Adaptive wavelet transform

Abstract

An apparatus and method including computer code configured to cause one or more processors to obtain a grid of encoded volumetric data representing at least one three-dimensional (3D) visual content from a bitstream, segment a plurality of vertices of the grid into a plurality of groups, and decoding the encoded volume data by predicting vertices in each of the plurality of groups.

Description

Cross - Reference to Related Applications

[0001] This application claims priority to U.S. Provisional Application No. 63 / 458,918, filed on April 12, 2023, U.S. Provisional Application No. 63 / 531,238, filed on August 7, 2023, and U.S. Application No. 18 / 632,467, filed on April 11, 2024, the disclosures of which are hereby incorporated by reference in their entireties. Background Art 1. Technical Field

[0002] This disclosure relates to a set of advanced video coding and decoding techniques, including adaptive wavelet transform. 2. Description of the Related Art

[0003] Advances in 3D (Three - Dimensional) capture, modeling, and rendering have contributed to the prevalence of 3D content across several platforms and devices. Today, it is possible to capture a baby's first steps on one continent and allow grandparents on another continent to see (and perhaps interact) and enjoy a fully immersive experience with the child. However, to achieve such realism, the models are becoming increasingly complex, and a large amount of data is associated with the creation and consumption of these models. 3D meshes are widely used to represent such immersive content.

[0004] A mesh is composed of a number of polygons that describe the surface of a volumetric object. Each polygon is defined by its vertices in 3D space and information on how the vertices are connected (referred to as connectivity information). Optionally, vertex attributes such as color, normal, etc. can be associated with the mesh vertices. Attributes can also be associated with the surface of the mesh by using mapping information that parameterizes the mesh using a 2D attribute map. Such a mapping can be described by a set of parametric coordinates called UV coordinates or texture coordinates, which are associated with the mesh vertices. The 2D attribute map is used to store high - resolution attribute information such as texture, normal, displacement, etc. Such information can be used for various purposes, such as texture mapping and shading.

[0005] Dynamic mesh sequences may require a large amount of data as they may include a large amount of information that changes over time. Therefore, efficient compression techniques are needed to store and transmit such content. Mesh compression standards such as IC, MESHGRID, and FAMC were previously developed by MPEG to handle dynamic meshes with constant connectivity and time-varying geometry and vertex attributes. However, these standards do not consider time-varying attribute maps and connectivity information. DCC (Digital Content Creation) tools typically generate such dynamic meshes. Correspondingly, for volumetric acquisition techniques, generating dynamic meshes with constant connectivity, especially under real-time constraints, is challenging. Existing standards do not support this type of content. MPEG plans to develop a new mesh compression standard to directly handle dynamic meshes with time-varying connectivity information and optionally time-varying attribute maps. The standard is for lossy and lossless compression for various applications such as real-time communication, storage, free-viewpoint video, AR, and VR. Features such as random access and scalable / progressive codec are also considered. Therefore, for any of these reasons, technical solutions to these problems that arise in video codec technologies are needed. Summary of the Invention

[0006] Including a method and an apparatus, the apparatus includes: a memory configured to store computer program code; and one or more processors configured to access the computer program code and operate according to what is indicated by the computer program code. The computer program is configured to cause the processor to implement: obtaining code configured to cause at least one processor to obtain a mesh of encoded volumetric data representing at least one three-dimensional (3D) visual content from a bitstream; segmenting code configured to cause at least one processor to segment a plurality of vertices of the mesh into a plurality of groups; and decoding code configured to cause at least one processor to: decode the encoded volumetric data by predicting vertices in each group based on an adaptive wavelet transform for each group of the plurality of groups, wherein the adaptive wavelet transform includes a prediction process and an update process, wherein the prediction process depends on a geometric attribute signal at a set of vertices among the plurality of vertices, at least one vertex in the set being between at least two other vertices in the set, and wherein the update process depends on adjacent vertices of the set, the adjacent vertices including at least two other vertices.

[0007] The prediction process may include: selecting at least two other vertices of a first set as endpoints of an edge of the mesh.

[0008] The update process may depend on the number of adjacent vertices including at least two other vertices.

[0009] The update process can depend on a scalar value.

[0010] The prediction process also depends on a scalar value.

[0011] Decoding the encoded volume data includes: applying an adaptive wavelet transform to an enhancement layer of a grid frame of a grid.

[0012] Decoding the encoded volume data can include: applying an adaptive wavelet transform to grid frames of a grid sequence of a grid.

[0013] Decoding the encoded volume data can include: selecting a region of a grid frame and applying an adaptive wavelet transform to the selected region of the grid frame.

[0014] Decoding the encoded volume data can include: applying an adaptive wavelet transform to a base layer of a grid frame of a grid.

[0015] Decoding the encoded volume data can include: applying an adaptive wavelet transform to at least one of an enhancement layer and a band of a grid frame of a grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, properties, and various advantages of the disclosed subject matter will become more apparent from the following detailed description and the accompanying drawings, in which:

[0017] Figure 1 is a schematic illustration of a diagram according to an embodiment;

[0018] Figure 2 is a simplified block diagram according to an embodiment;

[0019] Figure 3 is a simplified illustration according to an embodiment;

[0020] Figure 4 is a simplified illustration according to an embodiment;

[0021] Figure 5 is a simplified illustration according to an embodiment;

[0022] Figure 6 is a simplified illustration according to an embodiment;

[0023] Figure 7 is a simplified illustration according to an embodiment;

[0024] Figure 8 is a simplified illustration according to an embodiment;

[0025] Figure 9 is a simplified illustration according to an embodiment;

[0026] Figure 10is a simplified flowchart according to an embodiment;

[0027] Figure 11 is a simplified flowchart according to an embodiment;

[0028] Figure 12 is a simplified flowchart according to an embodiment;

[0029] Figure 13 is a simplified illustration according to an embodiment;

[0030] Figure 14 is a simplified illustration according to an embodiment;

[0031] Figure 15 is a simplified illustration according to an embodiment;

[0032] Figure 16 is a simplified illustration according to an embodiment;

[0033] Figure 17 is a simplified illustration according to an embodiment;

[0034] Figure 18 is a simplified illustration according to an embodiment;

[0035] Figure 19 is a simplified illustration according to an embodiment; and

[0036] Figure 20 is a simplified illustration according to an embodiment. Detailed implementation manners

[0037] The proposed features discussed below can be used alone or in any combination. In addition, the embodiments can be implemented by processing circuitry (e.g., one or more processors or one or more integrated circuits). In one example, one or more processors execute a program stored in a non-transitory computer-readable medium.

[0038] Figure 1 FIG. shows a simplified block diagram of a communication system 100 according to an embodiment of the present disclosure. The communication system 100 may include at least two terminals 102 and 103 interconnected via a network 105. For one-way transmission of data, the first terminal 103 may encode and decode video data at a local location for transmission to another terminal 102 via the network 105. The second terminal 102 may receive the encoded and decoded video data of another terminal from the network 105, decode the encoded and decoded data, and display the recovered video data. One-way data transmission may be common in media service applications and the like.

[0039] Figure 1A second pair of terminals 101 and 104 is shown, which are configured to support two-way transmission of encoded and decoded video that may occur, for example, during a video conference. For two-way transmission of data, each terminal 101 and 104 can encode and decode video data captured at a local location for transmission via network 105 to another terminal. Each terminal 101 and 104 can also receive encoded and decoded video data sent by another terminal, can decode the encoded and decoded data, and can display the recovered video data at a local display device.

[0040] In Figure 1 FIG., terminals 101, 102, 103, and 104 may be shown as servers, personal computers, and smart phones, but the principles of the present disclosure are not limited thereto. Embodiments of the present disclosure are applicable to laptop computers, tablet computers, media players, and / or dedicated video conferencing devices. Network 105 represents any number of networks that transfer encoded and decoded video data between terminals 101, 102, 103, and 104, including, for example, wired and / or wireless communication networks. Communication network 105 may exchange data in circuit-switched channels and / or packet-switched channels. Representative networks include telecommunications networks, local area networks, wide area networks, and / or the Internet. For the purposes of this discussion, unless otherwise stated below, the architecture and topology of network 105 may be immaterial to the operation of the present disclosure.

[0041] Figure 2 FIG. shows the placement of a video encoder and a video decoder in a streaming environment as an example of an application of the disclosed subject matter. The disclosed subject matter may be equally applicable to other video-enabled applications, including, for example, video conferencing, digital TV, storing compressed video on digital media including CD (Compact Disc), DVD (Digital Versatile Disc), memory sticks, etc.

[0042] A streaming system may include a capture subsystem 203, which may include a video source 201, such as a digital camera device, that creates a stream of video samples 213, such as an uncompressed stream of video samples. The sample stream 213 may be characterized as having a high data volume when compared to an encoded video bitstream and may be processed by an encoder 202 coupled to the video source 201, which may be, for example, a camera device as discussed above. The encoder 202 may include hardware, software, or a combination thereof to implement or effectuate aspects of the disclosed subject matter as described in more detail below. The encoded video bitstream 204 may be characterized as having a lower data volume when compared to the sample stream and may be stored on a streaming server 205 for future use. One or more streaming clients 212 and 207 may access the streaming server 205 to retrieve copies 208 and 206 of the encoded video bitstream 204. The client 212 may include a video decoder 211 that decodes an incoming copy of the encoded video bitstream 208 and creates an outgoing stream of video samples 210 that may be rendered on a display 209 or other rendering device (not shown). In some streaming systems, the video bitstreams 204, 206, and 208 may be encoded according to certain video codec / compression standards. Examples of such standards were mentioned above and are further described herein.

[0043] Figure 3 may be a functional block diagram of a video decoder 300 according to an embodiment of the present invention.

[0044] The receiver 302 may receive one or more codec video sequences to be decoded by the decoder 300; in the same or another embodiment, one decoded video sequence is received at a time, where the decoding of each decoded video sequence is independent of other decoded video sequences. The decoded video sequences may be received from the channel 301, which may be a hardware / software link to a storage device storing the encoded video data. The receiver 302 may receive the encoded video data as well as other data, such as decoded audio data and / or auxiliary data streams, which may be forwarded to their respective using entities (not depicted). The receiver 302 may separate the decoded video sequences from the other data. To prevent network jitter, the buffer memory 303 may be coupled between the receiver 302 and the entropy decoder / parser 304 (hereinafter referred to as "parser"). When the receiver 302 is receiving data from a storage / forward device with sufficient bandwidth and controllability or from an isosynchronous network, the buffer 303 may not be needed or the buffer 303 may be small. For use on a best effort packet network such as the Internet, the buffer 303 may be required, and the buffer 303 may be relatively large and may advantageously have an adaptive size.

