An Adaptive CU Size Decision Algorithm for HEVC Intra Prediction Based on Complexity Classification Using Machine Learning

High efficiency video coding (HEVC), which is the newest video coding standard currently, achieves the best coding efficiency compared with all the other existing video coding standards. However, the computational complexity of the typical HEVC encoder dramatically increases because of the recursive searching scheme for finding the best coding unit (CU) partitions. In this paper, an adaptive fast CU size decision algorithm for HEVC Intra prediction is proposed based on CU complexity classification (CC) by using machine learning (ML) technology. Firstly, certain image features are extracted to characterize the CU complexity, which has a strong relationship with CU partitions, and then, the support vector machine is employed to analyze and construct the classification model according to the CU complexity. Finally, the proposed adaptive fast CU size decision algorithm, named as CCML, is released based on the complexity classification. The experimental results show that the proposed algorithm could achieve around 60% encoding time reduction for various test video sequences on average with only 1.26% Bjontegaard delta bit rate increase compared with the reference test model HM15.0 of HEVC.