WO2023098803A1 - 点云编码处理方法、点云解码处理方法及相关设备 - Google Patents

点云编码处理方法、点云解码处理方法及相关设备 Download PDF

Info

Publication number
WO2023098803A1
WO2023098803A1 PCT/CN2022/135876 CN2022135876W WO2023098803A1 WO 2023098803 A1 WO2023098803 A1 WO 2023098803A1 CN 2022135876 W CN2022135876 W CN 2022135876W WO 2023098803 A1 WO2023098803 A1 WO 2023098803A1
Authority
WO
WIPO (PCT)
Prior art keywords
attribute
attribute information
encoded
information
target order
Prior art date
Application number
PCT/CN2022/135876
Other languages
English (en)
French (fr)
Inventor
张伟
杨付正
鲁静芸
吕卓逸
Original Assignee
维沃移动通信有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 维沃移动通信有限公司 filed Critical 维沃移动通信有限公司
Publication of WO2023098803A1 publication Critical patent/WO2023098803A1/zh

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
    • H04N13/106—Processing image signals
    • H04N13/161—Encoding, multiplexing or demultiplexing different image signal components
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/30—Image reproducers
    • 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
    • H04N19/13—Adaptive entropy coding, e.g. adaptive variable length coding [AVLC] or context adaptive binary arithmetic coding [CABAC]
    • 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

