present a strategy for performing view-adaptive, crack-free tessellation of Catmull-Clark subdivision surfaces entirely on programmable graphics hardware. Our scheme extends the concept of breadth-first subdivision, which up to this point has only been applied to parametric patches. While mesh representations designed for a CPU often involve pointer-based structures and irregular perelement storage, neither of these is well-suited to GPU execution. To solve this problem, we use a simple yet effective data structure for representing a subdivision mesh, and design a careful algorithm to update the mesh in a completely parallel manner. We demonstrate that in spite of the complexities of the subdivision procedure, real-time tessellation to pixel-sized primitives can be done. Our implementation does not rely on any approximation of the limit surface, and avoids both subdivision cracks and T-junctions in the subdivided mesh. Using the approach in this paper, we are able to perform real-time subdivision for several static as well as animated models. Rendering performance is scalable for increasingly complex models