Floyd M. Chitalu
Method for Efficient CPU-GPU Streaming for Walkthrough of Full Motion Lightfield Video
Chitalu, Floyd M.; Koniaris, Babis; Mitchell, Kenny
Abstract
Lightfield video, as a high-dimensional function, is very demanding in terms of storage. As such, lightfield video data, even in a compressed form, do not typically fit in GPU or main memory unless the capture area, resolution or duration is sufficiently small. Additionally, latency minimization--critical for viewer comfort in use-cases such as virtual reality--places further constraints in many compression schemes. In this paper, we propose a scalable method for streaming lightfield video, parameterized on viewer location and time, that efficiently handles RAM-to-GPU memory transfers of lightfield video in a compressed form, utilizing the GPU architecture for reduction of latency. We demonstrate the effectiveness of our method in a variety of compressed animated lightfield datasets.
Citation
Chitalu, F. M., Koniaris, B., & Mitchell, K. (2017, December). Method for Efficient CPU-GPU Streaming for Walkthrough of Full Motion Lightfield Video. Presented at 14th European Conference on Visual Media Production (CVMP 2017), London, United Kingdom
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | 14th European Conference on Visual Media Production (CVMP 2017) |
Start Date | Dec 11, 2017 |
End Date | Dec 13, 2017 |
Publication Date | 2017-12 |
Deposit Date | Mar 3, 2020 |
Publisher | Association for Computing Machinery (ACM) |
Book Title | CVMP 2017: Proceedings of the 14th European Conference on Visual Media Production (CVMP 2017) |
ISBN | 9781450353298 |
DOI | https://doi.org/10.1145/3150165.3150173 |
Public URL | http://researchrepository.napier.ac.uk/Output/2606347 |
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