# Conditional Entropy Coding for Efficient Video Compression

arXiv: https://arxiv.org/abs/2008.09180 · HTML: https://mlsanity.com/paper/2008.09180/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 30 of 42)

- [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)
- [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)
- [Temporal Context Mining for Learned Video Compression](https://mlsanity.com/paper/2111.13850/) (2021)
- [HNeRV: A Hybrid Neural Representation for Videos](https://mlsanity.com/paper/2304.02633/) (2023)
- [Towards Practical Real-Time Neural Video Compression](https://mlsanity.com/paper/2502.20762/) (2025)
- [VCT: A Video Compression Transformer](https://mlsanity.com/paper/2206.07307/) (2022)
- [HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation](https://mlsanity.com/paper/2306.09818/) (2023)
- [ELF-VC: Efficient Learned Flexible-Rate Video Coding](https://mlsanity.com/paper/2104.14335/) (2021)
- [Neural Video Compression using GANs for Detail Synthesis and Propagation](https://mlsanity.com/paper/2107.12038/) (2021)
- [Scalable Neural Video Representations with Learnable Positional Features](https://mlsanity.com/paper/2210.06823/) (2022)
- [AlphaVC: High-Performance and Efficient Learned Video Compression](https://mlsanity.com/paper/2207.14678/) (2022)
- [Neural Compression-Based Feature Learning for Video Restoration](https://mlsanity.com/paper/2203.09208/) (2022)
- [Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression](https://mlsanity.com/paper/2401.15864/) (2024)
- [CNeRV: Content-adaptive Neural Representation for Visual Data](https://mlsanity.com/paper/2211.10421/) (2022)
- [Bi-Directional Deep Contextual Video Compression](https://mlsanity.com/paper/2408.08604/) (2024)
- [MobileCodec: Neural Inter-frame Video Compression on Mobile Devices](https://mlsanity.com/paper/2207.08338/) (2022)
- [Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression](https://mlsanity.com/paper/2112.13309/) (2021)
- [Task-Oriented Communication for Edge Video Analytics](https://mlsanity.com/paper/2211.14049/) (2022)
- [VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision](https://mlsanity.com/paper/2306.10681/) (2023)
- [Fast Encoding and Decoding for Implicit Video Representation](https://mlsanity.com/paper/2409.19429/) (2024)
- [Versatile Learned Video Compression](https://mlsanity.com/paper/2111.03386/) (2021)
- [EHVC: Efficient Hierarchical Reference and Quality Structure for Neural Video Coding](https://mlsanity.com/paper/2509.04118/) (2025)
- [Ultra-Fast Neural Video Compression](https://mlsanity.com/paper/2606.04410/) (2026)
- [Prediction and Reference Quality Adaptation for Learned Video Compression](https://mlsanity.com/paper/2406.14118/) (2024)
- [Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement](https://mlsanity.com/paper/2202.00011/) (2022)
- [NVC-1B: A Large Neural Video Coding Model](https://mlsanity.com/paper/2407.19402/) (2024)
- [Context Guided Transformer Entropy Modeling for Video Compression](https://mlsanity.com/paper/2508.01852/) (2025)

## Abstract (arXiv)

We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. Unlike prior learning-based approaches, we reduce complexity by not performing any form of explicit transformations between frames and assume each frame is encoded with an independent state-of-the-art deep image compressor. We first show that a simple architecture modeling the entropy between the image latent codes is as competitive as other neural video compression works and video codecs while being much faster and easier to implement. We then propose a novel internal learning extension on top of this architecture that brings an additional 10% bitrate savings without trading off decoding speed. Importantly, we show that our approach outperforms H.265 and other deep learning baselines in MS-SSIM on higher bitrate UVG video, and against all video codecs on lower framerates, while being thousands of times faster in decoding than deep models utilizing an autoregressive entropy model.