CN115086672A - Point cloud attribute coding method and device, point cloud attribute decoding method and device and related equipment - Google Patents

Point cloud attribute coding method and device, point cloud attribute decoding method and device and related equipment Download PDF

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CN115086672A
CN115086672A CN202110264409.4A CN202110264409A CN115086672A CN 115086672 A CN115086672 A CN 115086672A CN 202110264409 A CN202110264409 A CN 202110264409A CN 115086672 A CN115086672 A CN 115086672A
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point cloud
group
attribute
direct current
coefficient
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陈悦汝
王静
李革
高文
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Peng Cheng Laboratory
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/124—Quantisation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/136—Incoming video signal characteristics or properties
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
    • H04N19/91—Entropy coding, e.g. variable length coding [VLC] or arithmetic coding

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Abstract

The invention discloses a point cloud attribute coding method, a point cloud attribute coding device, a point cloud decoding method, a point cloud attribute coding device and related equipment, wherein the point cloud attribute coding method comprises the following steps: the method comprises the steps of ordering and grouping point cloud data to be encoded to obtain a group to be encoded, wherein the point cloud data to be encoded is point cloud data with attributes to be encoded; respectively transforming each group to be coded based on a transformation matrix to obtain transformation coefficients, wherein the transformation coefficients comprise direct current coefficients and alternating current coefficients; and respectively obtaining the direct current coefficient predicted value of each group to be coded, and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient predicted value and the transformation coefficient. Compared with the prior art, the scheme of the invention is beneficial to improving the coding performance and obtaining better point cloud attribute compression effect.

Description

Point cloud attribute coding method and device, point cloud attribute decoding method and device and related equipment
Technical Field
The invention relates to the technical field of data processing, in particular to a point cloud attribute encoding method, a point cloud attribute encoding device, a point cloud attribute decoding method, a point cloud attribute decoding device and related equipment.
Background
With the rapid development of scientific technology, technologies such as three-dimensional reconstruction and the like are widely applied, and three-dimensional point cloud is an important expression form of real world digitization. With the rapid development of three-dimensional scanning devices (e.g., lasers, radars, etc.), the accuracy and resolution of the point cloud becomes higher. The point cloud is obtained by sampling the surface of an object by a three-dimensional scanning device, the number of points of one frame of point cloud is generally in the million level, each point contains geometric information and attribute information such as color and reflectivity, and the data volume is huge. Therefore, it is important to perform compression encoding and decoding on the point cloud.
In the prior art, the point cloud is usually compressed and encoded and decoded by a Region-adaptive Hierarchical Transform (RAHT) method. During encoding, the attribute of the point is converted into a transformation coefficient in a forward direction, then the transformation coefficient is quantized, and then the quantization coefficient is subjected to adaptive entropy encoding. During decoding, adaptive entropy decoding is firstly performed to obtain a quantized coefficient, then inverse quantization is performed to obtain a transform coefficient, and then RAHT inverse transform is used to generate a reconstructed attribute value of a point. The problem in the prior art is that the transformation in the process of compression coding based on RAHT is a multi-time one-dimensional transformation, each transformation only aims at two points, the relational characteristics between multiple points cannot be well reflected, and the compression coding performance is limited.
Thus, there is still a need for improvement and development of the prior art.
Disclosure of Invention
The invention mainly aims to provide a point cloud attribute coding method, a point cloud attribute coding device, a point cloud decoding method, a point cloud attribute coding device and related equipment, and aims to solve the problems that in the prior art, the transformation in the process of compression coding through RAHT only aims at two points each time, the relational characteristics among multiple points cannot be well reflected, and the compression coding performance is limited.
In order to achieve the above object, a first aspect of the present invention provides a point cloud attribute encoding method, wherein the method includes:
the method comprises the steps of ordering and grouping point cloud data to be encoded to obtain a group to be encoded, wherein the point cloud data to be encoded is point cloud data with attributes to be encoded;
respectively transforming each group to be coded based on a transformation matrix to obtain transformation coefficients, wherein the transformation coefficients comprise direct current coefficients and alternating current coefficients;
and respectively obtaining a direct current coefficient predicted value of each group to be coded, and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient predicted value and the transformation coefficient.
Optionally, the sorting and grouping of the point cloud data to be encoded to obtain a group to be encoded includes:
arranging the point cloud data to be coded into a one-dimensional sequence from a three-dimensional sequence according to a preset rule, and acquiring a reordered point cloud sequence;
and sequentially grouping the point cloud sequences based on the reordering, and acquiring the group to be encoded.
Optionally, the transformation matrix is a one-dimensional K-order transformation matrix, and K is the number of points in the group to be encoded.
Optionally, the obtaining the dc coefficient prediction values of the to-be-encoded groups respectively, and implementing point cloud attribute encoding on the to-be-encoded groups based on the dc coefficient prediction values and the transform coefficients includes:
acquiring the position of each group to be coded;
respectively acquiring direct current coefficient predicted values of the groups to be coded based on the positions of the groups to be coded and the positions of the coded geometric points;
calculating a residual error between the direct current coefficient predicted value and the direct current coefficient to serve as a direct current coefficient residual error;
and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient residual error and the alternating current coefficient.
Optionally, the implementing of the point cloud attribute coding of each group to be coded based on the dc coefficient residual and the ac coefficient includes:
quantizing the direct current coefficient residual error and the alternating current coefficient to obtain a quantized direct current coefficient residual error and a quantized alternating current coefficient;
and entropy coding is carried out on the quantized direct current coefficient residual error and the quantized alternating current coefficient so as to realize point cloud attribute coding of the group to be coded.
The second aspect of the present invention provides a point cloud attribute encoding apparatus, wherein the apparatus comprises:
the grouping module is used for sequencing and grouping the point cloud data to be coded to acquire a group to be coded, wherein the point cloud data to be coded is point cloud data with attributes to be coded;
a transform coefficient obtaining module, configured to transform each to-be-encoded group based on a transform matrix, and obtain a transform coefficient, where the transform coefficient includes a direct current coefficient and an alternating current coefficient;
and the encoding module is used for respectively acquiring the direct current coefficient predicted value of each group to be encoded and realizing point cloud attribute encoding of each group to be encoded based on the direct current coefficient predicted value and the transformation coefficient.
The third aspect of the present invention provides a point cloud attribute decoding method, wherein the method includes:
sequencing and grouping the point cloud data to be decoded to obtain a code group to be decoded, wherein the point cloud data to be decoded is the point cloud data with the attribute to be decoded;
performing inverse entropy coding and inverse quantization on the basis of each group to be decoded to obtain a reconstructed direct current coefficient residual and a reconstructed alternating current coefficient of each group to be decoded;
and respectively obtaining the direct current coefficient predicted value of each to-be-decoded group, and realizing point cloud attribute decoding of each to-be-decoded point cloud data based on the direct current coefficient predicted value, the reconstructed direct current coefficient residual and the reconstructed alternating current coefficient.
