WO2019153326A1 - 一种基于帧内预测的点云属性压缩方法 - Google Patents

一种基于帧内预测的点云属性压缩方法 Download PDF

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WO2019153326A1
WO2019153326A1 PCT/CN2018/076435 CN2018076435W WO2019153326A1 WO 2019153326 A1 WO2019153326 A1 WO 2019153326A1 CN 2018076435 W CN2018076435 W CN 2018076435W WO 2019153326 A1 WO2019153326 A1 WO 2019153326A1
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point cloud
prediction
block
coding
information
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李革
邵薏婷
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北京大学深圳研究生院
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    • H04N19/103—Selection of coding mode or of prediction mode
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    • H04N19/157—Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter
    • H04N19/159—Prediction type, e.g. intra-frame, inter-frame or bidirectional frame prediction
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    • H04N19/184—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being bits, e.g. of the compressed video stream
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    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/186—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
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    • H04N19/96—Tree coding, e.g. quad-tree coding

Definitions

  • the invention belongs to the technical field of point cloud data processing, and relates to a point cloud data compression method, in particular to a method for point cloud attribute compression based on intra prediction.
  • the 3D point cloud is an important manifestation of digital realism in the real world. With the rapid development of 3D scanning devices (laser, radar, etc.), the accuracy and resolution of point clouds are higher. High-precision point clouds are widely used in the construction of digital maps in cities, and they play a supporting role in many popular studies such as smart cities, driverless, and cultural relics protection.
  • the point cloud is obtained by sampling the surface of the object by the 3D scanning device.
  • the number of points of a frame point cloud is generally one million, and each point contains geometric information and attribute information such as color and texture, and the amount of data is very large. The huge amount of data in the 3D point cloud brings great challenges to data storage and transmission, so point cloud compression is necessary.
  • Point cloud compression is mainly divided into geometric compression and attribute compression.
  • the existing point cloud attribute compression framework mainly includes the following:
  • This method first uses the octree to spatially decompose the point cloud to obtain the coded block, then performs depth-first traversal on the tree, and writes the traversed node color values in a serpentine order.
  • Into the two-dimensional JPEG table and then use the existing JPEG encoder to encode the obtained point cloud color table, wherein the JPEG encoder uses DCT.
  • the method utilizes the existing encoder, and the computational complexity is low, but the spatial correlation between points is not fully utilized, and the compression performance needs to be improved;
  • the method first uses the octree to spatially decompose the point cloud, and divides it into a specific level to obtain a transform block; forming a graph in each transform block, along any axis The two points whose distance is less than 1 are connected by one edge, and the weight of the edge is inversely proportional to the Euclidean distance; then the attribute information of the node in the figure is transformed.
  • the compression performance of the method is good; but the computational complexity is high, and the patterning method may bring subgraph problems, affecting the efficiency of graph transformation, and there is still room for improvement;
  • This method first uses the KD tree to spatially decompose the point cloud, and divides it to a certain depth to obtain a transform block, wherein each transform block contains approximately the same number of points: within the transform block Each two points are connected by one edge, and the weight of the edge is related to the Euclidean distance.
  • the set edge distance Euclidean distance threshold determines the sparseness of the graph; then the graph transformation of the attribute information of the point in the graph is performed.
  • the method completely solves the subgraph problem, and at the same time, the compression performance is improved compared with the former two methods, but the operation complexity is high and the performance still needs to be improved.
  • the present invention provides a method for point cloud attribute compression based on intra prediction by using a new one. Based on the block structure-based intra prediction scheme, four prediction modes are provided to reduce the information redundancy between different coding blocks of the point cloud as much as possible, and improve the compression performance of the point cloud attribute.
  • a point cloud attribute compression method based on intra prediction for a point cloud data, a KD tree is used to divide a coding block of a point cloud, and the coding blocks are numbered according to the order of breadth first traversal, and four types are used according to the number order.
  • the prediction mode of the point cloud intra prediction scheme processes the coding blocks one by one, selects the best prediction mode by mode decision, and then transforms, quantizes and entropy codes the prediction residuals to achieve the best performance of point cloud attribute compression. ; includes the following steps:
  • Color space conversion of point cloud attribute read the point cloud attribute information to be processed, consider the visual characteristics of the human eye and the difficulty of compression processing, and convert the point cloud color space from RGB space to YUV space;
  • KD tree division is performed on the point cloud according to the geometric information. Each time the coordinate axis with the largest variance of the point cloud position coordinates is selected as the division axis, and the point where the coordinate size is the median value is selected. As a dividing point, iteratively divides until the set KD tree depth is reached; the block obtained by the last layer of the KD tree partition is the coded block of the point cloud, and the coded blocks are numbered according to the order of the breadth traversal;
  • the number order of the above point cloud coding blocks will be used as the prediction order of the post point intra prediction.
