Low complexity depth intra coding in 3D-HEVC based on depth classification

Huijie Zheng, Jianqing Zhu, Huanqiang Zeng, Jing Chen, Canhui Cai, Kai-Kuang Ma

Abstract: The latest high efficiency video coding-based three dimensional video coding (3D-HEVC) exploits sophisticated intra prediction scheme to improve the coding performance of the depth video, but incurring heavy computational complexity. To address this problem, a low complexity depth intra coding method is presented for 3D-HEVC based on depth classification. Firstly, a database of depth prediction units (PUs) with three kinds of complexities is collected based on their optimal intra prediction mode. Then, the histogram of oriented gradient (HOG) features are extracted on these established database to train the classifier using support vector machine (SVM). For the current depth PU, the trained classifier is applied to determine its most possible complexity class so as to select the corresponding modes for involving the mode decision process. Experimental results show that the proposed method is able to significantly reduce the computational complexity while keeping almost the same coding performance of depth video and video quality of the synthesized view, compared with the exhaustive mode decision in 3D-HEVC.