Definitions

  • the present application belongs to the field of computer technology, and in particular relates to a point cloud encoding and processing method, a point cloud decoding processing method and related equipment.
  • a point cloud is a form of representation of a three-dimensional object or scene, which is composed of a set of discrete point sets that are irregularly distributed in space and express the spatial structure and surface properties of a three-dimensional object or scene.
  • Point cloud data usually consists of geometric information describing a location, such as three-dimensional coordinates (x, y, z), and attribute information of the location, such as color (R, G, B) or reflectance.
  • attribute information such as color (R, G, B) or reflectance.
  • the process of encoding the attribute information of the point cloud has a poor effect of removing redundancy between information, resulting in low encoding efficiency.
  • Embodiments of the present application provide a point cloud encoding processing method, a point cloud decoding processing method, and related equipment, which can solve the problem of low encoding efficiency.
  • a point cloud encoding processing method comprising:
  • the distribution characteristic value being used to represent the distribution characteristic information of the attribute information to be encoded
  • the target order is an order of Exponential Golomb encoding
  • Entropy encoding is performed on the attribute information to be encoded based on the exponential Golomb encoding algorithm of the target order to obtain an attribute code stream.
  • a point cloud decoding processing method comprising:
  • the target order is the order of exponential Golomb decoding
  • Entropy decoding is performed on the attribute code stream to be decoded based on the exponential Golomb decoding algorithm of the target order.
  • a point cloud encoding processing device including:
  • the first determination module is configured to determine a distribution characteristic value, and the distribution characteristic value is used to represent the distribution characteristic information of the attribute information to be encoded;
  • the second determination module is configured to determine a target order corresponding to the attribute information to be encoded based on the distribution characteristic value, where the target order is an order of Exponential Golomb encoding;
  • An encoding module configured to perform entropy encoding on the attribute information to be encoded based on the exponential Golomb encoding algorithm of the target order to obtain an attribute code stream.
  • a point cloud decoding processing device including:
  • a determining module configured to determine a target order corresponding to the attribute code stream to be decoded, where the target order is the order of Exponential Golomb decoding
  • the decoding module is configured to perform entropy decoding on the to-be-decoded attribute code stream based on the exponential Golomb decoding algorithm of the target order.
  • the embodiment of the present application provides an electronic device, the electronic device includes a processor and a memory, the memory stores programs or instructions that can run on the processor, and the programs or instructions are processed by the The steps of the method described in the first aspect are realized when the processor is executed, or the steps of the method described in the second aspect are realized when the program or instruction is executed by the processor.
  • an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented Or, when the program or instruction is executed by the processor, the steps of the method as described in the second aspect are implemented.
  • the embodiment of the present application 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 achieve the first aspect Or the method described in the second aspect.
  • an embodiment of the present application provides a computer program product, the program product is stored in a storage medium, and the program product is executed by at least one processor to implement the method described in the first aspect or the second aspect.
  • the embodiment of the present application provides a communication device configured to implement the method described in the first aspect or the second aspect.
  • the distribution characteristic value is determined, and the distribution characteristic value is used to represent the distribution characteristic information of the attribute information to be encoded; the target order corresponding to the attribute information to be encoded is determined based on the distribution characteristic value, and the target The order is the order of the Exponential Golomb encoding; the Exponential Golomb encoding algorithm based on the target order entropy-encodes the attribute information to be encoded to obtain an attribute code stream.
  • the target order in the process of encoding the attribute information of the point cloud, can be determined adaptively based on the distribution characteristic information of the attribute information to be encoded, and the exponential Golomb encoding algorithm of the target order can be used to perform entropy encoding on the attribute information to be encoded, which can Improve the effect of removing redundancy between information, thereby improving coding efficiency.
  • Fig. 1 is one of framework schematic diagrams of a kind of point cloud AVS encoder
  • Fig. 2 is one of frame schematic diagrams of a kind of point cloud AVS decoder
  • FIG. 3 is a flow chart of a point cloud encoding processing method provided by an embodiment of the present application.
  • FIG. 4 is a flow chart of a point cloud decoding processing method provided by an embodiment of the present application.
  • Fig. 5 is a structural diagram of a point cloud encoding processing device provided by an embodiment of the present application.
  • FIG. 6 is a structural diagram of a point cloud decoding processing device provided by an embodiment of the present application.
  • FIG. 7 is one of the structural diagrams of an electronic device provided in an embodiment of the present application.
  • FIG. 8 is a second structural diagram of an electronic device provided by 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.
  • the codec terminal corresponding to the codec method in the embodiment of the present application can be a terminal, and the terminal can also be called a terminal device or a user terminal (User Equipment, UE), and the terminal can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop Laptop Computer (Laptop Computer) or Notebook Computer, Personal Digital Assistant (Personal Digital Assistant, PDA), PDA, Netbook, Ultra-mobile Personal 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 equipment (Vehicle User Equipment, VUE), pedestrian terminal (Pedestrian User Equipment , PUE) and other terminal-side devices, wearable devices include: smart watches, bracelets, earphones, glasses, etc. It should be noted that, the embodiment of the present application does not limit the specific type of the terminal.
  • the geometric information and attribute information of point cloud are encoded separately.
  • coordinate transformation is performed on the geometric information so that all point clouds are contained in a bounding box, and then the coordinates are quantized.
  • Quantization mainly plays the role of scaling. Since quantization will round the geometric coordinates, the geometric information of some points will be the same, which is called duplicate points. It is determined whether to remove duplicate points according to the parameters. Quantization and removal of duplicate points are two steps. Also known as the voxelization process.
  • the bounding box is divided into 8 sub-cubes, and the non-empty sub-cubes continue to be divided until the unit cube with leaf nodes of 1x1x1 is obtained.
  • the number of points in the point is encoded to generate a binary code stream.
  • Attribute coding is mainly aimed at color and reflectance information. First, judge whether to perform color space conversion according to the parameters. If color space conversion is performed, the color information is converted from Red Green Blue (RGB) color space to brightness color (YUV) color space. Then, the geometrically reconstructed point cloud is recolored with the original point cloud so that the unencoded attribute information corresponds to the reconstructed geometric information.
  • RGB Red Green Blue
  • YUV brightness color
  • the nearest neighbor of the point to be predicted is searched using the geometric spatial relationship, and the reconstructed attribute value of the found neighbor is used to predict the point to be predicted to obtain the predicted attribute value, and then the The real attribute value and the predicted attribute value are differentiated to obtain the prediction residual, and finally the prediction residual is quantized and encoded to generate a binary code stream.
  • attribute prediction 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 the difference prediction is performed.
  • 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 attribute prediction transformation process 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 the prediction 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 prediction residual and quantize it; finally, perform entropy encoding on the quantized transform coefficient and prediction residual to generate a binary code stream.
  • the AVS decoding process corresponds to the encoding process.
  • the AVS decoder framework is shown in Figure 2.
  • the point cloud encoding processing method provided in the embodiment of the present application involves the attribute information encoding part in the AVS encoder framework, and the point cloud decoding processing method involves the attribute information decoding part in the AVS decoder framework.
  • the point cloud encoding processing method provided in the embodiment of the present application involves entropy encoding in the attribute information encoding part
  • the point cloud decoding processing method involves entropy decoding in the attribute information decoding part.
  • FIG. 3 is a flow chart of a point cloud encoding processing method provided in an embodiment of the present application. As shown in FIG. 3, the point cloud encoding processing method includes the following steps:
  • Step 101 Determine a distribution characteristic value, where the distribution characteristic value is used to represent the distribution characteristic information of the attribute information to be encoded.
  • the point cloud encoding processing method can be used at the encoding end.
  • the distribution characteristic information of the attribute information to be encoded can represent the attribute distribution of the point cloud.
  • the attribute information to be encoded can be the attribute information of the point cloud sequence; or it can be the attribute information of a certain subset of the point cloud sequence; or it can be the attribute information of a subset subdivided by a certain subset of the point cloud sequence, etc. , which is not limited in this embodiment.
  • a certain subset of the point cloud sequence may be a certain slice of the point cloud sequence.
  • the attribute information to be encoded may be attribute information of a current point cloud sequence.
  • the distribution characteristic value may be related to the maximum value of the attribute information to be encoded; or the distribution characteristic value may be related to the minimum value of the attribute information to be encoded; or the distribution characteristic value may be related to the maximum value of the attribute information to be encoded It may be related to the difference of the minimum value; or the distribution characteristic value may be related to the average value of the absolute value of the attribute information to be encoded; etc., which are not limited in this embodiment.
  • the current point cloud sequence can be traversed, and the maximum and minimum values of the attribute information in the current point cloud sequence can be recorded, which can be expressed according to the maximum and minimum values of the attribute information
  • the distribution range of the attribute information of the current point cloud sequence can be the difference between the maximum value and the minimum value of the attribute information in the current point cloud sequence; or the current point cloud sequence can be traversed to calculate the attributes in the current point cloud sequence
  • the average value of the information, the distribution characteristic value can be the average value of the absolute value of the attribute information in the current point cloud sequence.
  • the distribution characteristic value disAttr can be the difference Attr min between the maximum value Attr max of the attribute information in the current point cloud sequence and the minimum value of the attribute information in the current point cloud sequence:
  • the distribution characteristic value may be determined based on the attribute information to be encoded, or may be determined based on target information corresponding to the attribute information to be encoded, and the target information includes at least one of a prediction residual and a transformation coefficient.
  • the distribution characteristic value may be the difference between the maximum value and the minimum value of the target information corresponding to the attribute information to be encoded; or the distribution characteristic value may be The average value of the absolute values of the target information corresponding to the attribute information to be encoded.
  • attribute information to be encoded may be color attribute information, or reflectance attribute information, or other types of attribute information, and the embodiment of the present application does not limit the attribute type of the attribute information to be encoded.
  • Step 102 Determine a target order corresponding to the attribute information to be encoded based on the distribution characteristic value, where the target order is an order of Exponential Golomb encoding.
  • the target order may be positively correlated with the distribution characteristic value.
  • the target order corresponding to the attribute information to be encoded may be determined based on the ratio of the distribution characteristic value to the attribute quantization step size .
  • the target order corresponding to the attribute information to be encoded may be positively correlated with the ratio of the distribution characteristic value to the attribute quantization step size.
  • the ratio disAttr' of the distribution characteristic value to the attribute quantization step size is:
  • disAttr is the distribution characteristic value
  • AttrQuantStep is the attribute quantization step size.
  • the ratio of the distribution characteristic value to the attribute quantization step size can represent the distribution characteristic information of the attribute information to be encoded at the current code rate point.
  • the target index value can be calculated according to the ratio of the distribution characteristic value to the attribute quantization step size, and the exponential Golomb coding order corresponding to the target index value can be searched in the stored lookup table according to the target index value, and the The exponential Golomb encoding order corresponding to the target index value is determined as the target order.
  • the lookup table is a preset lookup table according to the coding characteristics of the Exponential Golomb coding, and the lookup table stores the corresponding relationship between the index value and the order of the Exponential Golomb coding.
