# Video Coding for Machines: Partial transmission of SIFT features

arXiv: https://arxiv.org/abs/2201.02689 · HTML: https://mlsanity.com/paper/2201.02689/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 7 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics](https://mlsanity.com/paper/2001.03569/) (2020)
- [High efficiency compression for object detection](https://mlsanity.com/paper/1710.11151/) (2017)
- [Compact Descriptors for Video Analysis: the Emerging MPEG Standard](https://mlsanity.com/paper/1704.08141/) (2017)
- [Keypoint Encoding for Improved Feature Extraction from Compressed Video at Low Bitrates](https://mlsanity.com/paper/1506.08316/) (2015)

## Built on by (most cited first, 1 of 1)

- [Task Oriented Video Coding: A Survey](https://mlsanity.com/paper/2208.07313/) (2022)

## Abstract (arXiv)

The paper deals with Video Coding for Machines that is a new paradigm in video coding related to consumption of decoded video by humans and machines. For such tasks, joint transmission of compressed video and features is considered. In this paper, we focus our considerations of features on SIFT keypoints. They can be extracted from the decoded video with losses in number of keypoints and their parameters as compared to the SIFT keypoints extracted from the original video. Such losses are studied for HEVC and VVC as functions of the quantization parameter and the bitrate. In the paper, we propose to transmit the residual feature data together with the compressed video. Therefore, even for strongly compressed video, the transmission of whole all SIFT keypoint information is avoided.