# NeuralLVC: Neural Lossless Video Compression via Masked Diffusion with Temporal Conditioning

arXiv: https://arxiv.org/abs/2604.03353 · HTML: https://mlsanity.com/paper/2604.03353/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Deep Contextual Video Compression](https://mlsanity.com/paper/2109.15047/) (2021)
- [Neural Video Compression with Diverse Contexts](https://mlsanity.com/paper/2302.14402/) (2023)
- [Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression](https://mlsanity.com/paper/2207.05894/) (2022)
- [Neural Video Compression with Feature Modulation](https://mlsanity.com/paper/2402.17414/) (2024)
- [Towards Practical Real-Time Neural Video Compression](https://mlsanity.com/paper/2502.20762/) (2025)
- [VoCo-LLaMA: Towards Vision Compression with Large Language Models](https://mlsanity.com/paper/2406.12275/) (2024)
- [Diffusion-based Perceptual Neural Video Compression with Temporal Diffusion Information Reuse](https://mlsanity.com/paper/2501.13528/) (2025)
- [Lossless data compression by large models](https://mlsanity.com/paper/2407.07723/) (2024)
- [Large Language Model for Lossless Image Compression with Visual Prompts](https://mlsanity.com/paper/2502.16163/) (2025)
- [BVI-AOM: A New Training Dataset for Deep Video Compression Optimization](https://mlsanity.com/paper/2408.03265/) (2024)
- [When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding](https://mlsanity.com/paper/2408.08093/) (2024)
- [M3-CVC: Controllable Video Compression with Multimodal Generative Models](https://mlsanity.com/paper/2411.15798/) (2024)
- [Progressive Compression with Universally Quantized Diffusion Models](https://mlsanity.com/paper/2412.10935/) (2024)
- [High Efficiency Image Compression for Large Visual-Language Models](https://mlsanity.com/paper/2407.17060/) (2024)
- [Low-Bitrate Video Compression through Semantic-Conditioned Diffusion](https://mlsanity.com/paper/2512.00408/) (2025)
- [Diffusion-aided Extreme Video Compression with Lightweight Semantics Guidance](https://mlsanity.com/paper/2602.05201/) (2026)
- [Revisiting Data Compression with Language Modeling](https://mlsanity.com/paper/2601.02875/) (2026)
- [Rethinking Autoregressive Models for Lossless Image Compression via Hierarchical Parallelism and Progressive Adaptation](https://mlsanity.com/paper/2511.10991/) (2025)

## Abstract (arXiv)

While neural lossless image compression has advanced significantly with learned entropy models, lossless video compression remains largely unexplored in the neural setting. We present NeuralLVC, a neural lossless video codec that combines masked diffusion with an I/P-frame architecture for exploiting temporal redundancy. Our I-frame model compresses individual frames using bijective linear tokenization that guarantees exact pixel reconstruction. The P-frame model compresses temporal differences between consecutive frames, conditioned on the previous decoded frame via a lightweight reference embedding that adds only 1.3% trainable parameters. Group-wise decoding enables controllable speed-compression trade-offs. Our codec is lossless in the input domain: for video, it reconstructs YUV420 planes exactly; for image evaluation, RGB channels are reconstructed exactly. Experiments on 9 Xiph CIF sequences and on 24 HD sequences at 720p and 1080p (Xiph, UVG, and the modern BVI-AOM benchmark) show that NeuralLVC outperforms H.264 and H.265 lossless by a significant margin at every resolution tested. We verify exact reconstruction through end-to-end encode-decode testing with arithmetic coding. These results suggest that masked diffusion with temporal conditioning is a promising direction for neural lossless video compression.