基于多类支持向量机的3D-HEVC深度视频帧内编码快速算法

Multi-class support vector machine-based fast algorithm for 3D-HEVC depth video intra coding

深度视频编码中最优深度划分和模式选择过程具有非常高的计算复杂度。提出了基于多类支持向量机(MSVM,multi-class support vector machine)的深度视频帧内编码快速算法。该算法包括离线模型训练和快速编码2个部分。在离线模型训练中,用深度视频最大编码单元(LCU,largest coding unit)的最优划分深度作为标签,当前LCU的空域复杂度、空域相邻LCU的最优划分深度和彩色视频对应LCU的最优划分深度作为特征去构造MSVM模型。在编码时,提取LCU的特征,根据MSVM模型得到划分深度的预测值。根据该预测值提前终止编码单元递归划分和模式选择过程。实验结果表明,提出的算法在几乎不影响虚拟视点质量的情况下,平均节省35.91%的总体编码时间和40.04%的深度编码时间。

The recursive splitting process of largest coding unit （LCU） and the mode search process of coding unit imposed enormous computational complexity on encoder. A multi-class support vector machine-based （MSVM） fast coding unit （CU） size decision algorithm for 3D-HEVC depth video intra-coding was proposed. The algorithm included two steps： off-line training and fast CU size and mode decision. In the process of off-line training, a MSVM model was constructed, where the texture complexity of current LCU, the optimal partition depth of its spatial neighboring LCU and co-located LCU in texture video were treated as feature vectors, and the optimal partition depth of LCU was utilized as corresponding class label. In the process of fast CU size and mode decision, features of LCU were extracted before cod- hag a LCU, then, a MSVM model was used to predict the class label. Finally, the class label that represents the largest parti- tion depth of the current LCU was employed to terminate the CU recursive splitting process and CU mode search process. Experimental results show that the proposed algorithm saves the encoding time of 3D-HEVC by 35.91% on average, and the encoding time of depth video by 40.04% on average, with negligible rendered virtual view image degradation.