采用改进图形变换的3D点云压缩
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摘要
本文提出一种采用改进图形变换的3D点云压缩算法。所提算法首先通过改进图形变换将每个块中的所有子图连接为一个图,从源头减少直流系数个数。同时用每个块所有点的均值作为直流系数以降低直流量幅值,并对去平均的颜色值进行图形变换。考虑到量化后的交流系数的零系数占比比较大,本文采用了Run-Level的编码方法对非零的交流系数进行编码。对于直流系数,本文设计了一种预测编码方法对其进行有效编码。最后,编码完的交流系数和预测残差均采用霍夫曼编码器进行熵编码。实验结果表明所提算法相比多个现有3D点云压缩算法具有更高的压缩效率。
This paper proposes an improved graph transform-based 3D point cloud compression algorithm. The algorithm only generates one graph per processing block,so that each block will only produce a unique DC coefficient. In order to reduce the magnitude of the DC coefficient,we use the average of all the points in each block as the DC coefficient,and apply graph transform on the color value which has removed the average. In entropy coding,considering that a large amount of quantized AC coefficients is equal to zero,we use the Run-Level coding method to code non-zero coefficients. For DC coefficients,we use a predictive coding method which is similar to H. 264 to coding the DC coefficients. The last coded AC coefficients and prediction residuals are coded by Huffman encoder. The experimental results show that the performance of proposed algorithm is better than the latest 3D point cloud compression algorithm.
This paper proposes an improved graph transform-based 3D point cloud compression algorithm. The algorithm only generates one graph per processing block,so that each block will only produce a unique DC coefficient. In order to reduce the magnitude of the DC coefficient,we use the average of all the points in each block as the DC coefficient,and apply graph transform on the color value which has removed the average. In entropy coding,considering that a large amount of quantized AC coefficients is equal to zero,we use the Run-Level coding method to code non-zero coefficients. For DC coefficients,we use a predictive coding method which is similar to H. 264 to coding the DC coefficients. The last coded AC coefficients and prediction residuals are coded by Huffman encoder. The experimental results show that the performance of proposed algorithm is better than the latest 3D point cloud compression algorithm.
引文
[1]Zhang C,Cai Q,Chou P A,et al.Viewport:A distributed,immersive teleconferencing system with infrared dot pattern[J].IEEE Multi Media,2013,20(1):17-27.
[2]Zhang C,Florêncio D,Loop C.Point cloud attribute compression with graph transform[C]∥Proceedings of IEEE International Conference on Image Processing,2014:2066-2070.
[3]Mekuria R,Blom K,Cesar P.Design,implementation and evaluation of a point cloud codec for teleimmersive video[J].IEEE Transactions on Circuits and Systems for Video Technology,2017,27(4):828-842.
[4]Thanou D,Chou P A,Frossard P.Graph-based motion estimation and compensation for dynamic 3D point cloud compression[C]∥Proceedings of IEEE International Conference on Image Processing,2015:3235-3239.
[5]Cohen R,Tian D,Vetro A.Point cloud attribute compression using 3-d intra prediction and shapeadaptive transforms[C]∥Proceedings of Data Compression Conference(DCC),2016:141-150.
[6]Schnabel R,Klein R.Octree-based point-cloud compression[C]∥Proceedings of Eurographics Symposium on Point-Based Graphics,2006:111-121.
[7]Queiroz R L D,Chou P A.Compression of 3D point clouds using a region-adaptive hierarchical transform[J].IEEE Transactions on Image Processing,2016,25(8):3947-3956.
[8]Sikora T,Makai B.Shape-adaptive dct for generic coding of video[J].IEEE Transactions on Circuits and Systems for Video Technology,1995,5(1):59-62.
[9]Queiroz R L D,Chou P A.Transform Coding for Point Clouds Using a Gaussian Process Model[J].IEEE Transactions on Image Processing,2017,26(7):3507-3517.
[10]Shuman D I,Narang S K,Frossard P,et al.The emerging field of signal processing on graphs:Extending high-dimensional data analysis to networks and other irregular domains[J].Signal Processing Magazine,IEEE,2013,30(3):83-98.
[11]Hu W,Cheung G,Li X,et al.Depth map compression using multi-resolution graph-based transform for depthimage-based rendering[C]∥Proceedings of IEEE International Conference on Image Processing,2012:2066-2070.1297-1300.
[12]Zhang C,Florencio D.Analyzing the optimality of predictive transform coding using graph-based models[J].IEEESignal Processing Letters,2013,20(1):106-109.
