In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies. The proposed hybrid framework is founded upon the Enhanced Compression Model (ECM), which is a further enhancement of the Versatile Video Coding (VVC) standard. We have augmented the latest ECM reference software with well-designed coding techniques, including block partitioning, deep learning-based loop filter, and the activation of block importance mapping (BIM) which was integrated but previously inactive within ECM, further enhancing coding performance. Compared with ECM-10.0, our method achieves 6.26, 13.33, and 12.33 BD-rate savings for the Y, U, and V components under random access (RA) configuration, respectively.
@article{arxiv.2402.08397,
title = {A Neural-network Enhanced Video Coding Framework beyond ECM},
author = {Yanchen Zhao and Wenxuan He and Chuanmin Jia and Qizhe Wang and Junru Li and Yue Li and Chaoyi Lin and Kai Zhang and Li Zhang and Siwei Ma},
journal= {arXiv preprint arXiv:2402.08397},
year = {2024}
}