Online-learning-based Bayesian decision rule for fast intra mode and CU partitioning algorithm in HEVC screen content coding

Abstract

Screen content coding (SCC) is an extension of high efficiency video coding by adopting new coding modes to improve the coding efficiency of SCC at the expense of increased complexity. This paper proposes an online-learning approach for fast mode decision and coding unit (CU) size decision in SCC. To make a fast mode decision, the corner point is first extracted as a unique feature in screen content, which is an essential pre-processing step to guide Bayesian decision modeling. Second, the distinct color number in a CU is derived as another unique feature in screen content to build the precise model using online-learning for skipping unnecessary modes. Third, the correlation of the modes among spatial neighboring CUs is analyzed to further eliminate unnecessary mode candidates. Finally, the Bayesian decision rule using online-learning is applied again to make a fast CU size decision. To ensure the accuracy of the Bayesian decision models, new scene change detection is designed to update the models. Results show that the proposed algorithm achieves 36.69% encoding time reduction with 1.08% Bjontegaard delta bitrate (BDBR) increment under all intra configuration. By integrating into the existing fast SCC approach, the proposed algorithm reduces 48.83% encoding time with a 1.78% increase in BDBR.