[0045] Video decoder 300 may include a parser 304 to reconstruct symbols 313 based on an entropy-coded video sequence. The categories of these symbols include: information for managing the operation of decoder 300; and information potentially for controlling a rendering device such as display 312, which is not a part of the decoder but can be coupled to the decoder. The control information for the rendering device may be in the form of Supplementary Enhancement Information (SEI) messages or Video Usability Information (VUI) parameter set segments (not depicted). The parser 304 may parse / entropy-decode the received coded video sequence. The coding of the coded video sequence may be performed according to video coding techniques or standards and may follow principles well-known to those skilled in the art, including: variable length coding, Huffman coding, arithmetic coding with or without context sensitivity, etc. The parser 304 may extract subgroup parameter sets from the coded video sequence for at least one subgroup of pixels in the video decoder based on at least one parameter corresponding to the subgroup. The subgroup may include Group of Picture (GOP), picture, tile, slice, macroblock, Coding Unit (CU), block, Transform Unit (TU), Prediction Unit (PU), etc. The entropy decoder / parser may also extract information such as transform coefficients, quantizer parameter values, motion vectors, etc. from the coded video sequence.

[0046] The parser 304 may perform entropy decoding / parsing operations on the video sequence received from buffer 303 to create symbols 313. The parser 304 may receive the encoded data and selectively decode specific symbols 313. Additionally, the parser 304 may determine whether to provide a specific symbol 313 to the motion compensation prediction unit 306, scaler / inverse transform unit 305, intra prediction unit 307, or loop filter 311.

[0047] Depending on the type of the coded video picture or its part (e.g., inter-picture and intra-picture, inter-block and intra-block) and other factors, the reconstruction of symbols 313 may involve multiple different units. Which units are involved and the manner of involvement may be controlled by subgroup control information parsed by the parser 304 from the coded video sequence. For clarity, this subgroup control information flow between the parser 304 and the multiple units below is not depicted.

[0048] In addition to the functional blocks already mentioned, the decoder 300 can conceptually be subdivided into a number of functional units as described below. In a practical implementation operating under commercial constraints, many of these units interact closely with each other and can be at least partially integrated with each other. However, for the purpose of describing the disclosed subject matter, it is appropriate to conceptually subdivide into the following functional units.

[0049] The first unit is the scaler / inverse transform unit 305. The scaler / inverse transform unit 305 receives the quantized transform coefficients as symbols 313 and control information from the parser 304, including which transform to use, block size, quantization factor, quantization scaling matrix, etc. The scaler / inverse transform unit 305 can output a block including sample values, which can be input into the aggregator 310.

[0050] In some cases, the output samples of the scaler / inverse transform 305 can belong to an intra-coded block; that is: a block that does not use predictive information from a previously reconstructed picture, but can use predictive information from a previously reconstructed part of the current picture. Such predictive information can be provided by the intra-picture prediction unit 307. In some cases, the intra-picture prediction unit 307 uses the surrounding reconstructed information extracted from the current (partially reconstructed) picture 309 to generate a block of the same size and shape as the block being reconstructed. In some cases, the aggregator 310 adds the predictive information generated by the intra-picture prediction unit 307 to the output sample information provided by the scaler / inverse transform unit 305 on a per-sample basis.

[0051] In other cases, the output samples of the scaler / inverse transform unit 305 can belong to an inter-coded and possibly motion-compensated block. In this case, the motion compensation prediction unit 306 can access the reference picture memory 308 to extract samples for prediction. After motion compensating the extracted samples according to the symbols 313 belonging to the block, these samples can be added by the aggregator 310 to the output of the scaler / inverse transform unit (referred to as residual samples or residual signal in this case) to generate output sample information. The address in the reference picture memory from which the motion compensation unit extracts the prediction samples can be controlled by a motion vector, which is available to the motion compensation unit in the form of symbols 313, which can have, for example, an X component, a Y component, and a reference picture component. Motion compensation can also include: interpolation of sample values extracted from the reference picture memory when using sub-sample accurate motion vectors, motion vector prediction mechanisms, etc.

[0052] The output samples of aggregator 310 can undergo various loop filtering techniques in loop filter unit 311. Video compression techniques can include in-loop filter techniques that are controlled by parameters included in the decoded video bitstream and available to loop filter unit 311 as symbols 313 from parser 304, but video compression techniques can also respond to meta-information obtained during decoding of previous (in decoding order) portions of the decoded picture or decoded video sequence, and to previously reconstructed and loop-filtered sample values.

[0053] The output of loop filter unit 311 can be a sample stream that can be output to rendering device 312 and stored in reference picture memory 557 for future inter-picture prediction.

[0054] Once fully reconstructed, some decoded pictures can be used as reference pictures for future prediction. Once a decoded picture is fully reconstructed and the decoded picture has been identified as a reference picture (e.g., by parser 304), then the current reference picture 309 can become part of reference picture buffer 308, and a new current picture memory can be reallocated before starting to reconstruct subsequent decoded pictures.

[0055] Video decoder 300 can perform decoding operations according to predetermined video compression techniques that can be documented in standards such as ITU-T Recommendation H.265. In the sense that the decoded video sequence follows the syntax of the video compression technique or standard, the decoded video sequence can conform to the syntax specified by the video compression technique or standard being used, as specified in the video compression technique document or standard and explicitly in the profile document therein. For compliance, it is also required that the complexity of the decoded video sequence be within the range defined by the level of the video compression technique or standard. In some cases, the level limits the maximum picture size, maximum frame rate, maximum reconstruction sample rate (measured, for example, in megasamples per second), maximum reference picture size, etc. In some cases, the limits set by the level can be further restricted by the Hypothetical Reference Decoder (HRD) specification and the metadata for HRD buffer management signaled in the decoded video sequence.

[0056] In an embodiment, the receiver 302 may receive additional (redundant) data along with the encoded video. The additional data may be included as part of the (one or more) decoded video sequences. The additional data may be used by the video decoder 300 to properly decode the data and / or more accurately reconstruct the original video data. The additional data may be in the form of, for example, temporal, spatial, or signal-to-noise ratio (SNR) enhancement layers, redundant slices, redundant pictures, forward error correction codes, and the like.

[0057] Figure 4 may be a functional block diagram of a video encoder 400 according to an embodiment of the present disclosure.

[0058] The encoder 400 may receive video samples from a video source 401 (which is not part of the encoder), and the video source 401 may capture video images to be encoded and decoded by the encoder 400.

[0059] The video source 401 may provide a source video sequence in the form of a digital video sample stream to be encoded and decoded by the encoder (303), and the digital video sample stream may have any suitable bit depth (e.g., 8 bits, 10 bits, 12 bits,...), any color space (e.g., BT.601 YCrCb, RGB,...), and any suitable sampling structure (e.g., YCrCb 4:2:0, YCrCb 4:4:4). In a media service system, the video source 401 may be a storage device storing previously prepared videos. In a video conferencing system, the video source 401 may be a camera device that captures local image information as a video sequence. The video data may be provided as a plurality of individual pictures that are given motion when viewed in sequence. The pictures themselves may be organized as a spatial pixel array, where each pixel may include one or more samples depending on the sampling structure, color space, etc. used. Those skilled in the art can easily understand the relationship between pixels and samples. The following description focuses on samples.

[0060] According to an embodiment, the encoder 400 may encode, decode, and compress the pictures of the source video sequence into an encoded and decoded video sequence 410 in real time or according to any other time constraint required by the application. Implementing an appropriate encoding and decoding speed is a function of the controller 402. The controller controls the other functional units as described below and is functionally coupled to these units. For clarity, the couplings are not depicted. The parameters set by the controller may include: rate control related parameters (picture skipping, quantizer, λ value of rate distortion optimization techniques,...), picture size, group of pictures (GOP) layout, maximum motion vector search range, etc. Those skilled in the art can easily identify other functions of the controller 402, as these functions may belong to the video encoder 400 optimized for a specific system design.

[0061] Some video encoders operate in a manner that is readily recognizable by those skilled in the art as a "codec loop". As a gross simplification, the codec loop can include: an encoding portion of an encoder 400 (hereinafter referred to as the "source codec") that is responsible for creating symbols based on input pictures and reference pictures to be coded and decoded; and a (local) decoder 406 embedded in the encoder 400, where the (local) decoder 406 reconstructs the symbols to create sample data that the (remote) decoder will also create (since in the video compression techniques contemplated in the disclosed subject matter, any compression between the symbols and the coded and decoded video bitstream is lossless). This reconstructed sample stream is input to the reference picture memory 405. Since decoding the symbol stream results in a bit-exact result independent of the decoder location (local or remote), the reference picture buffer content is also bit-exact between the local encoder and the remote encoder. In other words, the reference picture samples "seen" by the prediction portion of the encoder are exactly the same as the sample values that the decoder will "see" when using prediction during decoding. The basic principle of this reference picture synchronization (and the resulting drift if synchronization cannot be maintained, for example due to channel errors) is well known to those skilled in the art.

[0062] The operation of the "local" decoder 406 can be the same as that of the "remote" decoder 300 already described in detail above. However, also briefly referring to Figure 3 , when symbols are available and the entropy codec 408 and parser 304 can losslessly encode / decode the symbols into a coded and decoded video sequence, the entropy decoding portion of the decoder 300 including the channel 301, receiver 302, buffer 303, and parser 304 may not be fully implemented in the local decoder 406. Figure 4

[0063] It can be observed at this time that any decoder technology other than parsing / entropy decoding present in the decoder must also necessarily exist in the corresponding encoder in substantially the same functional form. Since the encoder technology is inverse to the decoder technology that has been fully described, the description of the encoder technology can be simplified. More detailed descriptions will only be provided and are needed in certain places below.

[0064] As part of its operation, the source codec 403 can perform motion-compensated predictive coding and decoding, which predictively codes and decodes an input frame by referring to one or more previously coded and decoded frames in the video sequence designated as "reference frames". In this way, the codec engine 407 codes and decodes the difference between a pixel block of the input frame and a pixel block of the reference frame, and the reference frame can be selected as the prediction reference for the input frame.

