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Point cloud compression encoder key parameter optimization method based on support vector machine

A technology of support vector machines, key parameters, applied in the field of image processing

Active Publication Date: 2019-08-06
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] Aiming at the optimization problem of encoding parameters in the existing PCL-PCC point cloud encoder, the present invention provides an encoder parameter optimization method based on support vector machine SVM, which greatly improves the compression performance of point cloud

Method used

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  • Point cloud compression encoder key parameter optimization method based on support vector machine
  • Point cloud compression encoder key parameter optimization method based on support vector machine
  • Point cloud compression encoder key parameter optimization method based on support vector machine

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0110] A support vector machine based point cloud compression encoder key parameter optimization method, such as image 3 shown, including the following steps:

[0111] (1) The preprocessing process of point cloud data is as follows:

[0112] ① Uniform preprocessing of geometric information in point cloud information; refers to: reading point cloud The geometric information (x, y, z) of all points in the point cloud, calculate the minimum value of the geometric information of the point cloud (x min ,y min ,z min ) and the maximum value of point cloud geometric information (x max ,y max ,z max ).

[0113] ② Point cloud and the ith point p i The geometric coordinate information in (x i ,y i ,z i ) for homogenization; i∈{1, 2,...N}, N is the number of points in the point cloud; as shown in formula (I):

[0114]

[0115] In formula (I), Indicates the i-th point p after the homogenization process i geometric coordinate information.

[0116] ③ Perform RGB and Y...

Embodiment 2

[0134] According to a kind of support vector machine-based point cloud compression coder key parameter optimization method described in embodiment 1, its difference is:

[0135] In step ⑤, the point set obtained by projecting onto the X_Y plane i-th projected point S xy，i Indicates the i-th point projected onto the X_Y plane, and the calculation formula is shown in formula (IV), formula (V), formula (VI), formula (VII), and formula (VIII):

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] In the formula, p xy_i Represent a point cloud Projected to the position on the two-dimensional X_Y plane The set of points at the place, n represents p xy_i The number of points in the set, when n=1, it means one-to-one projection, when n>1, it means many-to-one mapping; represents the set p xy_i The average value of the luminance signal of all points in , represents the set p xy_i The average value of the red color difference signals of all points in ; re...

Embodiment 3

[0157] According to a kind of support vector machine-based point cloud compression coder key parameter optimization method described in embodiment 1, its difference is:

[0158] Step ⑥, extract the point set on the X_Y plane The distribution eigenvector of As shown in formula (XIX), formula (XX), formula (XXI), formula (XXII), formula (XXIII), formula (XXIV):

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165] In the formula, N xy express The number of midpoints.

[0166] Step ⑥, extract the point set on the Y_Z plane The distribution eigenvector of As shown in formula (XXV), formula (XXVI), formula (XXVII), formula (XXVIII), formula (XXIX), formula (XXX):

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173] In the formula, N yz express The number of midpoints.

[0174] Step ⑥, extract the point set on the X_Z plane The distribution eigenvector of As shown in formula (XXXI), formula (XXXII), formula (X...

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Abstract

The invention relates to a point cloud compression encoder key parameter optimization method based on a support vector machine, and the method comprises the steps: carrying out the preprocessing of the geometric information and color information of a point cloud; extracting a feature vector of the point cloud; for a given target code rate, finding an optimal parameter pair which minimizes distortion by using a full search method; extracting an optimal tag under a given target code rate from all point clouds in the training set; and writing the optimal label information and the feature vector information into a training set, performing training by using a support vector machine and the training set information to obtain a model, testing the feature vector information in the test set by using the model, and predicting an optimal test label on a continuous domain to obtain an optimal parameter pair of the test set. According to the method, the distribution characteristics of the point cloud are utilized, a support vector machine method is used for training to obtain the optimal coding parameter pair of the test point cloud, and the time cost is greatly reduced while the coding performance of the encoder under the condition of a given coding bit rate is ensured.

Description

technical field [0001] The invention relates to a three-dimensional point cloud compression coding optimization method based on a support vector machine, and belongs to the technical field of image processing. Background technique [0002] With the improvement of the capabilities of 3D data acquisition equipment, 3D point cloud has become an effective way to express objects or scenes. 3D point cloud (point cloud data) includes a collection of many points, namely To describe objects in space, where N represents the total number of points, p i Represents the i-th point, i∈{1,...N}, each point usually consists of geometric information (that is, the spatial coordinates x, y, z of the point, and the origin of the coordinate system is usually set as the center point of the acquisition device) and attribute components ( Usually includes the R, G, B three chromaticity attributes of the point, and color information, as well as reflectivity, normal vector, etc.). 3D point clouds a...

Claims

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Application Information

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IPC IPC(8): G06T9/00G06N20/10
CPCG06T9/00G06N20/10
Inventor 刘祺元辉王韦韦刘昊
Owner SHANDONG UNIV
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