Fast HEVC Transrating using Random Forests

This article describes a fast transrating solution for HEVC based on classification and machine learning techniques. Two classifiers are trained to predict the range of CTU quadtree depths that will be searched to find the best CTU partitioning. Three approaches are proposed for reducing the number of features used by the classifiers, two based on feature selection, and one based on feature transformation using autoencoders. A full transrating framework based on x265 is built for model training and evaluation. Experimental results using the x265 encoder show that an average 41.81% computational complexity reduction can be achieved at the cost of a tolerable 0.29% Bjontegaard-Delta bitrate, outperforming competing methods.