An adaptive CU size decision algorithm based on gradient boosting machines for 3D-HEVC inter-coding

3D high-efficiency video coding (3D-HEVC) is an extension of the HEVC standard for coding of texture videos and depth maps. 3D-HEVC inherits the same quadtree coding structure as HEVC for both texture and depth components, in which the coding units (CUs) are recursively conducted on different sizes, namely, depth levels. However, the recursive splitting process of the CU causes extensive computational complexity. To reduce this computational burden, this paper presents an adaptive CU size decision algorithm for texture videos and depth maps. The proposed algorithm is divided into three steps. In the first step, the average local variance (ALV) is extracted from each CU size to define their homogeneity. Then, a classification-based gradient boosting machines (GBM) is employed to analyze and build a binary classification model from the extracted ALV features. The GBM model is employed to extract and efficiently get suitable thresholds for texture and depth map CUs. In the last step, a fast CU size decision algorithm is performed based on adaptive thresholds for texture videos and depth maps. The experimental results show that the proposed algorithm reduces a significant amount of encoding time, while the loss in coding efficiency is negligible.