OpenDCVCs: A PyTorch Open Source Implementation and Performance Evaluation of the DCVC series Video Codecs
Yichi Zhang, Fengqing Zhu

TL;DR
OpenDCVCs is an open-source PyTorch framework that enables reproducible research, training, and benchmarking of advanced learned video compression models, significantly aiding community development and comparison.
Contribution
It provides the first comprehensive, training-ready implementation of multiple DCVC models, facilitating reproducibility, benchmarking, and further research in learned video compression.
Findings
DCVC models outperform classical codecs in bitrate reduction
OpenDCVCs enables consistent evaluation across datasets
Framework supports end-to-end training and benchmarking
Abstract
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…
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