Point cloud encoding method and decoding method, and encoder and decoder
Provided are a point cloud encoding method and decoding method, and an encoder and a decoder. The encoding method includes: processing attribute information of a target point in a point cloud, so as to obtain a predicted residual of the attribute information of the target point; quantizing the predicted residual on the basis of a quantized weight of the target point and a quantized stride of the target point, so as to obtain a quantized residual of the attribute information of the target point, wherein the quantized weight of the target point is the weight used when weighting the quantized stride of the target point; and encoding the quantized residual, so as to obtain a code stream.
1 . A method for encoding a point cloud, comprising:
processing attribute information of a target point in a point cloud to obtain a predicted residual value of the attribute information of the target point;
quantizing the predicted residual value based on a quantization weight of the target point and a quantization step of the target point to obtain a quantized residual value of the attribute information of the target point, the quantization weight of the target point being a weight used for performing weighting on the quantization step of the target point; and
encoding the quantized residual value to obtain a bitstream,
wherein the quantizing the predicted residual value based on a quantization weight of the target point and a quantization step of the target point to obtain a quantized residual value of the attribute information of the target point comprises:
determining an effective quantization weight of the target point based on the quantization weight of the target point; and
quantizing the predicted residual value by using the effective quantization weight of the target point and the quantization step of the target point to obtain the quantized residual value.
2 . The method of claim 1 , wherein a value of the effective quantization weight is equal to an integer power of 2.
3 . The method of claim 1 , wherein a value of the effective quantization weight is not equal to an integer power of 2; and determining the effective quantization weight of the target point based on the quantization weight of the target point comprises:
determining the integer power of 2 which is the closest to the quantization weight of the target point as the effective quantization weight based on the value of the quantization weight of the target point.
4 . The method of claim 1 , wherein the quantizing the predicted residual value by using the effective quantization weight of the target point and the quantization step of the target point to obtain the quantized residual value comprises:
multiplying the effective quantization weight by the predicted residual value to obtain a weighted residual value; and
quantizing the weighted residual value by using the quantization step of the target point to obtain the quantized residual value.
5 . The method of claim 1 , wherein the quantizing the predicted residual value by using the effective quantization weight of the target point and the quantization step of the target point to obtain the quantized residual value comprises:
updating the quantization step of the target point by using the following formula:
newQstep
=
⌈
Qstep
/
effectiveQuantWeight
⌉
,
wherein effectiveQuantWeight represents effective quantization weight of the target point, newQstep represents the quantization step of the target point after being updated based on the effective quantization weight of the target point, and Qstep represents the quantization step of the target point before being updated based on the effective quantization weight of the target point; and
quantizing the predicted residual value by using the quantization step after being updated of the target point to obtain the quantized residual value.
6 . The method of claim 1 , further comprising:
updating quantization weights of N nearest points of the present point based on the quantization weight of the present point by traversing points in the cloud point according to a reverse order of an encoding order of the point cloud, N being an integer greater than 0.
7 . The method of claim 6 , further comprising:
dividing the point cloud into one or more Level of Detail (LOD) layers, wherein each LOD layer comprises one or more points, and
an initial value of the quantization weight of a point in the previous M layers of LOD of the plurality of LOD layers is greater than an initial value of the quantization weight of a point in remaining LOD layers of the plurality of LOD layers.
8 . The method of claim 1 , wherein the processing the attribute information of the target point in the point cloud to obtain the predicted residual value of the attribute information of the target point comprises:
processing location information of the target point to obtain reconstructed information of the location information of the target point;
obtaining a predicted value of the attribute information of the target point according to the reconstructed information of the location information of the target point;
processing the attribute information of the target point in the point cloud to obtain a true value of the attribute information of the target point; and
obtaining the predicted residual value according to the predicted value of the attribute information of the target point and the true value of the attribute information of the target point.
