Machine Learning Based Choice of Characteristics for the One-Shot Determination of the HEVC Intra Coding Tree

Abstract

In the last few years, the Internet of Things (IoT) has become a reality. Forthcoming applications are likely to boost mobile video demand to an unprecedented level. A large number of systems are likely to integrate the latest MPEG video standard High Efficiency Video Coding (HEVC) in the long run and will particularly require energy efficiency. In this context, constraining the computational complexity of embedded HEVC encoders is a challenging task, especially in the case of software encoders. The most energy consuming part of a software intra encoder is the determination of the coding tree partitioning, i.e. the size of pixel blocks. This determination usually requires an iterative process that leads to repeating some encoding tasks. State-of-the-art studies have focused on predicting, from “easily” computed characteristics, an efficient coding tree. They have proposed and evaluated independently many characteristics for one-shot quad-tree prediction. In this paper, we present a fair comparison of these characteristics using a Machine Learning approach and a real-time HEVC encoder. Both computational complexity and information gain are considered, showing that characteristics are far from equivalent in terms of coding tree prediction performance.