[0065] ​The local video decoder 406 can decode the decoded video data of frames that can be designated as reference frames based on the symbols created by the source codec 403. The operation of the codec engine 407 can advantageously be lossy processing. When the decoded video data can be decoded at the video decoder ( Figure 4 not shown), the reconstructed video sequence can generally be a copy of the source video sequence with some errors. The local video decoder 406 replicates the decoding process that can be performed by the video decoder on the reference frames and can cause the reconstructed reference frames to be stored in the reference picture memory 405 that can be, for example, a cache memory. In this way, the encoder 400 can locally store a copy of the reconstructed reference frames, which has the same content (no transmission errors) as the reconstructed reference frames that will be obtained by the remote video decoder.

[0066] The predictor 404 can perform a prediction search for the codec engine 407. That is, for a new frame to be coded or decoded, the predictor 404 can search in the reference picture memory 405 for sample data (as candidate reference pixel blocks) or some metadata, such as reference picture motion vectors, block shapes, etc., that can be used as an appropriate prediction reference for the new picture. The predictor 404 can operate on a per-pixel basis for sample blocks to find an appropriate prediction reference. In some cases, as determined by the search results obtained by the predictor 404, the input picture can have prediction references extracted from multiple reference pictures stored in the reference picture memory 405.

[0067] The controller 402 can manage the coding and decoding operations of the source codec 403 that can be, for example, a video codec, including, for example, setting parameters and subgroup parameters for encoding video data.

[0068] The outputs of all the above-mentioned functional units can be subjected to entropy coding in the entropy codec 408. The entropy codec converts these symbols into a coded video sequence by losslessly compressing the symbols generated by the various functional units according to techniques known to those skilled in the art, such as Huffman coding, variable length coding, arithmetic coding, etc.

[0069] The transmitter 409 can buffer the coded video sequence(s) created by the entropy codec 408 in preparation for transmission via the communication channel 411, which can be a hardware / software link to a storage device that will store the encoded video data. The transmitter 409 can merge the coded video data from the source codec 403 with other data to be transmitted, such as coded audio data and / or an auxiliary data stream (source not shown).

[0070] The controller 402 may manage the operation of the encoder 400. During encoding and decoding, the controller 402 may assign a certain type of encoded / decoded picture to each encoded / decoded picture, which may affect the encoding and decoding techniques that can be applied to the corresponding picture. For example, pictures can generally be assigned to one of the following frame types:

[0071] Intra pictures (I pictures), which may be pictures that can be encoded and decoded without using any other frames in the sequence as a prediction source. Some video codecs allow different types of intra pictures, including for example independent decoder refresh pictures. Those skilled in the art are aware of these variations of I pictures and their corresponding applications and features.

[0072] Predictive pictures (P pictures), which may be pictures that can be encoded and decoded using intra prediction or inter prediction that utilizes at most one motion vector and a reference index to predict the sample values of each block.

[0073] Bi - directional predictive pictures (B pictures), which may be pictures that can be encoded and decoded using intra prediction or inter prediction that utilizes at most two motion vectors and reference indices to predict the sample values of each block. Similarly, multiple predictive pictures can use more than two reference pictures and associated metadata for reconstructing a single block.

[0074] Source pictures can generally be spatially subdivided into multiple sample blocks (e.g., blocks of 4×4, 8×8, 4×8, or 16×16 samples respectively), and are encoded and decoded on a block - by - block basis. These blocks can be predictively encoded and decoded with reference to other (encoded / decoded) blocks, which are determined by the encoding and decoding assignments applied to the corresponding pictures of the blocks. For example, blocks of an I picture can be non - predictively encoded, or can be predictively encoded (spatial prediction or intra prediction) with reference to the encoded / decoded blocks of the same picture. Pixel blocks of a P picture can be non - predictively encoded with reference to one previously encoded reference picture via spatial prediction or via temporal prediction. Blocks of a B picture can be non - predictively encoded with reference to one or two previously encoded reference pictures via spatial prediction or via temporal prediction.

[0075] The encoder 400, which may be, for example, a video codec, can perform encoding and decoding operations according to a predetermined video encoding and decoding technique or standard such as the ITU - T H.265 recommendation. In the operation of the encoder 400, the encoder 400 can perform various compression operations, including predictive encoding and decoding operations that utilize the temporal redundancy and spatial redundancy in the input video sequence. Thus, the encoded / decoded video data can conform to the syntax specified by the used video encoding and decoding technique or standard.

[0076] In an embodiment, the transmitter 409 may transmit additional data along with the encoded video. The source codec 403 may include such data as part of the decoded / encoded video sequence. The additional data may include temporal / spatial / SNR enhancement layers, other forms of redundant data such as redundant pictures and slices, supplementary enhancement information (SEI) messages, visual usability information (VUI) parameter set segments, and the like.

[0077] Figure 5 FIG. 500 is a simplified block diagram flowchart showing an exemplary viewport-related process in the Omnidirectional Media Application Format (OMAF), which may enable 360-degree Virtual Reality (VR360) streaming as described in OMAF.

[0078] At acquisition block 501, video data A is acquired, such as data of multiple images and audio at the same time instance in a case where the image data may represent a scene in VR360. At processing block 503, the images B at the same time instance are processed by one or more of the following i : being stitched; being mapped onto a projected picture with respect to one or more virtual reality (VR) angles or other angles / viewpoints; and being packed region by region. Additionally, metadata indicating any of such processed information and other information may be created to assist in the delivery and rendering processes.

[0079] Regarding data D, at image encoding block 505, the projected picture is encoded as data E i and is combined into a media file and combined in a viewport-independent stream; and at video encoding block 504, the video picture is encoded as data E v , such as as a single-layer bitstream; and regarding data B a , the audio data may also be encoded as data E at audio encoding block 502 a .

[0080] Data E a 、E v and E i 、the entire decoded / encoded bitstream F iAnd / or F can be stored at a (Content Delivery Network (CDN) / cloud) server and can generally be fully transmitted to the OMAF player 520, for example, at delivery block 507 or otherwise, and can be fully decoded by a decoder such that at least one region corresponding to the current viewport of the decoded picture is presented to the user at the display block 516 for various metadata, file playback, and orientation / viewport metadata from the head / eye tracking block 508, where the orientation / viewport metadata is, for example, the angle at which the user can view through the device with respect to the viewport specifications of the VR image device. A notable feature of VR360 is that only the viewport can be displayed at any given moment, and such a feature can be used to improve the performance of the omnidirectional video system by selective delivery according to the user's viewport (or any other criteria, such as recommended viewport timing metadata). For example, according to an exemplary embodiment, viewport-related delivery can be achieved through tile-based video coding and decoding.

[0081] Similar to the above encoding blocks, the OMAF player 520 according to an exemplary embodiment can perform file / segment de-encapsulation for one or more of the data F' and / or F' i and one or more aspects of such encoding are similarly reversed for metadata, and the audio data E' is decoded at the audio decoding block 510 i the video data E' is decoded at the video decoding block 513 v and the image data E' is decoded at the image decoding block 514 i to continue with the audio rendering of the data B' at the audio rendering block 511 a and the image rendering of the data D' at the image rendering block 515 to output the display data A' in VR360 format at the display block 516 according to various metadata such as orientation / viewport metadata i and output the audio data A' at the speaker / headphone block 512 s . Various metadata may affect the processing in the data decoding and rendering processes according to various tracks, languages, qualities, views that can be selected by or for the user of the OMAF player 520, and it should be understood that the processing order described herein is presented for exemplary embodiments and can be implemented in other orders according to other exemplary embodiments.

[0082] Figure 6FIG. 600 shows a simplified block-type content stream processing for (warp-decoded) point cloud data, where, regarding capturing / generating / (de)coding / rendering / displaying 6-degree-of-freedom media, view position and angle-related processing (herein referred to as "V-PCC") is performed on the point cloud data. It should be understood that according to the exemplary embodiments, the described features can be used individually or in any order of combination, and elements such as those shown for encoding and decoding can be implemented by processing circuitry (e.g., one or more processors or one or more integrated circuits), and one or more processors can execute programs stored in a non-transitory computer-readable medium.

[0083] FIG. 600 shows an exemplary embodiment for streaming warp-decoded point cloud data according to V-PCC.

[0084] At the volume data acquisition block 601, a real-world visual scene or a computer-generated visual scene (or a combination thereof) can be captured by a set of imaging devices, or these scenes can be synthesized by a computer into volume data, and the volume data, which can have any format, can be converted into a (quantized) point cloud data format through image processing at the conversion to point cloud block 602. For example, according to an exemplary embodiment, data from the volume data can be converted region-by-region data into points in the point cloud by extracting one or more values from the values in the volume data and any associated data as described below into the desired point cloud format. According to an exemplary embodiment, the volume data can be a 3D data set of 2D images, such as slices of 2D projections from which the 3D data set is projected. According to an exemplary embodiment, the point cloud data format includes representations of data points in one or more various spaces and can be used to represent the volume data, and can provide improvements in sampling and data compression, such as improvements regarding temporal redundancy, and for example, the point cloud data in x, y, z format represents color values (e.g., RGB, etc.), luminance, intensity, etc. at each of the multiple points of the cloud data, and can be used together with progressive decoding, polygon meshing, direct rendering, octree 3D representation of 2D quadtree data.

[0085] At the project onto image block 603, the acquired point cloud data can be projected onto a 2D image, and the acquired point cloud data can be encoded into an image / video picture using video-based point cloud coding (V-PCC). The projected point cloud data can include attributes, geometry, occupancy maps, and other metadata for reconstructing the point cloud data, such as using a painter's algorithm, a ray casting algorithm, a (3D) binary space partitioning algorithm, etc.

[0086] On the other hand, at the scene generator block 609, the scene generator can generate some metadata, for example, according to the director's intention or the user's preference, for rendering and displaying 6 Degrees-of-Freedom (DoF) media. Such 6DoF media can include 360VR, such as a 3D viewing of a scene according to rotational changes on the 3D axes X, Y, Z, in addition to an additional dimension that allows moving forward / backward, up / down, and left / right within or at least according to the point cloud codec data for a virtual experience. The scene description metadata defines one or more scenes composed of the decoded point cloud data and other media data including VR360, light field, audio, etc., and can be provided to one or more cloud servers and / or file / segment encapsulation / de-encapsulation processing as indicated by the relevant description. Figure 6 and the file / segment encapsulation / de-encapsulation processing as indicated by the relevant description.