  • index value Index may be positively correlated with the ratio disAttr' of the distribution characteristic value and the attribute quantization step size.
  • the target index value Index can be:
  • Step 103 Perform entropy encoding on the attribute information to be encoded based on the exponential Golomb encoding algorithm of the target order to obtain an attribute code stream.
  • the target information corresponding to the attribute information to be encoded can be obtained, the target information includes at least one of a prediction residual and a transformation coefficient, and the target information is entropy-entropyed by using the exponential Golomb coding algorithm of the target order coding.
  • attribute prediction may be performed on the attribute information to be encoded to obtain a prediction residual, and an exponential Golomb coding algorithm of the target order may be used to perform entropy encoding on the prediction residual.
  • the embodiment of the present application determines the target order corresponding to the attribute information to be encoded based on the distribution characteristic information of the attribute information to be encoded, and proposes an adaptive exponential Golomb encoding method. Using the same K-order exponential Golomb encoding, the embodiment of the present application can efficiently remove redundancy among information; and can make full use of attribute information distribution of different attribute types at different code rate points, and can further improve encoding efficiency.
  • the distribution characteristic value is determined, and the distribution characteristic value is used to represent the distribution characteristic information of the attribute information to be encoded; the target order corresponding to the attribute information to be encoded is determined based on the distribution characteristic value, and the target The order is the order of the Exponential Golomb encoding; the Exponential Golomb encoding algorithm based on the target order entropy-encodes the attribute information to be encoded to obtain an attribute code stream.
  • the target order in the process of encoding the attribute information of the point cloud, can be determined adaptively based on the distribution characteristic information of the attribute information to be encoded, and the exponential Golomb encoding algorithm of the target order can be used to perform entropy encoding on the attribute information to be encoded, which can Improve the effect of removing redundancy between information, thereby improving coding efficiency.
  • the attribute code stream carries indication information of the target order.
  • the indication information can be used to indicate the target order, taking the target order as K order as an example, the indication information of the target order can be K, or can be K order, or can be Degree K, etc. , this embodiment does not limit the specific expression form of the indication information of the target order.
  • the target order indicated by the indication information carried in the attribute code stream is the order of Exponential Golomb decoding when used for decoding.
  • the attribute code stream carries the indication information of the target order, so that the decoder can obtain the target order through the indication information of the target order carried in the attribute code stream, and adopt the index of the target order Golomb decoding algorithm for entropy decoding.
  • the attribute code stream carries an attribute information parameter set
  • the attribute information parameter set includes the indication information and the attribute type corresponding to the indication information
  • the attribute type is the attribute information to be encoded type.
  • the encoding end may write the indication information of the target order into the attribute information parameter set Adaptation Parameter Set (Adaptation Parameter Set, APS).
  • Adaptation Parameter Set Adaptation Parameter Set, APS
  • the parameter GolombNumber[num_attr_type] is introduced into the attribute information parameter set APS to store the order of the exponential Golomb code for different types of attribute selection.
  • num_attr_type is the total number of attribute types of the point cloud to be encoded
  • the value of GolombNumber[attrIdx] is an integer greater than or equal to 0, and attrIdx is used to identify different attribute types.
  • the current AVS point cloud data set mainly contains two types of attributes: color and reflectivity, then the correspondence between attrIdx and attribute types in the embodiment of the present application can be shown in Table 1:
  • the decoding end can analyze the exponential Columbus order K from the attribute code stream to be decoded, that is, the target order, and then decode the attribute code stream to be decoded according to the order K.
  • the decoder obtains the value of the syntax element GolombNumber[attrIdx] from the input code stream; obtains the exponential Golomb order K corresponding to the attribute type according to the value of GolombNumber[attrIdx].
  • the exponential Columbus order is the exponential Columbus coded order.
  • the exponential Golomb order K is equal to the value of kth_GolombNumber[0], and when decoding the attribute information, use the K-order exponential Golomb decoding algorithm to decode the attribute code stream corresponding to the color information;
  • the exponential Golomb order K is equal to the value of kth_GolombNumber[1].
  • the attribute information parameter set is carried in the attribute code stream, and the attribute information parameter set includes the indication information and the attribute type corresponding to the indication information, so that the decoding end can obtain the target object through the attribute information parameter set.
  • the target order is positively correlated with the distribution characteristic value.
  • the larger the value of the distribution characteristic is, the larger the distribution range of the attribute information to be encoded is, and the target order It is positively correlated with the distribution characteristic value, so that a larger exponential Columbus coding order is used for attribute information to be coded with a large distribution range, so that redundancy among information can be efficiently removed, and coding efficiency can be improved.
  • the target order is proportional to the distribution characteristic value.
  • the determining the target order corresponding to the attribute information to be encoded based on the distribution characteristic value includes:
  • a target order corresponding to the attribute information to be encoded is determined based on a rounded value of a ratio of the distribution characteristic value to the attribute quantization step size.
  • the attribute quantization step size may be the current attribute quantization step size, that is, the attribute quantization step size of the current encoding.
  • the target order corresponding to the attribute information to be encoded may be positively correlated with the rounded value of the ratio of the distribution characteristic value to the attribute quantization step size.
  • the target order corresponding to the attribute information to be encoded may be related to The distribution characteristic value is directly proportional to the rounded value of the ratio of the attribute quantization step size.
  • the corresponding relationship between the index value and the exponential Golomb coding order can be stored in the lookup table, and the rounded value of the ratio of the distribution characteristic value to the attribute quantization step size can be used as the target index value, and in the lookup table Find the Exponential Golomb coded order corresponding to the target index value as the target order.
  • the target order corresponding to the attribute information to be encoded is determined.
  • the distribution of attribute information at the code rate point comes from the adaptive selection of the order of the exponential Golomb coding. Using a more matching K-order exponential Golomb algorithm for entropy coding can more effectively reduce the redundancy between information and further improve the coding efficiency.
  • determining the target order corresponding to the attribute information to be encoded based on the rounded value of the ratio of the distribution characteristic value to the attribute quantization step size includes:
  • a target order corresponding to the attribute information to be encoded is determined based on a stored correspondence between an Exponential Golomb encoding order and an index value, and the target order corresponds to the target index value.
  • the order of the Exponential Golomb code is the order of the Exponential Golomb code.
  • the corresponding relationship between the exponential Columbus coded order and the index value can be stored in the form of a lookup table, so that the exponential Columbus coded order corresponding to the target index value can be found in the stored lookup table, and the exponential Columbus coded order corresponding to the target index value determined as the target order.
  • the lookup table may be a preset lookup table according to the coding characteristics of the Exponential Golomb code.
  • target index value may be positively correlated with the integer value of the ratio of the distribution characteristic value to the attribute quantization step size
  • the target index value may be: log 2 [disAttr'], where [ ] is a rounding symbol, and [disAttr'] is a rounding value of the ratio of the distribution characteristic value to the attribute quantization step size.
  • the target index value may be: [log 2 [disAttr′]].
  • the target index value is determined based on the rounded value of the ratio of the distribution characteristic value to the attribute quantization step size;
  • the target order of , the target order corresponds to the target index value.
  • the target order can be quickly determined based on the stored correspondence between the exponential Golomb encoding order and the index value, further improving the encoding efficiency.
  • the target order-based exponential Golomb coding algorithm performs entropy coding on the attribute information to be coded, including:
  • Acquire target information corresponding to the attribute information to be encoded where the target information includes at least one of a prediction residual and a transform coefficient
  • Entropy coding is performed on the target information by using an exponential Golomb coding algorithm of the target order.
  • attribute prediction can be performed on the attribute information to be encoded to obtain the prediction residual, and the exponential Golomb coding algorithm of the target order can be used to perform entropy encoding on the prediction residual; or, the attribute prediction transformation can be performed on the attribute information to be encoded to obtain the prediction residual Difference and transformation coefficient, using the exponential Golomb coding algorithm of the target order to perform entropy coding on the prediction residual and transformation coefficient; or, attribute information to be encoded can be transformed to obtain the prediction residual and transformation coefficient, using the target
  • the order exponential Golomb coding algorithm performs entropy coding on the prediction residual and the transformation coefficient; etc., which are not limited in this embodiment.
  • the target information corresponding to the property information to be encoded is obtained, and the target information includes at least one of prediction residual and transformation coefficient; the target information is encoded by using the exponential Golomb coding algorithm of the target order Do entropy encoding.
  • the effect of removing redundancy between information can be improved, thereby improving coding efficiency.
  • the distribution characteristic value is determined based on at least one of the following:
  • the maximum value of the attribute information to be encoded, the minimum value of the attribute information to be encoded, the difference between the maximum value and the minimum value of the attribute information to be encoded, and the absolute value of the attribute information to be encoded Any one of the average values of can better reflect the distribution of the attribute information to be encoded, so that the target order can be determined adaptively by using the distribution of the attribute information to be encoded, and the exponential Golomb encoding algorithm of the target order can be used to encode Entropy coding of attribute information can improve the effect of removing redundancy between information, thereby improving coding efficiency.
  • the AVSC1_ai representation in Table 2 is tested under geometrically lossy and attribute lossy encoding; the AVSC2_ai representation in Table 3 is tested under geometric lossless and attribute lossy encoding; the AVSC3_ai representation in Table 4 is under geometric lossless, And the attribute is limited and tested under the lossy encoding method; the AVSC4_ai representation in Table 5 is tested under the geometric lossless and attribute lossless encoding method.
  • average represents the average performance gain of all test sequences under this condition.
  • AVSCat1A is a test sequence whose attribute information is reflectance information
  • AVSCat1B is a test sequence whose attribute information is color information
  • AVSCat1C is a test sequence whose attribute information includes reflectance information and color information
  • AVSCat2 is a test sequence whose attribute information is reflectance information, and is multi The test sequence of the frame sequence
  • AVSCat3 is the attribute information is the color information, and it is the test sequence of the multi-frame sequence
  • AVSCat1A+AVSCat2average is the average performance gain of the two test sequences.
  • BD-AttrReate is a parameter used to measure the performance of attribute information encoding.
  • Bpip bits per input point
  • bpip ratio is the ratio of bpip between the test method and the reference method, in the form of a percentage. Under non-destructive conditions, if the bpip ratio is less than 100%, the performance of the test method is better. From the data in Table 2 to Table 5, it can be seen that the encoding performance of the point cloud encoding processing method adopted in the embodiment of the present application is better than that of the reference method, and the attribute information encoding performance is better.
  • FIG. 4 is a flow chart of a point cloud decoding processing method provided in an embodiment of the present application. As shown in FIG. 4, the point cloud decoding processing method includes the following steps:
  • Step 201 determining the target order corresponding to the attribute code stream to be decoded, the target order being the order of Exponential Golomb decoding;
  • Step 202 Perform entropy decoding on the attribute code stream to be decoded based on the exponential Golomb decoding algorithm of the target order.
  • the attribute code stream to be decoded carries indication information for indicating the target order.
  • the to-be-decoded attribute code stream carries an attribute information parameter set
  • the attribute information parameter set includes the indication information and the attribute type corresponding to the indication information
  • the attribute type is the attribute to be decoded The type of attribute information corresponding to the code stream.
  • this embodiment is an implementation manner of the decoding side corresponding to the embodiment shown in FIG. The embodiment will not be repeated, and the same beneficial effect can also be achieved.
  • the point cloud encoding processing method provided in the embodiment of the present application may be executed by a point cloud encoding processing device.
