# Scalable Video Coding for Humans and Machines

arXiv: https://arxiv.org/abs/2208.02512 · HTML: https://mlsanity.com/paper/2208.02512/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Image and Video Compression with Neural Networks: A Review](https://mlsanity.com/paper/1904.03567/) (2019)
- [Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics](https://mlsanity.com/paper/2001.03569/) (2020)
- [Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2107.08373/) (2021)
- [Towards Coding for Human and Machine Vision: A Scalable Image Coding Approach](https://mlsanity.com/paper/2001.02915/) (2020)
- [Near-Lossless Deep Feature Compression for Collaborative Intelligence](https://mlsanity.com/paper/1804.09963/) (2018)
- [Latent-space scalability for multi-task collaborative intelligence](https://mlsanity.com/paper/2105.10089/) (2021)
- [Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies](https://mlsanity.com/paper/2101.06341/) (2021)
- [Affine Transformation-Based Deep Frame Prediction](https://mlsanity.com/paper/2009.05666/) (2020)
- [Human-Machine Collaborative Video Coding Through Cuboidal Partitioning](https://mlsanity.com/paper/2102.01307/) (2021)

## Built on by (most cited first, 9 of 9)

- [Scalable Image Coding for Humans and Machines Using Feature Fusion Network](https://mlsanity.com/paper/2405.09152/) (2024)
- [Learned Scalable Video Coding For Humans and Machines](https://mlsanity.com/paper/2307.08978/) (2023)
- [Task Oriented Video Coding: A Survey](https://mlsanity.com/paper/2208.07313/) (2022)
- [VVC+M: Plug and Play Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2305.10453/) (2023)
- [Explicit Residual-Based Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2506.19297/) (2025)
- [Guided Diffusion for the Extension of Machine Vision to Human Visual Perception](https://mlsanity.com/paper/2503.17907/) (2025)
- [NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines](https://mlsanity.com/paper/2401.10761/) (2024)
- [High Dynamic Range Video Compression: A Large-Scale Benchmark Dataset and A Learned Bit-depth Scalable Compression Algorithm](https://mlsanity.com/paper/2503.00410/) (2025)
- [Scalable Neural Video Representation Compression](https://mlsanity.com/paper/2609.04273/) (2026)

## Abstract (arXiv)

Video content is watched not only by humans, but increasingly also by machines. For example, machine learning models analyze surveillance video for security and traffic monitoring, search through YouTube videos for inappropriate content, and so on. In this paper, we propose a scalable video coding framework that supports machine vision (specifically, object detection) through its base layer bitstream and human vision via its enhancement layer bitstream. The proposed framework includes components from both conventional and Deep Neural Network (DNN)-based video coding. The results show that on object detection, the proposed framework achieves 13-19% bit savings compared to state-of-the-art video codecs, while remaining competitive in terms of MS-SSIM on the human vision task.