通信学报2016，Vol.37Issue(11)：181-188,8.DOI:10.11959/j.issn.1000-436x.2016235
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
摘要
Abstract
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-ing 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.关键词
深度视频编码/3D-HEVC/帧内编码/最大编码单元/多类支持向量机Key words
depth video coding/3D-HEVC/intra coding/largest coding unit/multi-class support vector machine分类
信息技术与安全科学引用本文复制引用
刘晟,彭宗举,陈嘉丽,陈芬,郁梅,蒋刚毅..基于多类支持向量机的3D-HEVC深度视频帧内编码快速算法[J].通信学报,2016,37(11):181-188,8.基金项目
国家高技术研究发展计划（“863”计划）基金资助项目（No.2015AA015901）；国家自然科学基金资助项目（No.61620106012, No.U1301257, No.61271270）；浙江省自然科学基金资助项目（No.LY16F010002, No.LY15F010005, No.LY17F010005）；宁波市自然科学基金资助项目（No.2015A610127, No.2015A610124）；宁波大学科研基金(理)/学科基金资助项目(No.xkxl1502)Foundation Items：The National High-Tech R&D Program of China (863 Program)(No.2015AA015901), The National Natural Science Foundation of China (No.61620106012, No.U1301257, No.61271270), The Natural Science Foundation of Zhejiang Prov-ince (No.LY16F010002, No.LY15F010005, No.LY17F010005), The Natural Science Foundation of Ningbo (No.2015A610127, No.2015A610124), Ningbo University Research Foundation (Science)/Discipline Project(No.xkxl1502) （理）