  • Intra prediction is performed on the coded blocks in order according to the numbering order of the coding blocks, and a total of four prediction modes are used.
  • Intra prediction is performed on the first coding block b 1 , 128 is used as a reference value for the Y component prediction.
  • the prediction mode 1 uses 128 as the prediction reference value of Y i , U i , V i is not predicted; the prediction mode 2 is used The mean Y i-1 of all point Y components after the previous block b i-1 is reconstructed predicts Y i , U i , V i are not predicted; the prediction mode 3 is all points U and V after reconstruction using the previous block The mean values U i-1 and V i-1 of the color components are respectively predicted for U i and V i , and Y i is not predicted; the fourth mode of prediction is to use Y i-1 , U i-1 , V i-1 as Y i , U i , V i reference values for prediction, a total of four prediction modes;
  • Intra prediction mode decision There are four modes for predicting the color components Y i , U i , V i of the coding block b i (i ⁇ 1), and it is necessary to perform mode decision to select the best prediction mode, and the first The coding block does not need to make a mode decision; the absolute value of the prediction residual transform coefficient and the SATD are used to estimate the cost of the prediction mode, wherein the DCT is used to transform the prediction residual; the smaller the SATD value, the smaller the prediction mode cost. The better the prediction performance, the mode with the smallest SATD will be selected as the prediction mode of the current block;
  • the point p i in the point cloud has the color values of the RGB color space r i , g i , b i , and the RGB is converted into the YUV color space by the color space conversion matrix, and the color values are y i , u i , v i ;
  • the specific process of dividing and numbering the coding blocks in the above step 2) is as follows: when the KD tree is divided, the coordinate axis with the largest distribution variance of the selected points is used as the division axis, and the correlation of each point in the transformation block is fully considered; The point on which the coordinate on the axis is the median value is used as the division point, so that the number of points in the transformation block is substantially the same.
  • the point cloud to be processed has a total of N points, and the KD tree has a division depth of d. After dividing the point cloud by d times, 2d coding blocks are obtained; all coding blocks are numbered according to the order of breadth traversal.
  • Table 1 Color component reference values for coding block intra prediction
  • the mode decision of the intra prediction described in the above step 4) is as follows: the prediction reference values of the color components Y i , U i , V i of the coding block b i (i ⁇ 1) are respectively Y i_ref , U I_ref , V i_ref , the prediction residual b i(res) is calculated by Equation 2, the cost of the prediction mode SATD is calculated by Equation 3, and the mode with the smallest SATD is selected as the optimal prediction mode:
  • the performance of point cloud attribute compression is measured by the code rate and Peak Signal to Noise Ratio (PSNR), where the code rate is obtained by dividing the total number of bits of the code word by the number of points of the point cloud.
  • PSNR Peak Signal to Noise Ratio
  • Bpp bits per point
  • the unit of PSNR is decibel dB; the smaller the code rate, the larger the PSNR, and the better the compression performance of the point cloud attribute.
  • the above method uses intra prediction to reduce information redundancy between coding blocks, provides four prediction modes and selects the best mode by mode decision, and then uses the traditional DCT transform to compress the point cloud attributes, with low computational complexity and attribute compression. High efficiency and better point cloud compression performance.
  • the invention provides a point cloud attribute compression method based on intra prediction, which has the following technical advantages:
  • a new intra prediction scheme which supports four prediction modes, which effectively reduces the redundancy of attribute information between coding blocks.
  • FIG. 1 is a flow chart of a method provided by the present invention.
  • FIG. 2 is a diagram showing an example of KD tree division and coding block number of a point cloud.
  • FIG. 3 is a diagram showing an example of a code stream structure after compression of point cloud attribute information.
  • Figures 4a, b and c are graphs comparing the compression performance of the method of the invention with prior art methods.
  • Figure 4a is a comparison of the compression performance of the test longdress_vox10_1300.ply;
  • Figure 4b is a comparison chart of the compression performance of the test Shiva35.ply;
  • Figure 4c is a comparison of the compression performance of the test Queen_frame_0200.ply.
