  Versatile Video Coding (VVC), as the latest standard, significantly improves
the coding efficiency over its ancestor standard High Efficiency Video Coding
(HEVC), but at the expense of sharply increased complexity. In VVC, the
quad-tree plus multi-type tree (QTMT) structure of coding unit (CU) partition
accounts for over 97% of the encoding time, due to the brute-force search for
recursive rate-distortion (RD) optimization. Instead of the brute-force QTMT
search, this paper proposes a deep learning approach to predict the QTMT-based
CU partition, for drastically accelerating the encoding process of intra-mode
VVC. First, we establish a large-scale database containing sufficient CU
partition patterns with diverse video content, which can facilitate the
data-driven VVC complexity reduction. Next, we propose a multi-stage exit CNN
(MSE-CNN) model with an early-exit mechanism to determine the CU partition, in
accord with the flexible QTMT structure at multiple stages. Then, we design an
adaptive loss function for training the MSE-CNN model, synthesizing both the
uncertain number of split modes and the target on minimized RD cost. Finally, a
multi-threshold decision scheme is developed, achieving desirable trade-off
between complexity and RD performance. Experimental results demonstrate that
our approach can reduce the encoding time of VVC by 44.65%-66.88% with the
negligible Bj{\o}ntegaard delta bit-rate (BD-BR) of 1.322%-3.188%, which
significantly outperforms other state-of-the-art approaches.