[13]Hou J H,Chau L P,He Y,et al.Sparse representation for colors of 3D point cloud via virtual adaptive sampling[C]∥Proceedings of IEEE International Conference on A-coustics,Speech and Signal Processing(ICASSP),2017:2926-2930.
[14]Skretting K,Husy J H,Aase S O.Improved huffman coding using recursive splitting[C]∥Proceedings of Norwegian Signal Processing,1999.
[15]胡伟军,李克非,成建波.H.264/AVC视频编码标准的技术特点及应用分析[J].信号处理,2005,21(1):79-85.Hu Weijun,Li Kefei,Cheng Jianbo.Features and Applications of H.264/AVC[J].Signal Processing,2005,21(1):79-85.(in Chinese)
[16]Loop C,Cai Q,Escolano S O,et al.Microsoft voxelized upper bodies-a voxelized point cloud dataset[C]∥ISO/IEC JTC1/SC29 Joint WG11/WG1(MPEG/JPEG)Input Document m38673/M72012,2016.
[17]Loop C,Zhang C,Zhang Z.Real-time high-resolution sparse voxelization with application to image-based modeling[C]∥Proceedings of the 5th High Performance Graphics Conference.ACM,2013:73-79.
[2]Zhang C,Florêncio D,Loop C.Point cloud attribute compression with graph transform[C]∥Proceedings of IEEE International Conference on Image Processing,2014:2066-2070.
[3]Mekuria R,Blom K,Cesar P.Design,implementation and evaluation of a point cloud codec for teleimmersive video[J].IEEE Transactions on Circuits and Systems for Video Technology,2017,27(4):828-842.
[4]Thanou D,Chou P A,Frossard P.Graph-based motion estimation and compensation for dynamic 3D point cloud compression[C]∥Proceedings of IEEE International Conference on Image Processing,2015:3235-3239.
[5]Cohen R,Tian D,Vetro A.Point cloud attribute compression using 3-d intra prediction and shapeadaptive transforms[C]∥Proceedings of Data Compression Conference(DCC),2016:141-150.
[6]Schnabel R,Klein R.Octree-based point-cloud compression[C]∥Proceedings of Eurographics Symposium on Point-Based Graphics,2006:111-121.
[7]Queiroz R L D,Chou P A.Compression of 3D point clouds using a region-adaptive hierarchical transform[J].IEEE Transactions on Image Processing,2016,25(8):3947-3956.
[8]Sikora T,Makai B.Shape-adaptive dct for generic coding of video[J].IEEE Transactions on Circuits and Systems for Video Technology,1995,5(1):59-62.
[9]Queiroz R L D,Chou P A.Transform Coding for Point Clouds Using a Gaussian Process Model[J].IEEE Transactions on Image Processing,2017,26(7):3507-3517.
[10]Shuman D I,Narang S K,Frossard P,et al.The emerging field of signal processing on graphs:Extending high-dimensional data analysis to networks and other irregular domains[J].Signal Processing Magazine,IEEE,2013,30(3):83-98.
[11]Hu W,Cheung G,Li X,et al.Depth map compression using multi-resolution graph-based transform for depthimage-based rendering[C]∥Proceedings of IEEE International Conference on Image Processing,2012:2066-2070.1297-1300.
[12]Zhang C,Florencio D.Analyzing the optimality of predictive transform coding using graph-based models[J].IEEESignal Processing Letters,2013,20(1):106-109.
[13]Hou J H,Chau L P,He Y,et al.Sparse representation for colors of 3D point cloud via virtual adaptive sampling[C]∥Proceedings of IEEE International Conference on A-coustics,Speech and Signal Processing(ICASSP),2017:2926-2930.
[14]Skretting K,Husy J H,Aase S O.Improved huffman coding using recursive splitting[C]∥Proceedings of Norwegian Signal Processing,1999.
[15]胡伟军,李克非,成建波.H.264/AVC视频编码标准的技术特点及应用分析[J].信号处理,2005,21(1):79-85.Hu Weijun,Li Kefei,Cheng Jianbo.Features and Applications of H.264/AVC[J].Signal Processing,2005,21(1):79-85.(in Chinese)
[16]Loop C,Cai Q,Escolano S O,et al.Microsoft voxelized upper bodies-a voxelized point cloud dataset[C]∥ISO/IEC JTC1/SC29 Joint WG11/WG1(MPEG/JPEG)Input Document m38673/M72012,2016.
[17]Loop C,Zhang C,Zhang Z.Real-time high-resolution sparse voxelization with application to image-based modeling[C]∥Proceedings of the 5th High Performance Graphics Conference.ACM,2013:73-79.