Dr Buddhi Erabadda

The use of machine learning techniques for encoding complexity reduction in recent video coding standards such as High Efficiency Video Coding (HEVC) has received prominent attention in the recent past. Yet, the dynamically changing nature of the video contents makes it evermore challenging to use rigid traditional inference models for predicting the encoding decisions for a given content. In this context, this paper investigates the resulting implications on the coding efficiency and the encoding complexity, when using offline trained and online trained machine-learning models for coding unit size selection in the HEVC intra-prediction. The experimental results demonstrate that the ground truth encoding statistics of the content being encoded, is crucial to the efficient encoding decision prediction when using machine learning based prediction models.

This paper proposes a content adaptive fast CU size selection algorithm for HEVC intra-prediction using weighted support vector machines. The proposed algorithm demonstrates an average encoding time reduction of 52.38% with 1.19% average BDBR increase compared to HM16.1 reference encoder.