A Neural-network Enhanced Video Coding Framework beyond ECM
<|reference_start|>A Neural-network Enhanced Video Coding Framework beyond ECM: 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.<|reference_end|>
@article{zhao2024a,
    title={A Neural-network Enhanced Video Coding Framework beyond ECM},
    author={Yanchen Zhao, Wenxuan He, Chuanmin Jia, Qizhe Wang, Junru Li, Yue Li,
  Chaoyi Lin, Kai Zhang, Li Zhang, Siwei Ma},
    journal={arXiv preprint arXiv:2402.08397},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08397},
    primaryClass={cs.CV}
}