# Immersive Video Compression using Implicit Neural Representations

arXiv: https://arxiv.org/abs/2402.01596 · HTML: https://mlsanity.com/paper/2402.01596/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [NeRV: Neural Representations for Videos](https://mlsanity.com/paper/2110.13903/) (2021)
- [Deep Contextual Video Compression](https://mlsanity.com/paper/2109.15047/) (2021)
- [Neural Video Compression with Diverse Contexts](https://mlsanity.com/paper/2302.14402/) (2023)
- [Training with Quantization Noise for Extreme Model Compression](https://mlsanity.com/paper/2004.07320/) (2020)
- [HNeRV: A Hybrid Neural Representation for Videos](https://mlsanity.com/paper/2304.02633/) (2023)
- [HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation](https://mlsanity.com/paper/2306.09818/) (2023)
- [FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos](https://mlsanity.com/paper/2212.12294/) (2022)
- [Towards Scalable Neural Representation for Diverse Videos](https://mlsanity.com/paper/2303.14124/) (2023)
- [MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering](https://mlsanity.com/paper/2007.07099/) (2020)
- [Video Compression with CNN-based Post Processing](https://mlsanity.com/paper/2009.07583/) (2020)
- [Neural network-based arithmetic coding of intra prediction modes in HEVC](https://mlsanity.com/paper/1709.05737/) (2017)
- [ViSTRA2: Video Coding using Spatial Resolution and Effective Bit Depth Adaptation](https://mlsanity.com/paper/1911.02833/) (2019)

## Built on by (most cited first, 12 of 12)

- [NVRC: Neural Video Representation Compression](https://mlsanity.com/paper/2409.07414/) (2024)
- [Ultra-lightweight Neural Video Representation Compression](https://mlsanity.com/paper/2512.04019/) (2025)
- [BVI-CR: A Multi-View Human Dataset for Volumetric Video Compression](https://mlsanity.com/paper/2411.11199/) (2024)
- [Enhanced Neural Video Representation Compression Across Extreme Complexity and Quality Scales](https://mlsanity.com/paper/2606.28163/) (2026)
- [Splatography: Sparse multi-view dynamic Gaussian Splatting for filmmaking challenges](https://mlsanity.com/paper/2511.05152/) (2025)
- [ViVo: A Dataset for Volumetric Video Reconstruction and Compression](https://mlsanity.com/paper/2506.00558/) (2025)
- [NeRV360: Neural Representation for 360-Degree Videos with a Viewport Decoder](https://mlsanity.com/paper/2512.20871/) (2025)
- [Quantum Implicit Neural Compression](https://mlsanity.com/paper/2412.19828/) (2024)
- [DCVC-MV: Deep Contextual Multiview Video Compression with Efficient Inter-View Prediction](https://mlsanity.com/paper/2509.03922/) (2025)
- [NeR-SC: Adapting Neural Video Representation to Screen Content](https://mlsanity.com/paper/2605.27024/) (2026)
- [High-Fidelity Video Compression based on Invertible Neural Transform and Implicit Conditioning](https://mlsanity.com/paper/2606.13957/) (2026)
- [Gaussian Splatting-based Volumetric Video Compression with Sparse 4D Anchors](https://mlsanity.com/paper/2609.33969/) (2026)

## Abstract (arXiv)

Recent work on implicit neural representations (INRs) has evidenced their potential for efficiently representing and encoding conventional video content. In this paper we, for the first time, extend their application to immersive (multi-view) videos, by proposing MV-HiNeRV, a new INR-based immersive video codec. MV-HiNeRV is an enhanced version of a state-of-the-art INR-based video codec, HiNeRV, which was developed for single-view video compression. We have modified the model to learn a different group of feature grids for each view, and share the learnt network parameters among all views. This enables the model to effectively exploit the spatio-temporal and the inter-view redundancy that exists within multi-view videos. The proposed codec was used to compress multi-view texture and depth video sequences in the MPEG Immersive Video (MIV) Common Test Conditions, and tested against the MIV Test model (TMIV) that uses the VVenC video codec. The results demonstrate the superior performance of MV-HiNeRV, with significant coding gains (up to 72.33%) over TMIV. The implementation of MV-HiNeRV is published for further development and evaluation.