[0087] After the video encoding block 604 and the image encoding block 605 similar to the above video and image encoding (and as will be understood, audio encoding can also be provided as described above), the file / segment encapsulation block 606 processes such that the decoded point cloud data is combined into a media file for file playback or a series of initialization segments and media segments for streaming according to a specific media container file format such as one or more video container formats, and can be used, for example, for DASH etc. as described below when such a description represents an exemplary embodiment. The file container can also include scene description metadata such as from the scene generator block 1109 in the file or segment.

[0088] According to an exemplary embodiment, the file is encapsulated according to the scene description metadata to include at least one view position in the 6DoF media and at least one or more angular views at that view position / these view positions at one or more moments, such that such a file can be transmitted as required according to user or creator input. In addition, according to an exemplary embodiment, the segments of such a file can include one or more parts of such a file, for example, a part of the 6DoF media indicating a single viewpoint and the angles at that viewpoint at one or more moments; however, these are only exemplary embodiments and can vary according to various conditions such as the network, user, creator capabilities, and input.

[0089] According to an exemplary embodiment, point cloud data is segmented into multiple 2D / 3D regions, which are independently encoded and decoded, for example, at one or more of video coding block 604 and image coding block 605. Then, each independently encoded and decoded segmentation of the point cloud data can be encapsulated as a track in a file and / or segment at file / segment encapsulation block 606. According to an exemplary embodiment, each point cloud track and / or metadata track may include some useful metadata for view position / angle related processing.

[0090] According to an exemplary embodiment, metadata useful for view position / angle related processing includes, for example, one or more of the following in the metadata included in the file and / or segment encapsulated regarding the file / segment encapsulation block: layout information of 2D / 3D segmentation with indices; (dynamic) mapping information associating 3D volume segmentation with one or more 2D segmentations (e.g., any one of tiles / tile groups / slices / sub-pictures); 3D positions of each 3D segmentation on a 6DoF coordinate system; a list of representative view positions / angles; a list of selected view positions / angles corresponding to 3D volume segmentation; indices of 2D / 3D segmentations corresponding to the selected view positions / angles; quality (grade) information of each 2D / 3D segmentation; and rendering information of each 2D / 3D segmentation depending on each view position / angle, for example. Invoking such metadata when requested, for example, by a user of a V-PCC player or according to an instruction from a content creator for the user of a V-PCC player, can enable more efficient processing for a specific part of 6DoF media expected regarding such metadata, such that the V-PCC player can deliver an image of a focused part of 6DoF media with higher quality than the unused part of the media.

[0091] A file or one or more segments of a file can be directly delivered from file / segment encapsulation block 606 to either the V-PCC player 625 or a cloud server, such as the cloud server at cloud server block 607, using a delivery mechanism (e.g., via HTTP-based Dynamic Adaptive Streaming over HTTP (DASH)). At the cloud server block 607, the cloud server can extract one or more tracks and / or one or more specific 2D / 3D segmentations from the file and can merge multiple decoded point cloud data into one data.

[0092] Based on data such as that regarding the position / viewing angle tracking block 608, if the current viewing position and angle are defined on a 6DoF coordinate system at the client system, view position / angle metadata can be delivered from the file / segment encapsulation block 606 or otherwise processed based on the file or segment at the cloud server at the cloud server block 607 such that the cloud server can extract an appropriate segmentation from the stored file and merge the segmentation (if required) based on metadata, for example, from the client system with the V-PCC player 625, and the extracted data can be delivered to the client as a file or segment.

[0093] Regarding such data, at the file / segment de-encapsulation block 615, the file de-encapsulator processes the file or received segment, extracts the encoded / decoded bitstream and parses the metadata, and at the video decoding block 610 and the image decoding block 611, the encoded / decoded point cloud data is then decoded into decoded point cloud data, and the encoded / decoded point cloud data is reconstructed into point cloud data at the point cloud reconstruction block 612, and the reconstructed point cloud data can be displayed at the display block 614 and / or can first be synthesized at the scene synthesis block 613 based on one or more various scene descriptions regarding the scene description data from the scene generator block 609.

[0094] In view of the above, such an exemplary V-PCC stream represents advantages over the V-PCC standard, including one or more of the following: the described segmentation ability for multiple 2D / 3D regions; the ability to combine the encoded / decoded 2D / 3D segmentations in the compression domain into a single compliant encoded / decoded video bitstream; and the ability to extract the encoded / decoded 2D / 3D bitstream of the encoded / decoded pictures into a compliant encoded / decoded bitstream, where such V-PCC system support is further improved by forming a container including a VVC (Versatile Video Coding, VVC) bitstream to support a mechanism for carrying metadata including one or more of the above-mentioned metadata.

[0095] In view of this and according to the exemplary embodiments described further below, the term "mesh" indicates the composition of one or more polygons that describe the surface of a volumetric object. Each polygon is defined by its vertices in 3D space and information on how the vertices are connected (referred to as connectivity information). Optionally, vertex attributes such as color, normal, etc. can be associated with the mesh vertices. Attributes can also be associated with the surface of the mesh by using mapping information that parameterizes the mesh using a 2D attribute map. Such a mapping can be described by a set of parametric coordinates called UV coordinates or texture coordinates, which are associated with the mesh vertices. The 2D attribute map is used to store high-resolution attribute information such as texture, normal, displacement, etc. According to the exemplary embodiments, such information can be used for various purposes such as texture mapping and shading.

[0096] However, a dynamic mesh sequence may require a large amount of data because the dynamic mesh sequence may include a large amount of information that changes over time. For example, in contrast to a "static mesh" or "static mesh sequence" where the information of the mesh may not change from one frame to another, a "dynamic mesh" or "dynamic mesh sequence" indicates the movement of which vertices represented by the mesh change from one frame to another. Therefore, efficient compression techniques are needed to store and transmit such content. The mesh compression standards IC, MESHGRID, FAMC were previously developed by MPEG to handle dynamic meshes with constant connectivity and time-varying geometry and vertex attributes. However, these standards do not consider time-varying attribute maps and connectivity information. DCC (Digital Content Creation) tools typically generate such dynamic meshes. Correspondingly, for volumetric acquisition techniques, generating dynamic meshes with constant connectivity, especially under real-time constraints, is challenging. Existing standards do not support this type of content. According to the exemplary embodiments herein, aspects of a new mesh compression standard are described to directly handle dynamic meshes with time-varying connectivity information and optionally with time-varying attribute maps, which is targeted at lossy and lossless compression for various applications such as real-time communication, storage, free viewpoint video, AR, and VR. Functions such as random access and scalable / progressive codec are also considered.

[0097] Figure 7Shows an example framework 700 for dynamic mesh compression for, for example, a method based on 2D atlas sampling. Each frame of the input mesh 701 can be preprocessed through a series of operations (e.g., tracking, remeshing, parameterization, voxelization). Note that these operations can be encoder-only, which means these operations may not be part of the decoding process, and this possibility can be signaled in the metadata through the following flag: for example, for encoder-only, the flag indicates 0; and for others, the flag indicates 1. Thereafter, a mesh 702 with a 2D UV atlas can be obtained, where each vertex of the mesh has one or more associated UV coordinates on the 2D atlas. Then, by sampling the 2D atlas, the mesh can be converted into multiple graphs, including a geometry graph and an attribute graph. Then, these 2D graphs can be encoded and decoded by a video / image codec such as HEVC (High-Efficiency Video Coding, HEVC), VVC, AV1 (AOMedia Video 1, AV1), AVS3 (Audio Video Coding Standard 3, AVS3), etc. On the decoder 703 side, the mesh can be reconstructed from the decoded 2D graphs. Any post-processing and filtering can also be applied to the reconstructed mesh 704. Note that for the purpose of 3D mesh reconstruction, other metadata can be signaled to the decoder side. Note that the chart boundary information including uv coordinates and xyz coordinates of the boundary vertices can be predicted, quantized, and entropy encoded in the bitstream. The quantization step size can be configured on the encoder side to trade off quality and bitrate.

[0098] In some implementations, a 3D mesh can be divided into several segments (or patches / graphs), and according to an exemplary embodiment, one or more 3D mesh segments can be regarded as a "3D mesh". Each segment can consist of a set of connected vertices associated with its geometry, attribute, and connectivity information. As Figure 8 shown in the example 800 of volume data in, the UV parameterization process 802 that maps from a 3D mesh segment to a 2D graph, for example, to a block of the 2D UV atlas 702 mentioned above, maps one or more mesh segments 801 to a 2D graph 803 in the 2D UV atlas 804. Each vertex (v n ) in the mesh segment will be assigned 2D UV coordinates in the 2D UV atlas. Note that the vertices (v n)Form a connected component that is the 3D counterpart thereof. The geometric, attribute, and connectivity information of each vertex can also be inherited from its 3D counterpart. For example, information indicating that vertex v4 is directly connected to vertices v0, v5, v1, and v3 can be indicated, and similarly, information for each of the other vertices can also be indicated. Further, according to an exemplary embodiment, such a 2D texture mesh will further indicate information such as color information on a per-face basis, where the per-face is, for example, per triangle face, such as v2, v5, v3 as one "face".

[0099] For example, regarding Figure 8 the features of example 800, see Figure 9 example 900, where the 3D mesh segment 801 can also be mapped to multiple separate 2D charts 901 and 902. In this case, vertices in 3D can correspond to multiple vertices in a 2D UV atlas. As Figure 9 shown, the same 3D mesh segment is mapped to multiple 2D charts in a 2D UV atlas, rather than a single chart as in Figure 8 . For example, 3D vertices v1 and v4 each have two 2D counterparts v1, v1' and v4, v4' respectively. Thus, a general 2D UV atlas of the 3D mesh can include multiple charts as Figure 14 shown, where each chart can contain multiple (usually more than or equal to 3) vertices associated with its 3D geometry, attributes, and connectivity information.

