WO2023098814A1 - 属性量化、反量化方法、装置及设备 - Google Patents

属性量化、反量化方法、装置及设备 Download PDF

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WO2023098814A1
WO2023098814A1 PCT/CN2022/135908 CN2022135908W WO2023098814A1 WO 2023098814 A1 WO2023098814 A1 WO 2023098814A1 CN 2022135908 W CN2022135908 W CN 2022135908W WO 2023098814 A1 WO2023098814 A1 WO 2023098814A1
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quantization
information
target
attribute
correction factor
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PCT/CN2022/135908
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English (en)
French (fr)
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张伟
杨付正
鲁静芸
吕卓逸
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维沃移动通信有限公司
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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
    • H04N19/10—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
    • H04N19/42—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
    • H04N19/90—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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  • the present application belongs to the technical field of point cloud processing, and specifically relates to an attribute quantization and dequantization method, device and equipment.
  • the existing digital audio and video codec technology standard point cloud compression (Audio Video coding Standard, AVS) - point cloud compression (Point Cloud Compression, PCC) attribute encoding process under different bit rate points, use the default configuration Attribute information quantification.
  • AVS Anao Video coding Standard
  • PCC Point Cloud Compression
  • attribute interpolation on the reconstructed point cloud, that is, to recolor, and calculate a new attribute value for each point in the reconstructed point cloud, so that the attributes of the reconstructed point cloud and the original point cloud The error is minimal. This process will cause a mismatch between the reconstructed point cloud and the original point cloud attribute information, resulting in low coding efficiency.
  • Embodiments of the present application provide an attribute quantization and dequantization method, device and equipment, which can solve the problem of how to improve the matching degree between attribute quantization and recoloring process.
  • an attribute quantification method including:
  • the first device determines a quantization correction factor according to the target information, the target information includes at least one of geometric information and attribute information of the target point cloud, and the quantization correction factor is used to correct the target quantization step size;
  • the first device corrects the target quantization step size according to the quantization correction factor to obtain a corrected quantization step size
  • the first device performs quantization processing on the target point cloud according to the corrected quantization step size.
  • an attribute dequantization method including:
  • the second device obtains a quantization correction factor, the quantization correction factor is related to target information, and the target information includes at least one of geometric information and attribute information of the target point cloud, and the quantization correction factor is used to quantize the target step size make corrections;
  • the second device corrects the target quantization step size according to the quantization correction factor to obtain a corrected quantization step size
  • the second device performs inverse quantization processing on the target point cloud according to the corrected quantization step size.
  • an attribute quantification device including:
  • the first determination module is configured to determine a quantization correction factor according to target information, the target information including at least one of geometric information and attribute information of the target point cloud, and the quantization correction factor is used to correct the target quantization step size ;
  • a first correction module configured to correct the target quantization step size according to the quantization correction factor to obtain a corrected quantization step size
  • the first processing module is configured to perform quantization processing on the target point cloud according to the corrected quantization step.
  • an attribute dequantization device including:
  • the first acquisition module is used to acquire a quantized correction factor, the quantized correction factor is related to target information, and the target information includes at least one of geometric information and attribute information of the target point cloud, and the quantized correction factor is used for The target quantization step size is corrected;
  • the second correction module is used to correct the target quantization step size according to the quantization correction factor to obtain the corrected quantization step size
  • the second processing module is configured to perform inverse quantization processing on the target point cloud according to the corrected quantization step size.
  • a first device in a fifth aspect, includes a processor and a memory, the memory stores programs or instructions that can run on the processor, and the programs or instructions are executed by the processor When realizing the steps of the method as described in the first aspect.
  • an attribute quantification device including a processor and a communication interface, wherein the processor is used to determine the quantization correction factor according to the target information, and the target information includes the geometric information and attribute information of the target point cloud. At least one item, the quantization correction factor is used to correct the target quantization step; according to the quantization correction factor, the target quantization step is corrected to obtain the corrected quantization step; according to the corrected quantization step, Perform quantization processing on the target point cloud.
  • a second device in a seventh aspect, includes a processor and a memory, the memory stores programs or instructions that can run on the processor, and the programs or instructions are executed by the processor When executed, the steps of the method described in the second aspect are realized.
  • an attribute dequantization device including a processor and a communication interface, wherein the processor is used to obtain a quantization correction factor, and the quantization correction factor is related to target information, and the target information includes a target point cloud At least one of the geometric information and attribute information, the quantization correction factor is used to correct the target quantization step; according to the quantization correction factor, the target quantization step is corrected to obtain the corrected quantization step; Inverse quantization processing is performed on the target point cloud according to the corrected quantization step size.
  • a ninth aspect provides an attribute quantification system, including: an attribute quantization device and an attribute inverse quantization device, the attribute quantization device can be used to execute the steps of the attribute quantification method as described in the first aspect, and the attribute inverse quantization device It can be used to execute the steps of the attribute dequantization method described in the second aspect.
  • a readable storage medium is provided, and a program or an instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method as described in the first aspect are implemented, or the The steps of the method described in the second aspect.
  • a chip in an eleventh aspect, includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or an instruction to implement the method described in the first aspect. method, or implement the method as described in the second aspect.
  • a computer program/program product is provided, the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement the The steps of the method, or the steps to realize the method as described in the second aspect.
  • the quantization correction factor is determined according to the target information, and the target information includes at least one of the geometric information and attribute information of the target point cloud, so that the target quantization step size can be adjusted by the quantization correction factor Correction, and can use the corrected quantization step to quantize the target point cloud, which improves the matching of the attribute quantization process and the recoloring process, which in turn helps to improve the coding efficiency.
  • Figure 1 shows a schematic structural diagram of the point cloud AVS encoder framework
  • FIG. 2 shows a schematic flow chart of an attribute quantification method in an embodiment of the present application
  • FIG. 3 shows a schematic flow diagram of an attribute dequantization method in an embodiment of the present application
  • Fig. 4 shows the block diagram of the attribute quantification device of the embodiment of the present application
  • FIG. 5 shows one of the structural schematic diagrams of the attribute quantification device of the embodiment of the present application
  • FIG. 6 shows the second structural diagram of the attribute quantification device in the embodiment of the present application.
  • FIG. 7 shows a schematic diagram of modules of an attribute inverse quantization device according to an embodiment of the present application.
  • first, second and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific sequence or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or described herein and that "first" and “second” distinguish objects. It is usually one category, and the number of objects is not limited. For example, there may be one or more first objects.
  • “and/or” in the description and claims means at least one of the connected objects, and the character “/” generally means that the related objects are an "or” relationship.
  • Both the attribute quantization device corresponding to the attribute quantization method and the attribute inverse quantization device corresponding to the attribute inverse quantization method in the embodiment of the present application may be a terminal, and the terminal may also be called a terminal device or a user equipment (User Equipment, UE), and the terminal may be Mobile phone, tablet computer (Tablet Personal Computer), laptop computer (Laptop Computer) or notebook computer, personal digital assistant (Personal Digital Assistant, PDA), palmtop computer, netbook, ultra-mobile personal computer (ultra-mobile personal computer) computer, UMPC), mobile Internet device (Mobile Internet Device, MID), augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) equipment, robot, wearable device (Wearable Device) or vehicle-mounted equipment (Vehicle User Equipment, VUE), pedestrian terminals (Pedestrian User Equipment, PUE) and other terminal-side equipment, wearable devices include: smart watches, bracelets, earphones, glasses, etc. It should be noted that, the embodiment of the present application does not limit
  • the geometric information of the point cloud and the attribute information corresponding to each point are encoded separately.
  • the point cloud is preprocessed: first, the point cloud is constructed with a minimum cuboid containing all points in the input point cloud, which is called a bounding box.
  • the origin coordinates of the bounding box are the minimum values of the coordinates of each point in the point cloud in the three dimensions of x, y, and z.
  • coordinate transformation is performed on the points in the point cloud: based on the origin of the coordinates, the original coordinates of the points are transformed into relative coordinates relative to the origin of the coordinates.
  • the decoding end obtains the occupancy code of each node through continuous analysis in the order of breadth-first traversal, and divides the nodes in turn until the 1x1x1 unit cube is divided, and the analysis is obtained The number of points contained in each leaf node is finally recovered to obtain the geometrically reconstructed point cloud information.
  • attribute coding is mainly carried out for color and reflectance information. Firstly, it is judged whether to perform color space conversion, and if color space conversion is performed, the color information is converted from RGB color space to YUV color space. Then, in the case of geometrically lossy coding, it is necessary to perform attribute interpolation on the reconstructed point cloud, that is, to recolor, and calculate a new attribute value for each point in the reconstructed point cloud, so that the reconstructed point cloud and the original point cloud Attribute errors are minimal. There are three branches in attribute information coding: attribute prediction, attribute prediction transformation and attribute transformation.
  • the attribute prediction process is as follows: first the point cloud is re-ranked and then differentially predicted. In the current AVS coding framework, the Hilbert code is used to reorder the point cloud. Then perform attribute prediction on the sorted point cloud. If the geometric information of the current point to be encoded is the same as that of the previous encoded point, it is a repeated point, and the reconstructed attribute value of the repeated point is used as the attribute prediction value of the current point to be encoded. Otherwise, select the first m points of the Hilbert order as neighbor candidate points for the current point to be encoded, and then calculate the Manhattan distance between them and the geometric information of the current point to be encoded, and determine the nearest n points as the neighbors of the current point to be encoded.
  • the reciprocal of the distance is used as the weight to calculate the weighted average of the attributes of all neighbors, which is used as the attribute prediction value of the current point to be encoded.
  • the prediction residual is calculated through the attribute prediction value and the attribute value of the current point to be encoded, and finally the prediction residual is quantized and entropy encoded to generate a binary code stream.
  • the transformation process of attribute prediction is as follows: first, the point cloud sequence is grouped according to the spatial density of the point cloud, and then the attribute information of the point cloud is predicted. The obtained prediction residual is transformed, and the obtained transformation coefficient is quantized; finally, the quantized transformation coefficient and attribute residual are entropy encoded to generate a binary code stream.
  • the attribute transformation process is as follows: firstly, wavelet transform is performed on the point cloud attributes, and the transformation coefficients are quantized; secondly, the attribute reconstruction value is obtained through inverse quantization and inverse wavelet transformation; then the difference between the original attribute and the attribute reconstruction value is calculated to obtain the attribute residual and its Quantization; finally, entropy coding is performed on the quantized transform coefficients and attribute residuals to generate a binary code stream.
  • the embodiments of the present application are mainly aimed at the quantization and dequantization processes in the figure.
  • the embodiment of the present application provides an attribute quantification method, including:
  • Step 201 The first device determines a quantitative correction factor according to target information, where the target information includes at least one item of geometric information and attribute information of the target point cloud.