  • the method for performing the point cloud coding processing by the point cloud coding processing device is taken as an example to illustrate the point cloud coding processing device provided in the embodiment of the present application.
  • FIG. 5 is a structural diagram of a point cloud encoding processing device provided in an embodiment of the present application. As shown in FIG. 5, the point cloud encoding processing device 300 includes:
  • the first determining module 301 is configured to determine a distribution characteristic value, and the distribution characteristic value is used to represent the distribution characteristic information of the attribute information to be encoded;
  • the second determination module 302 is configured to determine a target order corresponding to the attribute information to be encoded based on the distribution characteristic value, where the target order is an order of Exponential Golomb encoding;
  • the encoding module 303 is configured to perform entropy encoding on the attribute information to be encoded based on the exponential Golomb encoding algorithm of the target order to obtain an attribute code stream.
  • the attribute code stream carries indication information of the target order.
  • the attribute code stream carries an attribute information parameter set
  • the attribute information parameter set includes the indication information and the attribute type corresponding to the indication information
  • the attribute type is the attribute information to be encoded type.
  • the target order is positively correlated with the distribution characteristic value.
  • the second determining module 302 is specifically configured to:
  • the second determination module 302 is specifically configured to:
  • a target order corresponding to the attribute information to be encoded is determined based on a stored correspondence between an Exponential Golomb encoding order and an index value, and the target order corresponds to the target index value.
  • the encoding module 303 is specifically configured to:
  • Acquire target information corresponding to the attribute information to be encoded where the target information includes at least one of a prediction residual and a transform coefficient
  • Entropy encoding is performed on the target information by using an exponential Golomb coding algorithm of the target order to obtain an attribute code stream.
  • the distribution characteristic value is determined based on at least one of the following:
  • the first determination module determines the distribution characteristic value, and the distribution characteristic value is used to represent the distribution characteristic information of the attribute information to be encoded; the second determination module determines the corresponding value of the attribute information to be encoded based on the distribution characteristic value.
  • the target order is the order of Exponential Golomb encoding; the encoding module performs entropy encoding on the attribute information to be encoded based on the Exponential Golomb encoding algorithm of the target order to obtain an attribute code stream.
  • the target order in the process of encoding the attribute information of the point cloud, can be determined adaptively based on the distribution characteristic information of the attribute information to be encoded, and the exponential Golomb encoding algorithm of the target order can be used to perform entropy encoding on the attribute information to be encoded, which can Improve the effect of removing redundancy between information, thereby improving coding efficiency.
  • the point cloud encoding processing apparatus in the embodiment of the present application may be an electronic device, 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.
  • the electronic device can be a mobile phone, a tablet computer, a notebook computer, a handheld computer, a vehicle electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/virtual reality (virtual reality, VR) ) equipment, robots, wearable devices, ultra-mobile personal computer (ultra-mobile personal computer, UMPC), netbook or personal digital assistant (personal digital assistant, PDA), etc.
  • the point cloud encoding processing device in the embodiment of the present application may be a device with an operating system.
  • the operating system may be an Android (Android) operating system, an ios operating system, or other possible operating systems, which are not specifically limited in this embodiment of the present application.
  • the point cloud encoding processing device provided in the embodiment of the present application can realize various processes realized in the method embodiment in FIG. 3 , and details are not repeated here to avoid repetition.
  • the point cloud decoding processing method provided in the embodiment of the present application may be executed by a point cloud decoding processing device.
  • the method for performing the point cloud decoding processing by the point cloud decoding processing device is taken as an example to illustrate the point cloud decoding processing device provided in the embodiment of the present application.
  • FIG. 6 is a structural diagram of a point cloud decoding processing device provided in an embodiment of the present application. As shown in FIG. 6, the point cloud decoding processing device 400 includes:
  • a determining module 401 configured to determine a target order corresponding to the attribute code stream to be decoded, where the target order is the order of Exponential Golomb decoding;
  • the decoding module 402 is configured to perform entropy decoding on the attribute code stream to be decoded based on the exponential Golomb decoding algorithm of the target order.
  • the attribute code stream to be decoded carries indication information for indicating the target order.
  • the to-be-decoded attribute code stream carries an attribute information parameter set
  • the attribute information parameter set includes the indication information and the attribute type corresponding to the indication information
  • the attribute type is the attribute to be decoded The type of attribute information corresponding to the code stream.
  • the point cloud decoding processing apparatus in the embodiment of the present application may be an electronic device, or may be 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.
  • the electronic device can be a mobile phone, a tablet computer, a notebook computer, a handheld computer, a vehicle electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/virtual reality (virtual reality, VR) ) equipment, robots, wearable devices, ultra-mobile personal computer (ultra-mobile personal computer, UMPC), netbook or personal digital assistant (personal digital assistant, PDA), etc.
  • the point cloud decoding processing device in the embodiment of the present application may be a device with an operating system.
  • the operating system may be an Android (Android) operating system, an ios operating system, or other possible operating systems, which are not specifically limited in this embodiment of the present application.
  • the point cloud decoding processing device provided in the embodiment of the present application can realize various processes realized in the method embodiment in FIG. 4 , and details are not repeated here to avoid repetition.
  • the embodiment of the present application also provides an electronic device 500, including a processor 501 and a memory 502.
  • the memory 502 stores programs or instructions that can run on the processor 501.
  • the programs or instructions are When executed by the processor 501, each step of the above-mentioned point cloud encoding processing method embodiment is realized, or, when the program or instruction is executed by the processor 501, each step of the above-mentioned point cloud decoding processing method embodiment is realized, and the same technical effect can be achieved , to avoid repetition, it will not be repeated here.
  • the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.
  • FIG. 8 is a schematic diagram of a hardware structure of an electronic device implementing an embodiment of the present application.
  • the electronic device 600 includes but is not limited to: a radio frequency unit 601, a network module 602, an audio output unit 603, an input unit 604, a sensor 605, a display unit 606, a user input unit 607, an interface unit 608, a memory 609, and a processor 610, etc. part.
  • the electronic device 600 can also include a power supply (such as a battery) for supplying power to various components, 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 function can be realized through the power management system. Consumption management and other functions.
  • a power supply such as a battery
  • the structure of the electronic device shown in FIG. 8 does not constitute a limitation to the electronic device.
  • the electronic device may include more or fewer components than shown in the figure, or combine some components, or arrange different components, and details will not be repeated here. .
  • the electronic device is used to execute the point cloud encoding processing method, wherein:
  • a processor 610 configured to determine a distribution characteristic value, where the distribution characteristic value is used to represent the distribution characteristic information of the attribute information to be encoded
  • the processor 610 is further configured to determine a target order corresponding to the attribute information to be encoded based on the distribution characteristic value, the target order being the order of Exponential Golomb encoding;
  • the processor 610 is further configured to perform entropy encoding on the attribute information to be encoded based on the exponential Golomb encoding algorithm of the target order to obtain an attribute code stream.
  • the attribute code stream carries indication information of the target order.
  • the attribute code stream carries an attribute information parameter set
  • the attribute information parameter set includes the indication information and the attribute type corresponding to the indication information
  • the attribute type is the attribute information to be encoded type.
  • the target order is positively correlated with the distribution characteristic value.
  • processor 610 is further configured to:
  • a target order corresponding to the attribute information to be encoded is determined based on a rounded value of a ratio of the distribution characteristic value to the attribute quantization step size.
  • processor 610 is further configured to:
  • a target order corresponding to the attribute information to be encoded is determined based on a stored correspondence between an Exponential Golomb encoding order and an index value, and the target order corresponds to the target index value.
  • processor 610 is further configured to:
  • Acquire target information corresponding to the attribute information to be encoded where the target information includes at least one of a prediction residual and a transform coefficient
  • Entropy coding is performed on the target information by using an exponential Golomb coding algorithm of the target order.
  • the distribution characteristic value is determined based on at least one of the following:
  • the processor 610 determines the distribution characteristic value, and the distribution characteristic value is used to represent the distribution characteristic information of the attribute information to be encoded; the processor 610 determines the target level corresponding to the attribute information to be encoded based on the distribution characteristic value.
  • the target order is the order of Exponential Golomb encoding; the processor 610 performs entropy encoding on the attribute information to be encoded based on the Exponential Golomb encoding algorithm of the target order to obtain an attribute code stream.
  • the target order in the process of encoding the attribute information of the point cloud, can be adaptively determined based on the distribution characteristic information of the attribute information to be encoded, and the exponential Golomb encoding algorithm of the target order can be used to perform entropy encoding on the attribute information to be encoded, which can Improve the effect of removing redundancy between information, thereby improving coding efficiency.
  • the electronic device is used to execute a point cloud decoding processing method, wherein:
  • the processor 610 is configured to determine a target order corresponding to the attribute code stream to be decoded, where the target order is the order of Exponential Golomb decoding;
  • the processor 610 is further configured to perform entropy decoding on the attribute code stream to be decoded based on the exponential Golomb decoding algorithm of the target order.
  • the attribute code stream to be decoded carries indication information for indicating the target order.
  • the to-be-decoded attribute code stream carries an attribute information parameter set
  • the attribute information parameter set includes the indication information and the attribute type corresponding to the indication information
  • the attribute type is the attribute to be decoded The type of attribute information corresponding to the code stream.
  • the electronic device provided in this implementation manner can implement each process implemented by the method embodiment in FIG. 4 , and details are not repeated here to avoid repetition.
  • the input unit 604 may include a graphics processor (Graphics Processing Unit, GPU) 6041 and a microphone 6042, and the graphics processor 6041 is used for the 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 memory 609 can be used to store software programs 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 handles 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 embodiment of the present application also provides a readable storage medium, the readable storage medium may be nonvolatile or volatile, the readable storage medium stores programs or instructions, and the programs or instructions are stored in
  • the processor is executed, the various processes of the above-mentioned point cloud encoding processing method embodiment are realized, or, when the program or instruction is executed by the processor, the various processes of the above-mentioned point cloud decoding processing method embodiment are realized, and the same technical effect can be achieved, for To avoid repetition, I won't go into details here.
  • the processor is the processor in the electronic device described in the above 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 point cloud encoding processing method Each process of the example, or, the processor is used to run programs or instructions to implement the various processes of the above-mentioned point cloud decoding processing method embodiment, and can achieve the same technical effect, in order to avoid repetition, it will not be repeated here.
  • chips mentioned in the embodiments of the present application may also be called system-on-chip, system-on-chip, system-on-a-chip, or system-on-a-chip.
  • An embodiment of the present application provides a computer program product, the program product is stored in a storage medium, and the program product is executed by at least one processor to implement the various processes in the above embodiment of the point cloud encoding processing method, or, the program product Executed by at least one processor to implement the various processes of the above-mentioned point cloud decoding processing method embodiment, and can achieve the same technical effect, in order to avoid repetition, no more details are given here.
  • 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.

