# Conditional Coding for Flexible Learned Video Compression

arXiv: https://arxiv.org/abs/2104.07930 · HTML: https://mlsanity.com/paper/2104.07930/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2021.

## Built on by (most cited first, 26 of 26)

- [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)
- [CANF-VC: Conditional Augmented Normalizing Flows for Video Compression](https://mlsanity.com/paper/2207.05315/) (2022)
- [B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flows](https://mlsanity.com/paper/2209.01769/) (2022)
- [Hierarchical B-frame Video Coding Using Two-Layer CANF without Motion Coding](https://mlsanity.com/paper/2304.02690/) (2023)
- [Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression](https://mlsanity.com/paper/2112.13309/) (2021)
- [Versatile Learned Video Compression](https://mlsanity.com/paper/2111.03386/) (2021)
- [Flexible-Rate Learned Hierarchical Bi-Directional Video Compression With Motion Refinement and Frame-Level Bit Allocation](https://mlsanity.com/paper/2206.13613/) (2022)
- [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)
- [Boosting neural video codecs by exploiting hierarchical redundancy](https://mlsanity.com/paper/2208.04303/) (2022)
- [Multi-Scale Deformable Alignment and Content-Adaptive Inference for Flexible-Rate Bi-Directional Video Compression](https://mlsanity.com/paper/2306.16544/) (2023)
- [Learned Scalable Video Coding For Humans and Machines](https://mlsanity.com/paper/2307.08978/) (2023)
- [Microdosing: Knowledge Distillation for GAN based Compression](https://mlsanity.com/paper/2201.02624/) (2022)
- [Enhanced Neural Video Representation Compression Across Extreme Complexity and Quality Scales](https://mlsanity.com/paper/2606.28163/) (2026)
- [Conditional and Residual Methods in Scalable Coding for Humans and Machines](https://mlsanity.com/paper/2305.02562/) (2023)
- [Low-Latency Neural Stereo Streaming](https://mlsanity.com/paper/2403.17879/) (2024)
- [Efficient Video Diffusion with Sparse Information Transmission for Video Compression](https://mlsanity.com/paper/2603.18501/) (2026)
- [CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability](https://mlsanity.com/paper/2202.00416/) (2022)
- [Learned Hierarchical B-frame Coding with Adaptive Feature Modulation for YUV 4:2:0 Content](https://mlsanity.com/paper/2212.14187/) (2022)
- [Efficient Compression of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling](https://mlsanity.com/paper/2412.01754/) (2024)
- [Neural Video Compression with In-Loop Contextual Filtering and Out-of-Loop Reconstruction Enhancement](https://mlsanity.com/paper/2509.04051/) (2025)
- [Event-guided Neural Video Compression](https://mlsanity.com/paper/2610.02265/) (2026)

## Abstract (arXiv)

This paper introduces a novel framework for end-to-end learned video coding. Image compression is generalized through conditional coding to exploit information from reference frames, allowing to process intra and inter frames with the same coder. The system is trained through the minimization of a rate-distortion cost, with no pre-training or proxy loss. Its flexibility is assessed under three coding configurations (All Intra, Low-delay P and Random Access), where it is shown to achieve performance competitive with the state-of-the-art video codec HEVC.