After the extension of depth modeling mode 4(DMM-4)in 3D high efficiency video coding(3D-HEVC),the computational complexity increases sharply,which causes the real-time performance of video coding to be impacted.To re...After the extension of depth modeling mode 4(DMM-4)in 3D high efficiency video coding(3D-HEVC),the computational complexity increases sharply,which causes the real-time performance of video coding to be impacted.To reduce the computational complexity of DMM-4,a simplified hardware-friendly contour prediction algorithm is proposed in this paper.Based on the similarity between texture and depth map,the proposed algorithm directly codes depth blocks to calculate edge regions to reduce the number of reference blocks.Through the verification of the test sequence on HTM16.1,the proposed algorithm coding time is reduced by 9.42%compared with the original algorithm.To avoid the time consuming of serial coding on HTM,a parallelization design of the proposed algorithm based on reconfigurable array processor(DPR-CODEC)is proposed.The parallelization design reduces the storage access time,configuration time and saves the storage cost.Verified with the Xilinx Virtex 6 FPGA,experimental results show that parallelization design is capable of processing HD 1080p at a speed above 30 frames per second.Compared with the related work,the scheme reduces the LUTs by 42.3%,the REG by 85.5%and the hardware resources by 66.7%.The data loading speedup ratio of parallel scheme can reach 3.4539.On average,the different sized templates serial/parallel speedup ratio of encoding time can reach 2.446.展开更多
综述了基于高效率视频编码HEVC(high efficiency video coding)标准的两种扩展,即MV-HEVC(high efficiency video coding based multiview)和3D-HEVC(high efficiency video coding based 3D video coding)的工作原理及其编码工具,分析...综述了基于高效率视频编码HEVC(high efficiency video coding)标准的两种扩展,即MV-HEVC(high efficiency video coding based multiview)和3D-HEVC(high efficiency video coding based 3D video coding)的工作原理及其编码工具,分析了3D-HEVC模型的特点、编码模块与方法,并将3D-HEVC与MV-HEVC进行了性能对比.总结发现,由于3D-HEVC采用纹理视频加深度格式来合成虚拟视点,从而降低了大量的编码码率,可方便应用于3D电视、自由立体视点电视和3D数字电影等多种三维体验中.随着智能移动设备的发展,手持终端采用3D-HEVC支持多视点3D视频将会成为未来的研究趋势.展开更多
基金Supported by the National Natural Science Foundation of China(No.61834005,61772417,61802304,61602377,61874087,61634004)the Shaanxi Province Key R&D Plan(No.2020JM-525,2021GY-029,2021KW-16)。
文摘After the extension of depth modeling mode 4(DMM-4)in 3D high efficiency video coding(3D-HEVC),the computational complexity increases sharply,which causes the real-time performance of video coding to be impacted.To reduce the computational complexity of DMM-4,a simplified hardware-friendly contour prediction algorithm is proposed in this paper.Based on the similarity between texture and depth map,the proposed algorithm directly codes depth blocks to calculate edge regions to reduce the number of reference blocks.Through the verification of the test sequence on HTM16.1,the proposed algorithm coding time is reduced by 9.42%compared with the original algorithm.To avoid the time consuming of serial coding on HTM,a parallelization design of the proposed algorithm based on reconfigurable array processor(DPR-CODEC)is proposed.The parallelization design reduces the storage access time,configuration time and saves the storage cost.Verified with the Xilinx Virtex 6 FPGA,experimental results show that parallelization design is capable of processing HD 1080p at a speed above 30 frames per second.Compared with the related work,the scheme reduces the LUTs by 42.3%,the REG by 85.5%and the hardware resources by 66.7%.The data loading speedup ratio of parallel scheme can reach 3.4539.On average,the different sized templates serial/parallel speedup ratio of encoding time can reach 2.446.
文摘综述了基于高效率视频编码HEVC(high efficiency video coding)标准的两种扩展,即MV-HEVC(high efficiency video coding based multiview)和3D-HEVC(high efficiency video coding based 3D video coding)的工作原理及其编码工具,分析了3D-HEVC模型的特点、编码模块与方法,并将3D-HEVC与MV-HEVC进行了性能对比.总结发现,由于3D-HEVC采用纹理视频加深度格式来合成虚拟视点,从而降低了大量的编码码率,可方便应用于3D电视、自由立体视点电视和3D数字电影等多种三维体验中.随着智能移动设备的发展,手持终端采用3D-HEVC支持多视点3D视频将会成为未来的研究趋势.
