Fast reference frame selection based on content similarity for low complexity HEVC encoder

The high efficiency video coding (HEVC) is the state-of-the-art video coding standard, which achieves about 50% bit rate saving while maintaining the same visual quality as compared to the H.264/AVC. This achieved coding efficiency benefits from a set of advanced coding tools, such as the multiple reference frames (MRF) based interframe prediction, which efficiently improves the coding efficiency of the HEVC encoder, while it also increases heavy computation into the HEVC encoder. The high encoding complexity becomes a bottleneck for the high definition videos and HEVC encoder to be widely used in real-time and low power multimedia applications. In this paper, we propose a content similarity based fast reference frame selection algorithm for reducing the computational complexity of the multiple reference frames based interframe prediction. Based the large content similarity between the parent prediction unit (Inter_2N × 2N) and the children prediction units (Inter_2N × N, Inter_N × 2N, Inter_N × N, Inter_2N × nU, Inter_2N × nD, Inter_nL × 2N, and Inter_nR × 2N), the reference frame selection information of the children prediction units are obtained by learning the results of their parent prediction unit. Experimental results show that the proposed algorithm can reduce about 54.29% and 43.46% MRF encoding time saving for the low-delay-main and random-access-main coding structures, respectively, while the rate distortion performance degradation is negligible.