  • the above-mentioned target point cloud is a point cloud sequence or a point cloud slice (slice) in the point cloud sequence.
  • the target point cloud refers to the point cloud after the target point cloud to be coded is preprocessed, and the preprocessing includes at least one of coordinate translation, quantization processing and removing duplicate points.
  • the above geometric information is obtained according to the size information of the bounding box corresponding to the target point cloud.
  • the above attribute information includes color, reflectance and the like.
  • the first device described above is applicable to an encoding device.
  • Step 202 The first device corrects the target quantization step size according to the quantization correction factor to obtain a corrected quantization step size.
  • the target quantization step size may be an initial quantization step size
  • the initial quantization step size may be understood as a default quantization step size in a configuration file.
  • the corrected quantization step size satisfies the following formula:
  • AttrQuantStep fin attrQuantStep+Correctfactor
  • AttrQuantStep fin attrQuantStep ⁇ Correctfactor
  • AttrQuanStep fin indicates the corrected attribute quantization step size
  • attrQuantStep indicates the target quantization step size
  • Correctfactor indicates the quantization correction factor
  • Step 203 The first device performs quantization processing on the target point cloud according to the corrected quantization step.
  • the target quantization step size is corrected based on the quantization correction factor, and the target point cloud is quantized based on the corrected quantization step size.
  • the quantization process specifically refers to attribute quantization processing. Same, no more details here.
  • the quantization correction factor is determined according to the target information, and the target information includes the geometric information and attribute information of the target point cloud, so that the target quantization step can be corrected by the quantization correction factor, and can be used
  • the corrected quantization step size quantizes the target point cloud, which improves the matching between the attribute quantization process and the recoloring process, which in turn helps to improve the coding efficiency.
  • the geometric information includes geometric bit width information of the target point cloud, and the geometric bit width information is used to indicate required accuracy information of bounding box size information corresponding to the target point cloud.
  • the geometric bit width information is determined according to a maximum side length of a bounding box corresponding to the target point cloud.
  • the above-mentioned geometric bit width information can be obtained by calculating according to the size information of the bounding box corresponding to the target point cloud, or can be obtained directly, that is, obtained from the point cloud sequence corresponding to the above-mentioned target point cloud.
  • the geometric bit width information satisfies the following formula:
  • bitdepth log2maxSize
  • bitdepth represents the geometric bit width information
  • maxSize is the maximum side length of the bounding box corresponding to the target point cloud.
  • the size information of the bounding box may be represented by maxSize.
  • maxSize max(boundingbox x , boundingbox y , boundingbox z ), where boundingbox x represents the side length corresponding to the bounding box in the x direction, boundingbox y represents the side length corresponding to the bounding box in the y direction, and boundingbox z represents the bounding box in the The side length corresponding to the z direction.
  • the attribute information of the target information further includes: a target quantization step size.
  • the quantization correction factor satisfies at least one of the following:
  • the quantization correction factor is proportional to the geometric bit width information, that is, the larger the geometric bit width information, the larger the quantization correction factor;
  • the quantization correction factor is inversely proportional to the target quantization step size, that is, the larger the target quantization step size, the smaller the quantization correction factor;
  • the quantization correction factor is proportional to the precision information of the attribute information, that is, the larger the precision information of the attribute information is, the larger the quantization correction factor is.
  • the quantization correction factor satisfies the following formula:
  • Correctfactor represents a quantization correction factor
  • bitdepth represents geometric bit width information
  • attrQuantStep represents precision information of the attribute information
  • m, n, p, and q are preset values respectively.
  • ">>" indicates a right shift operation
  • bitdepth>>m indicates that the bitdepth is shifted to the right by m bits
  • attrbitdepth>>q indicates that attrbitdepth is shifted to the right by q bits.
  • quantization correction factor can also be calculated by other formulas based on the above parameters.
  • the above-mentioned quantization correction factor can be added to the target code stream, so that the decoder can directly obtain the quantization correction factor from the target code stream, and based on this The quantization correction factor is dequantized, and the target code stream is obtained after coding the nodes in the target point cloud.
  • the quantization correction factor is determined according to the target information, and the target information includes the geometric information and attribute information of the target point cloud, so that the target quantization step can be corrected by the quantization correction factor, and can be used
  • the corrected quantization step size quantizes the target point cloud, which improves the matching between the attribute quantization process and the recoloring process, which in turn helps to improve the coding efficiency.
  • the embodiment of the present application also provides an attribute dequantization method, including:
  • Step 301 The second device acquires a quantitative correction factor, the quantitative correction factor is related to target information, and the target information includes at least one of geometric information and attribute information of the target point cloud, and the quantitative correction factor is used to correct the target The quantization step size is corrected.
  • the above-mentioned target point cloud is a point cloud sequence or a point cloud slice (slice) in the point cloud sequence.
  • the target point cloud refers to the point cloud after the target point cloud to be coded is preprocessed, and the preprocessing includes at least one of coordinate translation, quantization processing and removing duplicate points.
  • the above geometric information is obtained according to the size information of the bounding box corresponding to the target point cloud.
  • the second device described above is applicable to a decoding device.
  • Step 302 The second device corrects the target quantization step size according to the quantization correction factor to obtain a corrected quantization step size.
  • the target quantization step size has been explained in the above embodiments, and will not be repeated here.
  • Step 303 The second device performs dequantization processing on the target point cloud according to the corrected quantization step.
  • the target quantization step size is corrected based on the quantization correction factor, and the target point cloud is dequantized based on the corrected quantization step size.
  • the dequantization process specifically refers to the attribute dequantization process. It is the same as the prior art and will not be repeated here.
  • the property dequantization method of the embodiment of the present application obtains a quantization correction factor, and the quantization correction factor is related to the target information, and the target information includes the geometric information and attribute information of the target point cloud, so that the quantization correction factor can be used for the target
  • the quantization step is corrected, and the corrected quantization step can be used to dequantize the target point cloud, which improves the matching degree of attribute quantization and recoloring, which in turn helps to improve decoding efficiency.
  • said acquiring a quantized correction factor includes:
  • the target code stream is decoded to obtain the quantization correction factor Correctfactor, and the target code stream is obtained by encoding the nodes in the target point cloud.
  • the attribute dequantization device directly obtains the quantization correction factor from the target code stream.
  • said acquiring a quantized correction factor includes:
  • the quantitative correction factor is determined.
  • the attribute dequantization device itself determines the quantization correction factor according to the target information.
  • the geometric bit width information is obtained according to the target code stream.
  • the geometric information includes geometric bit width information of the target point cloud, and the geometric bit width information is used to indicate required accuracy information of bounding box size information corresponding to the target point cloud.
  • the geometric bit width information satisfies the following formula:
  • bitdepth log2maxSize
  • bitdepth represents the geometric bit width information
  • maxSize is the maximum side length of the bounding box corresponding to the target point cloud.
  • the target information further includes: a target quantization step size.
  • the quantization correction factor satisfies at least one of the following:
  • the quantization correction factor is proportional to the geometric bit width information, that is, the larger the geometric bit width information, the larger the quantization correction factor;
  • the quantization correction factor is inversely proportional to the target quantization step size, that is, the larger the target quantization step size, the smaller the quantization correction factor;
  • the quantization correction factor is proportional to the precision information of the attribute information, that is, the larger the precision information of the attribute information is, the larger the quantization correction factor is.
  • the quantization correction factor satisfies the following formula:
  • Correctfactor represents a quantization correction factor
  • bitdepth represents geometric bit width information
  • attrQuantStep represents precision information of the attribute information
  • m, n, p, and q are preset values respectively.
  • ">>" indicates a right shift operation
  • bitdepth>>m indicates that the bitdepth is shifted to the right by m bits
  • attrbitdepth>>q indicates that attrbitdepth is shifted to the right by q bits.
  • the corrected quantization step size satisfies the following formula:
  • AttrQuantStep fin attrQuantStep+Correctfactor
  • AttrQuantStep fin attrQuantStep ⁇ Correctfactor
  • AttrQuanStep fin indicates the corrected attribute quantization step size
  • attrQuantStep indicates the target quantization step size
  • Correctfactor indicates the quantization correction factor
  • the property dequantization method of the embodiment of the present application obtains a quantization correction factor, and the quantization correction factor is related to the target information, and the target information includes the geometric information and attribute information of the target point cloud, so that the quantization correction factor can be used for the target
  • the quantization step is corrected, and the corrected quantization step can be used to dequantize the target point cloud, which improves the matching degree of attribute quantization and recoloring, which in turn helps to improve decoding efficiency.
  • the first device and the second device in this embodiment of the present application may be the same device or different devices.
  • the foregoing first device and second device may specifically be terminals.
  • the above method in the embodiment of the present application selects a more suitable quantization correction factor for different types of attribute information through the geometric information and attribute information of the target point cloud, and performs attribute quantization based on the quantization correction factor, which further improves the encoding and decoding efficiency.
  • limit-lossy refers to the coding condition that the geometry is lossy and the attribute is lossy.
  • Luma, Chroma Cb, and Chroma Cr represent the three components of the color channel, and reflectance represents the reflectivity information. They are all attribute information of the point cloud. For the currently tested point cloud sequence, at least one of the above attribute information is included.
  • the attribute quantification method provided in the embodiment of the present application may be executed by an attribute quantification device.
  • an attribute quantification method performed by an attribute quantization device is taken as an example to describe the attribute quantization device provided in the embodiment of the present application.
  • the embodiment of the present application also provides an attribute quantification device 400, including:
  • the first determining module 401 is configured to determine a quantization correction factor according to the target information, the target information including at least one of the geometric information and attribute information of the target point cloud, and the quantization correction factor is used to perform the target quantization step size correction;
  • the first correction module 402 is configured to correct the target quantization step size according to the quantization correction factor to obtain a corrected quantization step size
  • the first processing module 403 is configured to perform quantization processing on the target point cloud according to the corrected quantization step.
  • the device of the embodiment of the present application determines the quantization correction factor according to the target information, and the target information includes the geometric information and attribute information of the target point cloud, so that the target quantization step can be corrected by the quantization correction factor, and can be used
  • the corrected quantization step size quantizes the target point cloud, which improves the matching between the attribute quantization process and the recoloring process, which in turn helps to improve the coding efficiency.
  • the geometric information includes geometric bit width information of the target point cloud, and the geometric bit width information is used to indicate accuracy information of bounding box size information corresponding to the target point cloud.
  • the geometric bit width information is determined according to a maximum side length of a bounding box corresponding to the target point cloud.
  • the geometric bit width information satisfies the following formula:
  • bitdepth log2maxSize
  • bitdepth represents the geometric bit width information
  • maxSize is the maximum side length of the bounding box corresponding to the target point cloud.
