  Versatile Video Coding (VVC) allows for large compression efficiency gains
over its predecessor, High Efficiency Video Coding (HEVC). The added efficiency
comes at the cost of increased runtime complexity, especially for encoding. It
is thus highly relevant to explore all available runtime reduction options.
This paper proposes a novel first pass for two-pass rate control in all-intra
configuration, using low-complexity video analysis and a Random Forest
(RF)-based machine learning model to derive the data required for driving the
second pass. The proposed method is validated using VVenC, an open and
optimized VVC encoder. Compared to the default two-pass rate control algorithm
in VVenC, the proposed method achieves around 32% reduction in encoding time
for the preset faster, while on average only causing 2% BD-rate increase and
achieving similar rate control accuracy.

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