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A simplified hardware-friendly contour prediction algorithm in 3D-HEVC and parallelization design 被引量:1
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作者 JIANG Lin DUAN Xueyao XIE Xiaoyan 《High Technology Letters》 EI CAS 2022年第4期392-400,共9页
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
关键词 depth modeling mode 4(DMM-4) contour prediction 3D high efficiency video coding(3d-hevc) PARALLELIZATION reconfigurable array processor
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基于注意力-残差双特征流卷积神经网络的深度图帧内编码单元快速划分算法
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作者 贾克斌 吴岳珩 《北京工业大学学报》 北大核心 2025年第5期539-551,共13页
针对三维高效视频编码(three-dimensional high efficiency video coding,3D-HEVC)深度图编码单元(coding unit,CU)划分复杂度高的问题,提出一种基于卷积神经网络(convolutional neural networks,CNN)的算法来实现快速深度图帧内编码。... 针对三维高效视频编码(three-dimensional high efficiency video coding,3D-HEVC)深度图编码单元(coding unit,CU)划分复杂度高的问题,提出一种基于卷积神经网络(convolutional neural networks,CNN)的算法来实现快速深度图帧内编码。首先,提出一种具有3个分支的注意力-残差双特征流卷积神经网络(attention-residual bi-feature stream convolutional neural networks,ARBS-CNN)模型,其中基于残差模块(residual module,RM)和特征蒸馏(feature distill,FD)模块的2个分支用于提取全局图像特征,基于动态模块(dynamic module,DM)和卷积-卷积块注意力模块(convolutional-convolutional block attention module,Conv-CBAM)的分支用于提取局部图像特征;然后,将提取到的特征进行整合并输出,得到对深度图CU划分结构的预测;最后,将ARBS-CNN嵌入到3D-HEVC测试平台中,利用预测结果加速深度图帧内编码。与原始算法相比,提出的算法能在维持率失真性能几乎不受影响的条件下,平均减少74.2%的编码时间。实验结果表明,该算法能够在保持率失真性能的条件下,有效降低3D-HEVC的编码复杂度。 展开更多
关键词 三维高效视频编码(three-dimensional high efficiency video coding 3d-hevc) 深度图 卷积神经网络(convolutional neural networks CNN) 编码单元(coding unit CU)划分 帧内编码 双特征流
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