  • a method for intra-frame prediction based point cloud attribute compression proposes a new block structure based intra prediction scheme for point cloud data, and provides four prediction modes to reduce point cloud different coding as much as possible. Information redundancy between blocks improves the compression performance of point cloud attributes;
  • Figure 1 is a block diagram of the method of the present invention.
  • the point cloud attribute compression is performed by the method of the present invention, as shown in FIG. 1 , and the specific implementation steps are as follows:
  • Color space conversion of point cloud attribute read the point cloud attribute information to be processed, the point p i in the point cloud has the color values of RGB color space r i , g i , b i , through the color space conversion matrix Convert RGB to YUV color space, the color values are y i , u i , v i , as shown in Equation 1:
  • the RGB color value of the first point p 1 of the point cloud longdress_vox10_1300.ply is (102, 94, 87), and the YUV color value is obtained by the color conversion matrix (54.4128, -2.7926, 50.3798).
  • the RGB color value of the first point p 1 of the point cloud Shiva35.ply is (125, 88, 54), and the YUV color value is obtained by the color conversion matrix (43.4902, 30.9580, 50.5518).
  • the RGB color value of the first point p 1 of the point cloud Queen_frame_0200.ply is (102, 80, 71), and the YUV color value is obtained by the color conversion matrix (48.0172, 9.8702, 44.1126).
  • the KD tree is essentially a binary tree.
  • the coordinate axis with the largest variance of the distribution in the position coordinates of the point cloud is selected as the division axis.
  • the point whose coordinate size is the median value is selected as the division point, and iteratively divides until the set KD tree depth is reached.
  • the KD tree and the coded block after the division are as shown in FIG. 2 .
  • the point cloud longdress_vox10_1300.ply has a total of 857966 points, and the KD tree division depth d is set to 13, and the number of points in the block after division is 104 or 105.
  • the point cloud Shiva35.ply has a total of 1010591 points, and the KD tree division depth d is set to 13, and the number of points in the block after division is 123 or 124.
  • the point cloud Queen_frame_0200.ply has a total of 1000993 points, and the KD tree division depth d is set to 13, and the number of points in the block after division is 122 or 123.
  • Intra prediction based on block structure The point cloud is divided into a certain number of coding blocks according to the spatial positional relationship through the spatial division of step (2).
  • the order of the coding blocks is determined by the breadth traversal order of the tree division.
  • the intra prediction is performed on the coded block according to the numbering order.
  • Point cloud longdress_vox10_1300.ply, Shiva35.ply and Queen_frame_0200.ply have 8192 code blocks, and all points in each block can be regarded as one class.
  • the first point of the first code block b 1 of the point cloud longdress_vox10_1300.ply RGB color value is (131, 96, 72), after the color conversion, the YUV color value is (52.85, 23.99, 54.16), after color prediction The post-residual difference is (-75.15, 23.99, 54.16).
  • the prediction mode 1 uses 128 as the prediction reference value of Y i
  • the prediction mode 2 uses the previous block
  • the mean Y i-1 of all the point Y color components in b i-1 is predicted by Y i
  • the prediction mode 3 is to use the mean values U i-1 and V i-1 of the U and V color components of all points in the previous block respectively.
  • Predicting U i and V i , prediction mode 4 is to use Y i-1 , U i-1 , V i-1 as reference values of Y i , U i , V i for prediction, and a total of four prediction modes;
  • the point cloud longdress_vox10_1300.ply first color block b 1 reconstructed color mean (105.25, - 18.04, 20.79), this color average will be used as the prediction reference of the second code block b 2 , b 2 code Blocks can use four prediction modes for color prediction.
  • Intra prediction mode decision There are 4 modes for predicting the color components Y i , U i , V i of the coded block b i (i ⁇ 1), and it is necessary to estimate the cost of each mode for mode decision, and select Optimal prediction mode; using SATD to estimate the cost of the prediction mode, wherein the prediction residual is transformed using DCT, and the mode with the smallest SATD will be selected as the prediction mode of the current block;
  • the second code block b 2 of the point cloud longdress_vox10_1300.ply is predicted by referring to the reconstructed color mean of the b 1 block, and the SATD values obtained by using the four modes are: 2632.7588 (mode one), 3457.2168 (mode two), 2698.4360 (Mode 3), 2378.7190 (Mode 4). Among them, mode 4 has the lowest SATD value, and mode 4 is selected as the best prediction mode.
