Complexity Reduction Algorithm for Quality Scalability in Scalable HEVC

SHVC, the scalable extension of high efficiency video coding (HEVC), can improve the compression performance by using advanced inter-layer prediction features at the cost of huge computational complexity. Recently, in order to reduce the encoding complexity of SHVC, a tempo-spatial searching order algorithm (TSSOA) and a fast CU depth range decision (FCUDRD) are proposed, respectively. However, every coding unit (CU) in these two methods still need perform motion estimation (ME) to find the best prediction mode. To further improve the performance of TSSOA and FCUDRD, we propose two encoding strategies including fast prediction unit (PU) prediction algorithm (FPUPA) and fast motion vector (MV) prediction algorithm (FMVPA) in this paper. Firstly, we use TSSOA to find the best candidate quadtree, and then the neighboring PU modes are considered as the best prediction mode of the current CU. Secondly, five causal neighboring MVs of the CUs are considered as the good candidate MV of the current CU due to temporal and spatial correlation of MV. Finally, we combine FPUPA and FMVPA into the SHVC system to further speed up the encoding process. Simulation results show that the proposed FPUPA and FMVPA can achieve an average of time improving ratio (TIR) about 69.39% and 71.70% for LD, when compared to original SHVC (SHM4.0). In addition, as compared with TSSOA and FCUDRD, the proposed method can further achieve an average of TIR about 13.52% and 14.35%, respectively. It is clear that the proposed algorithm can efficiently increase the speed of SHVC encoder with insignificant loss of image quality.