A fourth aspect of the present invention provides a point cloud attribute decoding apparatus, wherein the apparatus includes:
the grouping module is used for sequencing and grouping the point cloud data to be decoded to obtain a group to be decoded, wherein the point cloud data to be decoded is the point cloud data with the attribute to be decoded;
a reconstruction coefficient obtaining module, configured to perform inverse entropy coding and inverse quantization on the basis of each to-be-decoded group, and obtain a reconstructed direct current coefficient residual and a reconstructed alternating current coefficient of each to-be-decoded group;
and the decoding module is used for respectively obtaining the direct current coefficient predicted value of each to-be-decoded group and realizing point cloud attribute decoding of each to-be-decoded point cloud data based on the direct current coefficient predicted value, the reconstructed direct current coefficient residual and the reconstructed alternating current coefficient.
A fifth aspect of the present invention provides an intelligent terminal, where the intelligent terminal includes a memory, a processor, and a point cloud attribute decoding program stored in the memory and executable on the processor, and the point cloud attribute decoding program, when executed by the processor, implements any one of the steps of the point cloud attribute decoding method.
A sixth aspect of the present invention provides a computer-readable storage medium, in which a point cloud attribute decoding program is stored, and when the point cloud attribute decoding program is executed by a processor, the method for decoding the point cloud attribute includes any one of the steps of the point cloud attribute decoding method.
According to the scheme, the point cloud data to be coded are sorted and grouped to obtain the group to be coded, wherein the point cloud data to be coded is the point cloud data with the attribute to be coded; respectively transforming each group to be coded based on a transformation matrix to obtain transformation coefficients, wherein the transformation coefficients comprise direct current coefficients and alternating current coefficients; and respectively obtaining the direct current coefficient predicted value of each group to be coded, and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient predicted value and the transformation coefficient. Compared with the prior art, the point cloud data to be coded are grouped and then the point cloud data to be coded are transformed, and the relation characteristics among multiple points can be better reflected by each transformation aiming at multiple points; and for the direct current coefficient with higher correlation between the groups to be coded, the corresponding redundant information is reduced by a prediction method, thereby being beneficial to improving the coding performance and obtaining better point cloud attribute compression effect.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a point cloud attribute encoding method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the step S100 in FIG. 1 according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a group to be encoded according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating the step S300 in FIG. 1 according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating the step S304 in FIG. 4 according to an embodiment of the present invention;
fig. 6 is a schematic flowchart of a point cloud attribute encoding method with a coding residual processing step according to an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of an apparatus for encoding point cloud attributes according to an embodiment of the present invention;
FIG. 8 is a flowchart illustrating a method for decoding a point cloud attribute according to an embodiment of the present invention;
fig. 9 is a schematic flowchart of a point cloud attribute decoding method provided with a step of processing decoded residual errors according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a point cloud attribute decoding apparatus according to an embodiment of the present invention;
fig. 11 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when …" or "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted depending on the context to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings of the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
With the rapid development of scientific technology, technologies such as three-dimensional reconstruction and the like are widely applied, and three-dimensional point cloud is an important expression form of real world digitization. With the rapid development of three-dimensional scanning devices (e.g., lasers, radars, etc.), the accuracy and resolution of the point cloud becomes higher. The high-precision point cloud is widely applied to the construction of urban digital maps and plays a technical support role in numerous popular researches such as smart cities, unmanned driving, cultural relic protection and the like. The point cloud is obtained by sampling the surface of an object by a three-dimensional scanning device, the number of points of one frame of point cloud is generally in the million level, each point contains geometric information and attribute information such as color and reflectivity, and the data volume is huge. The huge data volume of the three-dimensional point cloud brings huge challenges to data storage, transmission and the like, so that the point cloud compression becomes very important.
In the prior art, when RAHT coding is generally adopted, the attributes of points are converted into transformation coefficients in a forward direction, then the transformation coefficients are quantized, and then the quantization coefficients are subjected to adaptive entropy coding. During decoding, adaptive entropy decoding is firstly performed to obtain a quantized coefficient, then inverse quantization is performed to obtain a transform coefficient, and then RAHT inverse transform is used to generate a reconstructed attribute value of a point. RAHT is designed based on one-dimensional transformation, and is multiple one-dimensional transformation, each transformation only aims at two points, and the relational characteristics among multiple points cannot be well reflected, so that the compression coding performance is limited. And its order of transformation in three-dimensional space affects the final algorithm performance. For example, in three-dimensional space, the current RAHT method transforms in the order of x-direction, y-direction, and z-direction, but this is not the best solution. Therefore, it is necessary to provide a better point cloud attribute encoding and corresponding point cloud attribute decoding scheme, so as to improve the point cloud compression performance and obtain a better compression effect.
In the embodiment of the invention, point cloud data to be coded are sorted and grouped to obtain a group to be coded, wherein the point cloud data to be coded is the point cloud data with the attribute to be coded; respectively transforming each group to be coded based on a transformation matrix to obtain transformation coefficients, wherein the transformation coefficients comprise direct current coefficients and alternating current coefficients; and respectively obtaining the direct current coefficient predicted value of each group to be coded, and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient predicted value and the transformation coefficient. Compared with the prior art, the point cloud data to be coded are grouped and then the point cloud data to be coded are transformed, each transformation can aim at multiple points, and the relation characteristics among the multiple points are better reflected; and for the direct current coefficient with higher correlation between the groups to be coded, the corresponding redundant information is reduced by a prediction method, thereby being beneficial to improving the coding performance and obtaining better point cloud attribute compression effect.
As shown in fig. 1, an embodiment of the present invention provides a method for encoding a point cloud attribute, specifically, the method includes the following steps:
step S100, point cloud data to be coded are sorted and grouped to obtain a group to be coded, wherein the point cloud data to be coded is point cloud data with attributes to be coded.
Specifically, after point cloud data to be encoded is obtained, the point cloud data to be encoded is sorted and grouped. The point cloud data to be encoded is the point cloud data which needs attribute compression encoding. The point cloud coding mainly comprises geometric coding and attribute coding, and the point cloud attribute coding is mainly realized in the embodiment of the invention, for example, the color attribute of the point cloud is coded.
Step S200, respectively transforming each group to be encoded based on the transformation matrix to obtain a transformation coefficient, where the transformation coefficient includes a direct current coefficient and an alternating current coefficient.
The transformation matrix may be a preset matrix, or may be set and adjusted according to actual requirements, which is not specifically limited herein.
