Fast HEVC intra coding unit size decision based on an improved Bayesian classification framework

HEVC achieves much higher coding efficiency compared with previous coding standards at cost of significant computation complexity. In this paper, an improved Bayesian classification framework is constructed to accelerate the coding unit size decision for HEVC intra coding. Under this framework, different decision mechanisms are applied for different regions of feature space. Specifically, the feature space is partitioned into two regions: high-discrimination region and low-discrimination region. In high-discrimination region, Bayesian classifier is employed to assist mode decision, while in low-discrimination region, Rate-Distortion-cost based decision is utilized. An optimal method of feature space partition is theoretically analyzed by modeling the problem as a constrained optimization problem. Moreover, the feature used in the improved Bayesian classification framework is carefully selected. Experimental results show that the proposed algorithm can save on average 45.3% of the total encoding time of HM 10.0 with only 0.5% increases in BD-rate.