  • the method of the present invention performs significantly better than the existing mainstream methods (based on octrees) under the condition of attribute compression high code rate.
  • DCT attribute compression RN Mekuria, K. Blom, and P. Cesar, "Design, Implementation and Evaluation of a Point Cloud Codec for Tele-Immersive Video," IEEE Trans.CSVT, vol. PP, no. 99, pp .1–1, 2016.).
  • This method uses the simple division method of KD tree, combined with intra-frame prediction, an efficient de-redundancy scheme. Under the condition of point cloud compression high code rate, the compression performance has obvious advantages and outstanding advantages.
  • the method of the invention uses intra prediction to reduce information redundancy between coded blocks, provides four prediction modes and selects the best mode by mode decision, and then uses the traditional DCT transform to compress the point cloud attributes, and the computational complexity is low.
  • the attribute compression efficiency is high, and the better point cloud compression performance is realized.
  • the important form of real-world digitalization is widely used in the construction of urban digital maps. It supports technical support in many popular research such as smart city, driverless, and cultural relic protection. effect. This is conducive to promotion in the market.

Abstract

一种基于帧内预测的点云属性压缩方法，针对点云属性信息，提出一种新的基于块结构的帧内预测方案，提供四种预测模式以尽可能地减少点云不同编码块之间的信息冗余，提高点云属性的压缩性能；包括：点云属性的颜色空间转换；采用K-dimension(KD)树划分点云得到编码块；基于块结构的帧内预测；帧内预测模式决策；变换、均匀量化和熵编码。该方法使用KD树对点云进行编码块的划分，并按照广度优先遍历的顺序对编码块进行编号，根据编号顺序使用具有四种预测模式的点云帧内预测方案对编码块进行逐一处理，由模式决策选出最佳预测模式，再对预测残差进行变换、量化和熵编码处理，以达到点云属性压缩的最佳性能。

Description

一种基于帧内预测的点云属性压缩方法 技术领域
本发明属于点云数据处理技术领域，涉及点云数据压缩方法，尤其涉及一种基于帧内预测的点云属性压缩的方法。
背景技术
三维点云是现实世界数字化的重要表现形式。随着三维扫描设备(激光、雷达等)的快速发展，点云的精度、分辨率更高。高精度点云广泛应用于城市数字化地图的构建，在如智慧城市、无人驾驶、文物保护等众多热门研究中起技术支撑作用。点云是三维扫描设备对物体表面采样所获取的，一帧点云的点数一般是百万级别，其中每个点包含几何信息和颜色、纹理等属性信息，数据量十分庞大。三维点云庞大的数据量给数据存储、传输等带来巨大挑战，所以点云压缩十分必要。
点云压缩主要分为几何压缩和属性压缩，现有的点云属性压缩框架主要包括以下几种：
