# Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies

arXiv: https://arxiv.org/abs/2101.06341 · HTML: https://mlsanity.com/paper/2101.06341/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2021.

## Built on by (most cited first, 14 of 14)

- [An Introduction to Neural Data Compression](https://mlsanity.com/paper/2202.06533/) (2022)
- [CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming](https://mlsanity.com/paper/2211.08428/) (2022)
- [Scalable Video Coding for Humans and Machines](https://mlsanity.com/paper/2208.02512/) (2022)
- [Neural B-frame Video Compression with Bi-directional Reference Harmonization](https://mlsanity.com/paper/2511.08938/) (2025)
- [Differentiable bit-rate estimation for neural-based video codec enhancement](https://mlsanity.com/paper/2301.09776/) (2023)
- [Probing Image Compression For Class-Incremental Learning](https://mlsanity.com/paper/2403.06288/) (2024)
- [High-Efficiency Neural Video Compression via Hierarchical Predictive Learning](https://mlsanity.com/paper/2410.02598/) (2024)
- [Optimized learned entropy coding parameters for practical neural-based image and video compression](https://mlsanity.com/paper/2301.08752/) (2023)
- [Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds](https://mlsanity.com/paper/2410.17823/) (2024)
- [Motion Free B-frame Coding for Neural Video Compression](https://mlsanity.com/paper/2411.17160/) (2024)
- [Neural Weight Step Video Compression](https://mlsanity.com/paper/2112.01504/) (2021)
- [Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving](https://mlsanity.com/paper/2402.14642/) (2024)
- [Understanding the Performance Plateau in Text-to-Video Retrieval: A Comprehensive Empirical and Linguistic Analysis](https://mlsanity.com/paper/2605.00826/) (2026)
- [BLUE: Semantics-Preserving Video Compression for Efficient Vision-Language Surveillance Analytics](https://mlsanity.com/paper/2607.19515/) (2026)

## Abstract (arXiv)

Significant advances in video compression system have been made in the past several decades to satisfy the nearly exponential growth of Internet-scale video traffic. From the application perspective, we have identified three major functional blocks including pre-processing, coding, and post-processing, that have been continuously investigated to maximize the end-user quality of experience (QoE) under a limited bit rate budget. Recently, artificial intelligence (AI) powered techniques have shown great potential to further increase the efficiency of the aforementioned functional blocks, both individually and jointly. In this article, we review extensively recent technical advances in video compression system, with an emphasis on deep neural network (DNN)-based approaches; and then present three comprehensive case studies. On pre-processing, we show a switchable texture-based video coding example that leverages DNN-based scene understanding to extract semantic areas for the improvement of subsequent video coder. On coding, we present an end-to-end neural video coding framework that takes advantage of the stacked DNNs to efficiently and compactly code input raw videos via fully data-driven learning. On post-processing, we demonstrate two neural adaptive filters to respectively facilitate the in-loop and post filtering for the enhancement of compressed frames. Finally, a companion website hosting the contents developed in this work can be accessed publicly at https://purdueviper.github.io/dnn-coding/.