mlsanityData to 14 Oct 2026

Towards Lossless Implicit Neural Representation via Bit Plane Decomposition

arXiv 2502.21001 ↗First posted 28 Feb 2025cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

We quantify the upper bound on the size of the implicit neural representation (INR) model from a digital perspective. The upper bound of the model size increases exponentially as the required bit-precision increases. To this end, we present a bit-plane decomposition method that makes INR predict bit-planes, producing the same effect as reducing the upper bound of the model size. We validate our hypothesis that reducing the upper bound leads to faster convergence with constant model size. Our method achieves lossless representation in 2D image and audio fitting, even for high bit-depth signals, such as 16-bit, which was previously unachievable. We pioneered the presence of bit bias, which INR prioritizes as the most significant bit (MSB). We expand the application of the INR task to bit depth expansion, lossless image compression, and extreme network quantization. Our source code is available at https://github.com/WooKyoungHan/LosslessINR

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 30 of its 49 references that are in the map. First posted on arXiv 28 Feb 2025.

Builds on

30 of 49 references.

References · 30

  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  2. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  4. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  5. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  6. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  7. 2018On the Spectral Bias of Neural Networkscited by 1,341
  8. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  9. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 552
  10. 2020Local Implicit Grid Representations for 3D Scenescited by 373
  11. 2024The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bitscited by 308
  12. 2021COIN: COmpression with Implicit Neural representationscited by 233
18 more
  1. 2023WIRE: Wavelet Implicit Neural Representationscited by 220
  2. 2023BitNet: Scaling 1-bit Transformers for Large Language Modelscited by 189
  3. 2021Implicit Neural Representations for Image Compressioncited by 162
  4. 2021BACON: Band-limited Coordinate Networks for Multiscale Scene Representationcited by 143
  5. 2021Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPscited by 135
  6. 2021Local Texture Estimator for Implicit Representation Functioncited by 127
  7. 2022COIN++: Neural Compression Across Modalitiescited by 82
  8. 2023FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functionscited by 78
  9. 2023NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functionscited by 65
  10. 2022DINER: Disorder-Invariant Implicit Neural Representationcited by 30
  11. 2023Compression with Bayesian Implicit Neural Representationscited by 25
  12. 2023Polynomial Neural Fields for Subband Decomposition and Manipulationcited by 22
  13. 2023Mathematical Introduction to Deep Learning: Methods, Implementations, and Theorycited by 20
  14. 2022Learning Local Implicit Fourier Representation for Image Warpingcited by 14
  15. 2023RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representationscited by 9
  16. 2020A Little Bit More: Bitplane-Wise Bit-Depth Recoverycited by 8
  17. 2024JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficientscited by 7
  18. 2019BitNet: Learning-Based Bit-Depth Expansioncited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Content-Aware Frequency Encoding for Implicit Neural Representations with Fourier-Chebyshev Featurescited by 1
  2. 2026Error Aware Distribution Prediction for Lightweight Implicit Neural Representationscited by 0
  3. 2026Lossless-INR: Lossless Volumetric Implicit Neural Representationscited by 0
  4. 2026Recurrent Sinusoidal INRs for Efficient High-Fidelity Representationcited by 0
  5. 2026RAID: Towards Robust AI-Generated Image Detection with Bit-Reversed Imagescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Extreme Compression of Adaptive Neural Images
  2. 2025SINR: Sparsity Driven Compressed Implicit Neural Representations
  3. 2023Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation
  4. 2021Compressive Neural Representations of Volumetric Scalar Fields
  5. 2026DiV-INR: Extreme Low-Bitrate Diffusion Video Compression with INR Conditioning
  6. 2025Information-computation trade-offs in non-linear transforms ↗
  7. 2025Input Resolution Downsizing as a Compression Technique for Vision Deep Learning Systems
  8. 2022On Quantizing Implicit Neural Representations