Landscapes

  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Compression Of Band Width Or Redundancy In Fax (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)

Abstract

本申请公开了一种点云编码处理方法、点云解码处理方法及相关设备，属于计算机技术领域，本申请实施例的点云编码处理方法包括：确定分布特性值，分布特性值用于表示待编码属性信息的分布特性信息（101）；基于分布特性值确定待编码属性信息对应的目标阶数，目标阶数为指数哥伦布编码的阶数（102）；基于目标阶数的指数哥伦布编码算法对待编码属性信息进行熵编码，得到属性码流（103）。

Description

点云编码处理方法、点云解码处理方法及相关设备
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No.202111467925.3的优先权，其全部内容通过引用包含于此。
技术领域
本申请属于计算机技术领域，具体涉及一种点云编码处理方法、点云解码处理方法及相关设备。
背景技术
点云是三维物体或场景的一种表现形式，是由空间中一组无规则分布、表达三维物体或场景空间结构和表面属性的离散点集所构成。为了准确反映空间中的信息，所需离散点的数量相当大，而为了减少点云数据存储和传输时所占用的带宽，需要对点云数据进行编码压缩处理。点云数据通常由描述位置的几何信息如三维坐标(x，y，z)，以及该位置的属性信息如颜色(R，G，B)或者反射率等构成。在点云编码压缩过程中对几何信息及属性信息的编码是分开进行的。
目前，对点云的属性信息进行编码的过程，去除信息间冗余的效果较差，从而导致编码效率较低。
发明内容
本申请实施例提供一种点云编码处理方法、点云解码处理方法及相关设备，能够解决编码效率较低的问题。
第一方面，提供了一种点云编码处理方法，该方法包括：
确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息；
基于所述分布特性值确定所述待编码属性信息对应的目标阶数，所述目标阶数为指数哥伦布编码的阶数；
基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。
第二方面，提供了一种点云解码处理方法，该方法包括：
确定待解码属性码流对应的目标阶数，所述目标阶数为指数哥伦布解码的阶数；
基于所述目标阶数的指数哥伦布解码算法对所述待解码属性码流进行熵解码。
第三方面，提供了一种点云编码处理装置，包括：
第一确定模块，用于确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息；
第二确定模块，用于基于所述分布特性值确定所述待编码属性信息对应的目标阶数，所述目标阶数为指数哥伦布编码的阶数；
编码模块，用于基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。
第四方面，提供了一种点云解码处理装置，包括：
确定模块，用于确定待解码属性码流对应的目标阶数，所述目标阶数为指数哥伦布解码的阶数；
解码模块，用于基于所述目标阶数的指数哥伦布解码算法对所述待解码属性码流进行熵解码。
第五方面，本申请实施例提供了一种电子设备，该电子设备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，或者，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。
第六方面，本申请实施例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，所述程序或指令被处理器执行时实现如第二方面所述的方法的步骤。
第七方面，本申请实施例提供了一种芯片，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， 实现如第一方面或第二方面所述的方法。
第八方面，本申请实施例提供一种计算机程序产品，该程序产品被存储在存储介质中，该程序产品被至少一个处理器执行以实现如第一方面或第二方面所述的方法。
第九方面，本申请实施例提供一种通信设备，被配置为执行以实现如第一方面或第二方面所述的方法。
本申请实施例中，确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息；基于所述分布特性值确定所述待编码属性信息对应的目标阶数，所述目标阶数为指数哥伦布编码的阶数；基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。这样，在对点云的属性信息进行编码的过程中，能够基于待编码属性信息的分布特性信息自适应确定目标阶数，采用目标阶数的指数哥伦布编码算法对待编码属性信息进行熵编码，能够提高去除信息间冗余的效果，从而提高编码效率。
附图说明
图1是一种点云AVS编码器框架示意图之一；
图2是一种点云AVS解码器框架示意图之一；
图3是本申请实施例提供的一种点云编码处理方法的流程图；
图4是本申请实施例提供的一种点云解码处理方法的流程图；
图5是本申请实施例提供的一种点云编码处理装置的结构图；
图6是本申请实施例提供的一种点云解码处理装置的结构图；
图7是本申请实施例提供的一种电子设备的结构图之一；
图8是本申请实施例提供的一种电子设备的结构图之二。
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。
本申请实施例中的编解码方法对应的编解码端可以为终端，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。
为了方便理解，以下对本申请实施例涉及的一些内容进行说明：
如图1所示，在点云数字音视频编解码技术标准(Audio Video coding Standard，AVS)编码器框架中，点云的几何信息和属性信息是分开编码的。首先对几何信息进行坐标转换，使点云全部包含在一个包围盒(bounding box)中，然后再进行坐标量化。量化主要起到缩放的作用，由于量化会对几何坐标取整，使得一部分点的几何信息相同，称为重复点，根据参数来决定是否移除重复点，量化和移除重复点这两个步骤又被称为体素化过程。接下来，对包围盒进行多叉树划分，例如八叉树、四叉树或二叉树划分。在基于多叉树的几何信息编码框架中，将包围盒八等分为8个子立方体，对非空的子立方体继续进行划分，直到划分得到叶子节点为1x1x1的单位立方体时停止划分，对叶子结点中的点数进行编码，生成二进制码流。
几何编码完成后，对几何信息进行重建，用于后面的重着色。属性编码主要针对的是颜色和反射率信息。首先根据参数判断是否进行颜色空间转换，若进行颜色空间转换，则将颜色信息从红绿蓝(Red Green Blue，RGB)颜色空间转换到亮度色彩(YUV)颜色空间。然后，利用原始点云对几何重建点云进行重着色，使得未编码的属性信息与重建的几何信息对应起来。在颜色信息编码中，通过莫顿码对点云进行排序后，利用几何空间关系搜索待预测点的最近邻，并利用所找到邻居的重建属性值对待预测点进行预测得到预测属性值，然后将真实属性值和预测属性值进行差分得到预测残差，最后对预测残差进行量化并编码，生成二进制码流。
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。
(1)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。
(2)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预测残差进行熵编码，生成二进制码流。
(3)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预测残差并对其量化；最后将量化后的变换系数和预测残差进行熵编码，生成二进制码流。
可选地，AVS解码流程与编码流程对应，具体的，AVS解码器框架如图 2所示。
可选地，本申请实施例提供的点云编码处理方法涉及AVS编码器框架中属性信息编码部分，点云解码处理方法涉及AVS解码器框架中属性信息解码部分。
可选地，本申请实施例提供的点云编码处理方法涉及属性信息编码部分中的熵编码，点云解码处理方法涉及属性信息解码部分的熵解码。
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点云编码处理方法及点云解码处理方法进行详细地说明。
参见图3，图3是本申请实施例提供的一种点云编码处理方法的流程图，如图3所示，点云编码处理方法包括以下步骤：
步骤101、确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息。
其中，点云编码处理方法可以用于编码端。待编码属性信息的分布特性信息可以表征点云的属性分布。待编码属性信息可以为点云序列的属性信息；或者可以为点云序列的某一子集的属性信息；或者可以为点云序列的某一子集再划分的子集的属性信息，等等，本实施例对此不进行限定。点云序列的某一子集可以为点云序列的某一个片(slice)。示例地，以待编码属性信息为点云序列的属性信息为例，待编码属性信息可以为当前点云序列的属性信息。
另外，分布特性值可以与所述待编码属性信息的最大值相关；或者分布特性值可以与所述待编码属性信息的最小值相关；或者分布特性值可以与所述待编码属性信息的最大值与最小值的差值相关；或者分布特性值可以与所述待编码属性信息的绝对值的平均值相关；等等，本实施例对此不进行限定。
以待编码属性信息为当前点云序列的属性信息为例，可以遍历当前点云序列，记录当前点云序列中属性信息的最大值和最小值，可以根据属性信息的最大值和最小值来表示当前点云序列属性信息的分布范围，示例地，分布特性值可以为当前点云序列中属性信息的最大值和最小值的差值；或者可以遍历当前点云序列，计算当前点云序列中属性信息的平均值，分布特性值可以为当前点云序列中属性信息的绝对值的平均值。
一种实施方式中，分布特性值disAttr可以为当前点云序列中属性信息的 最大值Attr max和当前点云序列中属性信息的最小值的差值Attr min：
disAttr＝Attr max-Attr min。
另外，分布特性值可以基于待编码属性信息确定，或者可以基于所述待编码属性信息对应的目标信息确定，所述目标信息包括预测残差和变换系数中的至少一项。以分布特性值基于所述待编码属性信息对应的目标信息确定为例，分布特性值可以为所述待编码属性信息对应的目标信息的最大值和最小值的差值；或者分布特性值可以为所述待编码属性信息对应的目标信息的绝对值的平均值。
需要说明的是，待编码属性信息可以为颜色属性信息，或者可以为反射率属性信息，或者还可以为其他类型的属性信息，本申请实施例对待编码属性信息的属性类型不进行限定。
步骤102、基于所述分布特性值确定所述待编码属性信息对应的目标阶数，所述目标阶数为指数哥伦布编码的阶数。
其中，所述目标阶数可以与所述分布特性值正相关，一种实施方式中，可以基于所述分布特性值与属性量化步长的比值，确定所述待编码属性信息对应的目标阶数。所述待编码属性信息对应的目标阶数，可以与所述分布特性值与属性量化步长的比值正相关。所述分布特性值与属性量化步长的比值越大，则待编码属性信息对应的目标阶数越大。所述分布特性值与属性量化步长的比值disAttr′为：
Figure PCTCN2022135876-appb-000001
其中，disAttr为分布特性值，AttrQuantStep为属性量化步长。分布特性值与属性量化步长的比值可以表征待编码属性信息在当前码率点下的分布特性信息。
一种实施方式中，可以根据所述分布特性值与属性量化步长的比值计算目标索引值，根据目标索引值在存储的查找表中查找该目标索引值对应的指数哥伦布编码阶数，将该目标索引值对应的指数哥伦布编码阶数确定为目标阶数。该查找表是根据指数哥伦布编码的编码特性预先设置好的查找表，该查找表存储有索引值与指数哥伦布编码阶数的对应关系。
另外，该索引值Index可以与所述分布特性值与属性量化步长的比值 disAttr′正相关。
一种实施方式中，该目标索引值Index可以为：
Index＝log 2disAttr′。
步骤103、基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。
其中，可以获取所述待编码属性信息对应的目标信息，所述目标信息包括预测残差和变换系数中的至少一项，采用所述目标阶数的指数哥伦布编码算法对所述目标信息进行熵编码。示例地，可以对待编码属性信息进行属性预测，得到预测残差，采用所述目标阶数的指数哥伦布编码算法对预测残差进行熵编码。
需要说明的是，在AVS-点云压缩算法(Point Cloud Compression，PCC)属性熵编码过程中，需要利用K阶指数哥伦布编码对预测残差进行熵编码。相关技术中，针对不同的属性类型，直接分配不同的K值进行熵编码，例如：当属性信息为颜色时，使用1阶指数哥伦布编码对预测残差进行熵编码；当属性信息为反射率时，使用3阶指数哥伦布编码对预测残差进行熵编码。然而，由于不同的点云序列在不同的码率点下，属性残差的分布范围也是具有差异的。本申请实施例基于待编码属性信息的分布特性信息确定所述待编码属性信息对应的目标阶数，提出了一种自适应指数哥伦布编码方法，相对于对同一属性类型在所有的码率点均使用相同的K阶指数哥伦布编码，本申请实施例能够高效的去除信息间的冗余；且能够充分利用不同属性类型在不同码率点下的属性信息分布情况，能够进一步提升编码效率。
本申请实施例中，确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息；基于所述分布特性值确定所述待编码属性信息对应的目标阶数，所述目标阶数为指数哥伦布编码的阶数；基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。这样，在对点云的属性信息进行编码的过程中，能够基于待编码属性信息的分布特性信息自适应确定目标阶数，采用目标阶数的指数哥伦布编码算法对待编码属性信息进行熵编码，能够提高去除信息间冗余的效果，从而提高编码效率。
可选地，所述属性码流中携带所述目标阶数的指示信息。
其中，所述指示信息可以用于指示所述目标阶数，以目标阶数为K阶为例，目标阶数的指示信息可以为K，或者可以为K阶，或者可以为Degree K，等等，本实施例对目标阶数的指示信息的具体表现形式不进行限定。
需要说明的是，所述属性码流中携带的指示信息所指示的目标阶数，在用于解码时为指数哥伦布解码的阶数。
该实施方式中，所述属性码流中携带所述目标阶数的指示信息，从而解码端可以通过属性码流中携带的目标阶数的指示信息获取目标阶数，采用该目标阶数的指数哥伦布解码算法进行熵解码。
可选地，所述属性码流中携带有属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的属性类型，所述属性类型为所述待编码属性信息的类型。