Step S300, respectively obtaining the direct current coefficient predicted value of each group to be coded, and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient predicted value and the transformation coefficient.
Specifically, each group to be encoded is respectively transformed to obtain a transform coefficient of the group to be encoded, and further, a predicted value of a direct current coefficient of the group to be encoded is obtained, and point cloud attribute encoding of the group to be encoded is achieved based on the corresponding predicted value of the direct current coefficient and the transform coefficient. Optionally, point cloud attribute encoding may be performed on each group to be encoded in sequence according to the step S200 and the step S300.
As can be seen from the above, the point cloud attribute encoding method provided by the embodiment of the present invention sorts and groups point cloud data to be encoded to obtain a group to be encoded, where the point cloud data to be encoded is point cloud data with attributes to be encoded; respectively transforming each group to be coded based on a transformation matrix to obtain transformation coefficients, wherein the transformation coefficients comprise direct current coefficients and alternating current coefficients; and respectively obtaining the direct current coefficient predicted value of each group to be coded, and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient predicted value and the transformation coefficient. Compared with the prior art, the point cloud data to be coded are grouped and then the point cloud data to be coded are transformed, and the relation characteristics among multiple points can be better reflected by each transformation aiming at multiple points; and for the direct current coefficient with higher correlation between the groups to be coded, the corresponding redundant information is reduced by a prediction method, thereby being beneficial to improving the coding performance and obtaining better point cloud attribute compression effect.
Specifically, in this embodiment, as shown in fig. 2, the step S100 includes:
and S101, arranging the point cloud data to be coded into a one-dimensional sequence from a three-dimensional sequence according to a preset rule, and acquiring a reordered point cloud sequence.
The preset rule is a preset sorting rule and can be set and adjusted according to actual requirements. Optionally, the preset rule may be an ordering rule based on a morton code or a hilbert code. Specifically, in this embodiment, the point cloud data to be encoded is reordered from small to large based on the morton code or the hilbert code, and a reordered point cloud sequence is obtained.
And S102, sequentially grouping the point cloud sequences based on the reordering point cloud sequences to obtain groups to be coded.
Specifically, for the reordered point cloud sequence, the reordered point cloud sequence is sequentially grouped as groups to be encoded, every K points are one group, and the number of the geometric points of the last group to be encoded is K end Wherein K is an integer greater than or equal to 2, K end And may be any integer between 1 and K (including 1 and K). In particular, K may represent the order in which the group to be encoded is transformed. Fig. 3 is a schematic diagram of a group to be encoded according to an embodiment of the present invention, in fig. 3, point cloud data to be encoded is sorted and grouped based on a sequence of small to large hilbert codes, where K is 4 and K is end 3. Further, in this embodiment, each group to be encoded is sequentially encoded, the first two groups to be encoded shown in fig. 3 have already completed encoding, and what is being processed is the 3 rd group to be encoded.
Optionally, the transformation matrix is a one-dimensional K-order transformation matrix, and K is the number of points in the group to be encoded.
The transformation matrix may be set and adjusted according to actual requirements, and optionally, the transformation matrix may be a discrete cosine transformation matrix or a haar wavelet transformation matrix.
Specifically, one-dimensional K-order transformation may be performed on the attribute values of K geometric points in the group to be encoded, the transform matrix may select a Discrete Cosine (DCT) transform matrix or a Haar wavelet (Haar) transform matrix, and transform coefficients including a direct current coefficient and K-1 alternating current coefficients are obtained after the transformation. In this embodiment, the specific transformation formula is that Y is TX, X and Y are K-dimensional column vectors, the pth dimension of X represents the attribute value of the pth point, the first dimension of Y is the transformation dc coefficient, and the remaining K-1 dimension is the transformation ac coefficient. Embodiments of the present invention further provide an example of a specific transformation matrix T when K is 4, as shown in the following formula (1):
Figure BDA0002971569610000091
specifically, in this embodiment, as shown in fig. 4, the step S300 includes:
step S301, acquiring the position of each group to be encoded.
Specifically, the position of the group to be encoded is a position coordinate of the group to be encoded. The position coordinates of the group to be encoded are calculated based on the spatial positions of the geometric points within the group to be encoded. Alternatively, K (or K) within the group to be encoded may be used end ) The average coordinate of the point is used as the position coordinate of the group to be encoded, or the position coordinate of a random point or the position coordinate of a central point in the group to be encoded may be used as the position coordinate of the group to be encoded, which is not specifically limited herein.
Step S302, respectively obtaining the dc coefficient prediction values of each to-be-encoded group based on the position of each to-be-encoded group and the position of the encoded geometric point.
Specifically, the dc coefficient prediction value is calculated based on the position of the group to be encoded and the position of the encoded geometric point obtained in step S301. The coded points are geometric points in the group to be coded which are already coded. Since each group to be encoded is sequentially encoded according to steps S200 and S300, when the dc coefficient prediction value is calculated for the current group to be encoded, the group to be encoded before the current group to be encoded has already completed encoding, as shown in fig. 3, when the dc coefficient prediction value is calculated for the 3 rd group to be encoded, the points in the 1 st and 2 nd groups to be encoded are the encoded geometric points. In this embodiment, the dc coefficient prediction value of the 1 st to-be-encoded block is set to 0.
In this embodiment, all or part of the code is encodedAnd searching M points which are closest to the position of the current group to be encoded or adjacent points in the geometric points as reference points. The direct current coefficient prediction value is the weighted average value of the reconstruction attributes of the M reference points multiplied by
Figure BDA0002971569610000101
Weight w of each reference point i Distance d from reference point to group to be encoded i Correlation, the reconstructed attribute value of each reference point is a i (i-1, 2, …, M). Distance d i The distance may be an euclidean distance or a manhattan distance, and is not particularly limited herein, and in this embodiment, M is 3. Specific direct current coefficient prediction value DC pred The calculation formula of (a) is as follows:
Figure BDA0002971569610000102
step S303 is to calculate a residual between the dc coefficient prediction value and the dc coefficient as a dc coefficient residual.
And step S304, realizing point cloud attribute coding of each group to be coded based on the direct current coefficient residual error and the alternating current coefficient.
