FCNR: Fast Compressive Neural Representation of Visualization Images

Yunfei Lu, Pengfei Gu, Chaoli Wang
7/23/2024
cs.CVeess.IV

No official code found. Best implementation: mikelyfei/FCNR (6 stars, MIT, last commit 2024-07-24).

Abstract

We present FCNR, a fast compressive neural representation for tens of thousands of visualization images under varying viewpoints and timesteps. The existing NeRVI solution, albeit enjoying a high compression ratio, incurs slow speeds in encoding and decoding. Built on the recent advances in stereo image compression, FCNR assimilates stereo context modules and joint context transfer modules to compress image pairs. Our solution significantly improves encoding and decoding speed while maintaining high reconstruction quality and satisfying compression ratio. To demonstrate its effectiveness, we compare FCNR with state-of-the-art neural compression methods, including E-NeRV, HNeRV, NeRVI, and ECSIC. The source code can be found at https://github.com/YunfeiLu0112/FCNR.

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Code Implementations(1)

mikelyfei/FCNRCommunity100%
MIT

FCNR: Fast Compressive Neural Representation of Visualization Images

60PythonJul 1, 20242 years agoMIT

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Cite this paper

@article{lu2024fcnr,
  title  = {FCNR: Fast Compressive Neural Representation of Visualization Images},
  author = {Yunfei Lu and Pengfei Gu and Chaoli Wang},
  year   = {2024},
  eprint = {2407.16369},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.16369v2}
}

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