[0100] Figure 9 Example 903 showing triangulation derived in a chart with boundary vertices B0, B1, B2, B3, B4, B5, B6, B7 is shown. When such information is provided, any triangulation method can be applied to create connections between vertices (including boundary vertices and sampled vertices). For example, for each vertex, find the two closest vertices. Or for all vertices, continuously generate triangles until a minimum number of triangles is reached after a set number of attempts. As shown in example 903, there are various regularly shaped, repeating triangles and various oddly shaped triangles, and these oddly shaped triangles are usually closest to the boundary vertices and have their own unique dimensions, which may or may not be shared with any other triangles in the triangle. Connectivity information can also be reconstructed by explicit signaling. If the polygon cannot be recovered by implicit rules, then according to an exemplary embodiment, the encoder can signal the connectivity information in the bitstream.

[0101] Define boundary vertices B0, B1, B2, B3, B4, B5, B6, B7 in 2D UV space. Boundary edges can be determined by checking if an edge appears in only one triangle. According to an exemplary embodiment, the following information of the boundary vertices is important and should be signaled in the bitstream: geometric information, e.g., 3D XYZ coordinates, even if currently in 2D UV parameter form; and 2D UV coordinates.

[0102] For the case where boundary vertices in 3D as shown Figure 9 correspond to multiple vertices in a 2D UV atlas, the mapping from 3D XUZ to 2D UV can be one-to-many. Thus, a UV-to-XYZ (or called UV2XYZ) index can be signaled to indicate the mapping function. UV2XYZ can be a 1D array of indices that map each 2D UV vertex to a 3D XYZ vertex.

[0103] According to an exemplary embodiment, to efficiently represent mesh signals, a subset of mesh vertices along with their connectivity information can be encoded and decoded first. In the original mesh, the connections between these vertices may not exist as these vertices are subsampled from the original mesh. There are different ways to signal the connectivity information between vertices, so such a subset is called a base mesh or base vertices.

[0104] According to an exemplary embodiment, multiple methods are implemented for dynamic mesh compression, and the multiple methods are part of the edge-based vertex prediction framework mentioned above, where the base mesh is encoded and decoded first, and then more additional vertices are predicted based on the connectivity information of the edges from the base mesh. Note that these methods can be applied individually or in any form of combination.

[0105] For example, consider Figure 10 the vertex grouping in the example flowchart 1001 of the prediction mode. At S101, vertices inside the mesh can be obtained, and at S102, the vertices can be divided into different groups for prediction purposes, e.g., see Figure 9。In one example, at S104, patch / chart segmentation is used for partitioning. In another example, partitioning is performed under each patch / chart S105. The decision whether to proceed to S104 or S105 can be signaled by a flag or the like at S103. In the case of S105, several vertices of the same patch / chart form a prediction group and will share the same prediction mode, while several other vertices of the same patch / chart can use another prediction mode. Herein, a "prediction mode" can be regarded as a specific mode used by a decoder for predicting video content including patches. The prediction modes can be classified into intra prediction modes and inter prediction modes, and within each category, there can be different specific modes from which the decoder selects. According to an exemplary embodiment, each group, the "prediction group", can share the same specific mode (e.g., an angular mode at a specific angle) or the same classified prediction mode (e.g., all intra prediction modes, but can be predicted at different angles) according to an exemplary embodiment. Such grouping at S106 can be assigned at different levels by determining the corresponding number of vertices involved in each group. For example, according to an exemplary embodiment, every 64, 32, or 16 vertices following the scan order within a patch / chart will be assigned the same prediction mode, while other vertices can be assigned differently. For each group, the prediction mode can be an intra prediction mode or an inter prediction mode. This can be signaled or assigned. According to the example flowchart 1000, if at S107 it is determined that a grid frame or a grid slice is of the intra type, for example, by checking whether the flag of the grid frame or the grid slice indicates the intra type, then the vertices of all groups within the grid frame or the grid slice should use the intra prediction mode; otherwise, at S108, for all vertices in each group, an intra prediction mode or an inter prediction mode can be selected.

[0106] In addition, for a group of grid vertices using the intra prediction mode, its vertices can be predicted only by using previously encoded and decoded vertices within the same sub - division of the current grid. According to an exemplary embodiment, sometimes the sub - division can be the current grid itself, and according to an exemplary embodiment, for a group of grid vertices using the inter prediction mode, its vertices can be predicted only by using previously encoded and decoded vertices from another grid frame. Each of the above - mentioned information can be determined and signaled by a flag or the like. The prediction feature can occur at S110, and the result of the prediction and signaling can occur at S111.

[0107] According to an exemplary embodiment, for each vertex in a set of vertices in the example flow chart 1000 and the flow chart 1100 described below, after prediction, the residual will be a 3D displacement vector indicating the offset from the current vertex to its predictor. The residuals of a set of vertices need to be further compressed. In one example, before entropy coding and decoding, the transformation at S111 and its signaling can be applied to the residuals of the set of vertices. The following methods can be implemented to handle the coding and decoding of a set of displacement vectors. For example, in one method, the cases where a set of displacement vectors, some displacement vectors, or their components have only zero values are signaled appropriately. In another embodiment, for each displacement vector, a flag indicating whether the vector has any non-zero components is signaled, and if not, the coding and decoding of all components of the displacement vector can be skipped. Further, in another embodiment, for each set of displacement vectors, a flag indicating whether the set has any non-zero vectors is signaled, and if not, the coding and decoding of all displacement vectors in the set can be skipped. Further, in another embodiment, for each component of a set of displacement vectors, a flag indicating whether the component of the set has any non-zero vectors is signaled, and if not, the coding and decoding of all displacement vectors in the set for that component can be skipped. Further, in another embodiment, there may be cases where it is signaled that a set of displacement vectors or the components of the set of displacement vectors need to be transformed, and if not, the transformation can be skipped, and quantization / entropy coding and decoding can be directly applied to the set or the set of components. Further, in another embodiment, for each set of displacement vectors, a flag indicating whether the set needs to undergo transformation is signaled, and if not, the transformation coding and decoding of all displacement vectors in the set can be skipped. Further, in another embodiment, for each component of a set of displacement vectors, a flag indicating whether the component of the set needs to undergo transformation is signaled, and if not, the transformation coding and decoding of all displacement vectors in the set for that component can be skipped. The above embodiments regarding the processing of vertex prediction residuals in this paragraph can also be combined and implemented in parallel for different patches respectively.

[0108] Figure 11FIG. 1100 shows an example flow chart, where at S121, a mesh frame that is encoded / decoded as an entire data unit can be obtained, which means that all vertices or attributes of the mesh frame may have correlations among them. Alternatively, according to the determination at S122, the mesh frame can be divided into smaller independent sub-divisions at S123, which conceptually is similar to slices or tiles in 2D video or images. At S124, a prediction type can be assigned to the encoded / decoded mesh frame or the encoded / decoded mesh sub-division. Possible prediction types include intra-frame encoding / decoding type and inter-frame encoding / decoding type. For the intra-frame encoding / decoding type, at S125, only predictions from the reconstructed parts of the same frame or slice are allowed. On the other hand, at S125, in addition to intra-frame prediction of the mesh frame, the inter-frame prediction type will allow predictions from previously encoded / decoded mesh frames. Furthermore, more subtypes such as P type or B type can be used to classify the inter-frame prediction type. In the P type, only one predictor can be used for prediction purposes, while in the B type, two predictors from two previously encoded / decoded mesh frames can be used to generate a predictor. A weighted average of the two predictors can be an example. When the mesh frame is encoded / decoded as a whole, the frame can be regarded as an intra-frame or inter-frame encoded / decoded mesh frame. In the case of an inter-frame mesh frame, the P type or B type can be further identified via signaling. Alternatively, if the mesh frame is encoded / decoded in the case of further intra-frame division, at S124, the assignment of the prediction type occurs for each sub-division in the sub-divisions. Each of the above information can be determined and signaled via flags, etc., and similar to Figure 10 S110 and S111, the prediction feature can occur at S126, and the result of the prediction and signaling can occur at S127.

[0109] Therefore, although a dynamic mesh sequence may require a large amount of data, because it may include a large amount of information that changes over time, an efficient compression technique is needed to store and transmit such content, and the features described herein represent such improved efficiency by enabling at least an improvement in the prediction of the 3D position of mesh vertices by using previously decoded vertices in the same mesh frame (intra-frame prediction) or from previously encoded / decoded mesh frames (inter-frame prediction).

[0110] In addition, an exemplary embodiment can generate a displacement vector for the third layer 1303 of the mesh based on one or more reconstructed vertices of a previous layer such as the second layer 1302 and the first layer 1301 of the third layer 1303. Assuming the index of the second layer 1302 is T, a predictor for the vertices in the third layer 1303 T+1 is generated based on at least the reconstructed vertices of the current layer or the second layer 1302. An example of such a layer-based prediction structure is as shown in Figure 13 example 1300 in Figure 13Shows reconstruction-based vertex prediction: Progressive vertex prediction is performed using edge-based interpolation, where the predictor is generated based on previously decoded vertices rather than predictor vertices. The first layer 1301 can be a mesh bounded by a first polygon 1340 that has decoded vertices as its vertices at its boundary and has interpolated vertices along lines among the vertices of these decoded vertices. As progressive encoding and decoding proceeds from the first layer 1301 to the second layer 1302, an additional polygon 1341 can be formed by displacement vectors from the interpolated vertices in the first layer to the additional vertices of the second layer 1302, so the total number of vertices in the second layer 1302 can be greater than the total number of vertices in the first layer 1301. Similarly, proceeding to the third layer 1303, the additional vertices of the second layer 1302 together with the decoded vertices from the first layer 1301 can then serve encoding and decoding in a manner similar to the decoded vertices served in proceeding from the first layer 1301 to the second layer 1303; i.e., multiple additional polygons can be formed. It should be noted that referring to Figure 14 Example 1400 in Figure 14 shows such progressive encoding and decoding, where, different from Figure 13 Example 1400 shows that in proceeding from the first layer 1401 to the second layer 1403 and then to the third layer 1403, each of the additionally formed polygons can be entirely within the polygon formed by the boundary of the first layer 1401.