Specifically, for a group to be coded, a direct current coefficient residual and K-1 alternating current coefficients are obtained, quantization and entropy coding are carried out on the direct current coefficient residual and the K-1 alternating current coefficients together, and point cloud attribute coding of the group to be coded is achieved. The entropy coding is performed on the direct current coefficient residual error, and compared with the direct current coefficient coding, the code length can be saved. And the combination of prediction and transformation is beneficial to utilizing the correlation of direct current coefficients among coding groups, and the corresponding redundant information is reduced by using a prediction method, so that the coding performance is improved, and a better point cloud attribute compression effect is obtained. Meanwhile, the transformed frequency domain direct current coefficients (when the transformation formula is Y (TX), X is a signal in a space domain, and Y is a signal in a frequency domain) are predicted by utilizing the spatial information and the reconstructed attribute value of the point cloud, so that the correlation and the influence between the direct current coefficients can be effectively reduced, and the efficient point cloud attribute compression is realized by combining the transformation and prediction methods.
Specifically, in this embodiment, as shown in fig. 5, the step S304 includes:
step S3041, quantize the dc coefficient residual and the ac coefficient to obtain a quantized dc coefficient residual and a quantized ac coefficient.
Step S3042, performing entropy encoding on the quantized dc coefficient residual and the quantized ac coefficient to implement point cloud attribute encoding on the group to be encoded.
In this embodiment, the dc coefficient residual and the ac coefficient are quantized according to a preset quantization step, and a specific quantization step may be set and adjusted according to actual requirements, which is not specifically limited herein. Alternatively, the quantization step size for the dc coefficient residue and the quantization step size for the ac coefficient may be the same or different, e.g. a larger quantization step size (e.g. 2 times the quantization step size for the dc coefficient residue) may be used for the ac coefficient. The quantization method may adopt uniform quantization, uniform quantization with dead zones, non-uniform quantization, and the like, and is not particularly limited herein.
Specifically, the entropy coding is to encode quantized coefficients (including quantized dc coefficient residuals and quantized ac coefficients) without losing any information according to the principle of entropy, and may use Shannon (Shannon) coding, Huffman (Huffman) coding, arithmetic coding (arithmetric coding), or the like, and is not limited herein. When corresponding inverse entropy coding is performed, the entropy coded quantized coefficients can be completely restored.
Further, for the last group to be encoded, if K end K, the encoding can be performed using the above-described specific steps. If K is end <K, the points of the group to be encoded can also be encoded individually. Specifically, for the qth point (q ═ 1, …, K) end ) And calculating the attribute predicted value of the q-th point based on the position of the q-th point and the position of the coded geometric point. The calculation method is that M points which are closest to the position of the q-th point or adjacent points are searched in all or part of the coded geometric points to be used as reference points. And the q-th point attribute predicted value is a reconstructed attribute weighted average value of the M reference points. Weight w of each reference point i And referenceDistance d from point to group to be encoded i Correlation, the reconstructed attribute value of each reference point is a i (i ═ 1, 2, …, M). Distance d i And may be a euclidean distance or a manhattan distance, and is not particularly limited thereto. Optionally, the number M of the reference points may be the same as or different from the number of the reference points in step S302, and is the same in this embodiment and is 3. Specific attribute prediction value A pred The calculation formula of (a) is as follows:
Figure BDA0002971569610000121
further, residual errors of the attribute predicted value and the actual attribute value are calculated, the residual errors are quantized to obtain quantized residual error coefficients, and then the quantized residual error coefficients are subjected to entropy coding so as to realize the encoding of the point cloud attributes of the last group to be encoded.
Optionally, after step S300, the method further includes: and reconstructing the group to be coded. Specifically, the quantized dc coefficient residual and the quantized ac coefficient are inversely quantized to obtain a reconstructed dc coefficient residual and a reconstructed ac coefficient. And adding the direct current coefficient predicted value corresponding to the group to be coded and the reconstructed direct current coefficient residual to obtain a reconstructed direct current coefficient. Forming a column matrix, denoted as Y, of the reconstructed DC coefficients and the K-1 reconstructed AC coefficients ′ Performing one-dimensional K-order inverse transformation to obtain K point reconstruction attribute values, which are recorded as X ′ . Inverse transformation formula is X ′ ＝T T Y ′ Wherein T is T Is a transpose of the transformation matrix T.
For the last group to be encoded, if K end Reconstructing by adopting the steps as K; if K is end <K, the reconstruction method that can also be used is as follows: for the qth point (q 1, …, K) end ) And inversely quantizing the quantized residual coefficient to obtain a reconstructed attribute residual coefficient, and adding a corresponding attribute predicted value obtained by calculation during coding to the reconstructed attribute residual coefficient to obtain a reconstructed attribute value.
In this way, after each group to be encoded is encoded, the reconstruction attribute values of the corresponding points are calculated, and when the next group to be encoded is encoded, the direct current coefficient prediction value of the next group to be encoded can be predicted based on the reconstruction attribute values of the corresponding points. Meanwhile, the method can also be used for ensuring the consistency of coding and decoding during decoding.
Furthermore, in order to reduce the loss in the attribute coding and decoding process and realize the non-calculation or near-lossless attribute coding and decoding, a coding residual processing step and a decoding residual processing step can be arranged, and the precision in the coding and decoding process is improved. Optionally, the coding residual processing step and the decoding residual processing step may be combined with other compression methods to implement lossless and near-lossless attribute compression, which is not limited herein.
Specifically, the point cloud attribute coding method may further include a coding residual processing step, fig. 6 is a schematic flow chart of the point cloud attribute coding method provided with the coding residual processing step in the embodiment of the present invention, and as shown in fig. 6, when performing attribute coding, an attribute residual value of each space point between a reconstructed point cloud and an original point cloud is obtained, then the attribute residual value is quantized according to requirements to obtain an attribute quantized residual coefficient, and finally the attribute quantized residual coefficient is coded. The reconstructed point cloud is obtained according to the point cloud attribute coding method and has a reconstructed attribute value, and the original point cloud is unprocessed point cloud in the point cloud data to be processed. Specifically, for the near-lossless condition (limited-lossy), quantization coding is performed according to a given quantization step for the attribute residual value, and the control of the Hausdorff (Hausdorff) error can be realized. For lossless conditions (lossless), the processing can be done by the following two methods: according to the method I, for the attribute residual value, the attribute residual value is directly coded without using quantization processing, namely the quantization step length is 1; and secondly, coding the attribute quantization residual remainder and the attribute quantization residual coefficient according to the attribute residual value. Wherein, for the encoding of the color, the calculation of the attribute residual value needs to be performed in the color space of the original point cloud. If the reconstructed attribute value of the point cloud generated by the inverse transformation is in a different color space from the attribute value of the original point cloud, for example, the original point cloud has the attribute value of RGB color space, and the reconstructed attribute value of the point cloud generated by the inverse transformation is the attribute value of YUV color space, the reconstructed attribute value of the point cloud generated by the inverse transformation needs to be subjected to color space conversion, converted into the same color space as the original point cloud, and then calculated.
Further, the present invention example tests the experimental results of the method of this example compared with Anchor based on the PCRM software version v2.0, and the results are shown in tables 1 to 3 below.