9 . A method for decoding a point cloud, comprising:
parsing a bitstream of a point cloud to obtain a quantized residual value of attribute information of a target point of the point cloud;
performing inverse quantization on the quantized residual value based on a quantization weight of the target point and a quantization step of the target point to obtain a reconstructed residual value of the attribute information of the target point, the quantization weight of the target point being a weight used for performing weighting on the quantization step of the target point;
obtaining a reconstructed value of the attribute information of the target point according to the reconstructed residual value and the predicted value of the attribute information of the target point; and
obtaining a decoded point cloud according to the reconstructed value of the attribute information of the target point,
wherein the performing the inverse quantization on the quantized residual value based on the quantization weight of the target point and the quantization step of the target point to obtain the reconstructed residual value of the attribute information of the target point comprises:
determining an effective quantization weight of the target point based on the quantization weight of the target point; and
performing inverse quantization on the quantized residual value by using the effective quantization weight of the target point and the quantization step of the target point to obtain the reconstructed residual value.
10 . The method of claim 9 , wherein a value of the effective quantization weight is not equal to an integer power of 2; and the determining the effective quantization weight of the target point based on the quantization weight of the target point comprises:
determining the integer power of 2 which is the closest to the quantization weight of the target point as the effective quantization weight based on the value of the quantization weight of the target point.
11 . The method of claim 9 , wherein the performing the inverse quantization on the quantized residual value by using the effective quantization weight of the target point and the quantization step of the target point to obtain the reconstructed residual value comprises:
performing inverse quantization on the quantized residual value by using the quantization step of the target point to obtain a weighted residual value; and
dividing the weighted residual value by the effective quantization weight to obtain the reconstructed residual value.
12 . The method of claim 9 , wherein the performing the inverse quantization on the quantized residual value by using the effective quantization weight of the target point and the quantization step of the target point to obtain the reconstructed residual value comprises:
updating the quantization step of the target point by using the following formula:
newQstep
=
⌈
Qstep
/
effectiveQuantWeight
⌉
,
wherein effectiveQuantWeight represents effective quantization weight of the target point, newQstep represents the quantization step of the target point after being updated based on the effective quantization weight of the target point, and Qstep represents the quantization step of the target point before being updated based on the effective quantization weight of the target point; and
performing inverse quantization on the quantized residual value by using the quantization step after being updated of the target point to obtain the reconstructed residual value.
13 . The method of claim 9 , further comprising:
updating quantization weights of N nearest points of a present point based on the quantization weight of the present point by traversing the points in the cloud point according to a reverse order of an encoding order of the point cloud, N being an integer greater than 0.
14 . The method of claim 13 , further comprising:
dividing the point cloud into one or more Level of Detail (LOD) layers, wherein each LOD layer comprises one or more points, and
an initial value of the quantization weight of a point in the previous M layers of LOD of the one or more LOD layers is greater than an initial value of the quantization weight of a point in remaining LOD layers of the plurality of LOD layers.
15 . The method of claim 13 , wherein the updating the quantization weights of the N nearest points of the present point based on the quantization weight of the present point comprises:
acquiring an influence weight of the present point on each of the N nearest points, the influence weight depending on location information of the present point and the N nearest points; and
updating the quantization weights of the N nearest points based on the quantization weight of the present point and the influence weight of the present point on each of the N nearest points.
16 . The method of claim 9 , further comprising:
parsing a bitstream to obtain reconstructed information of location information of the target point; and
obtaining a predicted value of the attribute information of the target point according to the reconstructed information of the location information of the target point.
17 . A point cloud encoder, comprising:
a processor and
a computer-readable storage medium for storing a computer program;
wherein the processor is configured to execute the computer program to:
process attribute information of a target point in a point cloud to obtain a predicted residual value of the attribute information of the target point;
quantize the quantized residual value based on a quantization weight of the target point and a quantization step of the target point to obtain a quantized residual value of the attribute information of the target point, the quantization weight of the target point being a weight used for performing weighting on the quantization step of the target point; and
encode the quantized residual value to obtain a bitstream,
wherein the processor is further configured to execute the computer program to:
determine an effective quantization weight of the target point based on the quantization weight of the target point; and
quantize the predicted residual value by using the effective quantization weight of the target point and the quantization step of the target point to obtain the quantized residual value.
18 . A point cloud decoder, comprising:
a processor and
a computer-readable storage medium for storing a computer program;
wherein the processor is configured to execute the computer program to implement the method of claim 9 .