# Diffusion-based Perceptual Neural Video Compression with Temporal Diffusion Information Reuse

arXiv: https://arxiv.org/abs/2501.13528 · HTML: https://mlsanity.com/paper/2501.13528/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Placed in

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

## Builds on (most cited first, 3 of 3)

- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [IBVC: Interpolation-driven B-frame Video Compression](https://mlsanity.com/paper/2309.13835/) (2023)

## Built on by (most cited first, 22 of 22)

- [Generative Neural Video Compression via Video Diffusion Prior](https://mlsanity.com/paper/2512.05016/) (2025)
- [Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance](https://mlsanity.com/paper/2512.07480/) (2025)
- [DiffVC-OSD: One-Step Diffusion-based Perceptual Neural Video Compression Framework](https://mlsanity.com/paper/2508.07682/) (2025)
- [Ultra-Fast Neural Video Compression](https://mlsanity.com/paper/2606.04410/) (2026)
- [Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models for Wireless Networks](https://mlsanity.com/paper/2502.05695/) (2025)
- [Generative Video Compression with One-Dimensional Latent Representation](https://mlsanity.com/paper/2603.15302/) (2026)
- [DiffVC-RT: Towards Practical Real-Time Diffusion-based Perceptual Neural Video Compression](https://mlsanity.com/paper/2601.20564/) (2026)
- [ProGVC: Progressive-based Generative Video Compression via Auto-Regressive Context Modeling](https://mlsanity.com/paper/2603.17546/) (2026)
- [ZeroGVC: Zero-Shot Generative Video Compression with Autoregressive Diffusion Priors](https://mlsanity.com/paper/2606.22371/) (2026)
- [Efficient Video Diffusion with Sparse Information Transmission for Video Compression](https://mlsanity.com/paper/2603.18501/) (2026)
- [Dense-Prior-Guided Generative Video Compression](https://mlsanity.com/paper/2604.06655/) (2026)
- [Generative Video Compression with Adaptive Score Distillation](https://mlsanity.com/paper/2607.22772/) (2026)
- [GVC-RT: Towards Real-Time Generative Video Compression at Ultra-Low Bitrates](https://mlsanity.com/paper/2608.04891/) (2026)
- [A Causal Diffusion Model for Video Reconstruction from Ultra-Low-Bitrate Representations](https://mlsanity.com/paper/2602.13837/) (2026)
- [Geometric Transformation-Embedded Mamba for Learned Video Compression](https://mlsanity.com/paper/2603.07912/) (2026)
- [NeuralLVC: Neural Lossless Video Compression via Masked Diffusion with Temporal Conditioning](https://mlsanity.com/paper/2604.03353/) (2026)
- [A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff](https://mlsanity.com/paper/2604.14603/) (2026)
- [Group-of-Latents: Perceptual Video Compression at Extreme Bitrates via Masked Latent Generative Modeling](https://mlsanity.com/paper/2607.19437/) (2026)
- [Generative Video Compression Based on Hierarchical Referencing](https://mlsanity.com/paper/2608.11618/) (2026)
- [DiffVC-ONE: Diffusion-based Generative Video Compression with One-Step Video Diffusion Transformer](https://mlsanity.com/paper/2608.20515/) (2026)
- [A Spatiotemporal Semantic Importance-Guided Unified Compression and Editing Framework for AI-Generated Videos](https://mlsanity.com/paper/2610.05779/) (2026)
- [Unified Generative Video Compression with Autoregressive and Bidirectional Video Diffusion](https://mlsanity.com/paper/2610.13023/) (2026)

## Abstract (arXiv)

Recently, foundational diffusion models have attracted considerable attention in image compression tasks, whereas their application to video compression remains largely unexplored. In this article, we introduce DiffVC, a diffusion-based perceptual neural video compression framework that effectively integrates foundational diffusion model with the video conditional coding paradigm. This framework uses temporal context from previously decoded frame and the reconstructed latent representation of the current frame to guide the diffusion model in generating high-quality results. To accelerate the iterative inference process of diffusion model, we propose the Temporal Diffusion Information Reuse (TDIR) strategy, which significantly enhances inference efficiency with minimal performance loss by reusing the diffusion information from previous frames. Additionally, to address the challenges posed by distortion differences across various bitrates, we propose the Quantization Parameter-based Prompting (QPP) mechanism, which utilizes quantization parameters as prompts fed into the foundational diffusion model to explicitly modulate intermediate features, thereby enabling a robust variable bitrate diffusion-based neural compression framework. Experimental results demonstrate that our proposed solution delivers excellent performance in both perception metrics and visual quality.