一、基于八叉树分解和DCT的方法：该方法先用八叉树对点云进行空间分解得到编码块，然后对树进行深度优先遍历，将遍历的结点颜色值按照蛇形的顺序写入二维的JPEG表，再利用现有的JPEG编码器对获得的点云颜色表进行编码，其中JPEG编码器使用的是DCT。该方法利用现有的编码器，计算复杂度低，但并未充分利用点与点之间的空间相关性，在压缩性能上有待提高；
二、基于八叉树分解和图变换的方法：该方法先用八叉树对点云进行空间分解，划分到特定层次得到变换块；在每个变换块内形成图，将沿着任意坐标轴的距离不超过1的两点用一条边连接起来，边的权重与欧氏距离成反比；再对图中结点的属性信息进行图变换。该方法的压缩性能佳；但运算复杂度较高，其构图方式可能会带来子图问题，影响图变换的效率，仍有改善的空间；
三、基于KD树分解和图变换的方法：该方法先用KD树对点云进行空间分解，划分到一定深度得到变换块，其中每个变换块内所包含的点数大致相同：在变换块内，每两点用一条边连接起来，边的权重与欧氏距离有关，设定的边欧式距离阈值决定图的稀疏度；然后再对图内点的属性信息进行图变换。该方法彻底解决了子图问题，同时在压缩性能方面较前两种方法都有较大的提升，但运算复杂度较高，性能仍待改善。
发明的公开
为了克服上述现有技术的不足，在考虑计算复杂度的条件下，进一步改善点云属性的压缩性能，本发明提供一种基于帧内预测的点云属性压缩的方法，通过使用一种新的基于块结构的帧内预测方案，提供四种预测模式以尽可能地减少点云不同编码块之间的信息冗余，提高点云属性的压缩性能。
本发明提供的技术方案是：
一种基于帧内预测的点云属性压缩方法，针对点云数据，使用KD树对点云进行编码块的划分，并按照广度优先遍历的顺序对编码块进行编号，根据编号顺序使用具有四种预测模式的点云帧内预测方案对编码块进行逐一处理，由模式决策选出最佳预测模式，再对预测残差进行变换、量化和熵编码处理，以达到点云属性压缩的最佳性能；包括如下步骤：
1)点云属性的颜色空间转换：读入待处理的点云属性信息，考虑人眼的视觉特性和压缩处理的难易程度，将点云颜色空间从RGB空间转换到YUV空间；
2)采用KD树划分点云得到编码块：根据几何信息对点云进行KD树划分，每次选择点云位置坐标中分布方差最大的坐标轴作为划分轴，选取坐标大小是中位值的点作为划分点，迭代划分直至达到设定的KD树深度；KD树划分的最后一层所得到的块即为点云的编码块，按照广度遍历的顺序对编码块进行编号；
上述点云编码块的编号顺序，将作为后期点云帧内预测的预测顺序。
3)基于块结构的帧内预测：根据编码块的编号顺序依次对编码块进行帧内预测，共四种预测模式。对第一个编码块b 1进行帧内预测时，使用128作为Y分量预测的参考值。处理其他块b i(i≠1)的颜色分量Y i、U i、V i时，预测模式一是使用128作为Y i的预测参考值，U i、V i不预测；预测模式二是用前一个块b i-1重构后所有点Y分量的均值Y i-1对Y i进行预测，U i、V i不预测；预测模式三是使用前一个块重构后所有点U、V颜色分量的均值U i-1、V i-1分别对U i、V i进行预测，Y i不预测；预测模式四是使用Y i-1、U i-1、V i-1作为Y i、U i、V i的参考值进行预测，共4种预测模式；
4)帧内预测模式决策：对编码块b i(i≠1)的颜色分量Y i、U i、V i预测有4种模式，需要进行模式决策选出最佳的预测模式，而第一个编码块不需要进行模式决策；使用预测残差变换系数的绝对值和SATD来估计预测模式的代价，其中使用DCT对预测残差进行变换处理；SATD值越小，代表预测模式代价越小，预测性能越好，具有最小SATD的模式将被选择为当前块的预测模式；
5)点云属性压缩码流的生成：按照编码顺序处理所有编码块，对预测后残差进行DCT变换、均匀量化和熵编码，得到点云属性压缩的最终码流；
上述步骤1)所述的颜色空间转换，其具体过程如下：
(1-1)点云中的点p i具有RGB颜色空间的颜色值为r i、g i、b i，通过颜色空间转换矩阵将RGB转换到YUV颜色空间，颜色值为y i、u i、v i；
(1-2)颜色空间转换的数学表达式为:
Figure PCTCN2018076435-appb-000001
上述步骤2)所述的编码块的划分和编号，其具体过程如下：KD树划分时，选择点的分布方差最大的坐标轴作为划分轴，充分考虑了变换块内各点的相关性；同时把划分轴上坐标是中位值的点作为划分点，使变换块内点的数量基本相同。设待处理的点云共有N个点，KD树设定的划分深度为d，经过对点云d次划分后，得到2 d个编码块；对所有的编码块按照广度遍历的顺序进行编号
Figure PCTCN2018076435-appb-000002
上述步骤3)所述的基于编码块顺序的帧内预测，编码块的预测参考值如表1所示：
表1编码块帧内预测的颜色分量参考值
Figure PCTCN2018076435-appb-000003
