Result Details

Efficient Random-Access GPU Video Decoding for Light-Field Rendering

CHLUBNA, T.; ZEMČÍK, P.; MILET, T. Efficient Random-Access GPU Video Decoding for Light-Field Rendering. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2024, vol. 2024, no. 102, p. 1-14. ISSN: 1047-3203.
Type
journal article
Language
English
Authors
Abstract

Compression method for GPU streaming of discrete light fields is proposed in this paper. Views on the scene are encoded with video codec to enable streaming in real time. Instead of using a classic scheme, all frames are encoded according to one reference frame. Any frame is decoded directly, in a random-access manner that is suitable for light-field rendering methods, where only few frames are necessary on the GPU. The proposed scheme reaches the best decoding quality/time ratio in comparison to other schemes, where all preceding frames need to be decompressed, and all-key-frame video that supports random access, but is extremely large. The proposed method solves the space-requirements and streaming-bandwidth issues using the GPU accelerated decoding, and enables incorporating light-field assets in real-time 3D simulations. Compared to existing methods, the proposal is easy to implement, does not depend on specific video format or extension and is efficient on consumer GPUs.

Keywords

light field, video decoding, GPU acceleration, image-based rendering

URL
Published
2024
Pages
1–14
Journal
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, vol. 2024, no. 102, ISSN 1047-3203
DOI
UT WoS
001258796500001
EID Scopus
BibTeX
@article{BUT189074,
  author="Tomáš {Chlubna} and Pavel {Zemčík} and Tomáš {Milet}",
  title="Efficient Random-Access GPU Video Decoding for Light-Field Rendering",
  journal="JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION",
  year="2024",
  volume="2024",
  number="102",
  pages="1--14",
  doi="10.1016/j.jvcir.2024.104201",
  issn="1047-3203",
  url="https://www.sciencedirect.com/science/article/pii/S1047320324001561"
}
Projects
AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems, EU, Horizon 2020, 8A21015, 101007350, start: 2021-04-01, end: 2024-03-31, running
Soudobé metody zpracování, analýzy a zobrazování multimediálních a 3D dat, BUT, Vnitřní projekty VUT, FIT-S-23-8278, start: 2023-03-01, end: 2026-02-28, running
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