mlsanityData to 7 Oct 2026

FCNR: Fast Compressive Neural Representation of Visualization Images

arXiv 2407.16369 ↗First posted 23 Jul 2024cs.CV eess.IVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

17 of 26 references.

References · 17

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  3. 2018Variational image compression with a scale hyperpriorcited by 910
  4. 2016End-to-end Optimized Image Compressioncited by 673
  5. 2020Channel-wise Autoregressive Entropy Models for Learned Image Compressioncited by 255
  6. 2021NeRV: Neural Representations for Videoscited by 255
  7. 2023HNeRV: A Hybrid Neural Representation for Videoscited by 100
  8. 2022E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Contextcited by 76
  9. 2023HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representationcited by 70
  10. 2022NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modelingcited by 50
  11. 2023DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videoscited by 42
  12. 2022DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualizationcited by 32
5 more
  1. 2022CNeRV: Content-adaptive Neural Representation for Visual Datacited by 21
  2. 2019DSIC: Deep Stereo Image Compressioncited by 20
  3. 2023ECNR: Efficient Compressive Neural Representation of Time-Varying Volumetric Datasetscited by 16
  4. 2023LDMIC: Learning-based Distributed Multi-view Image Codingcited by 13
  5. 2023ECSIC: Epipolar Cross Attention for Stereo Image Compressioncited by 8

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Cited by year

Citing papers per year: 2024: 1; 2025: 5.

6 papers.

  1. 2025NLI4VolVis: Natural Language Interaction for Volume Visualization via LLM Multi-Agents and Editable 3D Gaussian Splattingcited by 19
  2. 2025Meta-INR: Efficient Encoding of Volumetric Data via Meta-Learning Implicit Neural Representationcited by 11
  3. 2024StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualizationcited by 9
  4. 2025TexGS-VolVis: Expressive Scene Editing for Volume Visualization via Textured Gaussian Splattingcited by 9
  5. 2025MC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representationscited by 4
  6. 2025Topology Aware Neural Interpolation of Scalar Fieldscited by 0