上述步骤4)中所述的帧内预测的模式决策，其具体过程如下：编码块b i(i≠1)的颜色分量Y i、U i、V i的预测参考值分别为Y i_ref、U i_ref、V i_ref，预测残差b i(res)由式2计算可得，预测模式的代价SATD由式3计算可得，选择SATD最小的模式作为最佳预测模式：
b i(res)＝(Y i-Y i_ref)+(U i-U i_ref)+(V i-V i_ref)   (式2)
SATD＝sum(abs(DCT(b i(res))))   (式3)
上述步骤5)中所述的点云属性压缩码流的生成，其具体过程如下：
(5-1)经过上述步骤1)至4)的处理，得到带编号的编码块预测残差和其预测模式信息；对预测残差进行DCT变换和均匀量化，二值化后得到属性信息的码流，再结合编码块的预测模式信息，经过熵编码得到最终的点云属性压缩码流；
(5-2)点云属性压缩的性能由码率和峰值信噪比PSNR(Peak Signal to Noise Ratio)来衡量，其中码率由码字总比特数除以点云的点数可得，单位是bpp(bits per point)，PSNR的单位是分贝dB；码率越小，PSNR越大，点云属性压缩性能越好。
上述方法使用帧内预测减少编码块之间的信息冗余，提供四种预测模式并由 模式决策选出最佳模式，再使用传统DCT变换对点云属性进行压缩，计算复杂度低，属性压缩效率高，实现更优的点云压缩性能。
与现有技术相比，本发明的有益效果是：
本发明提供一种基于帧内预测的点云属性压缩方法，具有以下技术优势：
(一)提出一种新的帧内预测方案，支持4种预测模式，有效地降低了编码块之间的属性信息冗余。
(二)处理不同编码块时，通过模式决策选出最佳的预测模式，实现点云压缩的最佳压缩性能。
附图的简要说明
图1是本发明提供方法的流程框图。
图2是点云的KD树划分及编码块编号的示例图。
图3是点云属性信息压缩后的码流结构示例图。
图4a、b和c是本发明方法与现有传统方法的压缩性能对比图。
其中，
图4a为测试longdress_vox10_1300.ply的压缩性能对比图；
图4b为测试Shiva35.ply的压缩性能对比图；
图4c为测试Queen_frame_0200.ply的压缩性能对比图。
实施本发明的最佳实施方式
下面结合附图，通过实施例进一步描述本发明，但不以任何方式限制本发明的范围。
本发明的一种基于帧内预测的点云属性压缩的方法，针对点云数据，提出一种新的基于块结构的帧内预测方案，提供四种预测模式以尽可能地减少点云不同编码块之间的信息冗余，提高点云属性的压缩性能；图1是本发明方法的流程框图。
以下针对MPEG点云压缩工作组中的官方点云数据集longdress_vox10_1300.ply、Shiva35.ply和Queen_frame_0200.ply，采用本发明方法进行点云属性压缩，如图1所示，具体实施步骤为：
(1)点云属性的颜色空间转换：读入待处理的点云属性信息，点云中的点p i具有RGB颜色空间的颜色值为r i、g i、b i，通过颜色空间转换矩阵将RGB转换到YUV颜色空间，颜色值为y i、u i、v i，如式1所示：
Figure PCTCN2018076435-appb-000004
点云longdress_vox10_1300.ply的第一个点p 1的RGB颜色值为(102,94,87)，经过颜色转换矩阵的处理得到YUV颜色值为(54.4128,-2.7926,50.3798)。
点云Shiva35.ply的第一个点p 1的RGB颜色值为(125,88,54)，经过颜色转换矩阵的处理得到YUV颜色值为(43.4902,30.9580,50.5518)。
点云Queen_frame_0200.ply的第一个点p 1的RGB颜色值为(102,80,71)，经过颜色转换矩阵的处理得到YUV颜色值为(48.0172,9.8702,44.1126)。
(2)采用KD树划分点云得到编码块：KD树实质上是一种二叉树，对该点云进行KD树的划分时，每次选择点云位置坐标中分布方差最大的坐标轴作为划分轴，在该轴上选取坐标大小是中位值的点作为划分点，迭代划分直至达到设定的KD树深度，划分完成后的KD树及带编号的编码块如图2所示。
点云longdress_vox10_1300.ply共有857966个点，KD树划分深度d设为13，经过划分后块内点的数量为104或105。
点云Shiva35.ply共有1010591个点，KD树划分深度d设为13，经过划分后块内点的数量为123或124。
点云Queen_frame_0200.ply共有1000993个点，KD树划分深度d设为13，经过划分后块内点的数量为122或123。
