# VSS: A Storage System for Video Analytics [Technical Report]

arXiv: https://arxiv.org/abs/2103.16604 · HTML: https://mlsanity.com/paper/2103.16604/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2021.

## Built on by (most cited first, 5 of 5)

- [Extract-Transform-Load for Video Streams](https://mlsanity.com/paper/2310.04830/) (2023)
- [CoVA: Exploiting Compressed-Domain Analysis to Accelerate Video Analytics](https://mlsanity.com/paper/2207.00588/) (2022)
- [D'ej`a Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse](https://mlsanity.com/paper/2506.14107/) (2025)
- [A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications](https://mlsanity.com/paper/2507.15628/) (2025)
- [Search over the Visual World: Persistent Visual Memory, Layered Indexes, and Source-Grounded Evidence](https://mlsanity.com/paper/2608.08075/) (2026)

## Abstract (arXiv)

We present a new video storage system (VSS) designed to decouple high-level video operations from the low-level details required to store and efficiently retrieve video data. VSS is designed to be the storage subsystem of a video data management system (VDBMS) and is responsible for: (1) transparently and automatically arranging the data on disk in an efficient, granular format; (2) caching frequently-retrieved regions in the most useful formats; and (3) eliminating redundancies found in videos captured from multiple cameras with overlapping fields of view. Our results suggest that VSS can improve VDBMS read performance by up to 54%, reduce storage costs by up to 45%, and enable developers to focus on application logic rather than video storage and retrieval.