TABLE 1
Figure BDA0002971569610000131
TABLE 2
Figure BDA0002971569610000132
TABLE 3
Figure BDA0002971569610000141
Table 1 is a table of rate-distortion data for luminance, chrominance, and reflectivity under the limited lossy geometry and lossy attribute conditions, table 2 is a table of rate-distortion data for luminance, chrominance, and reflectivity under the lossless geometry and lossy attribute conditions, and table 3 is a table of rate-distortion data for color attributes and reflectivity of three color channels of red (R), green (G), and blue (B) under the lossless geometry and limited lossy attribute conditions. As can be seen from tables 1 to 3, compared with the reference result of the test platform PCRM, under the conditions of limited lossy geometry and lossy attributes, and under the conditions of lossless geometry and lossy attributes, the end-to-end attribute rate distortion of the invention is respectively reduced by 13.7% and 29.7% for the luminance attributes; for the chroma Cb attribute, the end-to-end attribute rate distortion of the present invention is reduced by 34.8% and 38.3%, respectively; for the chroma Cr attribute, the end-to-end attribute rate distortion of the present invention is reduced by 35.3% and 38.9%, respectively; under the condition of lossless geometry and limited lossy property, the rate distortion of the end-to-end Hausdorff property of the invention is reduced by 10.8% for the color properties of red (R), green (G) and blue (B) color channels.
As shown in fig. 7, an embodiment of the present invention further provides a point cloud attribute encoding apparatus corresponding to the point cloud attribute encoding method, where the point cloud attribute encoding apparatus includes:
the grouping module 410 is configured to sort and group point cloud data to be encoded, to obtain a group to be encoded, where the point cloud data to be encoded is point cloud data to be encoded according to an attribute.
Specifically, after point cloud data to be encoded is obtained, the point cloud data to be encoded is sorted and grouped. The point cloud data to be encoded is the point cloud data which needs attribute compression encoding. The point cloud coding mainly comprises geometric coding and attribute coding, and the point cloud attribute coding is mainly realized in the embodiment of the invention, for example, the color attribute of the point cloud is coded.
A transform coefficient obtaining module 420, configured to transform each to-be-encoded group based on a transform matrix, so as to obtain a transform coefficient, where the transform coefficient includes a direct current coefficient and an alternating current coefficient.
The transformation matrix may be a preset matrix, or may be set and adjusted according to actual requirements, which is not specifically limited herein.
And an encoding module 430, configured to obtain the dc coefficient prediction values of the to-be-encoded groups, respectively, and implement point cloud attribute encoding on the to-be-encoded groups based on the dc coefficient prediction values and the transform coefficients.
Specifically, each group to be encoded is respectively transformed to obtain a transform coefficient of the group to be encoded, and further, a predicted value of a direct current coefficient of the group to be encoded is obtained, and point cloud attribute encoding of the group to be encoded is achieved based on the corresponding predicted value of the direct current coefficient and the transform coefficient. Optionally, the point cloud attribute coding may be sequentially performed on each group to be coded through the transform coefficient obtaining module 420 and the coding module 430.
As can be seen from the above, the point cloud attribute encoding device provided in the embodiment of the present invention obtains a group to be encoded by sorting and grouping point cloud data to be encoded through the grouping module 410, where the point cloud data to be encoded is point cloud data whose attribute is to be encoded; transforming each group to be encoded respectively by a transform coefficient obtaining module 420 based on a transform matrix to obtain transform coefficients, where the transform coefficients include dc coefficients and ac coefficients; the direct current coefficient prediction values of the groups to be coded are respectively obtained through the coding module 430, and the point cloud attribute coding of the groups to be coded is realized based on the direct current coefficient prediction values and the transformation coefficients. Compared with the prior art, the point cloud data to be coded are grouped and then the point cloud data to be coded are transformed, and the relation characteristics among multiple points can be better reflected by each transformation aiming at multiple points; and for the direct current coefficient with higher correlation between the groups to be coded, the corresponding redundant information is reduced by a prediction method, thereby being beneficial to improving the coding performance and obtaining better point cloud attribute compression effect.
Specifically, in this embodiment, the grouping module 410 is specifically configured to: arranging the point cloud data to be coded into a one-dimensional sequence from a three-dimensional sequence according to a preset rule, and acquiring a reordered point cloud sequence; and sequentially grouping the point cloud sequences based on the reordering, and acquiring the group to be encoded.
The preset rule is a preset sorting rule and can be set and adjusted according to actual requirements. Optionally, the preset rule may be a ranking rule based on a morton code or a hilbert code. Specifically, in this embodiment, based on the morton code or the hilbert code, the point cloud data to be encoded is reordered according to a descending order, and a reordered point cloud sequence is obtained. For the reordered point cloud sequence, sequentially grouping the reordered point cloud sequence as groups to be encoded, wherein every K points form one group, and the number of geometric points of the last group to be encoded is K end Wherein K is an integer greater than or equal to 2, K end And may be any integer between 1 and K (including 1 and K). In particular, K may represent the order in which the group to be encoded is transformed.
Optionally, the transformation matrix is a one-dimensional K-order transformation matrix, and K is the number of points in the group to be encoded.
The transformation matrix may be set and adjusted according to actual requirements, and optionally, the transformation matrix may be a discrete cosine transformation matrix or a haar wavelet transformation matrix. The specific transformation mode and the corresponding transformation matrix example may refer to the specific description in the corresponding point cloud attribute encoding method, and are not described herein again.
Specifically, in this embodiment, the encoding module 430 is specifically configured to: acquiring the position of each group to be coded; respectively acquiring direct current coefficient predicted values of the groups to be coded based on the positions of the groups to be coded and the positions of the coded geometric points; calculating a residual error between the direct current coefficient predicted value and the direct current coefficient to serve as a direct current coefficient residual error; and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient residual error and the alternating current coefficient.
Further, the encoding module 430 may be further specifically configured to: quantizing the direct current coefficient residual error and the alternating current coefficient to obtain a quantized direct current coefficient residual error and a quantized alternating current coefficient; and entropy coding is carried out on the quantized direct current coefficient residual error and the quantized alternating current coefficient so as to realize point cloud attribute coding of the group to be coded.
The specific process of the encoding module 430 for performing the above processing may refer to specific descriptions in the corresponding point cloud attribute encoding method, which are not described herein again.
Optionally, the point cloud attribute encoding device may further include an encoding residual processing module (not shown in fig. 7) configured to obtain an attribute residual value of each space point between the reconstructed point cloud and the original point cloud, quantize the attribute residual value according to a requirement to obtain an attribute quantized residual coefficient, and finally encode the attribute quantized residual coefficient. Namely, the corresponding coding residual processing steps are executed, so that the compression precision is improved in cooperation with the corresponding decoding residual processing. The specific processing procedure of the coding residual processing module may refer to the corresponding description in the coding residual processing step, and is not described herein again.