(3)基于块结构的帧内预测：点云经过步骤(2)的空间划分，将所有的点按照空间位置关系划分成一定数量的编码块，编码块的顺序由树划分的广度遍历顺序决定，再根据编号顺序依次对编码块进行帧内预测。点云longdress_vox10_1300.ply、Shiva35.ply和Queen_frame_0200.ply均有8192个编码块，每个块内的所有点可以看成一个类。
(3-1)对第一个编码块b 1进行帧内预测时，使用128作为Y分量的预测参考值；
例如，点云longdress_vox10_1300.ply第一个编码块b 1的第一个点RGB颜色值为(131,96,72)，经过颜色转换后YUV颜色值为(52.85,23.99,54.16)，经过颜色预测后残差值为(-75.15,23.99,54.16)。
(3-2)处理其他块b i(i≠1)的颜色分量Y i、U i、V i时，预测模式一是使用128作为Y i的预测参考值，预测模式二是使用前一个块b i-1内所有点Y颜色分量的均值Y i-1对Y i进行预测，预测模式三是使用前一个块内所有点U、V颜色分量的均值U i-1、V i-1分别对U i、V i进行预测，预测模式四是使用Y i-1、U i-1、V i-1作为Y i、U i、V i的参考值进行预测，共4种预测模式；
例如，点云longdress_vox10_1300.ply第一个编码块b 1重构后颜色均值为(105.25,-18.04,20.79)，这一颜色均值将作为第二个编码块b 2的预测参考值，b 2编码块可以使用4种预测模式进行颜色预测。
(4)帧内预测模式决策：对编码块b i(i≠1)的颜色分量Y i、U i、V i预测有4 种模式，需要估计每种模式的代价来进行模式决策，选出最佳的预测模式；使用SATD来估计预测模式的代价，其中使用DCT对预测残差进行变换处理，具有最小SATD的模式将被选择为当前块的预测模式；
例如，点云longdress_vox10_1300.ply第二个编码块b 2参考b 1块的重构颜色均值进行预测，使用4种模式得到的SATD值分别为：2632.7588(模式一)、3457.2168(模式二)、2698.4360(模式三)、2378.7190(模式四)。其中，模式四的SATD值最小，选择模式四作为最佳预测模式。
(5)点云属性压缩码流的生成：针对点云longdress_vox10_1300.ply的8192个编码块、Shiva35.ply的8192个编码块、Queen_frame_0200.ply的8192个编码块，将块内的颜色信息依次经过预测、DCT变换、量化和熵编码处理，再结合预测模式和变换模式的码流信息，按照编码块的顺序写入码流文件中，最终码流文件的结构如图3所示。点云属性压缩的性能由码率和峰值信噪比PSNR(Peak Signal to Noise Ratio)来衡量，其中码率的单位是bpp(bits per point)，PSNR的单位是分贝dB。
为了验证本发明的一种基于帧内预测的点云属性压缩的方法的效果，我们使用上述3个数据集longdress_vox10_1300.ply、Shiva35.ply、Queen_frame_0200.ply进行实验，在压缩性能上与现有的方法对比结果如图4a、b和c所示。
从图4a、b和c可以看出，在测试的三类典型的点云序列上，本发明的方法在属性压缩高码率的条件下性能明显优于现有的主流方法(基于八叉树和DCT的属性压缩，R.N.Mekuria,K.Blom,and P.Cesar,“Design,Implementation and Evaluation of a Point Cloud Codec for Tele-Immersive Video,”IEEE Trans.CSVT,vol.PP,no.99,pp.1–1,2016.)。本方法使用KD树这一简单的划分方式，结合帧内预测这一高效的去冗余方案，在点云压缩高码率情况下，压缩性能的优势明显，优点突出。
需要注意的是，公布实施例的目的在于帮助进一步理解本发明，但是本领域的技术人员可以理解：在不脱离本发明及所附权利要求的精神和范围内，各种替换和修改都是可能的。因此，本发明不应局限于实施例所公开的内容，本发明要求保护的范围以权利要求书界定的范围为准。
工业实用性
本发明的方法使用帧内预测减少编码块之间的信息冗余，提供四种预测模式并由模式决策选出最佳模式，再使用传统DCT变换对点云属性进行压缩，计算复 杂度低，属性压缩效率高，实现更优的点云压缩性能，现实世界数字化的重要表现形式，广泛应用于城市数字化地图的构建，在如智慧城市、无人驾驶、文物保护等众多热门研究中起技术支撑作用。从而有利于在市场上推广。

Claims (6)

  1. 一种基于帧内预测的点云属性压缩方法，针对点云属性信息，提出一种新的基于块结构的帧内预测方案，提供四种预测模式以尽可能地减少点云不同编码块之间的信息冗余，提高点云属性的压缩性能；包括如下步骤：
