In order to speed up the encoding process of HEVC, there have been many fast encoding methods proposed to reduce the number of CUs and PUs. Besides, the early TU decision algorithm (ETDA) is another method selected to reduce the encoding complexity of TU. Recently, Chio et al. proposed a new ETDA by determining the number of nonzero DCT coefficients (NNZ) of RQT (called NNZ-EDTA) to accelerate the encoding process of TU module [6]. However, the NNZ-ETDA can't effectively reduce the computational load for sequences with active motion or rich texture. Therefore, in order to further improve the performance of NNZ-ETDA, we propose an adaptive RQT depth for NNZ-ETDA (called ARD-NNZ-ETDA) by exploiting the characteristics of high temporal-spatial correlation exists in nature video sequences. An adaptive depth of RQT is employed to the NNZ-ETDA to further reduce the computational load of TU. Simulation results show that the proposed method can achieve time improving ratio (TIR) about 61.26%~81.48% when compared to HEVC (HM 8.1) with insignificant loss of image quality. Compared with the NNZ-ETDA, the proposed method can further achieve an average TIR about 8.29%~17.92%.
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