Fast Online-Learning Parameters Decision Algorithm Based on Bayesian Decision Rule for 3D-HEVC

3D-HEVC, as the extension of High Efficiency Video Coding standard, achieves a significant improvement in the coding efficiency of 3D videos, compared with the Multi-View Video Coding (MVC). However the improvement causes a great computational complexity. In this paper, a fast coding parameters decision algorithm is proposed to reduce the computational complexity. The process of the selection of Coding Unit (CU), Prediction Unit (PU) and Transform Unit (TU) are modeled as the online-learning classification process based on the Bayesian decision rule. Through the training of the selected feature vectors, the classifiers can precisely predict the prediction mode of PUs and whether or not the current CU and TU should be partitioned. The experimental results show that the proposed algorithm can achieve about 56% time reduction with a slight RD degradation.

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