As shown in fig. 8, an embodiment of the present invention further provides a point cloud attribute decoding method corresponding to the point cloud attribute encoding method, where the method includes:
step A100, ordering and grouping point cloud data to be decoded to obtain a group to be decoded, wherein the point cloud data to be decoded is point cloud data with attributes to be decoded. In particular to point cloud data which is encoded based on the point cloud attribute encoding method provided by the embodiment of the invention.
Specifically, after point cloud data to be decoded is obtained, the point cloud data to be decoded is sorted and grouped, wherein the point cloud data to be decoded is the point cloud data which needs to be subjected to attribute decoding. The point cloud decoding mainly comprises geometric decoding and attribute decoding, and the point cloud attribute decoding is mainly realized in the embodiment of the invention, for example, the color attribute of the point cloud is decoded.
Step A200, inverse entropy coding and inverse quantization are carried out on the basis of each group to be decoded, and a reconstructed direct current coefficient residual and a reconstructed alternating current coefficient of each group to be decoded are obtained.
And step A300, respectively obtaining the direct current coefficient predicted values of the to-be-decoded groups, and realizing point cloud attribute decoding of the to-be-decoded point cloud data based on the direct current coefficient predicted values, the reconstructed direct current coefficient residuals and the reconstructed alternating current coefficients.
Specifically, point cloud data to be decoded are arranged into a one-dimensional sequence from a three-dimensional sequence according to a preset rule, and a reordered point cloud sequence is obtained. The preset rule may be a sorting rule based on a morton code or a hilbert code. Specifically, in this embodiment, reordering is performed from small to large based on the morton code or the hilbert code, a reordered sequence to be decoded is obtained, and points in the reordered sequence to be decoded are sequentially grouped as a group to be decoded. Specifically, every K points are in one group, and the number of the geometric points of the last group to be decoded is K end Wherein K is an integer greater than or equal to 2, K end And may be any integer between 1 and K (including 1 and K).
And decoding each group to be decoded respectively, specifically, performing inverse entropy coding and inverse quantization on the group to be decoded to obtain a reconstructed direct current coefficient residual and a reconstructed alternating current coefficient of each group to be decoded. And calculating a direct current coefficient predicted value of the group to be decoded (the specific calculation method can refer to a corresponding calculation method in the point cloud attribute coding method), and adding the direct current coefficient predicted value and the reconstructed direct current coefficient residual to obtain a reconstructed direct current coefficient. And performing one-dimensional K-order inverse transformation on the reconstructed direct current coefficient and the reconstructed alternating current coefficient to obtain K point reconstruction attribute values, thereby realizing point cloud attribute decoding of a to-be-decoded group. The inverse transformation matrix during inverse transformation may be set and adjusted according to actual requirements, and may specifically be set based on a corresponding transformation matrix during encoding.
Specifically, the reconstructed DC coefficients and K-1 reconstructed AC coefficients are combined into a column matrix, which is denoted as Y ′ Performing one-dimensional K-order inverse transformation to obtain a reconstruction attribute value of K points, which is marked as X ′ . Inverse transformation formula is X ′ ＝T T Y ′ Wherein T is T Is a transpose of the transformation matrix T.
Further, for the last group to be decoded, if K end The decoding may be performed using the above-described specific steps. If K is end <K, the points of the block to be decoded can also be decoded separately. Specifically, for the qth point (q ═ 1, …, K) end ) And decoding and inverse quantizing to obtain a value of the reconstructed attribute residual of the q-th point, and meanwhile, obtaining an attribute predicted value (the specific obtaining method can refer to a corresponding obtaining method in a point cloud attribute coding method), and adding the attribute predicted value and the reconstructed attribute residual to obtain a reconstructed attribute value.
Optionally, in this embodiment, the point cloud attribute decoding method may further refer to specific steps in the point cloud attribute encoding method to perform corresponding decoding, for example, performing inverse quantization and the like based on corresponding quantization step lengths in the point cloud attribute encoding method, which is not described herein again. Therefore, the data coded by the point cloud attribute coding method can be decoded.
Further, in order to reduce the loss in the attribute encoding and decoding process and realize the non-calculation or near-lossless attribute encoding and decoding, a decoding residual processing step may be provided corresponding to the above-mentioned encoding residual processing step, so as to improve the precision in the encoding and decoding process.
Specifically, the point cloud attribute decoding method may further include a decoding residual error processing step, and fig. 9 is a schematic flow chart of the point cloud attribute decoding method provided with the decoding residual error processing step in the embodiment of the present invention, as shown in fig. 9, when performing attribute decoding, a reconstructed point cloud attribute value and a quantized residual error coefficient code stream are obtained, entropy decoding is performed on the quantized residual error coefficient code stream to obtain an attribute quantized residual error coefficient, then inverse quantization is performed on the attribute quantized residual error coefficient to obtain a reconstructed attribute residual error value (i.e., a residual error between a reconstructed point cloud attribute value and an original point cloud attribute value), and finally the reconstructed attribute residual error value and the reconstructed point cloud attribute value are added to obtain a final point cloud attribute decoding result. The reconstructed point cloud attribute value is a point cloud attribute value with reconstruction attribute obtained in the process of reconstructing the group to be decoded, and the quantized residual error coefficient code stream is code stream data of a quantized residual error coefficient processed and output based on the residual error processing step. Specifically, for the near lossless condition, for the quantized residual coefficient code stream, entropy decoding is performed on the quantized residual coefficient code stream to obtain a quantized attribute residual coefficient, and then inverse quantization processing is performed according to a given quantization step (the same as a corresponding quantization step in the coding residual processing step) to obtain an attribute residual value. For lossless conditions, the treatment can be performed by the following two methods: according to the method I, for an existing attribute residual value code stream, entropy decoding is firstly carried out on the existing attribute residual value code stream to obtain an attribute residual value, inverse quantization processing is not needed, and the attribute residual value and a reconstructed point cloud attribute value are directly added to obtain a final point cloud attribute decoding result; and secondly, performing entropy decoding on the existing attribute quantization residual residue code stream and attribute quantization residual coefficient code stream respectively to obtain an attribute quantization residual residue and an attribute quantization residual coefficient, performing inverse quantization on the attribute quantization residual residue and the attribute quantization residual coefficient respectively to obtain a reconstructed attribute residual residue and a reconstructed attribute residual coefficient, and finally adding the reconstructed attribute residual residue, the reconstructed attribute residual coefficient and the reconstructed point cloud attribute value to obtain a final point cloud attribute decoding result. Wherein, for the decoding of the color, the decoding residual processing needs to be performed in the color space of the original point cloud. If the point cloud reconstructed attribute value generated by the inverse transformation is located in a different color space from the attribute value of the original point cloud, for example, the original point cloud has the attribute value of RGB color space, and the attribute value generated by the inverse transformation is the attribute value of YUV color space, the point cloud reconstructed attribute value generated by the inverse transformation needs to be subjected to color space conversion, and converted into the same color space as the original point cloud for calculation.