  • the geometric bit width information is obtained according to a point cloud sequence corresponding to the target point cloud.
  • the attribute information of the target information further includes: a target quantization step size.
  • the quantization correction factor satisfies at least one of the following:
  • the quantization correction factor is proportional to the geometric bit width information
  • the quantization correction factor is inversely proportional to the target quantization step size
  • the quantization correction factor is proportional to the precision information of the attribute information.
  • the quantization correction factor satisfies the following formula:
  • Correctfactor indicates the quantization correction factor
  • bitdepth indicates the geometric bit width information
  • attrQuantStep indicates the precision information of the attribute information
  • m, n, p and q are preset values respectively
  • bitdepth>>m indicates that bitdepth is shifted to the right by m bits
  • attrbitdepth>>q means that attrbitdepth is shifted to the right by q bits.
  • the corrected quantization step size satisfies the following formula:
  • AttrQuantStep fin attrQuantStep+Correctfactor
  • AttrQuantStep fin attrQuantStep ⁇ Correctfactor
  • AttrQuanStep fin indicates the corrected attribute quantization step size
  • attrQuantStep indicates the target quantization step size
  • Correctfactor indicates the quantization correction factor
  • the device of the embodiment of the present application determines the quantization correction factor according to the target information, and the target information includes the geometric information and attribute information of the target point cloud, so that the target quantization step can be corrected by the quantization correction factor, and can be used
  • the corrected quantization step size quantizes the target point cloud, which improves the matching between the attribute quantization process and the recoloring process, which in turn helps to improve the coding efficiency.
  • the attribute quantification apparatus in the embodiment of the present application may be an electronic device, such as an electronic device with an operating system, or a component in the electronic device, such as an integrated circuit or a chip.
  • the electronic device may be a terminal, or other devices other than the terminal.
  • Other exemplary devices may be servers, network attached storage (Network Attached Storage, NAS), etc., which are not specifically limited in this embodiment of the present application.
  • the attribute quantification device provided by the embodiment of the present application can realize each process realized by the method embodiment in FIG. 2 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the embodiment of the present application also provides a first device, including a processor 501 and a memory 502, and the memory 502 stores programs or instructions that can run on the processor 501.
  • a first device including a processor 501 and a memory 502
  • the memory 502 stores programs or instructions that can run on the processor 501.
  • the embodiment of the present application also provides an attribute quantification device, including a processor and a communication interface, the processor is used to determine the quantization correction factor according to the target information, and the target information includes at least one of the geometric information and attribute information of the target point cloud , the quantization correction factor is used to correct the target quantization step; according to the quantization correction factor, the target quantization step is corrected to obtain the corrected quantization step; according to the corrected quantization step, the target The point cloud is quantified.
  • This device embodiment corresponds to the above-mentioned attribute quantification method embodiment, and each implementation process and implementation mode of the above-mentioned method embodiment can be applied to this device embodiment, and can achieve the same technical effect.
  • FIG. 6 is a schematic diagram of a hardware structure of an attribute quantification device implementing an embodiment of the present application.
  • the attribute quantification device includes but not limited to: radio frequency unit 601, network module 602, audio output unit 603, input unit 604, sensor 605, display unit 606, user input unit 607, interface unit 608, memory 609, processor 610, etc. at least some of the components.
  • the attribute quantification device can also include a power supply (such as a battery) for supplying power to each component. Consumption management and other functions.
  • a power supply such as a battery
  • the structure of the device shown in FIG. 6 does not constitute a limitation to the device. The device may include more or fewer components than shown in the figure, or combine some components, or arrange different components, which will not be repeated here.
  • the input unit 604 may include a graphics processing unit (Graphics Processing Unit, GPU) 6041 and a microphone 6042, and the graphics processor 6041 is used in a video capture mode or an image capture mode by an image capture device (such as the image data of the still picture or video obtained by the camera) for processing.
  • the display unit 606 may include a display panel 6061, and the display panel 6061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
  • the user input unit 607 includes at least one of a touch panel 6071 and other input devices 6072 .
  • the touch panel 6071 is also called a touch screen.
  • the touch panel 6071 may include two parts, a touch detection device and a touch controller.
  • Other input devices 6072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, and joysticks, which will not be repeated here.
  • the radio frequency unit 601 may transmit it to the processor 610 for processing; in addition, the radio frequency unit 601 may send the uplink data to the network side device.
  • the radio frequency unit 601 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.
  • the memory 609 can be used to store software programs or instructions as well as various data.
  • the memory 609 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required by at least one function (such as a sound playing function, image playback function, etc.), etc.
  • memory 609 may include volatile memory or nonvolatile memory, or, memory 609 may include both volatile and nonvolatile memory.
  • the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electronically programmable Erase Programmable Read-Only Memory (Electrically EPROM, EEPROM) or Flash.
  • ROM Read-Only Memory
  • PROM programmable read-only memory
  • Erasable PROM Erasable PROM
  • EPROM erasable programmable read-only memory
  • Electrical EPROM Electrical EPROM
  • EEPROM electronically programmable Erase Programmable Read-Only Memory
  • Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (Synch link DRAM , SLDRAM) and Direct Memory Bus Random Access Memory (Direct Rambus RAM, DRRAM).
  • RAM Random Access Memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • SDRAM double data rate synchronous dynamic random access memory
  • Double Data Rate SDRAM Double Data Rate SDRAM
  • DDRSDRAM double data rate synchronous dynamic random access memory
  • Enhanced SDRAM, ESDRAM enhanced synchronous dynamic random access memory
  • Synch link DRAM , SLDRAM
  • Direct Memory Bus Random Access Memory Direct Rambus
  • the processor 610 may include one or more processing units; optionally, the processor 610 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., Modem processors mainly process wireless communication signals, such as baseband processors. It can be understood that the foregoing modem processor may not be integrated into the processor 610 .
  • the processor 610 is configured to determine a quantization correction factor according to the target information, the target information includes at least one item of geometric information and attribute information of the target point cloud, and the quantization correction factor is used to perform the target quantization step size Correcting: correcting the target quantization step size according to the quantization correction factor and the target quantization step size to obtain a corrected quantization step size; performing quantization processing on the target point cloud according to the corrected quantization step size.
  • the device of the embodiment of the present application determines the quantization correction factor according to the target information, and the target information includes the geometric information and attribute information of the target point cloud, so that the target quantization step can be corrected by the quantization correction factor, and can be used
  • the corrected quantization step size quantizes the target point cloud, which improves the matching between the attribute quantization process and the recoloring process, which in turn helps to improve the coding efficiency.
  • the geometric information includes geometric bit width information of the target point cloud, and the geometric bit width information is used to indicate accuracy information of bounding box size information corresponding to the target point cloud.
  • the geometric bit width information is determined according to a maximum side length of a bounding box corresponding to the target point cloud.
  • the geometric bit width information satisfies the following formula:
  • bitdepth log2maxSize
  • bitdepth represents the geometric bit width information
  • maxSize is the maximum side length of the bounding box corresponding to the target point cloud.
  • the geometric bit width information is obtained according to a point cloud sequence corresponding to the target point cloud.
  • the attribute information of the target information further includes: a target quantization step size.
  • the quantization correction factor satisfies at least one of the following:
  • the quantization correction factor is proportional to the geometric bit width information
  • the quantization correction factor is inversely proportional to the target quantization step size
  • the quantization correction factor is proportional to the precision information of the attribute information.
  • the quantization correction factor satisfies the following formula:
  • Correctfactor indicates the quantization correction factor
  • bitdepth indicates the geometric bit width information
  • attrQuantStep indicates the precision information of the attribute information
  • m, n, p and q are preset values respectively
  • bitdepth>>m indicates that bitdepth is shifted to the right by m bits
  • attrbitdepth>>q means that attrbitdepth is shifted to the right by q bits.
  • the corrected quantization step size satisfies the following formula:
  • AttrQuantStep fin attrQuantStep+Correctfactor
  • AttrQuantStep fin attrQuantStep ⁇ Correctfactor
  • AttrQuanStep fin indicates the corrected attribute quantization step size
  • attrQuantStep indicates the target quantization step size
  • Correctfactor indicates the quantization correction factor
  • the device of the embodiment of the present application determines the quantization correction factor according to the target information, and the target information includes the geometric information and attribute information of the target point cloud, so that the target quantization step can be corrected by the quantization correction factor, and can be used
  • the corrected quantization step size quantizes the target point cloud, which improves the matching between the attribute quantization process and the recoloring process, which in turn helps to improve the coding efficiency.
  • the attribute dequantization method provided in the embodiment of the present application may be executed by an attribute dequantization device.
  • an attribute dequantization method performed by an attribute dequantization device is taken as an example to describe the attribute dequantization device provided in the embodiment of the present application.
  • an attribute inverse quantization device 700 including:
  • the first acquisition module 701 is configured to acquire a quantized correction factor, the quantized correction factor is related to target information, and the target information includes at least one of geometric information and attribute information of the target point cloud, and the quantized correction factor is used for Correct the target quantization step size;
  • the second correction module 702 is configured to correct the target quantization step size according to the quantization correction factor to obtain a corrected quantization step size
  • the second processing module 703 is configured to perform inverse quantization processing on the target point cloud according to the corrected quantization step size.
  • the device of the embodiment of the present application obtains a quantization correction factor, and the quantization correction factor is related to target information, and the target information includes geometric information and attribute information of the target point cloud, so that the quantization correction factor can be used to determine the target quantization step size Correction is carried out, and the target point cloud can be dequantized using the corrected quantization step size, which improves the matching degree of attribute quantization and recoloring, which in turn helps to improve decoding efficiency.
  • the first acquisition module is configured to decode a target code stream to obtain the quantization correction factor Correctfactor, and the target code stream is obtained by encoding nodes in the target point cloud.
  • the first obtaining module is configured to determine a quantization correction factor according to the target information.
  • the geometric information includes geometric bit width information of the target point cloud, and the geometric bit width information is used to indicate accuracy information of bounding box size information corresponding to the target point cloud.
  • the geometric bit width information is determined according to a maximum side length of a bounding box corresponding to the target point cloud.
  • the geometric bit width information satisfies the following formula:
  • bitdepth log2maxSize
  • bitdepth represents the geometric bit width information
  • maxSize is the maximum side length of the bounding box corresponding to the target point cloud.
  • the geometric bit width information is obtained according to the target code stream.
  • the target information further includes: a target quantization step size.
  • the quantization correction factor satisfies at least one of the following:
  • the quantization correction factor is proportional to the geometric bit width information
  • the quantization correction factor is inversely proportional to the target quantization step size
  • the quantization correction factor is proportional to the precision information of the attribute information.
