A Neural-network Enhanced Video Coding Framework beyond ECM
This work addresses video coding efficiency for multimedia applications, but it is incremental as it builds upon existing ECM standards.
The paper tackles video compression by proposing a hybrid framework that enhances the Enhanced Compression Model (ECM) with deep learning techniques, achieving BD-rate savings of 6.26%, 13.33%, and 12.33% for Y, U, and V components compared to ECM-10.0 under random access configuration.
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.