# DMVC: Multi-Camera Video Compression Network aimed at Improving Deep Learning Accuracy

arXiv: https://arxiv.org/abs/2410.18400 · HTML: https://mlsanity.com/paper/2410.18400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Channel-wise Autoregressive Entropy Models for Learned Image Compression](https://mlsanity.com/paper/2007.08739/) (2020)
- [Temporal Context Mining for Learned Video Compression](https://mlsanity.com/paper/2111.13850/) (2021)
- [M-LVC: Multiple Frames Prediction for Learned Video Compression](https://mlsanity.com/paper/2004.10290/) (2020)
- [Video Compression With Rate-Distortion Autoencoders](https://mlsanity.com/paper/1908.05717/) (2019)
- [Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model](https://mlsanity.com/paper/2006.13560/) (2020)
- [Learning for Video Compression](https://mlsanity.com/paper/1804.09869/) (2018)
- [Convolutional Neural Network-Based Block Up-sampling for Intra Frame Coding](https://mlsanity.com/paper/1702.06728/) (2017)
- [DeepRT: A Soft Real Time Scheduler for Computer Vision Applications on the Edge](https://mlsanity.com/paper/2105.01803/) (2021)
- [AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics](https://mlsanity.com/paper/2301.08664/) (2023)
- [ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale](https://mlsanity.com/paper/1811.01268/) (2018)
- [Convolutional Neural Networks based Intra Prediction for HEVC](https://mlsanity.com/paper/1808.05734/) (2018)

## Abstract (arXiv)

We introduce a cutting-edge video compression framework tailored for the age of ubiquitous video data, uniquely designed to serve machine learning applications. Unlike traditional compression methods that prioritize human visual perception, our innovative approach focuses on preserving semantic information critical for deep learning accuracy, while efficiently reducing data size. The framework operates on a batch basis, capable of handling multiple video streams simultaneously, thereby enhancing scalability and processing efficiency. It features a dual reconstruction mode: lightweight for real-time applications requiring swift responses, and high-precision for scenarios where accuracy is crucial. Based on a designed deep learning algorithms, it adeptly segregates essential information from redundancy, ensuring machine learning tasks are fed with data of the highest relevance. Our experimental results, derived from diverse datasets including urban surveillance and autonomous vehicle navigation, showcase DMVC's superiority in maintaining or improving machine learning task accuracy, while achieving significant data compression. This breakthrough paves the way for smarter, scalable video analysis systems, promising immense potential across various applications from smart city infrastructure to autonomous systems, establishing a new benchmark for integrating video compression with machine learning.