As shown in fig. 10, an embodiment of the present invention further provides a point cloud attribute decoding apparatus corresponding to the point cloud attribute decoding method, where the point cloud attribute decoding apparatus includes:
the grouping module 510 is configured to sort and group point cloud data to be decoded, and obtain a group to be decoded, where the point cloud data to be decoded is point cloud data to be decoded with an attribute.
Specifically, after point cloud data to be decoded is obtained, the point cloud data to be decoded is sorted and grouped. The point cloud data to be decoded is the point cloud data which needs attribute decompression. The point cloud decoding mainly comprises geometric decoding and attribute decoding, and the point cloud attribute decoding is mainly realized in the embodiment of the invention, for example, the color attribute of the point cloud is decoded.
A reconstruction coefficient obtaining module 520, configured to perform inverse entropy coding and inverse quantization on the basis of each to-be-decoded group, so as to obtain a reconstructed dc coefficient residual and a reconstructed ac coefficient of each to-be-decoded group.
A decoding module 530, configured to obtain the dc coefficient predicted values of the to-be-decoded groups, respectively, and implement point cloud attribute decoding on the point cloud data to be decoded based on the dc coefficient predicted values, the reconstructed dc coefficient residuals, and the reconstructed ac coefficients.
Specifically, the point cloud data to be decoded are arranged into a one-dimensional sequence from a three-dimensional sequence according to a preset rule, and a reordered point cloud sequence is obtained. The preset rule may be a ranking rule based on a morton code or a hilbert code. Specifically, in this embodiment, reordering is performed from small to large based on the morton code or the hilbert code, a reordered sequence to be decoded is obtained, and points in the reordered sequence to be decoded are sequentially grouped as a group to be decoded. Specifically, every K points are grouped, and the last point is to be decodedThe number of geometric points is K end Wherein K is an integer greater than or equal to 2, K end And may be any integer between 1 and K (including 1 and K).
And decoding each group to be decoded respectively, specifically, performing inverse entropy coding and inverse quantization on the group to be decoded to obtain a reconstructed direct current coefficient residual and a reconstructed alternating current coefficient of each group to be decoded. And calculating a direct current coefficient predicted value of the group to be decoded (the specific calculation process can refer to the corresponding calculation process in the point cloud attribute coding method or device), and adding the direct current coefficient predicted value and the reconstructed direct current coefficient residual to obtain the reconstructed direct current coefficient. And performing one-dimensional K-order inverse transformation on the reconstructed direct current coefficient and the reconstructed alternating current coefficient to obtain K point reconstruction attribute values, thereby realizing point cloud attribute decoding of a to-be-decoded group. The inverse transformation matrix during inverse transformation may be set and adjusted according to actual requirements, and may specifically be set based on a corresponding transformation matrix during encoding.
Further, for the last group to be decoded, if K end The decoding may be performed using the above-described specific steps. If K is end <K, the points of the block to be decoded can also be decoded separately. Specifically, for the qth point (q ═ 1, …, K) end ) And decoding and inverse quantizing to obtain a value of the reconstructed attribute residual of the q-th point, and meanwhile, obtaining an attribute predicted value (the specific obtaining process can refer to a corresponding obtaining process in a point cloud attribute coding method or device), and adding the attribute predicted value and the reconstructed attribute residual to obtain a reconstructed attribute value.
Optionally, in this embodiment, the point cloud attribute decoding device may further perform corresponding decoding with reference to specific steps in the point cloud attribute encoding method, for example, perform inverse quantization based on a corresponding quantization step in the point cloud attribute encoding method, and so on, which is not described herein again. Therefore, the data coded by the point cloud attribute coding method or device can be decoded.
Optionally, the point cloud attribute decoding apparatus may further include a decoding residual processing module (not shown in fig. 10) configured to obtain a reconstructed point cloud attribute value and a quantized residual coefficient code stream, perform entropy decoding on the quantized residual coefficient code stream to obtain an attribute quantized residual coefficient, perform inverse quantization on the attribute quantized residual coefficient to obtain a reconstructed attribute residual value (i.e., a residual between a reconstructed point cloud attribute value and an original point cloud attribute value), and finally add the reconstructed attribute residual value and the reconstructed point cloud attribute value to obtain a final point cloud attribute decoding result. The reconstructed point cloud attribute value is a point cloud attribute value with reconstruction attribute obtained in the process of reconstructing the group to be coded, and the quantized residual error coefficient is code stream data of the quantized residual error coefficient processed and output based on the residual error processing step. Namely, the corresponding decoding residual processing steps are executed, so that the compression precision is improved by matching with the corresponding coding residual processing. The specific processing procedure of the decoding residual processing module may refer to the corresponding description in the decoding residual processing step, and is not described herein again.
Based on the above embodiment, the present invention further provides an intelligent terminal, and a schematic block diagram thereof may be as shown in fig. 11. The intelligent terminal comprises a processor, a memory, a network interface and a display screen which are connected through a system bus. Wherein, the processor of the intelligent terminal is used for providing calculation and control capability. The memory of the intelligent terminal comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a point cloud attribute decoding program. The internal memory provides an environment for the operation of an operating system and a point cloud attribute decoding program in the non-volatile storage medium. The network interface of the intelligent terminal is used for being connected and communicated with an external terminal through a network. The point cloud attribute decoding program realizes the steps of any one of the point cloud attribute decoding methods when being executed by a processor. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen.
It will be understood by those skilled in the art that the block diagram of fig. 11 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation to the intelligent terminal to which the solution of the present invention is applied, and a specific intelligent terminal may include more or less components than those shown in the figure, or combine some components, or have different arrangements of components.
In one embodiment, an intelligent terminal is provided, where the intelligent terminal includes a memory, a processor, and a point cloud attribute decoding program stored in the memory and executable on the processor, and when executed by the processor, the point cloud attribute decoding program performs the following operations:
ordering and grouping point cloud data to be decoded to obtain a code group to be decoded, wherein the point cloud data to be decoded is point cloud data with attributes to be decoded;
performing inverse entropy coding and inverse quantization on the basis of each group to be decoded to obtain a reconstructed direct current coefficient residual and a reconstructed alternating current coefficient of each group to be decoded;
and respectively obtaining the direct current coefficient predicted value of each to-be-decoded group, and realizing point cloud attribute decoding of each to-be-decoded point cloud data based on the direct current coefficient predicted value, the reconstructed direct current coefficient residual and the reconstructed alternating current coefficient.
