  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.
