Técnica de aprendizagem automática aplicada a um codificador HEVC em tempo real.

Abstract

The most recent video coding standard, the High Efficiency Video Coding (HEVC), has a higher encoding complexity when compared with H.264/AVC, which means a higher computational cost. This thesis presents a review of the recent literature and proposes an algorithm that reduces such complexity. Therefore, a fast CU (Coding Unit) splitting algorithm is proposed for the HEVC encoder, which terminates the CU partitioning process at an early phase, based on an adaptive classification model. This model is generated by an online learning method based on the Primal Estimated sub-GrAdient SOlver for SVM (Pegasos) algorithm. The proposed method is implemented and integrated in the HEVC reference source code on its version 16.7. Experimental results show that the proposed method reduces the computational complexity of the HEVC encoder, up to 50% in some cases, with negligible losses, and shows an average computational reduction of 30%. This process results in reduced coding efficiency losses, however, some results showed a nearby 1% of BD-Rate (Bjontegaard Delta) gains in the Low Delay B configuration, without using an offline training phase.