The embodiment of the invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is stored with a point cloud attribute decoding program, and the point cloud attribute decoding program is executed by a processor to realize the steps of any point cloud attribute decoding method provided by the embodiment of the invention.
Optionally, the intelligent terminal and the computer readable storage medium may also store a point cloud attribute encoding program to implement the steps of the point cloud attribute encoding method.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the apparatus may be divided into different functional units or modules to implement all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art would appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the above modules or units is only one logical division, and the actual implementation may be implemented by another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The integrated modules/units described above, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium and can implement the steps of the embodiments of the method when the computer program is executed by a processor. The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form. The computer readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc. It should be noted that the contents contained in the computer-readable storage medium can be increased or decreased as required by legislation and patent practice in the jurisdiction.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.

Claims (10)

1. A method for point cloud attribute encoding, the method comprising:
the method comprises the steps of ordering and grouping point cloud data to be encoded to obtain a group to be encoded, wherein the point cloud data to be encoded is point cloud data with attributes to be encoded;
respectively transforming each group to be coded based on a transformation matrix to obtain transformation coefficients, wherein the transformation coefficients comprise direct current coefficients and alternating current coefficients;
and respectively obtaining the direct current coefficient predicted value of each group to be coded, and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient predicted value and the transformation coefficient.
2. The point cloud attribute coding method according to claim 1, wherein the sorting and grouping of the point cloud data to be coded to obtain a group to be coded comprises:
arranging the point cloud data to be coded into a one-dimensional sequence from a three-dimensional sequence according to a preset rule, and acquiring a reordered point cloud sequence;
and sequentially grouping based on the reordered point cloud sequence to obtain a group to be encoded.
3. The point cloud attribute encoding method of claim 1, wherein the transformation matrix is a one-dimensional K-th order transformation matrix, and K is the number of points in the group to be encoded.
4. The point cloud attribute encoding method according to claim 1, wherein the respectively obtaining the dc coefficient prediction values of the groups to be encoded and implementing point cloud attribute encoding for the groups to be encoded based on the dc coefficient prediction values and the transform coefficients comprises:
acquiring the position of each group to be coded;
respectively acquiring direct current coefficient predicted values of the groups to be coded based on the positions of the groups to be coded and the positions of the coded geometric points;
calculating a residual error between the direct current coefficient predicted value and the direct current coefficient to serve as a direct current coefficient residual error;
and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient residual error and the alternating current coefficient.
5. The point cloud attribute coding method according to claim 4, wherein the performing of the point cloud attribute coding on each group to be coded based on the direct current coefficient residuals and the alternating current coefficients comprises:
quantizing the direct current coefficient residual error and the alternating current coefficient to obtain a quantized direct current coefficient residual error and a quantized alternating current coefficient;
and entropy coding is carried out on the quantized direct current coefficient residual error and the quantized alternating current coefficient so as to realize point cloud attribute coding of the group to be coded.
6. A point cloud attribute encoding apparatus, the apparatus comprising:
the grouping module is used for sequencing and grouping point cloud data to be encoded to acquire a group to be encoded, wherein the point cloud data to be encoded is point cloud data with attributes to be encoded;
a transform coefficient obtaining module, configured to transform each to-be-encoded group based on a transform matrix, and obtain a transform coefficient, where the transform coefficient includes a direct current coefficient and an alternating current coefficient;
and the coding module is used for respectively obtaining the direct current coefficient predicted value of each group to be coded and realizing point cloud attribute coding of each group to be coded based on the direct current coefficient predicted value and the transformation coefficient.
7. A method for decoding point cloud attributes, the method comprising:
ordering and grouping point cloud data to be decoded to obtain a group to be decoded, wherein the point cloud data to be decoded is point cloud data with attributes to be decoded;
performing inverse entropy coding and inverse quantization on the basis of each group to be decoded to obtain a reconstructed direct current coefficient residual error and a reconstructed alternating current coefficient of each group to be decoded;
and respectively acquiring a direct current coefficient predicted value of each group to be decoded, and realizing point cloud attribute decoding of each point cloud data to be decoded based on the direct current coefficient predicted value, the reconstructed direct current coefficient residual and the reconstructed alternating current coefficient.
8. A point cloud attribute decoding apparatus, the apparatus comprising:
the device comprises a grouping module, a decoding module and a processing module, wherein the grouping module is used for sequencing and grouping point cloud data to be decoded to acquire a group to be decoded, and the point cloud data to be decoded is point cloud data with attributes to be decoded;
the reconstruction coefficient acquisition module is used for carrying out inverse entropy coding and inverse quantization on the basis of each group to be decoded to acquire a reconstruction direct current coefficient residual error and a reconstruction alternating current coefficient of each group to be decoded;
and the decoding module is used for respectively obtaining the direct current coefficient predicted value of each to-be-decoded group and realizing point cloud attribute decoding of each to-be-decoded point cloud data based on the direct current coefficient predicted value, the reconstructed direct current coefficient residual and the reconstructed alternating current coefficient.
9. An intelligent terminal, characterized in that the intelligent terminal comprises a memory, a processor and a point cloud attribute decoding program stored on the memory and executable on the processor, the point cloud attribute decoding program when executed by the processor implementing the steps of the point cloud attribute decoding method according to claim 7.
10. A computer-readable storage medium, on which a point cloud attribute decoding program is stored, which when executed by a processor implements the steps of the point cloud attribute decoding method of claim 7.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024074121A1 (en) * 2022-10-04 2024-04-11 Douyin Vision Co., Ltd. Method, apparatus, and medium for point cloud coding
WO2024124901A1 (en) * 2022-12-12 2024-06-20 腾讯科技（深圳）有限公司 Point cloud processing method and apparatus, device, medium, and program product
WO2024149142A1 (en) * 2023-01-11 2024-07-18 维沃移动通信有限公司 Transform coefficient encoding method, transform coefficient decoding method, and terminal

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024074121A1 (en) * 2022-10-04 2024-04-11 Douyin Vision Co., Ltd. Method, apparatus, and medium for point cloud coding
WO2024124901A1 (en) * 2022-12-12 2024-06-20 腾讯科技（深圳）有限公司 Point cloud processing method and apparatus, device, medium, and program product
WO2024149142A1 (en) * 2023-01-11 2024-07-18 维沃移动通信有限公司 Transform coefficient encoding method, transform coefficient decoding method, and terminal

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