  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.
