mlsanityData to 7 Oct 2026

Recent Standard Development Activities on Video Coding for Machines

arXiv 2105.12653 ↗First posted 26 May 2021cs.CVcited by 14

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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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 6 of its 50 references that are in the map. First posted on arXiv 26 May 2021.

Builds on

6 of 50 references.

References · 6

  1. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  2. 2018Variational image compression with a scale hyperpriorcited by 910
  3. 2018Joint Autoregressive and Hierarchical Priors for Learned Image Compressioncited by 642
  4. 2020Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modulescited by 570
  5. 2020Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analyticscited by 80
  6. 2018Learning based Facial Image Compression with Semantic Fidelity Metriccited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 1; 2025: 2; 2026: 4.

14 papers.

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