    1)点云属性的颜色空间转换：
    读入待处理的点云属性信息，考虑人眼的视觉特性和压缩处理的难易程度，将点云颜色空间从RGB空间转换到YUV空间；
    2)采用KD树划分点云得到编码块，并按照广度遍历顺序对编码块进行编号：
    读入点云的几何信息，根据几何信息对点云进行KD树划分，每次选择点云位置坐标中分布方差最大的坐标轴作为划分轴，选取坐标大小是中位值的点作为划分点，迭代划分直至达到设定的KD树深度；KD树划分的最后一层所得到的块即为点云的编码块，按照广度遍历的顺序对编码块进行编号，该编号将作为编码块后期处理的顺序；
    3)基于编号顺序对点云编码块的属性信息进行帧内预测，有四种预测模式：
    对第一个编码块b 1进行帧内预测时，使用128作为Y分量预测的参考值。处理其他块b i(i≠1)的颜色分量Y i、U i、V i时，预测模式一是使用128作为Y i的预测参考值，U i、V i不预测；预测模式二是用前一个块b i-1重构后所有点Y分量的均值Y i-1对Y i进行预测，U i、V i不预测；预测模式三是使用前一个块重构后所有点U、V颜色分量的均值U i-1、V i-1分别对U i、V i进行预测，Y i不预测；预测模式四是使用Y i-1、U i-1、V i-1作为Y i、U i、V i的参考值进行预测，共4种预测模式；
    4)帧内预测的模式决策：
    对编码块b i(i≠1)的颜色分量Y i、U i、V i预测有4种模式，需要进行模式决策选出最佳的预测模式，而第一个编码块不需要进行模式决策；使用预测残差变换系数的绝对值和SATD(Sum of Absolute Transformed Difference)来估计预测模式的代价，其中使用离散余弦变换DCT(Discrete cosine transform)对预测残差进行变换处理；SATD值越小，代表预测模式代价越小，预测性能越好，具有最小SATD的模式将被选择为当前块的预测模式；
    5)点云属性压缩码流的生成：按照编码顺序处理所有编码块，对预测后残差进行DCT变换、均匀量化和熵编码，得到点云属性压缩的最终码流；
  2. 如权利要求1所述点云属性压缩方法，其特征是，步骤1)中颜色空间转换的具体过程是：点云中的点p i具有RGB颜色空间的颜色值为r i、g i、b i，通过式 1转换到YUV颜色空间，颜色值为y i、u i、v i：
    Figure PCTCN2018076435-appb-100001
  3. 如权利要求1所述点云属性压缩方法，其特征是，步骤2)中KD树划分方法为二元划分方法；设待处理的点云共有N个点，KD树设定的划分深度为d，经过对点云d次划分后，得到2 d个编码块；对所有的编码块按照广度遍历的顺序进行编号
    Figure PCTCN2018076435-appb-100002
  4. 如权利要求1所述点云属性压缩方法，其特征是，步骤3)中对编码块进行帧内预测所用的参考值如下表1所示：
    表1编码块帧内预测的颜色分量参考值
    Figure PCTCN2018076435-appb-100003
  5. 如权利要求1所述点云属性压缩方法，其特征是，步骤4)中编码块b i(i≠1)的颜色分量Y i、U i、V i的预测参考值分别为Y i_ref、U i_ref、V i_ref，预测残差b i(res)由式2计算可得，预测模式的代价SATD由式3计算可得，具体过程如下：
    b i(res)＝(Y i-Y i_ref)+(U i-U i_ref)+(V i-V i_ref)    (式2)
    SATD＝sum(abs(DCT(b i(res))))    (式3)
  6. 如权利要求1所述点云属性压缩方法，其特征是，步骤5)中具体细节如下：
    (6-1)经过步骤1)至4)的处理，得到带编号的编码块预测残差和其预测模式信息；对预测残差进行DCT变换和均匀量化，二值化后得到属性信息的码流，再结合编码块的预测模式信息，经过熵编码得到最终的点云属性压缩码流；
    (6-2)点云属性信息的码流点云属性信息的码流主要由压缩头信息和编码块信息两大部分组成。其中，头信息主要包括量化步长等；编码块信息流以编码块为单位，按照编码块的顺序排列，每个块内主要包括编码块的预测模式信息和颜色残差信息。
    (6-3)点云属性压缩的性能由码率和峰值信噪比PSNR(Peak Signal to Noise Ratio)来衡量，其中码率的单位是bpp(bits per point)，PSNR的单位是分贝dB；码率越小，PSNR越大，点云属性压缩性能越好。
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