A Neural-network Enhanced Video Coding Framework beyond ECM

Yanchen Zhao, Wenxuan He, Chuanmin Jia, Qizhe Wang, Junru Li, Yue Li, Chaoyi Lin, Kai Zhang, Li Zhang, Siwei Ma

Abstract: 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. We evaluate the coding performance of the proposed framework with extensive experiments on the JVET dataset compared with ECM10.0 and VTM-11.0. Due to the testing environment and the coding complexity of the ECM, we did not conduct testing on Class A. The QPs are set as 22, 27, 32, 37, and 42. 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. The traditional hybrid coding framework combined with the three coding tools can further improve compression efficiency and has great potential for performance improvement.