  • the quantization correction factor satisfies the following formula:
  • Correctfactor indicates the quantization correction factor
  • bitdepth indicates the geometric bit width information
  • attrQuantStep indicates the precision information of the attribute information
  • m, n, p and q are preset values respectively
  • bitdepth>>m indicates that bitdepth is shifted to the right by m bits
  • attrbitdepth>>q means that attrbitdepth is shifted to the right by q bits.
  • the corrected quantization step size satisfies the following formula:
  • AttrQuantStep fin attrQuantStep+Correctfactor
  • AttrQuantStep fin attrQuantStep ⁇ Correctfactor
  • AttrQuanStep fin indicates the corrected attribute quantization step size
  • attrQuantStep indicates the target quantization step size
  • Correctfactor indicates the quantization correction factor
  • the device of the embodiment of the present application obtains a quantization correction factor, and the quantization correction factor is related to target information, and the target information includes geometric information and attribute information of the target point cloud, so that the quantization correction factor can be used to determine the target quantization step size Correction is carried out, and the target point cloud can be dequantized using the corrected quantization step size, which improves the matching degree of attribute quantization and recoloring, which in turn helps to improve decoding efficiency.
  • the attribute dequantization apparatus in the embodiment of the present application may be an electronic device, such as an electronic device with an operating system, or a component in the electronic device, such as an integrated circuit or a chip.
  • the electronic device may be a terminal, or other devices other than the terminal.
  • Other exemplary devices may be servers, network attached storage (Network Attached Storage, NAS), etc., which are not specifically limited in this embodiment of the present application.
  • the attribute dequantization device provided by the embodiment of the present application can realize each process realized by the method embodiment in FIG. 3 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the embodiment of the present application also provides a second device (for a schematic structural diagram, refer to FIG. 5 ), including a processor 501 and a memory 502, and the memory 502 stores programs or programs that can run on the processor 501.
  • a second device for a schematic structural diagram, refer to FIG. 5
  • the memory 502 stores programs or programs that can run on the processor 501.
  • the embodiment of the present application also provides an attribute dequantization device, including a processor and a communication interface, the processor is used to obtain a quantization correction factor, the quantization correction factor is related to target information, and the target information includes the geometric information of the target point cloud and Attribute information: perform dequantization processing on the target point cloud according to the quantization correction factor and the target quantization step size.
  • This device embodiment corresponds to the above-mentioned attribute dequantization method embodiment, and each implementation process and implementation mode of the above-mentioned method embodiment can be applied to this device embodiment, and can achieve the same technical effect.
  • the embodiment of the present application also provides an attribute quantification device, and its hardware structure diagram can be seen in FIG. 6 .
  • the property dequantization device includes but not limited to: radio frequency unit 601, network module 602, audio output unit 603, input unit 604, sensor 605, display unit 606, user input unit 607, interface unit 608, memory 609, processor 610, etc. at least some of the components.
  • the attribute dequantization device can also include a power supply (such as a battery) for supplying power to each component, and the power supply can be logically connected to the processor 610 through the power management system, so that the management of charging, discharging, and functions such as power management.
  • a power supply such as a battery
  • the structure of the device shown in FIG. 6 does not constitute a limitation to the device.
  • the device may include more or fewer components than shown in the figure, or combine some components, or arrange different components, which will not be repeated here.
  • the input unit 604 may include a graphics processing unit (Graphics Processing Unit, GPU) 6041 and a microphone 6042, and the graphics processor 6041 is used in a video capture mode or an image capture mode by an image capture device (such as the image data of the still picture or video obtained by the camera) for processing.
  • the display unit 606 may include a display panel 6061, and the display panel 6061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
  • the user input unit 607 includes at least one of a touch panel 6071 and other input devices 6072 .
  • the touch panel 6071 is also called a touch screen.
  • the touch panel 6071 may include two parts, a touch detection device and a touch controller.
  • Other input devices 6072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, and joysticks, which will not be repeated here.
  • the radio frequency unit 601 may transmit it to the processor 610 for processing; in addition, the radio frequency unit 601 may send the uplink data to the network side device.
  • the radio frequency unit 601 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.
  • the memory 609 can be used to store software programs or instructions as well as various data.
  • the memory 609 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required by at least one function (such as a sound playing function, image playback function, etc.), etc.
  • memory 609 may include volatile memory or nonvolatile memory, or, memory 609 may include both volatile and nonvolatile memory.
  • the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electronically programmable Erase Programmable Read-Only Memory (Electrically EPROM, EEPROM) or Flash.
  • ROM Read-Only Memory
  • PROM programmable read-only memory
  • Erasable PROM Erasable PROM
  • EPROM erasable programmable read-only memory
  • Electrical EPROM Electrical EPROM
  • EEPROM electronically programmable Erase Programmable Read-Only Memory
  • Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (Synch link DRAM , SLDRAM) and Direct Memory Bus Random Access Memory (Direct Rambus RAM, DRRAM).
  • RAM Random Access Memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • SDRAM double data rate synchronous dynamic random access memory
  • Double Data Rate SDRAM Double Data Rate SDRAM
  • DDRSDRAM double data rate synchronous dynamic random access memory
  • Enhanced SDRAM, ESDRAM enhanced synchronous dynamic random access memory
  • Synch link DRAM , SLDRAM
  • Direct Memory Bus Random Access Memory Direct Rambus
  • the processor 610 may include one or more processing units; optionally, the processor 610 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., Modem processors mainly process wireless communication signals, such as baseband processors. It can be understood that the foregoing modem processor may not be integrated into the processor 610 .
  • the processor 610 is configured to obtain a quantized correction factor, the quantized correction factor is related to target information, and the target information includes at least one of geometric information and attribute information of the target point cloud, and the quantized correction factor is used for Correcting the target quantization step; correcting the target quantization step according to the quantization correction factor to obtain a corrected quantization step; dequantizing the target point cloud according to the corrected quantization step deal with.
  • the device in the embodiment of the present application obtains a quantized correction factor, the quantized correction factor is related to target information, and the target information includes geometric information and attribute information of the target point cloud; according to the quantized correction factor and the target quantization step size, the The target point cloud is dequantized, so that the quantization correction factor can be used to correct the target quantization step, and the corrected quantization step can be used to perform dequantization on the target point cloud, which improves the attribute quantization process and The matching degree of the recoloring process is beneficial to improve the decoding efficiency.
  • processor 610 is further configured to:
  • the target code stream is decoded to obtain the quantization correction factor Correctfactor, and the target code stream is obtained by encoding the nodes in the target point cloud.
  • processor 610 is further configured to:
  • a quantitative correction factor is determined according to the target information.
  • the geometric information includes geometric bit width information of the target point cloud, and the geometric bit width information is used to indicate accuracy information of bounding box size information corresponding to the target point cloud.
  • the geometric bit width information is determined according to a maximum side length of a bounding box corresponding to the target point cloud.
  • the geometric bit width information satisfies the following formula:
  • bitdepth log2maxSize
  • bitdepth represents the geometric bit width information
  • maxSize is the maximum side length of the bounding box corresponding to the target point cloud.
  • the geometric bit width information is obtained according to the target code stream.
  • the target information further includes: a target quantization step size.
  • the quantization correction factor satisfies at least one of the following:
  • the quantization correction factor is proportional to the geometric bit width information
  • the quantization correction factor is inversely proportional to the target quantization step size
  • the quantization correction factor is proportional to the precision information of the attribute information.
  • the quantization correction factor satisfies the following formula:
  • Correctfactor indicates the quantization correction factor
  • bitdepth indicates the geometric bit width information
  • attrQuantStep indicates the precision information of the attribute information
  • m, n, p and q are preset values respectively
  • bitdepth>>m indicates that bitdepth is shifted to the right by m bits
  • attrbitdepth>>q means that attrbitdepth is shifted to the right by q bits.
  • the corrected quantization step size satisfies the following formula:
  • AttrQuantStep fin attrQuantStep+Correctfactor
  • AttrQuantStep fin attrQuantStep ⁇ Correctfactor
  • AttrQuanStep fin indicates the corrected attribute quantization step size
  • attrQuantStep indicates the target quantization step size
  • Correctfactor indicates the quantization correction factor
  • the device in the embodiment of the present application obtains a quantized correction factor, the quantized correction factor is related to target information, and the target information includes geometric information and attribute information of the target point cloud; according to the quantized correction factor and the target quantization step size, the The target point cloud is dequantized, so that the quantization correction factor can be used to correct the target quantization step, and the corrected quantization step can be used to perform dequantization on the target point cloud, which improves the attribute quantization process and The matching degree of the recoloring process is beneficial to improve the decoding efficiency.
  • the embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, each process of the above-mentioned attribute quantification method or attribute dequantization method embodiment is realized , and can achieve the same technical effect, in order to avoid repetition, it will not be repeated here.
  • the processor is the processor in the device described in the foregoing embodiments.
  • the readable storage medium includes a computer-readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk, and the like.
  • the embodiment of the present application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the above attribute quantification method or attribute reflection
  • the chip includes a processor and a communication interface
  • the communication interface is coupled to the processor
  • the processor is used to run programs or instructions to implement the above attribute quantification method or attribute reflection
  • the chip mentioned in the embodiment of the present application may also be called a system-on-chip, a system-on-chip, a system-on-a-chip, or a system-on-a-chip.
  • the embodiment of the present application further provides a computer program/program product, the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement the above attribute quantification method or attribute
  • the various processes of the embodiment of the inverse quantization method can achieve the same technical effect, so in order to avoid repetition, details are not repeated here.
  • the embodiment of the present application also provides an attribute quantization system, including: an attribute quantization device and an attribute inverse quantization device, the attribute quantization device can be used to execute the steps of the attribute quantization method as described above, and the attribute inverse quantization device can be used for The steps of the property dequantization method described above are performed.
  • an attribute quantization system including: an attribute quantization device and an attribute inverse quantization device, the attribute quantization device can be used to execute the steps of the attribute quantization method as described above, and the attribute inverse quantization device can be used for The steps of the property dequantization method described above are performed.
  • the term “comprising”, “comprising” or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase “comprising a " does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.
  • the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions are performed, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
  • the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation.
  • the technical solution of the present application can be embodied in the form of computer software products, which are stored in a storage medium (such as ROM/RAM, magnetic disk, etc.) , CD-ROM), including several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.

