采用改进图形变换的3D点云压缩

3D Point Cloud Compression Using Improved Graph Transform

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.