其中，编码端可以将目标阶数的指示信息写入属性信息参数集自适应参数集(Adaptation Parameter Set，APS)中。
一种实施方式中，在属性信息参数集APS中引入参数GolombNumber[num_attr_type]来存储不同类型的属性选择的指数哥伦布编码的阶数。其中，num_attr_type为待编码点云的属性类型的总个数，GolombNumber[attrIdx]的值指示了第attrIdx个属性的指数哥伦布编码的阶数，attrIdx＝0，1，……，num_attr_type-1。GolombNumber[attrIdx]取值为大于或等于0的整数，attrIdx用以标识不同的属性类型。例如，当前AVS点云数据集主要包含两种类型的属性：颜色和反射率，则本申请实施例中attrIdx与属性类型之间的对应关系可以如表1所示：
表1.attrIdx与属性类型的对应关系
attrIdx 属性类型
0 颜色
1 反射率
需要说明的是，解码端可以从待解码属性码流中解析得到指数哥伦布阶数K，即目标阶数，然后根据该阶数K对待解码属性码流进行解码。一种实施方式中，解码端从输入码流中获取语法元素GolombNumber[attrIdx]的值； 根据GolombNumber[attrIdx]的值获得属性类型对应的指数哥伦布阶数K。其中，指数哥伦布阶数，即指数哥伦布编码阶数。
针对颜色属性(即attrIdx为0时)，指数哥伦布阶数K等于kth_GolombNumber[0]的值，在解码属性信息时，使用K阶指数哥伦布解码算法对颜色信息对应的属性码流进行解码；
针对反射率属性(即attrIdx为1时)，指数哥伦布阶数K等于kth_GolombNumber[1]的值，在解码属性信息时，使用K阶指数哥伦布解码算法对反射率对应的属性码流进行解码。
该实施方式中，所述属性码流中携带有属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的属性类型，从而解码端可以通过属性信息参数集获取目标阶数与属性类型的对应关系，从而可以根据待解码的属性信息的属性类型确定目标阶数，采用该目标阶数的指数哥伦布解码算法进行熵解码。
可选地，所述目标阶数与所述分布特性值成正相关。
其中，以分布特性值为待编码属性信息的最大值与所述待编码属性信息的最小值的差值为例，分布特性值越大，表征待编码属性信息的分布范围较大，目标阶数与所述分布特性值成正相关，从而对于分布范围较大的待编码属性信息采用较大的指数哥伦布编码阶数，从而能够高效的去除信息间的冗余，提升编码效率。
一种实施方式中，所述目标阶数与所述分布特性值成正比。
可选地，所述基于所述分布特性值确定所述待编码属性信息对应的目标阶数，包括：
基于所述分布特性值与属性量化步长的比值的取整值，确定所述待编码属性信息对应的目标阶数。
其中，所述属性量化步长可以为当前属性量化步长，即当前编码的属性量化步长。所述待编码属性信息对应的目标阶数，可以与所述分布特性值与属性量化步长的比值的取整值正相关，示例地，所述待编码属性信息对应的目标阶数，可以与所述分布特性值与属性量化步长的比值的取整值成正比。
一种实施方式中，可以在查找表中存储索引值与指数哥伦布编码阶数的 对应关系，将所述分布特性值与属性量化步长的比值的取整值作为目标索引值，在查找表中查找与目标索引值对应的指数哥伦布编码阶数作为目标阶数。
该实施方式中，基于所述分布特性值与属性量化步长的比值的取整值，确定所述待编码属性信息对应的目标阶数，通过在属性熵编码过程中，根据不同属性类型在不同码率点下属性信息的分布来自适应的选择指数哥伦布编码的阶数，使用更匹配的K阶指数哥伦布算法进行熵编码能够更有效的减少信息间的冗余，进一步提升编码效率。
可选地，所述基于所述分布特性值与属性量化步长的比值的取整值，确定所述待编码属性信息对应的目标阶数，包括：
基于所述分布特性值与属性量化步长的比值的取整值，确定目标索引值；
基于存储的指数哥伦布编码阶数与索引值的对应关系确定所述待编码属性信息对应的目标阶数，所述目标阶数与所述目标索引值对应。
其中，指数哥伦布编码阶数，即指数哥伦布编码的阶数。可以以查找表的方式存储指数哥伦布编码阶数与索引值的对应关系，从而可以在存储的查找表中查找目标索引值对应的指数哥伦布编码阶数，将目标索引值对应的指数哥伦布编码阶数确定为目标阶数。该查找表可以是根据指数哥伦布编码的编码特性预先设置好的查找表。
另外，目标索引值可以与所述分布特性值与属性量化步长的比值的取整值成正相关，
一种实施方式中，目标索引值可以为：log 2[disAttr′]，其中，[ ]为取整符号，[disAttr′]为所述分布特性值与属性量化步长的比值的取整值。
一种实施方式中，目标索引值可以为：[log 2[disAttr′]]。
该实施方式中，基于所述分布特性值与属性量化步长的比值的取整值，确定目标索引值；基于存储的指数哥伦布编码阶数与索引值的对应关系确定所述待编码属性信息对应的目标阶数，所述目标阶数与所述目标索引值对应。这样，能够基于存储的指数哥伦布编码阶数与索引值的对应关系快速地确定目标阶数，进一步提高编码效率。
可选地，所述基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，包括：
获取所述待编码属性信息对应的目标信息，所述目标信息包括预测残差和变换系数中的至少一项；
采用所述目标阶数的指数哥伦布编码算法对所述目标信息进行熵编码。
其中，可以对待编码属性信息进行属性预测，得到预测残差，采用所述目标阶数的指数哥伦布编码算法对预测残差进行熵编码；或者，可以对待编码属性信息进行属性预测变换，得到预测残差和变换系数，采用所述目标阶数的指数哥伦布编码算法对预测残差和变换系数进行熵编码；或者，可以对待编码属性信息进行属性变换，得到预测残差和变换系数，采用所述目标阶数的指数哥伦布编码算法对预测残差和变换系数进行熵编码；等等，本实施例对此不进行限定。
该实施方式中，获取所述待编码属性信息对应的目标信息，所述目标信息包括预测残差和变换系数中的至少一项；采用所述目标阶数的指数哥伦布编码算法对所述目标信息进行熵编码。这样，在对预测残差或变换系数进行熵编码时能够提高去除信息间冗余的效果，从而提高编码效率。
可选地，所述分布特性值基于如下至少一项确定：
所述待编码属性信息的最大值；
所述待编码属性信息的最小值；
所述待编码属性信息的最大值与最小值的差值；
所述待编码属性信息的绝对值的平均值。
该实施方式中，所述待编码属性信息的最大值，所述待编码属性信息的最小值，所述待编码属性信息的最大值与最小值的差值及所述待编码属性信息的绝对值的平均值中的任意一项均能够较好地体现待编码属性信息的分布状况，从而能够利用待编码属性信息的分布状况自适应确定目标阶数，采用目标阶数的指数哥伦布编码算法对待编码属性信息进行熵编码，能够提高去除信息间冗余的效果，从而提高编码效率。
如表2至表5所示，在多种测试条件下，采用本申请实施例的点云编码处理方法进行点云编码，相对PCRMV5.0压缩效率更高，能够提升编码性能。其中，表2中AVSC1_ai表征在几何有损，且属性有损编码方式下进行测试；表3中AVSC2_ai表征在几何无损，且属性有损编码方式下进行测试；表4 中AVSC3_ai表征在几何无损，且属性有限度有损编码方式下进行测试；表5中AVSC4_ai表征在几何无损，且属性无损编码方式下进行测试。average表征该条件下所有测试序列的平均性能增益。Overall average表征所有测试序列的平均性能增益。AVSCat1A为属性信息是反射率信息的测试序列；AVSCat1B为属性信息是颜色信息的测试序列；AVSCat1C为属性信息包括反射率信息和颜色信息的测试序列；AVSCat2为属性信息是反射率信息，且为多帧序列的测试序列；AVSCat3为属性信息是颜色信息，且为多帧序列的测试序列；AVSCat1A+AVSCat2average为该两个测试序列的平均性能增益。表2至表4中，明亮度(Luma)、色度(Chroma)Cb及Chroma Cr表征三个通道，Reflectance表征反射率。表5中，Total表示总码流的变化，Colour表征颜色，Geometry表征几何信息。表2至表5中“-”表示该项无数据。
需要说明的是，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。每输入点的比特数(bits per input point，Bpip)为压缩完成之后的每个输入点的平均比特数，比特数越少代表着压缩效率越高。bpip ratio为测试方法与参考方法这两种方法的bpip之比，为百分比的形式。在无损条件下，如果bpip ratio小于100％，说明测试方法的性能要更好。通过表2至表5的数据可知，采用本申请实施例的点云编码处理方法编码性能相比参考方法，属性信息编码性能较好。
表2.AVSC1_ai条件下测试结果
Figure PCTCN2022135876-appb-000002
表3.AVSC2_ai条件下测试结果
Figure PCTCN2022135876-appb-000003
表4.AVSC3_ai条件下测试结果
Figure PCTCN2022135876-appb-000004
表5.AVSC4_ai条件下测试结果
Figure PCTCN2022135876-appb-000005
参见图4，图4是本申请实施例提供的一种点云解码处理方法的流程图，如图4所示，点云解码处理方法包括以下步骤：
步骤201、确定待解码属性码流对应的目标阶数，所述目标阶数为指数哥伦布解码的阶数；
步骤202、基于所述目标阶数的指数哥伦布解码算法对所述待解码属性码流进行熵解码。
可选地，所述待解码属性码流中携带用于指示所述目标阶数的指示信息。
可选地，所述待解码属性码流中携带有属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的属性类型，所述属性类型为所述待解码属性码流对应的属性信息的类型。
需要说明的是，本实施例作为与图3所示的实施例中对应的解码侧的实施方式，其具体的实施方式可以参见图3所示的实施例的相关说明，为了避免重复说明，本实施例不再赘述，且还可以达到相同有益效果。
本申请实施例提供的点云编码处理方法，执行主体可以为点云编码处理装置。本申请实施例中以点云编码处理装置执行点云编码处理的方法为例，说明本申请实施例提供的点云编码处理的装置。
请参见图5，图5是本申请实施例提供的一种点云编码处理装置的结构图，如图5所示，点云编码处理装置300包括：
第一确定模块301，用于确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息；
第二确定模块302，用于基于所述分布特性值确定所述待编码属性信息对应的目标阶数，所述目标阶数为指数哥伦布编码的阶数；
编码模块303，用于基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。
可选地，所述属性码流中携带所述目标阶数的指示信息。
可选地，所述属性码流中携带有属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的属性类型，所述属性类型为所述待编码属性信息的类型。
可选地，所述目标阶数与所述分布特性值成正相关。
可选地，所述第二确定模块302具体用于：
基于所述分布特性值与属性量化步长的比值的取整值，确定所述待编码 属性信息对应的目标阶数。
可选地，所述第二确定模块302具体用于：
基于所述分布特性值与属性量化步长的比值的取整值，确定目标索引值；
基于存储的指数哥伦布编码阶数与索引值的对应关系确定所述待编码属性信息对应的目标阶数，所述目标阶数与所述目标索引值对应。
可选地，所述编码模块303具体用于：
获取所述待编码属性信息对应的目标信息，所述目标信息包括预测残差和变换系数中的至少一项；
采用所述目标阶数的指数哥伦布编码算法对所述目标信息进行熵编码，得到属性码流。
可选地，所述分布特性值基于如下至少一项确定：
所述待编码属性信息的最大值；
所述待编码属性信息的最小值；
所述待编码属性信息的最大值与最小值的差值；
所述待编码属性信息的绝对值的平均值。
本申请实施例中，第一确定模块确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息；第二确定模块基于所述分布特性值确定所述待编码属性信息对应的目标阶数，所述目标阶数为指数哥伦布编码的阶数；编码模块基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。这样，在对点云的属性信息进行编码的过程中，能够基于待编码属性信息的分布特性信息自适应确定目标阶数，采用目标阶数的指数哥伦布编码算法对待编码属性信息进行熵编码，能够提高去除信息间冗余的效果，从而提高编码效率。
本申请实施例中的点云编码处理装置可以是电子设备，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，电子设备可以为手机、平板电脑、笔记本电脑、掌上电脑、车载电子设备、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、可穿戴设备、超级移动个人计算机(ultra-mobile personal computer，UMPC)、 上网本或者个人数字助理(personal digital assistant，PDA)等，还可以为服务器、网络附属存储器(Network Attached Storage，NAS)、个人计算机(personal computer，PC)、电视机(television，TV)、柜员机或者自助机等，本申请实施例不作具体限定。
本申请实施例中的点云编码处理装置可以为具有操作系统的装置。该操作系统可以为安卓(Android)操作系统，可以为ios操作系统，还可以为其他可能的操作系统，本申请实施例不作具体限定。
本申请实施例提供的点云编码处理装置能够实现图3的方法实施例实现的各个过程，为避免重复，这里不再赘述。
本申请实施例提供的点云解码处理方法，执行主体可以为点云解码处理装置。本申请实施例中以点云解码处理装置执行点云解码处理的方法为例，说明本申请实施例提供的点云解码处理的装置。
请参见图6，图6是本申请实施例提供的一种点云解码处理装置的结构图，如图6所示，点云解码处理装置400包括：
确定模块401，用于确定待解码属性码流对应的目标阶数，所述目标阶数为指数哥伦布解码的阶数；
解码模块402，用于基于所述目标阶数的指数哥伦布解码算法对所述待解码属性码流进行熵解码。
可选地，所述待解码属性码流中携带用于指示所述目标阶数的指示信息。
可选地，所述待解码属性码流中携带有属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的属性类型，所述属性类型为所述待解码属性码流对应的属性信息的类型。
