Fast HEVC Encoding Decisions Using Data Mining

Abstract

The High Efficiency Video Coding standard provides improved compression ratio in comparison with its predecessors at the cost of large increases in the encoding computational complexity. An important share of this increase is due to the new flexible partitioning structures, namely the coding trees, the prediction units, and the residual quadtrees, with the best configurations decided through an exhaustive rate-distortion optimization (RDO) process. In this paper, we propose a set of procedures for deciding whether the partition structure optimization algorithm should be terminated early or run to the end of an exhaustive search for the best configuration. The proposed schemes are based on decision trees obtained through data mining techniques. By extracting intermediate data, such as encoding variables from a training set of video sequences, three sets of decision trees are built and implemented to avoid running the RDO algorithm to its full extent. When separately implemented, these schemes achieve average computational complexity reductions (CCRs) of up to 50% at a negligible cost of 0.56% in terms of Bjontegaard Delta (BD) rate increase. When the schemes are jointly implemented, an average CCR of up to 65% is achieved, with a small BD-rate increase of 1.36%. Extensive experiments and comparisons with similar works demonstrate that the proposed early termination schemes achieve the best rate-distortion-complexity tradeoffs among all the compared works.