# SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data arXiv: https://arxiv.org/abs/2209.15180 ยท HTML: https://mlsanity.com/paper/2209.15180/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 44 references that are in the map. - First posted on arXiv 30 Sep 2022. ## Builds on (most cited first, 25 of 25) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020) - [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020) - [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015) - [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018) - [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019) - [GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields](https://mlsanity.com/paper/2011.12100/) (2020) - [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018) - [Space-time Neural Irradiance Fields for Free-Viewpoint Video](https://mlsanity.com/paper/2011.12950/) (2020) - [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018) - [Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction](https://mlsanity.com/paper/2003.10983/) (2020) - [NeRV: Neural Representations for Videos](https://mlsanity.com/paper/2110.13903/) (2021) - [Learning Shape Templates with Structured Implicit Functions](https://mlsanity.com/paper/1904.06447/) (2019) - [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020) - [Modulated Periodic Activations for Generalizable Local Functional Representations](https://mlsanity.com/paper/2104.03960/) (2021) - [Improving Inference for Neural Image Compression](https://mlsanity.com/paper/2006.04240/) (2020) - [Hypernetwork functional image representation](https://mlsanity.com/paper/1902.10404/) (2019) - [Compressive Neural Representations of Volumetric Scalar Fields](https://mlsanity.com/paper/2104.04523/) (2021) - [Reconstructing continuous distributions of 3D protein structure from cryo-EM images](https://mlsanity.com/paper/1909.05215/) (2019) - [Seeing Implicit Neural Representations as Fourier Series](https://mlsanity.com/paper/2109.00249/) (2021) - [Learning Signal-Agnostic Manifolds of Neural Fields](https://mlsanity.com/paper/2111.06387/) (2021) - [Fast Training of Neural Lumigraph Representations using Meta Learning](https://mlsanity.com/paper/2106.14942/) (2021) - [Local Deep Implicit Functions for 3D Shape](https://mlsanity.com/paper/1912.06126/) (2019) - [Multi-Head ReLU Implicit Neural Representation Networks](https://mlsanity.com/paper/2110.03448/) (2021) - [Ensemble Neural Representation Networks](https://mlsanity.com/paper/2110.04124/) (2021) ## Built on by (most cited first, 13 of 13) - [Implicit Neural Representation in Medical Imaging: A Comparative Survey](https://mlsanity.com/paper/2307.16142/) (2023) - [NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions](https://mlsanity.com/paper/2309.15426/) (2023) - [Boosting Neural Representations for Videos with a Conditional Decoder](https://mlsanity.com/paper/2402.18152/) (2024) - [TINC: Tree-structured Implicit Neural Compression](https://mlsanity.com/paper/2211.06689/) (2022) - [SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep](https://mlsanity.com/paper/2412.11579/) (2024) - [Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark](https://mlsanity.com/paper/2507.11931/) (2025) - [MoEC: Mixture of Experts Implicit Neural Compression](https://mlsanity.com/paper/2312.01361/) (2023) - [MC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representations](https://mlsanity.com/paper/2507.02494/) (2025) - [ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis](https://mlsanity.com/paper/2406.00227/) (2024) - [HOPE: High-order Polynomial Expansion of Black-box Neural Networks](https://mlsanity.com/paper/2307.08192/) (2023) - [A Compact Implicit Neural Representation for Efficient Storage of Massive 4D Functional Magnetic Resonance Imaging](https://mlsanity.com/paper/2312.00082/) (2023) - [HUB: Guiding Learned Optimizers with Continuous Prompt Tuning](https://mlsanity.com/paper/2305.16823/) (2023) - [Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos](https://mlsanity.com/paper/2311.13134/) (2023) ## Abstract (arXiv) Massive collection and explosive growth of biomedical data, demands effective compression for efficient storage, transmission and sharing. Readily available visual data compression techniques have been studied extensively but tailored for natural images/videos, and thus show limited performance on biomedical data which are of different features and larger diversity. Emerging implicit neural representation (INR) is gaining momentum and demonstrates high promise for fitting diverse visual data in target-data-specific manner, but a general compression scheme covering diverse biomedical data is so far absent. To address this issue, we firstly derive a mathematical explanation for INR's spectrum concentration property and an analytical insight on the design of INR based compressor. Further, we propose a Spectrum Concentrated Implicit neural compression (SCI) which adaptively partitions the complex biomedical data into blocks matching INR's concentrated spectrum envelop, and design a funnel shaped neural network capable of representing each block with a small number of parameters. Based on this design, we conduct compression via optimization under given budget and allocate the available parameters with high representation accuracy. The experiments show SCI's superior performance to state-of-the-art methods including commercial compressors, data-driven ones, and INR based counterparts on diverse biomedical data. The source code can be found at https://github.com/RichealYoung/ImplicitNeuralCompression.git.