本申请实施例中的点云解码处理装置可以是电子设备，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，电子设备可以为手机、平板电脑、笔记本电脑、掌上电脑、车载电子设备、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、可穿戴设备、超级移动个人计算机(ultra-mobile personal computer，UMPC)、上网本或者个人数字助理(personal digital assistant，PDA)等，还可以为服 务器、网络附属存储器(Network Attached Storage，NAS)、个人计算机(personal computer，PC)、电视机(television，TV)、柜员机或者自助机等，本申请实施例不作具体限定。
本申请实施例中的点云解码处理装置可以为具有操作系统的装置。该操作系统可以为安卓(Android)操作系统，可以为ios操作系统，还可以为其他可能的操作系统，本申请实施例不作具体限定。
本申请实施例提供的点云解码处理装置能够实现图4的方法实施例实现的各个过程，为避免重复，这里不再赘述。
如图7所示，本申请实施例还提供一种电子设备500，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执行时实现上述点云编码处理方法实施例的各个步骤，或者，该程序或指令被处理器501执行时实现上述点云解码处理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。
需要说明的是，本申请实施例中的电子设备包括上述所述的移动电子设备和非移动电子设备。
图8为实现本申请实施例的一种电子设备的硬件结构示意图。
该电子设备600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等部件。
本领域技术人员可以理解，电子设备600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8中示出的电子设备结构并不构成对电子设备的限定，电子设备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。
一种实施方式中，该电子设备用于执行点云编码处理方法，其中：
处理器610，用于确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息；
处理器610，还用于基于所述分布特性值确定所述待编码属性信息对应 的目标阶数，所述目标阶数为指数哥伦布编码的阶数；
处理器610，还用于基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。
可选地，所述属性码流中携带所述目标阶数的指示信息。
可选地，所述属性码流中携带有属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的属性类型，所述属性类型为所述待编码属性信息的类型。
可选地，所述目标阶数与所述分布特性值成正相关。
可选地，所述处理器610，还用于：
基于所述分布特性值与属性量化步长的比值的取整值，确定所述待编码属性信息对应的目标阶数。
可选地，所述处理器610，还用于：
基于所述分布特性值与属性量化步长的比值的取整值，确定目标索引值；
基于存储的指数哥伦布编码阶数与索引值的对应关系确定所述待编码属性信息对应的目标阶数，所述目标阶数与所述目标索引值对应。
可选地，所述处理器610，还用于：
获取所述待编码属性信息对应的目标信息，所述目标信息包括预测残差和变换系数中的至少一项；
采用所述目标阶数的指数哥伦布编码算法对所述目标信息进行熵编码。
可选地，所述分布特性值基于如下至少一项确定：
所述待编码属性信息的最大值；
所述待编码属性信息的最小值；
所述待编码属性信息的最大值与最小值的差值；
所述待编码属性信息的绝对值的平均值。
本实施方式中，处理器610确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息；处理器610基于所述分布特性值确定所述待编码属性信息对应的目标阶数，所述目标阶数为指数哥伦布编码的阶数；处理器610基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。这样，在对点云的属性信息进行编码的过程中， 能够基于待编码属性信息的分布特性信息自适应确定目标阶数，采用目标阶数的指数哥伦布编码算法对待编码属性信息进行熵编码，能够提高去除信息间冗余的效果，从而提高编码效率。
一种实施方式中，该电子设备用于执行点云解码处理方法，其中：
处理器610，用于确定待解码属性码流对应的目标阶数，所述目标阶数为指数哥伦布解码的阶数；
处理器610，还用于基于所述目标阶数的指数哥伦布解码算法对所述待解码属性码流进行熵解码。
可选地，所述待解码属性码流中携带用于指示所述目标阶数的指示信息。
可选地，所述待解码属性码流中携带有属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的属性类型，所述属性类型为所述待解码属性码流对应的属性信息的类型。
本实施方式提供的电子设备能够实现图4的方法实施例实现的各个过程，为避免重复，这里不再赘述。
应理解的是，本申请实施例中，输入单元604可以包括图形处理器(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。
存储器609可用于存储软件程序以及各种数据。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；可选地，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。
本申请实施例还提供一种可读存储介质，所述可读存储介质可以是非易失的，也可以是易失的，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点云编码处理方法实施例的各个过程，或者，该程序或指令被处理器执行时实现上述点云解码处理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电子设备中的处理器。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，实现上述点云编码处理方法实施例的各个过程，或者，所述处理器用于运行程序或指令，实现上述点云解码处理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。
本申请实施例提供一种计算机程序产品，该程序产品被存储在存储介质中，该程序产品被至少一个处理器执行以实现如上述点云编码处理方法实施例的各个过程，或者，该程序产品被至少一个处理器执行以实现如上述点云解码处理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。
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相关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，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. 一种点云编码处理方法，包括：
    确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息；
    基于所述分布特性值确定所述待编码属性信息对应的目标阶数，所述目标阶数为指数哥伦布编码的阶数；
    基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。
  2. 根据权利要求1所述的方法，所述属性码流中携带用于指示所述目标阶数的指示信息。
  3. 根据权利要求2所述的方法，其中，所述属性码流中携带有属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的属性类型，所述属性类型为所述待编码属性信息的类型。
  4. 根据权利要求1-3任一项所述的方法，其中，所述目标阶数与所述分布特性值成正相关。
  5. 根据权利要求1所述的方法，其中，所述基于所述分布特性值确定所述待编码属性信息对应的目标阶数，包括：
    基于所述分布特性值与属性量化步长的比值的取整值，确定所述待编码属性信息对应的目标阶数。
  6. 根据权利要求5所述的方法，其中，所述基于所述分布特性值与属性量化步长的比值的取整值，确定所述待编码属性信息对应的目标阶数，包括：
    基于所述分布特性值与属性量化步长的比值的取整值，确定目标索引值；
    基于存储的指数哥伦布编码阶数与索引值的对应关系确定所述待编码属性信息对应的目标阶数，所述目标阶数与所述目标索引值对应。
  7. 根据权利要求1所述的方法，其中，所述基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，包括：
    获取所述待编码属性信息对应的目标信息，所述目标信息包括预测残差和变换系数中的至少一项；
    采用所述目标阶数的指数哥伦布编码算法对所述目标信息进行熵编码。
  8. 根据权利要求1-3中任一项或5-7中任一项所述的方法，其中，所述分布特性值基于如下至少一项确定：
    所述待编码属性信息的最大值；
    所述待编码属性信息的最小值；
    所述待编码属性信息的最大值与最小值的差值；
    所述待编码属性信息的绝对值的平均值。
  9. 一种点云解码处理方法，包括：
    确定待解码属性码流对应的目标阶数，所述目标阶数为指数哥伦布解码的阶数；
    基于所述目标阶数的指数哥伦布解码算法对所述待解码属性码流进行熵解码。
  10. 根据权利要求9所述的方法，其中，所述待解码属性码流中携带用于指示所述目标阶数的指示信息。
  11. 根据权利要求10所述的方法，其中，所述待解码属性码流中携带有属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的属性类型，所述属性类型为所述待解码属性码流对应的属性信息的类型。
  12. 一种点云编码处理装置，包括：
    第一确定模块，用于确定分布特性值，所述分布特性值用于表示待编码属性信息的分布特性信息；
    第二确定模块，用于基于所述分布特性值确定所述待编码属性信息对应的目标阶数，所述目标阶数为指数哥伦布编码的阶数；
    编码模块，用于基于所述目标阶数的指数哥伦布编码算法对所述待编码属性信息进行熵编码，得到属性码流。
  13. 根据权利要求12所述的装置，其中，所述属性码流中携带所述目标阶数的指示信息。
  14. 根据权利要求13所述的装置，其中，所述属性码流中携带有属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的 属性类型，所述属性类型为所述待编码属性信息的类型。
  15. 根据权利要求12至14中任一项所述的装置，其中，所述目标阶数与所述分布特性值成正相关。
  16. 根据权利要求12所述的装置，其中，所述第二确定模块具体用于：
    基于所述分布特性值与属性量化步长的比值的取整值，确定所述待编码属性信息对应的目标阶数。
  17. 根据权利要求16所述的装置，其中，所述第二确定模块具体用于：
    基于所述分布特性值与属性量化步长的比值的取整值，确定目标索引值；
    基于存储的指数哥伦布编码阶数与索引值的对应关系确定所述待编码属性信息对应的目标阶数，所述目标阶数与所述目标索引值对应。
  18. 根据权利要求12所述的装置，其中，所述编码模块具体用于：
    获取所述待编码属性信息对应的目标信息，所述目标信息包括预测残差和变换系数中的至少一项；
    采用所述目标阶数的指数哥伦布编码算法对所述目标信息进行熵编码，得到属性码流。
  19. 根据权利要求12-14中任一项或16-18中任一项所述的装置，其中，所述分布特性值基于如下至少一项确定：
    所述待编码属性信息的最大值；
    所述待编码属性信息的最小值；
    所述待编码属性信息的最大值与最小值的差值；
    所述待编码属性信息的绝对值的平均值。
  20. 一种点云解码处理装置，包括：
    确定模块，用于确定待解码属性码流对应的目标阶数，所述目标阶数为指数哥伦布解码的阶数；
    解码模块，用于基于所述目标阶数的指数哥伦布解码算法对所述待解码属性码流进行熵解码。
  21. 根据权利要求20所述的装置，其中，所述待解码属性码流中携带用于指示所述目标阶数的指示信息。
  22. 根据权利要求21所述的装置，其中，所述待解码属性码流中携带有 属性信息参数集，所述属性信息参数集包括所述指示信息以及所述指示信息对应的属性类型，所述属性类型为所述待解码属性码流对应的属性信息的类型。
  23. 一种电子设备，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1-8中任一项所述的点云编码处理方法的步骤；或者，所述程序或指令被所述处理器执行时实现如权利要求9-11中任一项所述的点云解码处理方法的步骤。
  24. 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权利要求1-8中任一项所述的点云编码处理方法的步骤；或者，所述程序或指令被处理器执行时实现如权利要求9-11中任一项所述的点云解码处理方法的步骤。
  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1-8中任一项所述的点云编码处理方法的步骤，或者，实现如权利要求9-11中任一项所述的点云解码处理方法的步骤。
  26. 一种计算机程序产品，其中，所述计算机程序产品被存储在非瞬态的可读存储介质中，所述计算机程序产品被至少一个处理器执行以实现如权利要求1-8中任一项所述的点云编码处理方法的步骤，或者，所述计算机程序产品被至少一个处理器执行以实现如权利要求9-11中任一项所述的点云解码处理方法的步骤。
  27. 一种通信设备，被配置为执行如权利要求1-8中任一项所述的点云编码处理方法的步骤，或者，被配置为执行如权利要求9-11中任一项所述的点云解码处理方法的步骤。
PCT/CN2022/135876 2021-12-03 2022-12-01 点云编码处理方法、点云解码处理方法及相关设备 WO2023098803A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202111467925.3 2021-12-03
CN202111467925.3A CN116233389A (zh) 2021-12-03 2021-12-03 点云编码处理方法、点云解码处理方法及相关设备

