CART-based fast CU size decision and mode decision algorithm for 3D-HEVC

Abstract

Despite three-dimensional high efficiency video coding (3D-HEVC) has a good performance of 3D video coding and synthesized views, the recursive splitting process of the largest coding unit (LCU) and the best mode deciding process caused huge computational complexity. To reduce this computational burden, this paper presents a classification and regression tree-based (CART) fast coding level decision and mode decision algorithm for 3D depth video. The algorithm contained two parts: CART model training, fast coding level and mode decision. In the part of CART model training, we constructed a CART decision tree, where the complexity of depth map, the optimal depth level of co-located texture and the relativity of neighbor LCU were regarded as feature vectors, and the best coding unit depth level was regarded as class label. In the part of fast coding process, features were extracted to predict the depth level of each LCU of depth map; furthermore, some decision of coding mode could be skipped early. Experimental results show that proposed algorithm can save the times of coding process by 24.6% on average while maintaining almost the same rate distortion performance as the 3D-HEVC reference software.