  The latest video coding standard, Versatile Video Coding (VVC), achieves
almost twice coding efficiency compared to its predecessor, the High Efficiency
Video Coding (HEVC). However, achieving this efficiency (for intra coding)
requires 31x computational complexity compared to HEVC, making it challenging
for low power and real-time applications. This paper, proposes a novel machine
learning approach that jointly and separately employs two modalities of
features, to simplify the intra coding decision. First a set of features are
extracted that use the existing DCT core of VVC, to assess the texture
characteristics, and forms the first modality of data. This produces high
quality features with almost no overhead. The distribution of intra modes at
the neighboring blocks is also used to form the second modality of data, which
provides statistical information about the frame. Second, a two-step feature
reduction method is designed that reduces the size of feature set, such that a
lightweight model with a limited number of parameters can be used to learn the
intra mode decision task. Third, three separate training strategies are
proposed (1) an offline training strategy using the first (single) modality of
data, (2) an online training strategy that uses the second (single) modality,
and (3) a mixed online-offline strategy that uses bimodal learning. Finally, a
low-complexity encoding algorithms is proposed based on the proposed learning
strategies. Extensive experimental results show that the proposed methods can
reduce up to 24% of encoding time, with a negligible loss of coding efficiency.
Moreover, it is demonstrated how a bimodal learning strategy can boost the
performance of learning. Lastly, the proposed method has a very low
computational overhead (0.2%), and uses existing components of a VVC encoder,
which makes it much more practical compared to competing solutions.
