  In intra coding, Rate Distortion Optimization (RDO) is performed to achieve
the optimal intra mode from a pre-defined candidate list. The optimal intra
mode is also required to be encoded and transmitted to the decoder side besides
the residual signal, where lots of coding bits are consumed. To further improve
the performance of intra coding in Versatile Video Coding (VVC), an intelligent
intra mode derivation method is proposed in this paper, termed as Deep Learning
based Intra Mode Derivation (DLIMD). In specific, the process of intra mode
derivation is formulated as a multi-class classification task, which aims to
skip the module of intra mode signaling for coding bits reduction. The
architecture of DLIMD is developed to adapt to different quantization parameter
settings and variable coding blocks including non-square ones, which are
handled by one single trained model. Different from the existing deep learning
based classification problems, the hand-crafted features are also fed into the
intra mode derivation network besides the learned features from feature
learning network. To compete with traditional method, one additional binary
flag is utilized in the video codec to indicate the selected scheme with RDO.
Extensive experimental results reveal that the proposed method can achieve
2.28%, 1.74%, and 2.18% bit rate reduction on average for Y, U, and V
components on the platform of VVC test model, which outperforms the
state-of-the-art works.
