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.

翻译：本文提出FCNR，一种针对不同视角和时间步下数万张可视化图像的快速压缩神经表示方法。现有NeRVI方案虽具有高压缩比，但其编码与解码速度缓慢。基于立体图像压缩的最新进展，FCNR融合立体上下文模块与联合上下文传递模块以压缩图像对。本方案在保持高重建质量与理想压缩比的同时，显著提升了编码与解码速度。为验证其有效性，我们将FCNR与前沿神经压缩方法（包括E-NeRV、HNeRV、NeRVI及ECSIC）进行对比。源代码可见于https://github.com/YunfeiLu0112/FCNR。