Minimizing bandwidth utilization for streaming noisy Monte-Carlo renders
For the 2018 individual research pilot
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Abstract
This study focuses on the question how the bandwidth utilization of a high-quality video-stream from the Exposure Rendering framework can be minimized. Exposure Render uses a Monte-Carlo based rendering system to render volumetric data. The earliest estimation of lighting show high degrees of noise, leading to grainy images before convergence is complete. Exposure Render is planned to be turned into a web-service, where clients can upload volumetric data to view and interact with it. This necessitates a streaming service, which encountered difficulties regarding efficient compression. Using only JPEG compression to send still frames showed poor compression performance.
To answer this question, it was established what the noise characteristics of the frames produced by Exposure Render are. In addition, a survey was done on recent advanced in screen-space de-noising techniques to see which image-filtering techniques would be effective. This survey concluded that most of the state of the art could not readily be applied to Exposure Render, because the methods either are not designed for real-time Monte-Carlo rendering, or because they rely on additional rendering data, such as surface normals, which are not available in Exposure.
Three experiments were executed, namely a region-experiment on single image regions, where the best filtering methods were selected for local regions only. These were integrated in proposed enhancements to Exposure Render. The second experiment tested the similarity of a converging image sequence, before and after filtering. It was concluded that the delta-encoder and Median Blur performed the best in terms of speeding up the convergence in similarity over time.
The third experiment tested the bandwidth consumption of the methods and concluded that the Adaptive Gaussian Pyramid methods performed the best.
The best combination of algorithms to minimize the bandwidth utilization was found to be a Macro-block based bandwidth limiter in combination with an Adaptive Gaussian Pyramid resolution scaler, which increased compression ratio to 18.7 in comparison with the reference solution.