[0111] For such examples 1300 and / or 1400, according to an exemplary embodiment, referring to Figure 12 Example flowchart 1200, where, since the interpolated vertices on the current layer are predicted values, such values need to be reconstructed before being used as predictors for generating vertices on the next layer. This is done by: decoding the base mesh at S131; performing vertex prediction at S132; and then adding the decoded displacement vectors of the current layer to the predictors of the vertices, e.g., the predictors of the vertices of layer 1302 at S133. Then, the reconstructed vertices of this layer together with all the decoded vertices of the previous layer - e.g., checking the additional vertex values of such a layer at S134 - can be used to generate and signal the predictor vertices of the next layer 1303 at S135. This process can also be summarized as follows: Assume P[t](Vi) represents the predictor of vertex Vi on layer t; assume R[t](Vi) represents the reconstructed vertex Vi on layer t; assume D[t](Vi) represents the displacement vector of vertex Vi on layer t; assume f(*) represents the predictor generator, which can in particular be the average of two existing vertices. Then, according to an exemplary embodiment, for each layer t, there is the following: P[t](Vi) = f(R[s|s<t](Vj), R[m|m<t](Vk)), where Vj and Vk are the reconstructed vertices of the previous layer R[t](Vi) = P[t](Vi) + D[t](Vi) - Equation (1)

[0112] Then, for all vertices in a mesh frame, the vertices are divided into Layer 0 (base mesh), Layer 1, Layer 2, and so on. Then, the reconstruction of the vertices on a layer depends on the reconstruction of the vertices on the previous layer. Herein, each of P, R, and D represents a 3D vector in the context of a 3D mesh representation. D is the decoded displacement vector, and quantization may or may not be applied to this vector.

[0113] According to an exemplary embodiment, vertex prediction using the reconstructed vertices may be applied only to certain layers. For example, Layer 0 and Layer 1. For other layers, vertex prediction may still use neighboring predictor vertices without adding a displacement vector to the vertices for reconstruction. Such that these other layers can be processed simultaneously without waiting for a previous layer to be reconstructed. According to an exemplary embodiment, for each layer, it may be signaled whether to select vertex prediction based on the reconstructed vertices or to select vertex prediction based on the predictor vertices, or it may be signaled that the layer (and its subsequent layers) does not use vertex prediction based on the reconstructed vertices.

[0114] For the displacement vector whose vertex predictor is generated by the reconstructed vertices, quantization can be applied to it without further transformation, such as wavelet transformation, etc. For the displacement vector whose vertex predictor is generated by other predictor vertices, transformation may be required, and quantization can be applied to the transformation coefficients of these displacement vectors.

[0115] Therefore, a dynamic mesh sequence may require a large amount of data because the dynamic mesh sequence may include a large amount of information that changes over time. Therefore, efficient compression techniques are needed to store and transmit such content. In the framework of the above interpolation-based vertex prediction method, an important process is to compress the displacement vector, and this occupies a major part in the encoded / decoded bitstream. The focus of the present disclosure and the features of the present disclosure, for example, alleviate this problem by providing such compression.

[0116] In addition, similar to the other examples above, even in those embodiments, the dynamic mesh sequence may require a large amount of data because the dynamic mesh sequence may include a large amount of information that changes over time, and thus efficient compression techniques are needed to store and transmit such content. In the framework of the above-described method based on 2D atlas sampling, important advantages can be achieved by inferring connectivity information on the decoder side based on the sampled vertices plus the boundary vertices. This is the main part of the decoding process and is the focus of the other examples described below.

[0117] According to an exemplary embodiment, the connectivity information of the base mesh can be inferred (derived) based on the decoded boundary vertices and sampled vertices for each patch on both the encoder side and the decoder side.

[0118] Similarly as described above, any triangulation method can be applied to create connections between vertices (including boundary vertices and sampled vertices). According to an exemplary embodiment, the connectivity type can be signaled in a high-level syntax such as a sequence header, a slice header.

[0119] As mentioned above, for example, for an irregular-shaped triangular mesh, the connectivity information can also be reconstructed by explicit signaling. That is, if it is determined that the polygon cannot be recovered by implicit rules, the encoder can signal the connectivity information in the bitstream. And according to an exemplary embodiment, the overhead of such explicit signaling can be reduced based on the boundary of the polygon.

[0120] According to an embodiment, only the connectivity information between the boundary vertices and the sampled positions is determined to be signaled, while the connectivity information between the sampled positions themselves is inferred.

[0121] In addition, in any of the embodiments, the connectivity information can be signaled by prediction such that only the difference in the inferred connectivity (as a prediction) from one mesh to another mesh can be signaled in the bitstream.

[0122] It should be noted that according to an exemplary embodiment, the orientation of the inferred triangles (e.g., each triangle is inferred in a clockwise manner or a counterclockwise manner) can be signaled in a high-level syntax such as a sequence header, a slice header, etc. for all patches, or the orientation can be fixed (assumed) by the encoder and the decoder. The orientation of the inferred triangles can also be signaled differently for each patch.

[0123] As a further note, any reconstructed mesh can have a different connectivity from the original mesh. For example, the original mesh can be a triangular mesh, while the reconstructed mesh can be a polygon mesh (e.g., a quadrilateral mesh).

[0124] According to an exemplary embodiment, instead of signaling the connectivity information of any base vertices, the same algorithm can be used at both the encoder side and the decoder side to derive the edges between the base vertices. And according to an exemplary embodiment, the interpolation of the predicted vertices of the additional mesh vertices can be based on the derived edges of the base mesh.

[0125] According to an exemplary embodiment, a flag can be used to signal whether to signal or derive the connectivity information of the base vertices, and such a flag can be signaled at different levels of the bitstream, such as the sequence level, the frame level, etc.

[0126] According to an exemplary embodiment, first the same algorithm is used at both the encoder side and the decoder side to derive the edges between the base vertices. Then, by comparing with the original connectivity of the base mesh vertices, the difference between the signaled derived edges and the actual edges is signaled. Thus, after decoding the difference, the original connectivity of the base vertices can be restored.

[0127] In one example, for the derived edges, if determined to be incorrect compared to the original edges, such information can be signaled in the bitstream (by indicating the vertex pairs forming the edge); and for the original edges, if not derived, they can be signaled in the bitstream (by indicating the vertex pairs forming the edge). In addition, the connectivity on the boundary edges and the vertex interpolation involving the boundary edges can be performed separately from the internal vertices and edges.

[0128] Thus, through the exemplary embodiments described herein, the above technical problems can be advantageously improved by one or more of these technical solutions. For example, since a dynamic mesh sequence may require a large amount of data because the dynamic mesh sequence may include a large amount of information that changes over time, the exemplary embodiments described herein represent at least an efficient compression technique for storing and transmitting such content.

[0129] The above embodiments can be further applied to instance-based mesh coding and decoding, where an instance can be a mesh of an object or a part of an object. For example, Figure 15 The illustrated example 1500 shows a mesh example 1501, where there are various instances 1502 (mesh representing a cup), 1503 (mesh representing a spoon), and 1504 (mesh representing a plate), and these instances can be separately and respectively coded and decoded. And each of the instances 1501, 1502, 1503, and 1504 is shown in a corresponding bounding box in the bounding box, however, it should be noted that the instance 1501 can be considered to be shown as bounded by a "mesh-based bounding box", while each of the instances 1502, 1503, and 1504 can be considered to be shown as bounded by a corresponding bounding box in an "instance-based bounding box".

[0130] Figure 16 Example 1600 according to an exemplary embodiment is shown. According to an embodiment, the grid encoding process begins with preprocessing. The preprocessing converts the input dynamic grid (represented as M(i)) into a base grid m(i) and a set of displacements d(i). The encoder compresses this new representation and generates a compressed bitstream b(i).

[0131] According to an embodiment, the preprocessing includes mesh decimation 1601, followed by atlas parameterization 1602, and then subdivision surface fitting 1603, as Figure 16 shown. Mesh decimation 1601 uses a simplification technique to decimate the input grid M(i) and produce a decimated grid dm(i). The decimated grid dm(i) is then re-parameterized, for example, by atlas parameterization 1602. The resulting grid is represented as pm(i). Subdivision surface fitting 1604 takes the re-parameterized grid pm(i) and the input grid M(i) as inputs and produces a base grid m(i) and a set of displacements d(i).

[0132] More specifically, first, pm(i) is subdivided by applying a midpoint subdivision scheme to an element S 0 at each subdivision, for example, from S 0 to S 1 and then to S 2 , and the midpoint subdivision scheme iteratively subdivides each triangle into 4 sub-triangles, as Figure 17 described in example 1700 of. The displacement field d(i) is calculated by determining the nearest point on the surface of the original grid M(i) for each vertex of the subdivided grid.

[0133] According to an embodiment, the encoder optionally encodes a set of displacement vectors associated with the vertices of the subdivided grid, and this set of displacement vectors is referred to as the displacement field d(i). First, the displacement field d(i) is updated using the reconstructed quantized base grid m′(i) to generate an updated displacement field d′(i). Then a wavelet transform is applied to d′(i), and a set of wavelet coefficients is generated. Then the wavelet coefficients are quantized, and the wavelet coefficients can be compressed by using arithmetic coding and decoding, a traditional image / video encoder, or some other encoder.

[0134] The wavelet transform can be a linear wavelet transform. It consists of a prediction process and an update process. The prediction process is defined as follows: where, ·v is a vertex introduced in the middle of the edge (v1, v2), and ·Signal(v), Signal(v1), and Signal(v2) are the values of the geometric / vertex attribute signals at vertices v, v1, and v2, respectively.

[0135] And the update process is defined as follows: where v* is the set of neighboring vertices of vertex v.

[0136] The proposed method can be used alone or in any order of combination. In addition, each of the method (or embodiment), encoder, and decoder can be implemented by a processing circuitry (e.g., one or more processors or one or more integrated circuits). In one example, one or more processors execute a program stored in a non-transitory computer-readable medium.

[0137] In the present disclosure, multiple methods and systems for adaptive wavelet transform for encoding and decoding vertex positions or displacement vectors in mesh compression are proposed. It should be noted that they can be applied alone or in any form of combination. In addition, the disclosed methods and systems are not limited to mesh compression. They can also be applied to, for example, audio processing, image processing, video processing, or general signal processing.

[0138] Embodiments herein disclose an adaptive wavelet transform in which the weights in the update process are adapted to the number of neighboring vertices. The adaptive wavelet transform consists of a prediction process and an update process.

[0139] The prediction process is defined as follows: where ·v is the vertex introduced in the middle of the edge (v1, v2), and ·Signal(v), Signal(v1), and Signal(v2) are the values of the geometric / vertex attribute signals at vertices v, v1, and v2, respectively.

[0140] And the update process according to such an embodiment is defined as follows: where v* is the set of neighboring vertices of vertex v, and |v*| is the cardinality of v*, i.e., the number of vertices in the set v*.

