  This paper proposes a method for complexity reduction in practical video
encoders using multiple decision tree classifiers. The method is demonstrated
for the fast implementation of the 'High Efficiency Video Coding' (HEVC)
standard, chosen because of its high bit rate reduction capability but large
complexity overhead. Optimal partitioning of each video frame into coding units
(CUs) is the main source of complexity as a vast number of combinations are
tested. The decision tree models were trained to identify when the CU testing
process, a time-consuming Lagrangian optimisation, can be skipped i.e a high
probability that the CU can remain whole. A novel approach to finding the
simplest and most effective decision tree model called 'manual pruning' is
described. Implementing the skip criteria reduced the average encoding time by
42.1% for a Bj{\o}ntegaard Delta rate detriment of 0.7%, for 17 standard test
sequences in a range of resolutions and quantisation parameters.