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Abstract

本申请公开了一种属性量化、反量化方法、装置及设备，属于点云处理技术领域，本申请实施例的属性量化方法包括：第一设备根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；所述第一设备根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长，根据矫正后的量化步长，对所述目标点云进行量化处理。

Description

属性量化、反量化方法、装置及设备
相关申请的交叉引用
本申请主张在2021年12月03日在中国提交的中国专利申请号No.202111465429.4的优先权，其全部内容通过引用包含于此。
技术领域
本申请属于点云处理技术领域，具体涉及一种属性量化、反量化方法、装置及设备。
背景技术
现有的数字音视频编解码技术标准的点云压缩(Audio Video coding Standard，AVS)-点云压缩(Point Cloud Compression，PCC)属性编码过程中，在不同的码率点下，使用默认配置进行属性信息量化。然而，在几何有损的情况下，需要对重建点云进行属性插值，即重上色，为重建点云中的每个点都计算新的属性值，使得重建点云和原始点云的属性误差最小。这一过程会造成重建点云和原始点云属性信息不匹配的情况，造成编码效率低下。
发明内容
本申请实施例提供一种属性量化、反量化方法、装置及设备，能够解决如何提高属性量化与重着色过程的匹配度的问题。
第一方面，提供了一种属性量化方法，包括：
第一设备根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；
所述第一设备根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长；
所述第一设备根据矫正后的量化步长，对所述目标点云进行量化处理。
第二方面，提供了一种属性反量化方法，包括：
第二设备获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；
所述第二设备根据所述量化矫正因子对目标量化步长进行矫正处理，得到矫正后的量化步长；
所述第二设备根据矫正后的量化步长，对所述目标点云进行反量化处理。
第三方面，提供了一种属性量化装置，包括：
第一确定模块，用于根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；
第一矫正模块，用于根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长；
第一处理模块，用于根据矫正后的量化步长，对所述目标点云进行量化处理。
第四方面，提供了一种属性反量化装置，包括：
第一获取模块，用于获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；
第二矫正模块，用于根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长；
第二处理模块，用于根据矫正后的量化步长，对所述目标点云进行反量化处理。
第五方面，提供了一种第一设备，该属性量化装置包括处理器和存储器，所述存储器存储可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如第一方面所述的方法的步骤。
第六方面，提供了一种属性量化装置，包括处理器及通信接口，其中，所述处理器用于根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长；根据矫正后的量化步长，对所述目标点云进行量化处理。
第七方面，提供了一种第二设备，该属性反量化装置包括处理器和存储器，所述存储器存储可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如第二方面所述的方法的步骤。
第八方面，提供了一种属性反量化装置，包括处理器及通信接口，其中，所述处理器用于获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；根据所述量化矫正因子对目标量化步长进行矫正处理，得到矫正后的量化步长；根据矫正后的量化步长，对所述目标点云进行反量化处理。
第九方面，提供了一种属性量化系统，包括：属性量化装置及属性反量化装置，所述属性量化装置可用于执行如第一方面所述的属性量化方法的步骤，所述属性反量化装置可用于执行如第二方面所述的属性反量化方法的步骤。
第十方面，提供了一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如第一方面所述的方法的步骤，或者实现如第二方面所述的方法的步骤。
第十一方面，提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如第一方面所述的方法，或实现如第二方面所述的方法。
第十二方面，提供了一种计算机程序/程序产品，所述计算机程序/程序产品被存储在存储介质中，所述计算机程序/程序产品被至少一个处理器执行以实现如第一方面所述的方法的步骤，或实现如第二方面所述的方法的步骤。
在本申请实施例中，根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行量化处理，提高了属性量化过程与重着色过程的匹配，进而有利于提高编码效率。
附图说明
图1表示点云AVS编码器框架的结构示意图；
图2表示本申请实施例的属性量化方法的流程示意图；
图3表示本申请实施例的属性反量化方法的流程示意图；
图4表示本申请实施例的属性量化装置的模块示意图；
图5表示本申请实施例的属性量化装置的结构示意图之一；
图6表示本申请实施例的属性量化装置的结构示意图之二；
图7表示本申请实施例的属性反量化装置的模块示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员所获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书和权利要求书中的术语“第一”、“第二”等是用于区别类似的对象，而不用于描述特定的顺序或先后次序。应该理解这样使用的术语在适当情况下可以互换，以便本申请的实施例能够以除了在这里图示或描述的那 些以外的顺序实施，且“第一”、“第二”所区别的对象通常为一类，并不限定对象的个数，例如第一对象可以是一个，也可以是多个。此外，说明书以及权利要求中“和/或”表示所连接对象的至少其中之一，字符“/”一般表示前后关联对象是一种“或”的关系。
本申请实施例中的属性量化方法对应的属性量化装置和属性反量化方法对应的属性反量化装置均可以为终端，该终端也可以称作终端设备或者用户终端(User Equipment，UE)，终端可以是手机、平板电脑(Tablet Personal Computer)、膝上型电脑(Laptop Computer)或称为笔记本电脑、个人数字助理(Personal Digital Assistant，PDA)、掌上电脑、上网本、超级移动个人计算机(ultra-mobile personal computer，UMPC)、移动上网装置(Mobile Internet Device，MID)、增强现实(augmented reality，AR)/虚拟现实(virtual reality，VR)设备、机器人、可穿戴式设备(Wearable Device)或车载设备(Vehicle User Equipment，VUE)、行人终端(Pedestrian User Equipment，PUE)等终端侧设备，可穿戴式设备包括：智能手表、手环、耳机、眼镜等。需要说明的是，在本申请实施例并不限定终端的具体类型。
为使本领域技术人员能够更好地理解本申请实施例，先对AVS编解码器框架进行如下说明。
如图1所示，在点云AVS编码器框架中，点云的几何信息和每点所对应的属性信息是分开编码的。首先对点云进行预处理：首先对点云构建一个包含全部输入点云中所有点的最小长方体，称为包围盒(bounding box)。包围盒的原点坐标即为点云中各点坐标在x、y、z三个维度上的最小值组成。接下来对点云的中的点进行坐标转换：以此坐标原点为基准，使点的原始坐标转变为相对坐标原点的相对坐标。然后再对点的几何坐标进行量化，这一步量化主要起到缩放的作用。由于量化取整，使得一部分点的几何信息相同，根据参数来决定是否移除这些几何信息重复的点。接下来，按照广度优先遍历的顺序对预处理之后的点云进行八叉树(四叉树或二叉树)划分：以经过预处理之后的包围盒为根节点，对其八等分，产生八个子立方体作为其子节点，并用八个比特表示其子节点的占用信息，称为空间占用码。如果子立方体中有点，则表示该子节点被占据，则对应占用比特取值为1，否则取值为0。对被占据的子立方体继续进行划分，直到划分得到的叶子节点为1x1x1的单位立方体时停止划分，完成几何八叉树的编码。在八叉树编码的过程中，对产生的空间占用码以及最终的叶子节点中包含的点数进行熵编码，得到输出码流。在基于八叉树的几何解码过程中，解码端按照广度优先遍历的顺序，通过不断解析得到每个节点的占用码，并且依次不断划分节点，直至划分得到1x1x1的单位立方体时停止划分，解析得到每个叶子节点中包含的点数，最终恢复得到几何重构点云信息。
几何编码完成后，对几何信息进行重建，利用重建的几何信息来对属性信息进行编码。目前，属性编码主要针对颜色、反射率信息进行。首先判断是否进行颜色空间的转换，若进行颜色空间转换，则将颜色信息从RGB颜色空间转换到YUV颜色空间。然后，在几何有损编码的情况下，需要对重建点云进行属性插值，即重上色，为重建点云中的每个点都计算新的属性值，使得重建点云和原始点云的属性误差最小。在属性信息编码中分为三个分支：属性预测、属性预测变换与属性变换。属性预测过程如下：首先对点云进行重排序，然后进行差分预测。当前AVS编码框架中均采用希尔伯特(Hilbert)码对点云进行重排序。然后对排序之后的点云进行属性预测，若当前待编码点与前一个已编码点的几何信息相同，即为重复点，则利用重复点的重建属性值作为当前待编码点的属性预测值，否则对当前待编码点选择Hilbert序的前m个点作为邻居候选点，然后分别计算它们同当前待编码点的几何信息的曼哈顿距离，确定距离最近的n个点作为当前待编码点的邻居，以距离的倒数作为权重，计算所有邻居的属性的加权平均，作为当前待编码点的属性预测值。通过属性预测值和当前待编码点的属性值，计算出预测残差，最后对预测残差进行量化并熵编码，生成二进制码流。属性预测变换过程如下：首先按照点云的空间疏密程度对点云序列进行分组，然后对点云属性信息进行预测。对得到的预测残差进行变换，将得到的变换系数进行量化；最后将量化后的变换系数和属性残差进行熵编码，生成二进制码流。属性变换过程如下：首先对点云属性做小波变换，对变换系数做量化；其次通过逆量化、逆小波变换得到属性重建值；然后计算原始属性和属性重建值的差得到属性残差并对其量化；最后将量化后的变换系数和属性残差进行熵编码，生成二进制码流。本申请实施例主要是针对图中的量化和反量化过程。
下面结合附图，通过一些实施例及其应用场景对本申请实施例提供的属性量化方法进行详细地说明。
如图2所示，本申请实施例提供了一种属性量化方法，包括：
步骤201：第一设备根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项。
可选地，上述目标点云为点云序列或点云序列中的点云片(slice)。该目标点云是指对待编码的目标点云进行预处理之后的点云，所述预处理包括坐标平移、量化处理和去除重复点中的至少一项。
上述几何信息是根据目标点云对应的包围盒尺寸信息得到的。上述属性信息包括颜色、反射率等。
上述第一设备可应用于编码设备。
步骤202：所述第一设备根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长。
这里，目标量化步长可以为初始量化步长，该初始量化步长可以理解为配置文件中默认的量化步长。
可选地，所述矫正后的量化步长满足以下公式：
AttrQuanStep fin＝attrQuantStep+Correctfactor；
或者
AttrQuanStep fin＝attrQuantStep×Correctfactor；
其中，AttrQuanStep fin表示矫正后的属性量化步长，attrQuantStep表示目标量化步长，Correctfactor表示量化矫正因子。
步骤203：所述第一设备根据矫正后的量化步长，对所述目标点云进行量化处理。
这里，基于量化矫正因子对目标量化步长进行矫正，并基于矫正后的量化步长对目标点云进行量化处理，该量化处理具体是指属性量化处理，该属性量化处理的过程与现有技术相同，此处不再赘述。
本申请实施例的方法，根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行量化处理，提高了属性量化过程与重着色过程的匹配，进而有利于提高编码效率。
