# Improved Positional Encoding for Implicit Neural Representation based Compact Data Representation

arXiv: https://arxiv.org/abs/2311.06059 · HTML: https://mlsanity.com/paper/2311.06059/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [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)
- [NeRV: Neural Representations for Videos](https://mlsanity.com/paper/2110.13903/) (2021)
- [NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior](https://mlsanity.com/paper/2212.07388/) (2022)
- [COIN: COmpression with Implicit Neural representations](https://mlsanity.com/paper/2103.03123/) (2021)
- [From data to functa: Your data point is a function and you can treat it like one](https://mlsanity.com/paper/2201.12204/) (2022)
- [Spatial Functa: Scaling Functa to ImageNet Classification and Generation](https://mlsanity.com/paper/2302.03130/) (2023)
- [Meta-Learning Sparse Compression Networks](https://mlsanity.com/paper/2205.08957/) (2022)
- [Modality-Agnostic Variational Compression of Implicit Neural Representations](https://mlsanity.com/paper/2301.09479/) (2023)
- [RQAT-INR: Improved Implicit Neural Image Compression](https://mlsanity.com/paper/2303.03028/) (2023)
- [Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression](https://mlsanity.com/paper/2209.00964/) (2022)
- [Video Coding Using Learned Latent GAN Compression](https://mlsanity.com/paper/2207.04324/) (2022)
- [Latent-Shift: Gradient of Entropy Helps Neural Codecs](https://mlsanity.com/paper/2308.00725/) (2023)

## Built on by (most cited first, 4 of 4)

- [Data-Efficient Deep Operator Network for Unsteady Flow: A Multi-Fidelity Approach with Physics-Guided Subsampling](https://mlsanity.com/paper/2503.17941/) (2025)
- [High-Frequency Enhanced Hybrid Neural Representation for Video Compression](https://mlsanity.com/paper/2411.06685/) (2024)
- [Improving Variational Autoencoder using Random Fourier Transformation: An Aviation Safety Anomaly Detection Case-Study](https://mlsanity.com/paper/2601.01016/) (2026)
- [Subtractive Modulative Network with Learnable Periodic Activations](https://mlsanity.com/paper/2602.16337/) (2026)

## Abstract (arXiv)

Positional encodings are employed to capture the high frequency information of the encoded signals in implicit neural representation (INR). In this paper, we propose a novel positional encoding method which improves the reconstruction quality of the INR. The proposed embedding method is more advantageous for the compact data representation because it has a greater number of frequency basis than the existing methods. Our experiments shows that the proposed method achieves significant gain in the rate-distortion performance without introducing any additional complexity in the compression task and higher reconstruction quality in novel view synthesis.