Fast H.265 video encoder using data mining technique and DSP implementation.

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H.265 achieves significantly better coding efficiency than those of existing video coding standards. This is because H.265 adopts some new coding structures including coding unit (CU), prediction unit (PU) and transform unit (TU). The CU can be split by coding quad-tree (CQT) structure of depth = 4 and the TU can be split by residual quad-tree (RQT) of depth = 3. The optimal partitions of PU are according to the size of CU and the different prediction modes. Although H.265 can achieve the highest coding efficiency, it requires a very high computational complexity such as that its real-time application is limited. In order to reduce the computational complexity of H.265 encoder, G. Correa et al. proposed fast HEVC encoding decisions using data mining [7] recently. They firstly classified the coding structure into PU, RQT and CQT, and used data mining to find appropriate attributes for PU, RQT and CQT, respectively. Then, the corresponding to data is extracted from individual attribute, and is saved as attribute-relation file format (ARFF). And then, the ARFF is performed on WEKA of machine learning tool to train the PU, RQT and CQT decision trees using the C4.5 algorithm, respectively [17]. Finally, they applied the created decision trees to reduce the computational complexity of H.265 encoder. However, G. Correa et al. ignored the important attribute of SkipFlag in PU structure and the correlation attributes of neighboring block in CQT structure. This leads to reduce the accuracy for these decision trees. To further improve the accuracies of PU and CQT decision trees, we add the attribute of SkipFlag in PU structure and also consider the attribute of correlation existing neighboring blocks in CQT structure. In addition to train three decision trees for PU, RQT and CQT structure, there still exists a high computational complexity for ME module of H.265. Therefore, we propose a fast motion vector decision algorithm (FMVDA) to further speed up encoding process of H.265 encoder. Firstly, we find that there is a high temporal-spatial correlation of motion vector (MV) existing successive frame. And then, we find appropriate attributes including rate-distortion optimization (RDO), MV, MergeFlag and SkipFlag from neighboring blocks. Then, we train the MV decision trees using these selected attributes. Finally, FMVDA employs the created MVs decision tree to ME module, and achieves a fast H.265 video encoder. In addition, to further achieve the DSP realization for the proposed fast H.265 encoder, we embed the codec on the ADSP-BF609. We re-allocate the function of consuming module from L3 DDR-RAM to L1 and L2 SRAM to speed up the encoding time of H.265. Simulation results show that the proposed method can achieve an average time improving ratio (TIR) 75%~91% when compared to H.265 (HM16.7). Compared with G. Correa’s method, the proposed algorithm can achieve an average TIR about 22%~27%. It is clear that the proposed method can efficiently increase the speed of H.265 encoder with insignificant loss of image quality.