可选地，所述几何信息包括目标点云的几何位宽信息，所述几何位宽信息用于指示所述目标点云对应的包围盒尺寸信息的所需要的精度信息。
可选地，所述几何位宽信息是根据所述目标点云对应的包围盒的最大边长确定的。
本申请实施例中，上述几何位宽信息可以根据目标点云对应的包围盒尺寸信息计算得到，也可以直接获取，即从根据上述目标点云对应的点云序列得到。
可选地，所述几何位宽信息满足以下公式：
bitdepth＝log 2max Size；
其中，bitdepth表示所述几何位宽信息，所述maxSize为所述目标点云对应的包围盒的最大边长。
在本申请的具体实施例中，包围盒的尺寸信息可通过maxSize表示。其中，maxSize＝max(boundingbox x,boundingbox y,boundingbox z)，其中，boundingbox x表示包围盒在x方向对应的边长，boundingbox y表示包围盒在y方向对应的边长,boundingbox z表示包围盒在z方向对应的边长。在包围盒的三个方向的边长中选取最大的边长maxSize，然后利用上述公式得到几何位宽信息。
可选地，所述目标信息的属性信息还包括：目标量化步长。
可选地，所述量化矫正因子满足以下至少一项：
所述量化矫正因子与所述几何位宽信息成正比，即几何位宽信息越大，量化矫正因子越大；
所述量化矫正因子与所述目标量化步长成反比，即目标量化步长越大，量化矫正因子越小；
所述量化矫正因子与所述属性信息的精度信息成正比，即属性信息的精度信息越大，量化矫正因子就越大。
可选地，所述量化矫正因子满足以下公式：
Correctfactor＝((bitdepth>>m)+(n-attrQuantStep/p))×(attrbitdepth>>q)；
其中，Correctfactor表示量化矫正因子，bitdepth表示几何位宽信息，attrQuantStep表示所述属性信息的精度信息，m、n、p和q分别为预设的数值。其中，“>>”表示右移运算，bitdepth>>m表示bitdepth右移m位，attrbitdepth>>q表示attrbitdepth右移q位。
当然，该量化矫正因子也可以基于上述参数通过其他公式计算得到。
可选地，本申请实施例中，在得到上述量化矫正因子后，可将上述量化矫正因子添加至目标码流中，以便于解码端直接从该目标码流中获取量化矫正因子，并基于该量化矫正因子进行反量化处理，所述目标码流是对目标点云中的节点进行编码处理后得到的。
本申请实施例的方法，根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行量化处理，提高了属性量化过程与重着色过程的匹配，进而有利于提高编码效率。
如图3所示，本申请实施例还提供了一种属性反量化方法，包括：
步骤301：第二设备获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正。
可选地，上述目标点云为点云序列或点云序列中的点云片(slice)。该目标点云是指对待编码的目标点云进行预处理之后的点云，所述预处理包括坐标平移、量化处理和去除重复点中的至少一项。
上述几何信息是根据目标点云对应的包围盒尺寸信息得到的。
上述第二设备可应用于解码设备。
步骤302：所述第二设备根据所述量化矫正因子对所述目标量化步长进行矫正处理，得到矫正后的量化步长。
该目标量化步长已在上述实施例中进行解释，此处不再赘述。
步骤303：所述第二设备根据矫正后的量化步长，对所述目标点云进行反量化处理。
这里，基于量化矫正因子对目标量化步长进行矫正，并基于矫正后的量化步长对目标点云进行反量化处理，该反量化处理具体是指属性反量化处理，该属性反量化处理的过程与现有技术相同，此处不再赘述。
本申请实施例的属性反量化方法，获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行反量化处理，提高了属性量化与重着色的匹配程度，进而有利于提高解码效率。
可选地，所述获取量化矫正因子，包括：
对目标码流进行解码处理，得到所述量化矫正因子Correctfactor，所述目标码流是对目标点云中的节点进行编码处理后得到的。
这里，属性反量化装置直接从目标码流中获取量化矫正因子。
可选地，所述获取量化矫正因子，包括：
根据目标信息，确定量化矫正因子。
这里，属性反量化装置自身根据目标信息来确定量化矫正因子。
所述几何位宽信息是根据所述目标码流得到的。
可选地，所述几何信息包括目标点云的几何位宽信息，所述几何位宽信息用于指示所述目标点云对应的包围盒尺寸信息的所需要的精度信息。
可选地，所述几何位宽信息满足以下公式：
bitdepth＝log 2max Size；
其中，bitdepth表示所述几何位宽信息，所述maxSize为所述目标点云对应的包围盒的最大边长。
可选地，所述目标信息还包括：目标量化步长。
可选地，所述量化矫正因子满足以下至少一项：
所述量化矫正因子与所述几何位宽信息成正比，即几何位宽信息越大，量化矫正因子越大；
所述量化矫正因子与所述目标量化步长成反比，即目标量化步长越大，量化矫正因子越小；
所述量化矫正因子与所述属性信息的精度信息成正比即属性信息的精度信息越大，量化矫正因子就越大。
可选地，所述量化矫正因子满足以下公式：
Correctfactor＝((bitdepth>>m)+(n-attrQuantStep/p))×(attrbitdepth>>q)；
其中，Correctfactor表示量化矫正因子，bitdepth表示几何位宽信息，attrQuantStep表示所述属性信息的精度信息，m、n、p和q分别为预设的数值。其中，“>>”表示右移运算，bitdepth>>m表示bitdepth右移m位，attrbitdepth>>q表示attrbitdepth右移q位。
可选地，所述矫正后的量化步长满足以下公式：
AttrQuanStep fin＝attrQuantStep+Correctfactor；
或者
AttrQuanStep fin＝attrQuantStep×Correctfactor；
其中，AttrQuanStep fin表示矫正后的属性量化步长，attrQuantStep表示目标量化步长，Correctfactor表示量化矫正因子。
本申请实施例的属性反量化方法，获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行反量化处理，提高了属性量化与重着色的匹配程度，进而有利于提高解码效率。
本申请实施例的第一设备和第二设备可以是同一个设备，也可以是不同的设备。上述第一设备和第二设备可具体为终端。
本申请实施例的上述方法，通过目标点云的几何信息和属性信息，为不同类型的属性信息选择更适合的量化矫正因子，并基于该量化矫正因子进行属性量化，进一步提升了编解码效率。
实验结果表明，利用本申请的方案可以提升编码性能，如表1所示，在各种测试条件下，本申请相对PCRMV5.0压缩效率更高。几何峰值信噪比(Peak Signal to Noise Ratio，PSNR)是一种图像评价的客观标准，PSNR越大则图像的质量越好)。其中，End-to-End BD-AttrReate是用来衡量属性信息编码性能好坏的参数，BD-AttrReate为负时表示性能变好，在此基础上BD-AttrReate的绝对值越大，则性能的增益越大。其中，limit-lossy是指几何有损，属性有损这一编码条件。Luma、Chroma Cb、Chroma Cr代表颜色通道的三个分量，reflectance代表反射率信息，它们都是点云的属性信息，对于目前测试的点云序列来说，至少包含以上一种属性信息。
表1
Figure PCTCN2022135908-appb-000001
Figure PCTCN2022135908-appb-000002
本申请实施例提供的属性量化方法，执行主体可以为属性量化装置。本申请实施例中以属性量化装置执行属性量化方法为例，说明本申请实施例提供的属性量化装置。
如图4所示，本申请实施例还提供了一种属性量化装置400，包括：
第一确定模块401，用于根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；
第一矫正模块402，用于根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长；
第一处理模块403，用于根据矫正后的量化步长，对所述目标点云进行量化处理。
本申请实施例的装置，根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行量化处理，提高了属性量化过程与重着色过程的匹配，进而有利于提高编码效率。
可选地，所述几何信息包括目标点云的几何位宽信息，所述几何位宽信息用于指示所述目标点云对应的包围盒尺寸信息的精度信息。
可选地，所述几何位宽信息是根据所述目标点云对应的包围盒的最大边长确定的。
可选地，所述几何位宽信息满足以下公式：
bitdepth＝log 2max Size；
其中，bitdepth表示所述几何位宽信息，所述maxSize为所述目标点云对应的包围盒的最大边长。
可选地，所述几何位宽信息是根据目标点云对应的点云序列得到的。
可选地，所述目标信息的属性信息还包括：目标量化步长。
可选地，所述量化矫正因子满足以下至少一项：
所述量化矫正因子与所述几何位宽信息成正比；
所述量化矫正因子与所述目标量化步长成反比；
所述量化矫正因子与所述属性信息的精度信息成正比。
可选地，所述量化矫正因子满足以下公式：
Correctfactor＝((bitdepth>>m)+(n-attrQuantStep/p))×(attrbitdepth>>q)；
其中，Correctfactor表示量化矫正因子，bitdepth表示几何位宽信息，attrQuantStep表示所述属性信息的精度信息，m、n、p和q分别为预设的数值，bitdepth>>m表示bitdepth右移m位，attrbitdepth>>q表示attrbitdepth右移q位。
可选地，所述矫正后的量化步长满足以下公式：
AttrQuanStep fin＝attrQuantStep+Correctfactor；
或者
AttrQuanStep fin＝attrQuantStep×Correctfactor；
其中，AttrQuanStep fin表示矫正后的属性量化步长，attrQuantStep表示目标量化步长，Correctfactor表示量化矫正因子。
本申请实施例的装置，根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行量化处理，提高了属性量化过程与重着色过程的匹配，进而有利于提高编码效率。
本申请实施例中的属性量化装置可以是电子设备，例如具有操作系统的电子设备，也可以是电子设备中的部件，例如集成电路或芯片。该电子设备可以 是终端，也可以为除终端之外的其他设备。示例性的其他设备可以为服务器、网络附属存储器(Network Attached Storage，NAS)等，本申请实施例不作具体限定。
本申请实施例提供的属性量化装置能够实现图2的方法实施例实现的各个过程，并达到相同的技术效果，为避免重复，这里不再赘述。
可选的，如图5所示，本申请实施例还提供一种第一设备，包括处理器501和存储器502，存储器502上存储有可在所述处理器501上运行的程序或指令，该程序或指令被处理器501执行时实现上述属性量化方法实施例的各个步骤，且能达到相同的技术效果，为避免重复，这里不再赘述。
本申请实施例还提供一种属性量化装置，包括处理器和通信接口，处理器用于根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长；根据矫正后的量化步长，对所述目标点云进行量化处理。该装置实施例与上述属性量化方法实施例对应，上述方法实施例的各个实施过程和实现方式均可适用于该装置实施例中，且能达到相同的技术效果。具体地，图6为实现本申请实施例的一种属性量化装置的硬件结构示意图。
该属性量化装置包括但不限于：射频单元601、网络模块602、音频输出单元603、输入单元604、传感器605、显示单元606、用户输入单元607、接口单元608、存储器609以及处理器610等中的至少部分部件。
本领域技术人员可以理解，属性量化装置还可以包括给各个部件供电的电源(比如电池)，电源可以通过电源管理系统与处理器610逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。图6中示出的装置结构并不构成对装置的限定，装置可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置，在此不再赘述。
应理解的是，本申请实施例中，输入单元604可以包括图形处理单元(Graphics Processing Unit，GPU)6041和麦克风6042，图形处理器6041对在视频捕获模式或图像捕获模式中由图像捕获装置(如摄像头)获得的静态图片或视频的图像数据进行处理。显示单元606可包括显示面板6061，可以采用液晶显示器、有机发光二极管等形式来配置显示面板6061。用户输入单元607包括触控面板6071以及其他输入设备6072中的至少一种。触控面板6071，也称为触摸屏。触控面板6071可包括触摸检测装置和触摸控制器两个部分。其他输入设备6072可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆，在此不再赘述。
