# OpenDCVCs: A PyTorch Open Source Implementation and Performance Evaluation of the DCVC series Video Codecs

arXiv: https://arxiv.org/abs/2508.04491 · HTML: https://mlsanity.com/paper/2508.04491/

## Key facts

- Cited by 1 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 6 Aug 2025.

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

- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [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)
- [Deep Contextual Video Compression](https://mlsanity.com/paper/2109.15047/) (2021)
- [FVC: A New Framework towards Deep Video Compression in Feature Space](https://mlsanity.com/paper/2105.09600/) (2021)
- [Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression](https://mlsanity.com/paper/2207.05894/) (2022)
- [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)
- [Content Adaptive and Error Propagation Aware Deep Video Compression](https://mlsanity.com/paper/2003.11282/) (2020)
- [DVC-P: Deep Video Compression with Perceptual Optimizations](https://mlsanity.com/paper/2109.10849/) (2021)
- [OpenDVC: An Open Source Implementation of the DVC Video Compression Method](https://mlsanity.com/paper/2006.15862/) (2020)
- [Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs](https://mlsanity.com/paper/2403.18535/) (2024)

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

- [Real-Time Neural Video Compression with Unified Intra and Inter Coding](https://mlsanity.com/paper/2510.14431/) (2025)

## Abstract (arXiv)

We present OpenDCVCs, an open-source PyTorch implementation designed to advance reproducible research in learned video compression. OpenDCVCs provides unified and training-ready implementations of four representative Deep Contextual Video Compression (DCVC) models--DCVC, DCVC with Temporal Context Modeling (DCVC-TCM), DCVC with Hybrid Entropy Modeling (DCVC-HEM), and DCVC with Diverse Contexts (DCVC-DC). While the DCVC series achieves substantial bitrate reductions over both classical codecs and advanced learned models, previous public code releases have been limited to evaluation codes, presenting significant barriers to reproducibility, benchmarking, and further development. OpenDCVCs bridges this gap by offering a comprehensive, self-contained framework that supports both end-to-end training and evaluation for all included algorithms. The implementation includes detailed documentation, evaluation protocols, and extensive benchmarking results across diverse datasets, providing a transparent and consistent foundation for comparison and extension. All code and experimental tools are publicly available at https://gitlab.com/viper-purdue/opendcvcs, empowering the community to accelerate research and foster collaboration.