# DVC-P: Deep Video Compression with Perceptual Optimizations

arXiv: https://arxiv.org/abs/2109.10849 · HTML: https://mlsanity.com/paper/2109.10849/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [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)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [M-LVC: Multiple Frames Prediction for Learned Video Compression](https://mlsanity.com/paper/2004.10290/) (2020)
- [Content Adaptive and Error Propagation Aware Deep Video Compression](https://mlsanity.com/paper/2003.11282/) (2020)
- [Adversarial Distortion for Learned Video Compression](https://mlsanity.com/paper/2004.09508/) (2020)
- [OpenDVC: An Open Source Implementation of the DVC Video Compression Method](https://mlsanity.com/paper/2006.15862/) (2020)

## Built on by (most cited first, 13 of 13)

- [Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance](https://mlsanity.com/paper/2512.07480/) (2025)
- [DiffVC-OSD: One-Step Diffusion-based Perceptual Neural Video Compression Framework](https://mlsanity.com/paper/2508.07682/) (2025)
- [On the Choice of Perception Loss Function for Learned Video Compression](https://mlsanity.com/paper/2305.19301/) (2023)
- [DiffVC-RT: Towards Practical Real-Time Diffusion-based Perceptual Neural Video Compression](https://mlsanity.com/paper/2601.20564/) (2026)
- [ProGVC: Progressive-based Generative Video Compression via Auto-Regressive Context Modeling](https://mlsanity.com/paper/2603.17546/) (2026)
- [Rate-Perception Optimized Preprocessing for Video Coding](https://mlsanity.com/paper/2301.10455/) (2023)
- [OpenDCVCs: A PyTorch Open Source Implementation and Performance Evaluation of the DCVC series Video Codecs](https://mlsanity.com/paper/2508.04491/) (2025)
- [Efficient Video Diffusion with Sparse Information Transmission for Video Compression](https://mlsanity.com/paper/2603.18501/) (2026)
- [A Causal Diffusion Model for Video Reconstruction from Ultra-Low-Bitrate Representations](https://mlsanity.com/paper/2602.13837/) (2026)
- [Geometric Transformation-Embedded Mamba for Learned Video Compression](https://mlsanity.com/paper/2603.07912/) (2026)
- [A Mamba-based Perceptual Loss Function for Learning-based UGC Transcoding](https://mlsanity.com/paper/2603.25566/) (2026)
- [Generative Video Compression Based on Hierarchical Referencing](https://mlsanity.com/paper/2608.11618/) (2026)
- [DiffVC-ONE: Diffusion-based Generative Video Compression with One-Step Video Diffusion Transformer](https://mlsanity.com/paper/2608.20515/) (2026)

## Abstract (arXiv)

Recent years have witnessed the significant development of learning-based video compression methods, which aim at optimizing objective or perceptual quality and bit rates. In this paper, we introduce deep video compression with perceptual optimizations (DVC-P), which aims at increasing perceptual quality of decoded videos. Our proposed DVC-P is based on Deep Video Compression (DVC) network, but improves it with perceptual optimizations. Specifically, a discriminator network and a mixed loss are employed to help our network trade off among distortion, perception and rate. Furthermore, nearest-neighbor interpolation is used to eliminate checkerboard artifacts which can appear in sequences encoded with DVC frameworks. Thanks to these two improvements, the perceptual quality of decoded sequences is improved. Experimental results demonstrate that, compared with the baseline DVC, our proposed method can generate videos with higher perceptual quality achieving 12.27% reduction in a perceptual BD-rate equivalent, on average.