Publications (1)

Publication Number Publication Date
WO2023098803A1 true WO2023098803A1 (zh) 2023-06-08

Family

ID=86589667

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/135876 WO2023098803A1 (zh) 2021-12-03 2022-12-01 点云编码处理方法、点云解码处理方法及相关设备

Country Status (2)

Country Link
CN (1) CN116233389A (zh)
WO (1) WO2023098803A1 (zh)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101198055A (zh) * 2006-12-05 2008-06-11 华为技术有限公司 一种编码方法及编码器
CN101355700A (zh) * 2008-09-09 2009-01-28 北京大学软件与微电子学院 一种并行类熵编码方法及其装置
CN103152567A (zh) * 2013-02-26 2013-06-12 中国科学院光电技术研究所 一种任意阶数指数哥伦布编码器及其方法
US20190311501A1 (en) * 2018-04-10 2019-10-10 Apple Inc. Hierarchical point cloud compression with smoothing
CN111247798A (zh) * 2019-01-10 2020-06-05 深圳市大疆创新科技有限公司 对三维数据点集进行编码或解码的方法和设备
WO2021045130A1 (en) * 2019-09-03 2021-03-11 Panasonic Intellectual Property Corporation Of America System and method for video coding
CN112565734A (zh) * 2020-12-03 2021-03-26 西安电子科技大学 基于混合编码的点云属性编解码方法及装置
US20210319593A1 (en) * 2020-04-14 2021-10-14 Apple Inc. Significant coefficient flag encoding for point cloud attribute compression

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101198055A (zh) * 2006-12-05 2008-06-11 华为技术有限公司 一种编码方法及编码器
CN101355700A (zh) * 2008-09-09 2009-01-28 北京大学软件与微电子学院 一种并行类熵编码方法及其装置
CN103152567A (zh) * 2013-02-26 2013-06-12 中国科学院光电技术研究所 一种任意阶数指数哥伦布编码器及其方法
US20190311501A1 (en) * 2018-04-10 2019-10-10 Apple Inc. Hierarchical point cloud compression with smoothing
CN111247798A (zh) * 2019-01-10 2020-06-05 深圳市大疆创新科技有限公司 对三维数据点集进行编码或解码的方法和设备
WO2021045130A1 (en) * 2019-09-03 2021-03-11 Panasonic Intellectual Property Corporation Of America System and method for video coding
US20210319593A1 (en) * 2020-04-14 2021-10-14 Apple Inc. Significant coefficient flag encoding for point cloud attribute compression
CN112565734A (zh) * 2020-12-03 2021-03-26 西安电子科技大学 基于混合编码的点云属性编解码方法及装置

Also Published As

Publication number Publication date
CN116233389A (zh) 2023-06-06

Similar Documents

Publication Publication Date Title
WO2022257971A1 (zh) 点云编码处理方法、点云解码处理方法及相关设备
WO2023098803A1 (zh) 点云编码处理方法、点云解码处理方法及相关设备
WO2023025024A1 (zh) 点云属性编码方法、点云属性解码方法及终端
WO2023098820A1 (zh) 点云编码、解码方法、装置及通信设备
WO2023098802A1 (zh) 点云属性编码方法、点云属性解码方法及终端
WO2022257968A1 (zh) 点云编码方法、点云解码方法及终端
WO2022258055A1 (zh) 点云属性信息编码方法、解码方法、装置及相关设备
WO2022258010A1 (zh) 点云编码处理方法、解码处理方法及装置
WO2023098801A1 (zh) 点云属性编码方法、点云解码方法及终端
WO2022258009A1 (zh) 熵编码、解码方法及装置
WO2022257978A1 (zh) 点云编码方法、解码方法及装置
WO2022257972A1 (zh) 编码、解码控制方法及装置
WO2023098814A1 (zh) 属性量化、反量化方法、装置及设备
WO2024120323A1 (zh) 属性变换编码方法、属性变换解码方法及终端
WO2022257969A1 (zh) 点云编码处理方法、解码处理方法及相关设备
WO2022257970A1 (zh) 点云的几何信息编码处理方法、解码处理方法及相关设备
WO2023155779A1 (zh) 编码方法、解码方法、装置及通信设备
WO2024120325A1 (zh) 点云编码方法、点云解码方法及终端
WO2024120324A1 (zh) 属性变换解码方法、属性变换编码方法及终端
WO2024120326A1 (zh) 几何编码方法、几何解码方法及终端
WO2023024842A1 (zh) 点云编解码方法、装置、设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22900607

Country of ref document: EP

Kind code of ref document: A1