Probabilistic graphical model based fast HEVC inter prediction

The High Efficiency Video Coding (HEVC) standard achieves 50% improvement in coding efficiency compared with H.264/ AVC by introducing many more video encoding tools achieving different coding performance and complexity tradeoffs [1]. Various techniques have been proposed to reduce the complexity of HEVC encoding. In this paper, an effective Bayesian network based complexity reduction framework for HEVC encoding is proposed. The proposed framework is able to calculate the conditional probabilities of different status in the encoding process, which can be used to speedup the encoders by neglecting small probability events. Experimental results show that the proposed algorithm can save 52.5% of computational complexity with a loss of 1.41% in compression performance on average.