[0141] The inverse adaptive wavelet transform consists of an update process and a prediction process, where the update process is before the prediction process.

[0142] The update process in the inverse adaptive transform is defined as follows: Where \(v^*\) is the set of adjacent vertices of vertex \(v\), and \(|v^*|\) is the cardinality of \(v^*\), i.e., the number of vertices in the set \(v^*\).

[0143] The prediction process in the inverse adaptive wavelet transform is defined as follows: Where · \(v\) is a vertex introduced in the middle of the edge \((v_1, v_2)\), and · Signal\((v)\), Signal\((v_1)\) and Signal\((v_2)\) are the values of the geometric / vertex attribute signals at vertices \(v\), \(v_1\) and \(v_2\) respectively.

[0144] In some embodiments, the adaptive wavelet transform is applied to one or more selected mesh frames of a mesh sequence. In some embodiments, the adaptive wavelet transform is applied to one or more selected regions of a mesh frame. In some embodiments, the adaptive wavelet transform is applied to the base layer of a mesh frame. In some embodiments, the adaptive wavelet transform is applied to some enhancement layers of a mesh frame. In some embodiments, the adaptive wavelet transform is applied to one or more selected frequency bands of a mesh frame.

[0145] The proposed methods can be used individually or in any order of combination. In addition, each of the methods (or embodiments), encoders and decoders can be implemented by a processing circuitry (e.g., one or more processors or one or more integrated circuits). In one example, one or more processors execute a program stored in a non-transitory computer-readable medium.

[0146] According to embodiments, a plurality of methods and systems for adaptive wavelet transform for encoding and decoding vertex positions or displacement vectors in mesh compression are proposed. It should be noted that they can be applied individually or in any form of combination. In addition, the disclosed methods and systems are not limited to mesh compression. They can also be applied to, for example, audio processing, image processing, video processing or general signal processing.

[0147] Embodiments herein disclose an adaptive wavelet transform in which the weights in the update process are adapted to the number of adjacent vertices. The adaptive wavelet transform consists of a prediction process and an update process.

[0148] The prediction process is defined as follows: Where · \(v\) is a vertex introduced in the middle of the edge \((v_1, v_2)\), and ·Signal(v), Signal(v1), and Signal(v2) are the values of the geometric / vertex attribute signals at vertices v, v1, and v2, respectively.

[0149] And the update process according to such an embodiment is defined as follows: where c is a scalar, v* is the set of adjacent vertices of vertex v, and |v*| is the cardinality of v*, i.e., the number of vertices in the set v*.

[0150] The inverse adaptive wavelet transform consists of an update process and a prediction process, where the update process comes before the prediction process.

[0151] The update process in the inverse adaptive transform is defined as follows: where c is the same scalar, v* is the set of adjacent vertices of vertex v, |v*| is the cardinality of v*, i.e., the number of vertices in the set v*.

[0152] The prediction process in the inverse adaptive wavelet transform is defined as follows: where, ·v is the vertex introduced in the middle of the edge (v1, v2), and ·Signal(v), Signal(v1), and Signal(v2) are the values of the geometric / vertex attribute signals at vertices v, v1, and v2, respectively.

[0153] In some embodiments, the scalar c in any update process is a constant. The scalar c can be an integer, a rational number, a real number, etc. In some embodiments, the scalar c is set to the value 1.25.

[0154] In some embodiments, the scalar c in any update process is level-dependent, where the level is the level in the midpoint subdivision.

[0155] In some embodiments, the scalar c in any update process is a multiple of a power, where the midpoint subdivision level is the exponent, c = c0 * b 1 - Equation 2 where b l is the base of the power, l is the level in the midpoint subdivision, and c0 is the multiple. In some implementations, the base b is set to the value 2.0.

[0156] In some embodiments, the adaptive wavelet transform is applied to one or more selected regions of a grid frame. In some embodiments, the adaptive wavelet transform is applied to one or more selected grid frames of a grid sequence. In some embodiments, the adaptive wavelet transform is applied to the base layer of a grid frame. In some embodiments, the adaptive wavelet transform is applied to some enhancement layers of a grid frame. In some embodiments, the adaptive wavelet transform is applied to one or more selected frequency bands of a grid frame.

[0157] In some embodiments, the adaptive wavelet transform is applied to one or more selected grid frames of a grid sequence. In some embodiments, the adaptive wavelet transform is applied to one or more selected regions of a grid frame. In some embodiments, the adaptive wavelet transform is applied to the base layer of a grid frame. In some embodiments, the adaptive wavelet transform is applied to some enhancement layers of a grid frame. In some embodiments, the adaptive wavelet transform is applied to one or more selected frequency bands of a grid frame.

[0158] Accordingly, the embodiments herein provide valence-based adaptive lifting updates in enhancing wavelet transform and its HLS and semantics.

[0159] According to the embodiments herein, it is possible to signal lifting transform parameter syntax according to Figure 18 Example 1800 herein, such as 8.3.6.1.4. Lifting transform parameter syntax, where vltp_lifting_valence_update_weight_flag[ltpIndex] being equal to 1 specifies performing valence adaptive lifting update weight, and where vltp_lifting_valence_update_weight_flag[ltpIndex] being equal to 0 specifies not performing valence adaptive lifting update weight.

[0160] And for vltp_lifting_update_weight[ltpIndex], vltp_log2_lifting_update_weight[ltpIndex] (indicating the weighted coefficients of the update filter for wavelet transform, where ltpIndex is the index of the lifting transform parameter set) can be removed. vltp_lifting_update_weight[IltpIndex] indicates the weighted coefficients of the update filter for wavelet transform at the i-th detail level, where ltpIndex is the index of the lifting transform parameter set, such that (if vltp_lifting_valence_update_weight_flag[ltpIndex] is equal to 0, then vltp_lifting_update_weight[ltpIndex] is used to calculate the update weight as follows: update_weight = 1 / (1 << vItp_lifting_update_weight[ltpIndex]) - Equation 3 And if vltp_lifting_valence_update_weight_flag[ltpIndex] is equal to 1, then vltp_lifting_update_weight[ltpIndex] is used to calculate the update weight as follows: update_weight = 1.0 + vltp_lifting_update_weight[ltpIndex] * 0.1 - Equation 4

[0161] vdmc_lifting_update_weight_numerator[ltpIndex][i] indicates the numerator part of the update weight applied to the i-th component of the displaced wavelet coefficients for each detail level.

[0162] And vdmc_lifting_update_weight_denominator[ltpIndex][i] indicates the denominator part of the update weight applied to the i-th component of the displaced wavelet coefficients for each detail level.

[0163] To evaluate the performance of the proposed method, the method proposed in this paper is compared with TMM v6.0, and it is found that the improvement of this paper is achieved by adaptively improving the unified setting update process as described above, and it is similarly reiterated here:

[0164] That is to say, the embodiments of this paper provide an improved update process: Signal(v1) ← Signal(v1) + Signal(v) * w val *update_weight(i) Signal(v2) ← Signal(v2) + Signal(v) * w val *update_weight(i) (3) Among them, the meanings of each parameter are listed as follows: - Signal(v): Residual displacement - w val : - update_weight(i) = K * LOD Scale (n-i-1) - Equation 5 Where n represents the total number of LOD levels, and i represents the current LOD level in the lifting operation.

[0165] And experiments were conducted using various parameter settings including K and LOD Scale For the embodiments herein, K was set to 1.4 and LOD Scale was set to 0.8, which can be considered as the recommended values. Figure 19 The table in Example 19 of shows the experimental results for all sequences (300 frames). According to the improved update process herein, in the AI and LD configurations, the overall gains of the geometry are 0.7% and 0.5% respectively.

[0166] Therefore, the present disclosure provides a valence - based adaptive lifting update applied in lifting wavelet transform and its HLS and semantics. The above experimental results show the improved performance of the embodiments herein and can be considered as a contribution to V - DMCWD.

[0167] The above - mentioned technology can be implemented as computer software using computer - readable instructions and physically stored in one or more computer - readable media, or implemented by one or more specially configured hardware processors. For example, Figure 20 shows a computer system 2000 suitable for implementing certain embodiments of the disclosed subject matter.

[0168] Any suitable machine code or computer language can be used to encode and decode computer software, and the any suitable machine code or computer language can be subject to mechanisms such as assembly, compilation, linking, etc. to create code including instructions that can be directly executed by a computer central processing unit (CPU), a graphics processing unit (GPU), etc. or executed through interpretation, microcode execution, etc.

[0169] Instructions can be executed on various types of computers or their components including, for example, personal computers, tablet computers, servers, smart phones, gaming devices, Internet of Things devices, etc.

[0170] Figure 20 The components shown for computer system 2000 are exemplary in nature and are not intended to impose any limitation on the scope of use or functionality of the computer software implementing the present disclosure. The configuration of the components should not be construed as having any dependency or requirement related to any one component or combination of components shown in the exemplary embodiments of computer system 2000.

[0171] Computer system 2000 may include certain human-machine interface input devices. Such human-machine interface input devices can respond to inputs made by one or more human users through, for example, tactile inputs (e.g., keystrokes, swipes, data glove movements), audio inputs (e.g., voice, taps), visual inputs (e.g., gestures), olfactory inputs (not depicted). The human-machine interface devices can also be used to capture certain media not necessarily directly related to conscious input from humans, such as audio (e.g., voice, music, ambient sound), images (e.g., scanned images, photographic images obtained from still image cameras), video (e.g., two-dimensional video, three-dimensional video including stereoscopic video).

[0172] The input human-machine interface devices may include one or more of the following (only one of each is depicted): keyboard 2001, mouse 2002, touchpad 2003, touch screen 2010, joystick 2005, microphone 2006, scanner 2008, camera device 2007.

[0173] The computer system 2000 may also include certain human-machine interface output devices. Such human-machine interface output devices may stimulate the senses of one or more human users through, for example, haptic output, sound, light, and smell / taste. Such human-machine interface output devices may include: haptic output devices (e.g., haptic feedback through the touch screen 2010 or the joystick 2005, but there may also be haptic feedback devices that do not serve as input devices); audio output devices (e.g., speakers 2009, headphones (not depicted)); visual output devices (e.g., the screen 2010, including a CRT (Cathode Ray Tube) screen, an LCD (Liquid Crystal Display) screen, a plasma screen, an OLED (Organic Light Emitting Diode) screen, each with or without touch screen input capabilities, each with or without haptic feedback capabilities - some of which may be capable of outputting two-dimensional visual output or more than three-dimensional output in ways such as stereoscopic image output; virtual reality glasses (not depicted); holographic displays, and smoke machines (not depicted)); and printers (not depicted).