本申请实施例中，射频单元601接收来自网络侧设备的下行数据后，可以传输给处理器610进行处理；另外，射频单元601可以向网络侧设备发送上行数据。通常，射频单元601包括但不限于天线、放大器、收发信机、耦合器、低噪声放大器、双工器等。
存储器609可用于存储软件程序或指令以及各种数据。存储器609可主要包括存储程序或指令的第一存储区和存储数据的第二存储区，其中，第一存储区可存储操作系统、至少一个功能所需的应用程序或指令(比如声音播放功能、图像播放功能等)等。此外，存储器609可以包括易失性存储器或非易失性存储器，或者，存储器609可以包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，RAM)，静态随机存取存储器(Static RAM，SRAM)、动态随机存取存储器(Dynamic RAM，DRAM)、同步动态随机存取存储器(Synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，DDRSDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(Synch link DRAM，SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，DRRAM)。本申请实施例中的存储器609包括但不限于这些和任意其它适合类型的存储器。
处理器610可包括一个或多个处理单元；可选的，处理器610集成应用处理器和调制解调处理器，其中，应用处理器主要处理涉及操作系统、用户界面和应用程序等的操作，调制解调处理器主要处理无线通信信号，如基带处理器。可以理解的是，上述调制解调处理器也可以不集成到处理器610中。
其中，处理器610，用于根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；根据所述量化矫正因子和目标量化步长对目标量化步长进行矫正，得到矫正后的量化步长；根据矫正后的量化步长，对所述目标点云进行量化处理。
本申请实施例的装置，根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行量化处理，提高了属性量化过程与重着色过程的匹配，进而有利于提高编码效率。
可选地，所述几何信息包括目标点云的几何位宽信息，所述几何位宽信息用于指示所述目标点云对应的包围盒尺寸信息的精度信息。
可选地，所述几何位宽信息是根据所述目标点云对应的包围盒的最大边长确定的。
可选地，所述几何位宽信息满足以下公式：
bitdepth＝log 2max Size；
其中，bitdepth表示所述几何位宽信息，所述maxSize为所述目标点云对应的包围盒的最大边长。
可选地，所述几何位宽信息是根据目标点云对应的点云序列得到的。
可选地，所述目标信息的属性信息还包括：目标量化步长。
可选地，所述量化矫正因子满足以下至少一项：
所述量化矫正因子与所述几何位宽信息成正比；
所述量化矫正因子与所述目标量化步长成反比；
所述量化矫正因子与所述属性信息的精度信息成正比。
可选地，所述量化矫正因子满足以下公式：
Correctfactor＝((bitdepth>>m)+(n-attrQuantStep/p))×(attrbitdepth>>q)；
其中，Correctfactor表示量化矫正因子，bitdepth表示几何位宽信息，attrQuantStep表示所述属性信息的精度信息，m、n、p和q分别为预设的数值，bitdepth>>m表示bitdepth右移m位，attrbitdepth>>q表示attrbitdepth右移q位。
可选地，所述矫正后的量化步长满足以下公式：
AttrQuanStep fin＝attrQuantStep+Correctfactor；
或者
AttrQuanStep fin＝attrQuantStep×Correctfactor；
其中，AttrQuanStep fin表示矫正后的属性量化步长，attrQuantStep表示目标量化步长，Correctfactor表示量化矫正因子。
本申请实施例的装置，根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行量化处理，提高了属性量化过程与重着色过程的匹配，进而有利于提高编码效率。
本申请实施例提供的属性反量化方法，执行主体可以为属性反量化装置。本申请实施例中以属性反量化装置执行属性反量化方法为例，说明本申请实施例提供的属性反量化装置。
如图7所示，本申请实施例还提供了一种属性反量化装置700，包括：
第一获取模块701，用于获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；
第二矫正模块702，用于根据所述量化矫正因子对目标量化步长进行矫正处理，得到矫正后的量化步长；
第二处理模块703，用于根据矫正后的量化步长，对所述目标点云进行反量化处理。
本申请实施例的装置，获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行反量化处理，提高了属性量化与重着色的匹配程度，进而有利于提高解码效率。
可选地，所述第一获取模块用于对目标码流进行解码处理，得到所述量化矫正因子Correctfactor，所述目标码流是对目标点云中的节点进行编码处理后得到的。
可选地，所述第一获取模块用于根据所述目标信息，确定量化矫正因子。
可选地，所述几何信息包括目标点云的几何位宽信息，所述几何位宽信息用于指示所述目标点云对应的包围盒尺寸信息的精度信息。
可选地，所述几何位宽信息是根据所述目标点云对应的包围盒的最大边长确定的。可选地，所述几何位宽信息满足以下公式：
bitdepth＝log 2max Size；
其中，bitdepth表示所述几何位宽信息，所述maxSize为所述目标点云对应的包围盒的最大边长。
可选地，所述几何位宽信息是根据所述目标码流得到的。
可选地，所述目标信息还包括：目标量化步长。
可选地，所述量化矫正因子满足以下至少一项：
所述量化矫正因子与所述几何位宽信息成正比；
所述量化矫正因子与所述目标量化步长成反比；
所述量化矫正因子与所述属性信息的精度信息成正比。
可选地，所述量化矫正因子满足以下公式：
Correctfactor＝((bitdepth>>m)+(n-attrQuantStep/p))×(attrbitdepth>>q)；
其中，Correctfactor表示量化矫正因子，bitdepth表示几何位宽信息，attrQuantStep表示所述属性信息的精度信息，m、n、p和q分别为预设的数值，bitdepth>>m表示bitdepth右移m位，attrbitdepth>>q表示attrbitdepth右移q位。
可选地，所述矫正后的量化步长满足以下公式：
AttrQuanStep fin＝attrQuantStep+Correctfactor；
或者
AttrQuanStep fin＝attrQuantStep×Correctfactor；
其中，AttrQuanStep fin表示矫正后的属性量化步长，attrQuantStep表示目标量化步长，Correctfactor表示量化矫正因子。
本申请实施例的装置，获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行反量化处理，提高了属性量化与重着色的匹配程度，进而有利于提高解码效率。
本申请实施例中的属性反量化装置可以是电子设备，例如具有操作系统的电子设备，也可以是电子设备中的部件，例如集成电路或芯片。该电子设备可以是终端，也可以为除终端之外的其他设备。示例性的其他设备可以为服务器、网络附属存储器(Network Attached Storage，NAS)等，本申请实施例不作具体限定。
本申请实施例提供的属性反量化装置能够实现图3的方法实施例实现的各个过程，并达到相同的技术效果，为避免重复，这里不再赘述。
可选的，本申请实施例还提供一种第二设备(其结构示意可参见图5)，包括处理器501和存储器502，存储器502上存储有可在所述处理器501上运行的程序或指令，该程序或指令被处理器501执行时实现上述属性反量化方法实施例的各个步骤，且能达到相同的技术效果，为避免重复，这里不再赘述。
本申请实施例还提供一种属性反量化装置，包括处理器和通信接口，处理器用于获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息；根据所述量化矫正因子和目标量化步长，对所述目标点云进行反量化处理。该装置实施例与上述属性反量化方法实施例对应，上述方法实施例的各个实施过程和实现方式均可适用于该装置实施例中，且能达到相同的技术效果。
本申请实施例还提供了一种属性量化装置，其硬件结构示意图可参见图6。该属性反量化装置包括但不限于：射频单元601、网络模块602、音频输出单元603、输入单元604、传感器605、显示单元606、用户输入单元607、接口单元608、存储器609以及处理器610等中的至少部分部件。
本领域技术人员可以理解，属性反量化装置还可以包括给各个部件供电的电源(比如电池)，电源可以通过电源管理系统与处理器610逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。图6中示出的装置结构并不构成对装置的限定，装置可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置，在此不再赘述。
应理解的是，本申请实施例中，输入单元604可以包括图形处理单元(Graphics Processing Unit，GPU)6041和麦克风6042，图形处理器6041对 在视频捕获模式或图像捕获模式中由图像捕获装置(如摄像头)获得的静态图片或视频的图像数据进行处理。显示单元606可包括显示面板6061，可以采用液晶显示器、有机发光二极管等形式来配置显示面板6061。用户输入单元607包括触控面板6071以及其他输入设备6072中的至少一种。触控面板6071，也称为触摸屏。触控面板6071可包括触摸检测装置和触摸控制器两个部分。其他输入设备6072可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆，在此不再赘述。
本申请实施例中，射频单元601接收来自网络侧设备的下行数据后，可以传输给处理器610进行处理；另外，射频单元601可以向网络侧设备发送上行数据。通常，射频单元601包括但不限于天线、放大器、收发信机、耦合器、低噪声放大器、双工器等。
存储器609可用于存储软件程序或指令以及各种数据。存储器609可主要包括存储程序或指令的第一存储区和存储数据的第二存储区，其中，第一存储区可存储操作系统、至少一个功能所需的应用程序或指令(比如声音播放功能、图像播放功能等)等。此外，存储器609可以包括易失性存储器或非易失性存储器，或者，存储器609可以包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，RAM)，静态随机存取存储器(Static RAM，SRAM)、动态随机存取存储器(Dynamic RAM，DRAM)、同步动态随机存取存储器(Synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，DDRSDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(Synch link DRAM，SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，DRRAM)。本申请实施例中的存储器609包括但不限于这些和任意其它适合类型的存储器。
处理器610可包括一个或多个处理单元；可选的，处理器610集成应用处理器和调制解调处理器，其中，应用处理器主要处理涉及操作系统、用户界面和应用程序等的操作，调制解调处理器主要处理无线通信信号，如基带处理器。可以理解的是，上述调制解调处理器也可以不集成到处理器610中。
其中，处理器610，用于获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；根据所述量化矫正因子对所述目标量化步长进行矫正处理，得到矫正后的量化步长；根据矫正后的量化步长，对所述目标点云进行反量化处理。
本申请实施例的装置，获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息；根据所述量化矫正因子和目标量化步长，对所述目标点云进行反量化处理，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行反量化处理，提高了属性量化过程和重着色过程的匹配度，进而有利于提高解码效率。
可选地，所述处理器610还用于：
