# Recent Standard Development Activities on Video Coding for Machines

arXiv: https://arxiv.org/abs/2105.12653 · HTML: https://mlsanity.com/paper/2105.12653/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 6 of 6)

- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics](https://mlsanity.com/paper/2001.03569/) (2020)
- [Learning based Facial Image Compression with Semantic Fidelity Metric](https://mlsanity.com/paper/1812.10067/) (2018)

## Built on by (most cited first, 14 of 14)

- [VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision](https://mlsanity.com/paper/2306.10681/) (2023)
- [Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics](https://mlsanity.com/paper/2110.09241/) (2021)
- [AccMPEG: Optimizing Video Encoding for Video Analytics](https://mlsanity.com/paper/2204.12534/) (2022)
- [Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling](https://mlsanity.com/paper/2211.06797/) (2022)
- [Task Oriented Video Coding: A Survey](https://mlsanity.com/paper/2208.07313/) (2022)
- [Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines](https://mlsanity.com/paper/2503.19278/) (2025)
- [Privacy-Preserving Feature Coding for Machines](https://mlsanity.com/paper/2210.00727/) (2022)
- [Deep Video Codec Control for Vision Models](https://mlsanity.com/paper/2308.16215/) (2023)
- [Competitive Learning for Achieving Content-specific Filters in Video Coding for Machines](https://mlsanity.com/paper/2406.12367/) (2024)
- [Emerging Standards for Machine-to-Machine Video Coding](https://mlsanity.com/paper/2512.10230/) (2025)
- [Recent Advances of End-to-End Video Coding Technologies for AVS Standard Development](https://mlsanity.com/paper/2602.00483/) (2026)
- [Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Application](https://mlsanity.com/paper/2604.09421/) (2026)
- [BLUE: Semantics-Preserving Video Compression for Efficient Vision-Language Surveillance Analytics](https://mlsanity.com/paper/2607.19515/) (2026)
- [You've Seen Enough: Quality-Constrained Image Coding for Machines](https://mlsanity.com/paper/2609.25108/) (2026)

## Abstract (arXiv)

In recent years, video data has dominated internet traffic and becomes one of the major data formats. With the emerging 5G and internet of things (IoT) technologies, more and more videos are generated by edge devices, sent across networks, and consumed by machines. The volume of video consumed by machine is exceeding the volume of video consumed by humans. Machine vision tasks include object detection, segmentation, tracking, and other machine-based applications, which are quite different from those for human consumption. On the other hand, due to large volumes of video data, it is essential to compress video before transmission. Thus, efficient video coding for machines (VCM) has become an important topic in academia and industry. In July 2019, the international standardization organization, i.e., MPEG, created an Ad-Hoc group named VCM to study the requirements for potential standardization work. In this paper, we will address the recent development activities in the MPEG VCM group. Specifically, we will first provide an overview of the MPEG VCM group including use cases, requirements, processing pipelines, plan for potential VCM standards, followed by the evaluation framework including machine-vision tasks, dataset, evaluation metrics, and anchor generation. We then introduce technology solutions proposed so far and discuss the recent responses to the Call for Evidence issued by MPEG VCM group.