# Learning for Video Compression

arXiv: https://arxiv.org/abs/1804.09869 · HTML: https://mlsanity.com/paper/1804.09869/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2018.

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

- [Temporal Context Mining for Learned Video Compression](https://mlsanity.com/paper/2111.13850/) (2021)
- [Implicit Neural Video Compression](https://mlsanity.com/paper/2112.11312/) (2021)
- [Image Coding for Machines with Omnipotent Feature Learning](https://mlsanity.com/paper/2207.01932/) (2022)
- [4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video](https://mlsanity.com/paper/2503.18421/) (2025)
- [DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codes](https://mlsanity.com/paper/2403.15679/) (2024)
- [Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression](https://mlsanity.com/paper/2401.15864/) (2024)
- [Bi-Directional Deep Contextual Video Compression](https://mlsanity.com/paper/2408.08604/) (2024)
- [Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set](https://mlsanity.com/paper/2111.10302/) (2021)
- [B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flows](https://mlsanity.com/paper/2209.01769/) (2022)
- [RGB no more: Minimally-decoded JPEG Vision Transformers](https://mlsanity.com/paper/2211.16421/) (2022)
- [SC2 Benchmark: Supervised Compression for Split Computing](https://mlsanity.com/paper/2203.08875/) (2022)
- [Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression](https://mlsanity.com/paper/2112.13309/) (2021)
- [HPC: Hierarchical Progressive Coding Framework for Volumetric Video](https://mlsanity.com/paper/2407.09026/) (2024)
- [Insights from Generative Modeling for Neural Video Compression](https://mlsanity.com/paper/2107.13136/) (2021)
- [Versatile Learned Video Compression](https://mlsanity.com/paper/2111.03386/) (2021)
- [Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks](https://mlsanity.com/paper/2201.10162/) (2022)
- [MS-RNN: A Flexible Multi-Scale Framework for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2206.03010/) (2022)
- [Prediction and Reference Quality Adaptation for Learned Video Compression](https://mlsanity.com/paper/2406.14118/) (2024)
- [NVC-1B: A Large Neural Video Coding Model](https://mlsanity.com/paper/2407.19402/) (2024)
- [Learned Video Compression with Residual Prediction and Loop Filter](https://mlsanity.com/paper/2108.08551/) (2021)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [DeepSketch: A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression](https://mlsanity.com/paper/2202.10584/) (2022)
- [CWRNN-INVR: A Coupled WarpRNN based Implicit Neural Video Representation](https://mlsanity.com/paper/2604.06564/) (2026)
- [Neural-based Video Compression on Solar Dynamics Observatory Images](https://mlsanity.com/paper/2407.15730/) (2024)
- [DMVC: Multi-Camera Video Compression Network aimed at Improving Deep Learning Accuracy](https://mlsanity.com/paper/2410.18400/) (2024)
- [Neural Video Compression with In-Loop Contextual Filtering and Out-of-Loop Reconstruction Enhancement](https://mlsanity.com/paper/2509.04051/) (2025)

## Abstract (arXiv)

One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network. In this paper we propose the concept of PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction networks. PMCNN can model spatiotemporal coherence to effectively perform predictive coding inside the learning network. On the basis of PMCNN, we further explore a learning-based framework for video compression with additional components of iterative analysis/synthesis, binarization, etc. Experimental results demonstrate the effectiveness of the proposed scheme. Although entropy coding and complex configurations are not employed in this paper, we still demonstrate superior performance compared with MPEG-2 and achieve comparable results with H.264 codec. The proposed learning-based scheme provides a possible new direction to further improve compression efficiency and functionalities of future video coding.