对目标码流进行解码处理，得到所述量化矫正因子Correctfactor，所述目标码流是对目标点云中的节点进行编码处理后得到的。
可选地，所述处理器610还用于：
根据所述目标信息，确定量化矫正因子。
可选地，所述几何信息包括目标点云的几何位宽信息，所述几何位宽信息用于指示所述目标点云对应的包围盒尺寸信息的精度信息。
可选地，所述几何位宽信息是根据所述目标点云对应的包围盒的最大边长确定的。
可选地，所述几何位宽信息满足以下公式：
bitdepth＝log 2max Size；
其中，bitdepth表示所述几何位宽信息，所述maxSize为所述目标点云对应的包围盒的最大边长。
可选地，，所述几何位宽信息是根据所述目标码流得到的。
可选地，所述目标信息还包括：目标量化步长。
可选地，所述量化矫正因子满足以下至少一项：
所述量化矫正因子与所述几何位宽信息成正比；
所述量化矫正因子与所述目标量化步长成反比；
所述量化矫正因子与所述属性信息的精度信息成正比。
可选地，所述量化矫正因子满足以下公式：
Correctfactor＝((bitdepth>>m)+(n-attrQuantStep/p))×(attrbitdepth>>q)；
其中，Correctfactor表示量化矫正因子，bitdepth表示几何位宽信息，attrQuantStep表示所述属性信息的精度信息，m、n、p和q分别为预设的数值，bitdepth>>m表示bitdepth右移m位，attrbitdepth>>q表示attrbitdepth右移q位。
可选地，所述矫正后的量化步长满足以下公式：
AttrQuanStep fin＝attrQuantStep+Correctfactor；
或者
AttrQuanStep fin＝attrQuantStep×Correctfactor；
其中，AttrQuanStep fin表示矫正后的属性量化步长，attrQuantStep表示目标量化步长，Correctfactor表示量化矫正因子。
本申请实施例的装置，获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息；根据所述量化矫正因子和目标量化步长，对所述目标点云进行反量化处理，这样，通过该量化矫正因子能够对目标量化步长进行矫正，并能够利用矫正后的量化步长对目标点云进行反量化处理，提高了属性量化过程和重着色过程的匹配度，进而有利于提高解码效率。
本申请实施例还提供一种可读存储介质，所述可读存储介质上存储有程序或指令，该程序或指令被处理器执行时实现上述属性量化方法或属性反量化方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
其中，所述处理器为上述实施例中所述的装置中的处理器。所述可读存储介质，包括计算机可读存储介质，如计算机只读存储器ROM、随机存取存储器RAM、磁碟或者光盘等。
本申请实施例另提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现上述属性量化方法或属性反量化方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
应理解，本申请实施例提到的芯片还可以称为系统级芯片，系统芯片，芯片系统或片上系统芯片等。
本申请实施例另提供了一种计算机程序/程序产品，所述计算机程序/程序产品被存储在存储介质中，所述计算机程序/程序产品被至少一个处理器执行以实现上述属性量化方法或属性反量化方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
本申请实施例还提供了一种属性量化系统，包括：属性量化装置及属性反量化装置，所述属性量化装置可用于执行如上所述的属性量化方法的步骤，所述属性反量化装置可用于执行如上所述的属性反量化方法的步骤。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。此外，需要指出的是，本申请实施方式中的方法和装置的范围不限按示出或讨论的顺序来执行功能，还可包括根据所涉及的功能按基本同时的方式或按相反的顺序来执行功能，例如，可以按不同 于所描述的次序来执行所描述的方法，并且还可以添加、省去、或组合各种步骤。另外，参照某些示例所描述的特征可在其他示例中被组合。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以计算机软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
上面结合附图对本申请的实施例进行了描述，但是本申请并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本申请的启示下，在不脱离本申请宗旨和权利要求所保护的范围情况下，还可做出很多形式，均属于本申请的保护之内。

Claims (27)

  1. 一种属性量化方法，包括：
    第一设备根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；
    所述第一设备根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长；
    所述第一设备根据矫正后的量化步长，对所述目标点云进行量化处理。
  2. 根据权利要求1所述的方法，其中，所述几何信息包括目标点云的几何位宽信息，所述几何位宽信息用于指示所述目标点云对应的包围盒尺寸信息的精度信息。
  3. 根据权利要求2所述的方法，其中，所述几何位宽信息是根据所述目标点云对应的包围盒的最大边长确定的。
  4. 根据权利要求3所述的方法，其中，所述几何位宽信息满足以下公式：
    bitdepth＝log 2max Size；
    其中，bitdepth表示所述几何位宽信息，所述maxSize为所述目标点云对应的包围盒的最大边长。
  5. 根据权利要求2所述的方法，其中，所述几何位宽信息是根据目标点云对应的点云序列得到的。
  6. 根据权利要求1所述的方法，其中，所述目标信息的属性信息还包括：目标量化步长。
  7. 根据权利要求6所述的方法，其中，所述量化矫正因子满足以下至少一项：
    所述量化矫正因子与几何位宽信息成正比；
    所述量化矫正因子与所述目标量化步长成反比；
    所述量化矫正因子与所述属性信息的精度信息成正比。
  8. 根据权利要求2所述的方法，其中，所述量化矫正因子满足以下公式：
    Correctfactor＝((bitdepth>>m)+(n-attrQuantStep/p))×(attrbitdepth>>q)；
    其中，Correctfactor表示量化矫正因子，bitdepth表示几何位宽信息，attrQuantStep表示所述属性信息的精度信息，m、n、p和q分别为预设的数值，bitdepth>>m表示bitdepth右移m位，attrbitdepth>>q表示attrbitdepth右移q位。
  9. 根据权利要求1所述的方法，其中，所述矫正后的量化步长满足以下公式：
    AttrQuanStep fin＝attrQuantStep+Correctfactor；
    或者
    AttrQuanStep fin＝attrQuantStep×Correctfactor；
    其中，AttrQuanStep fin表示矫正后的属性量化步长，attrQuantStep表示目标量化步长，Correctfactor表示量化矫正因子。
  10. 一种属性反量化方法，其中，包括：
    第二设备获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；
    所述第二设备根据所述量化矫正因子对所述目标量化步长进行矫正处理，得到矫正后的量化步长；
    所述第二设备根据矫正后的量化步长，对所述目标点云进行反量化处理。
  11. 根据权利要求10所述的方法，其中，所述获取量化矫正因子，包括：
    对目标码流进行解码处理，得到所述量化矫正因子Correctfactor，所述目标码流是对目标点云中的节点进行编码处理后得到的。
  12. 根据权利要求10所述的方法，其中，所述几何信息包括目标点云的几何位宽信息，所述几何位宽信息用于指示所述目标点云对应的包围盒尺寸信息的精度信息。
  13. 根据权利要求12所述的方法，其中，所述几何位宽信息是根据所述目标点云对应的包围盒的最大边长确定的。
  14. 根据权利要求13所述的方法，其中，所述几何位宽信息满足以下公式：
    bitdepth＝log 2max Size；
    其中，bitdepth表示所述几何位宽信息，所述maxSize为所述目标点云对应的包围盒的最大边长。
  15. 根据权利要求13所述的方法，其中，所述几何位宽信息是根据目标码流得到的，所述目标码流是对目标点云中的节点进行编码处理后得到的。
  16. 根据权利要求10所述的方法，其中，所述目标信息还包括：目标量化步长。
  17. 根据权利要求16所述的方法，其中，所述量化矫正因子满足以下至少一项：
    所述量化矫正因子与几何位宽信息成正比；
    所述量化矫正因子与所述目标量化步长成反比；
    所述量化矫正因子与所述属性信息的精度信息成正比。
  18. 根据权利要求10所述的方法，其中，所述量化矫正因子满足以下公式：
    Correctfactor＝((bitdepth>>m)+(n-attrQuantStep/p))×(attrbitdepth>>q)；
    其中，Correctfactor表示量化矫正因子，bitdepth表示几何位宽信息，attrQuantStep表示所述属性信息的精度信息，m、n、p和q分别为预设的数值，bitdepth>>m表示bitdepth右移m位，attrbitdepth>>q表示attrbitdepth右移q位。
  19. 根据权利要求10所述的方法，其中，所述矫正后的量化步长满足以下公式：
    AttrQuanStep fin＝attrQuantStep+Correctfactor；
    或者
    AttrQuanStep fin＝attrQuantStep×Correctfactor；
    其中，AttrQuanStep fin表示矫正后的属性量化步长，attrQuantStep表示目标量化步长，Correctfactor表示量化矫正因子。
  20. 一种属性量化装置，应用于第一设备，其中，包括：
    第一确定模块，用于根据目标信息，确定量化矫正因子，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；
    第一矫正模块，用于根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长；
    第一处理模块，用于根据矫正后的量化步长，对所述目标点云进行量化处理。
  21. 一种第一设备，其中，包括处理器和存储器，所述存储器存储可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如权利要求1至9任一项所述的属性量化方法的步骤。
  22. 一种属性反量化装置，应用于第二设备，其中，包括：
    第一获取模块，用于获取量化矫正因子，所述量化矫正因子与目标信息相关，所述目标信息包括目标点云的几何信息和属性信息中的至少一项，所述量化矫正因子用于对目标量化步长进行矫正；
    第二矫正模块，用于根据所述量化矫正因子对目标量化步长进行矫正，得到矫正后的量化步长；
    第二处理模块，用于根据矫正后的量化步长，对所述目标点云进行反量化处理。
  23. 一种第二设备，其中，包括处理器和存储器，所述存储器存储可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如权利要求10至19任一项所述的属性反量化方法的步骤。
  24. 一种可读存储介质，其中，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如权利要求1至9任一项所述的属性量化方法的步骤，或者实现如权利要求10至19任一项所述的属性反量化方法的步骤。
  25. 一种芯片，包括处理器和通信接口，其中，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如权利要求1至9任一项所述的属性量化方法的步骤，或者实现如权利要求10至19任一项所述的属性反量化方法的步骤。
  26. 一种计算机程序产品，其中，所述程序产品被存储在非易失的存储介质中，所述程序产品被处理器执行时实现如权利要求1至9任一项所述的属性量化方法的步骤，或者实现如权利要求10至19任一项所述的属性反量化方法的步骤。
  27. 一种属性量化系统，包括：属性量化装置及属性反量化装置，所述属性量化装置可用于执行如权利要求1至9任一项所述的属性量化方法的步骤，所述属性反量化装置可用于执行如权利要求10至19任一项所述的属性反量化方法的步骤。
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