[0174] The computer system 2000 may also include human-accessible storage devices and their associated media, such as optical media including CD / DVD ROM (Read Only Memory) / RW 2020 with media such as CD / DVD 2011, thumb drives 2022, removable hard disk drives or solid state drives 2023, traditional magnetic media such as tapes and floppy disks (not depicted), devices based on dedicated ROM / ASIC (Application Specific Integrated Circuit) / PLD (Programmable Logic Device) such as security dongles (not depicted), etc.

[0175] Those skilled in the art should also understand that the term "computer-readable medium" used in connection with the presently disclosed subject matter does not include transmission media, carrier waves, or other transient signals.

[0176] The computer system 2000 may also include an interface 2099 to one or more communication networks 2098. The network 2098 may be, for example, wireless, wired, or optical. The network 2098 may also be local, wide area, metropolitan area, vehicle and industrial, real-time, delay tolerant, etc. Examples of the network 2098 include: local area networks such as Ethernet; wireless LAN (Local Area Network, LAN); cellular networks including GSM (Global System for Mobile Communications, GSM), 3G (the Third Generation, 3G), 4G (the Fourth Generation, 4G), 5G (the Fifth Generation, 5G), LTE (Long Term Evolution, LTE), etc.; TV wired or wireless wide area digital networks including cable TV, satellite TV, and terrestrial broadcast TV; vehicle and industrial networks including CANBus (Controller Area Network Bus, CANBus), etc. Certain networks 2098 typically require an external network interface adapter attached to certain general data ports or peripheral buses (2050 and 2051) (such as, for example, the USB (Universal Serial Bus, USB) port of the computer system 2000); other networks are typically integrated into the core of the computer system 2000 by attaching to the system bus as described below (for example, an Ethernet interface in a PC computer system or a cellular network interface in a smart phone computer system). The computer system 2000 may use any of these networks 2098 to communicate with other entities. Such communication may be unidirectional receive-only (such as broadcast TV), unidirectional transmit-only (such as CANbus to certain CANbus devices), or bidirectional, such as to other computer systems using local digital networks or wide area digital networks. Certain protocols and protocol stacks may be used on each of these networks and network interfaces as described above.

[0177] The above-mentioned human-machine interface devices, human-accessible storage devices, and network interfaces may be attached to the core 2040 of the computer system 2000.

[0178] The core 2040 may include one or more central processing units (CPUs) 2041, a graphics processing unit (GPU) 2042, a graphics adapter 2017, a dedicated programmable processing unit in the form of a Field Programmable Gate Area (FPGA) 2043, a hardware accelerator 2044 for certain tasks, etc. These devices, together with a read-only memory (ROM) 2045, a random access memory 2046, and an internal mass storage device such as an internal hard disk drive that is not accessible to users, a solid-state drive (SSD), etc. 2047, may be connected via a system bus 2048. In some computer systems, the system bus 2048 may be accessible in the form of one or more physical plugs to enable expansion by attaching additional CPUs, GPUs, etc. Peripheral devices may be attached directly or via a peripheral bus 2049 to the system bus 2048 of the core. The architecture of the peripheral bus includes PCI (Peripheral Component Interconnect / Interface), USB, etc.

[0179] The CPU 2041, GPU 2042, FPGA 2043, and accelerator 2044 may execute certain instructions that, when combined, may constitute the computer code mentioned above. The computer code may be stored in the ROM 2045 or a random access memory (RAM) 2046. Transient data may also be stored in the RAM 2046, while permanent data may be stored in, for example, the internal mass storage device 2047. Fast storage and retrieval of any memory device in the memory devices may be achieved by using a cache memory that may be closely associated with one or more CPUs 2041, GPUs 2042, the mass storage device 2047, the ROM 2045, the RAM 2046, etc.

[0180] A computer-readable medium may have computer code thereon for performing various computer-implemented operations. The medium and the computer code may be media and computer code that are specially designed and constructed for the purposes of this disclosure, or the medium and the computer code may be of the types well-known and available to those skilled in the art of computer software.

[0181] By way of example and not limitation, a computer system having architecture 2000 and particularly core 2040 can provide functionality due to a processor (including CPU, GPU, FPGA, accelerator, etc.) executing software contained in one or more tangible computer-readable media. Such computer-readable media can be media associated with a user-accessible mass storage device as introduced above, as well as certain storage devices of core 2040 having a non-transitory nature such as on-core mass storage 2047 or ROM 2045. The software implementing the various embodiments of the present disclosure can be stored in such devices and executed by core 2040. Depending on specific needs, the computer-readable media can include one or more memory devices or chips. The software can cause core 2040 and particularly the processors therein (including CPU, GPU, FPGA, etc.) to perform the specific processes or specific portions of specific processes described herein, including defining data structures stored in RAM 2046 and modifying such data structures according to software-defined processes. Additionally or alternatively, the computer system can provide functionality due to being logically hardwired or otherwise implemented in circuitry (e.g., accelerator 2044), which can operate in place of or in conjunction with the software to perform the specific processes or specific portions of specific processes described herein. In appropriate cases, references to software can include logic, and references to logic can also include software. In appropriate cases, references to computer-readable media can include circuitry (e.g., an integrated circuit (IC)) storing software for execution, circuitry implementing logic for execution, or both. The present disclosure encompasses any suitable combination of hardware and software.

[0182] Although the present disclosure has described several exemplary embodiments, there are changes, permutations, and various alternative equivalents that fall within the scope of the present disclosure. Accordingly, it will be recognized that those skilled in the art will be able to design many systems and methods that, although not explicitly shown or described herein, embody the principles of the present disclosure and are thus within the spirit and scope of the present disclosure.

Claims

1. A method for video decoding, the method being executed by at least one processor and comprising: Obtaining, from a bitstream, a mesh of encoded volumetric data representing at least one three-dimensional (3D) visual content; Dividing a plurality of vertices of the mesh into a plurality of groups; And Decoding the encoded volumetric data by predicting vertices in each of the groups based on an adaptive wavelet transform for each of the plurality of groups, wherein the adaptive wavelet transform includes a prediction process and an update process, wherein the prediction process depends on a geometric attribute signal at a vertex set among the plurality of vertices, at least one vertex in the set being between at least two other vertices in the set, and wherein the update process depends on adjacent vertices of the set, the adjacent vertices including the at least two other vertices.

2. The method according to claim 1, wherein, The prediction process includes: selecting at least two other vertices in a first set as endpoints of an edge of the mesh.

3. The method according to claim 1, wherein, The update process depends on the number of adjacent vertices including the at least two other vertices.

4. The method according to claim 1, wherein, The update process depends on a scalar value.

5. The method according to claim 4, wherein, The prediction process also depends on the scalar value.

6. The method according to claim 1, wherein Decoding the encoded volumetric data includes: applying the adaptive wavelet transform to an enhancement layer of a mesh frame of the mesh.

7. The method according to claim 1, wherein Decoding the encoded volumetric data includes: applying the adaptive wavelet transform to mesh frames of a mesh sequence of the mesh.

8. The method according to claim 7, wherein Decoding the encoded volumetric data includes: selecting a region of the mesh frame and applying the adaptive wavelet transform to the selected region of the mesh frame.

9. The method according to claim 1, wherein Decoding the encoded volumetric data includes: applying the adaptive wavelet transform to a base layer of a mesh frame of the mesh.

10. The method according to claim 1, wherein, Decoding the encoded volumetric data includes: applying the adaptive wavelet transform to a band of a mesh frame of the mesh.

11. A device for video decoding, the device comprising: At least one memory configured to store computer program code; At least one processor configured to access the computer program code and operate according to instructions of the computer program code, the computer program code including: An obtaining code configured to cause the at least one processor to: obtain, from a bitstream, a mesh of encoded volumetric data representing at least one three-dimensional (3D) visual content; A dividing code configured to cause the at least one processor to: divide a plurality of vertices of the mesh into a plurality of groups; and A decoding code configured to cause the at least one processor to: decode the encoded volumetric data by predicting vertices in each of the groups based on an adaptive wavelet transform for each of the plurality of groups, wherein the adaptive wavelet transform includes a prediction process and an update process, wherein the prediction process depends on a geometric attribute signal at a vertex set among the plurality of vertices, at least one vertex in the set being between at least two other vertices in the set, and Wherein, the updating process depends on adjacent vertices of the set, and the adjacent vertices include the at least two other vertices.

12. The device according to claim 11, wherein, The prediction process includes: selecting the at least two other vertices of the first set as endpoints of an edge of the mesh.

13. The device according to claim 11, wherein, The updating process depends on the number of adjacent vertices including the at least two other vertices.

14. The apparatus according to claim 11, wherein, The updating process depends on a scalar value.

15. The device according to claim 14, wherein The prediction process also depends on the scalar value.

16. The device according to claim 14, wherein Decoding the encoded volumetric data includes: applying the adaptive wavelet transform to an enhancement layer of a mesh frame of the mesh.

17. The apparatus according to claim 11, wherein, Decoding the encoded volumetric data includes: applying the adaptive wavelet transform to mesh frames of a mesh sequence of the mesh.

18. The device according to claim 17, wherein, Decoding the encoded volumetric data includes: selecting a region of the mesh frame and applying the adaptive wavelet transform to the selected region of the mesh frame.

19. The apparatus according to claim 11, wherein Decoding the encoded volumetric data includes: applying the adaptive wavelet transform to a base layer of a mesh frame of the mesh.

20. A non-transitory computer-readable medium storing a program, the program causing a computer to: obtain, from a bitstream, a mesh of encoded volumetric data representing at least one three-dimensional (3D) visual content; divide a plurality of vertices of the mesh into a plurality of groups; and decode the encoded volumetric data by predicting vertices in each of the groups based on an adaptive wavelet transform for each of the plurality of groups, Among them, wherein the adaptive wavelet transform includes a prediction process and an updating process, wherein the prediction process depends on a geometric attribute signal at a set of vertices among the plurality of vertices, at least one vertex in the set being between at least two other vertices in the set, and wherein the updating process depends on adjacent vertices of the set, the adjacent vertices including the at least two other vertices.

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