8786 results about "Bitstream" patented technology

By implementing a new bitstream for a Delta-Quantization Parameter (DQP), a decoder is able to implement parallel decoding of multiple coding units within a largest coding unit. In some embodiments, the DQP is placed immediately after the mode information of the first coding unit. In some embodiments, the DQP is placed after the mode information of the first non-skipped coding unit. In some embodiments, the DQP is placed after the first non-zero coded block flag.

Systems and methods are described for video coding using generalized bi-prediction. In an exemplary embodiment, to code a current block of a video in a bitstream, a first reference block is selected from a first reference picture and a second reference block is selected from a second reference picture. Each reference block is associated with a weight, where the weight may be an arbitrary weight ranging, e.g., between 0 and 1. The current block is predicted using a weighted sum of the reference blocks. The weights may be selected from among a plurality of candidate weights. Candidate weights may be signaled in the bitstream or may be derived implicitly based on a template. Candidate weights may be pruned to avoid out-of-range or substantially duplicate candidate weights. Generalized bi-prediction may additionally be used in frame rate up conversion.

The disclosure relates to a data processing method and a related device for point cloud media. The method includes: obtaining a media file of point cloud media, the media file comprising a point cloud bitstream and cross-attribute dependency indication information of the point cloud media, and the cross-attribute dependency indication information being for indicating an encoding and decoding dependency relationship between attribute data in the point cloud bitstream; and decoding the point cloud bitstream based on the cross-attribute dependency indication information to present the point cloud media.

A method for processing media data is disclosed. In an embodiment, the method includes determining a value of a neural-network post-filter characteristics (NNPFC) purpose (nnpfc_purpose) in a NNPFC supplemental enhancement information (SEI) message, wherein the nnpfc_purpose is configured to be set to include output picture size downsampling, and wherein at least one of (1) an output picture width is not equal to an input picture width and (2) an output picture height is not equal to an input picture height; and performing a conversion between a visual media data and a bitstream based on the nnpfc_purpose.

A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream comprising point cloud data; and decoding the point cloud data.

A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream including the point cloud data. A point cloud data receiving method according to embodiments may comprise the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data.

A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream including the point cloud data. In addition, a point cloud data transmission device according to embodiments may comprise: an encoder which encodes point cloud data; and a transmitter which transmits a bitstream including the point cloud data.

A computer-implemented method for lossy image or video compression, transmission and decoding, the method including the steps of: (i) receiving an input image at a first computer system; (ii) encoding the input image using a first trained neural network, using the first computer system, to produce a latent representation; (iii) quantizing the latent representation using the first computer system to produce a quantized latent; (iv) entropy encoding the quantized latent into a bitstream, using the first computer system; (v) transmitting the bitstream to a second computer system; (vi) the second computer system entropy decoding the bitstream to produce the quantized latent; (vii) the second computer system using a second trained neural network to produce an output image from the quantized latent, wherein the output image is an approximation of the input image.

Various embodiments provide example apparatus, method, and computer program product. An example apparatus includes: receiving or generating a first encoded bitstream comprising independently encoded subpictures; receiving or generating a second encoded bitstream comprising independently encoded subpictures; wherein resolution of subpictures in the second encoded bitstream is same or substantially same as resolution of corresponding subpictures in the first encoded bitstream; generating an encapsulated file with a first track and second track, the first track comprises the first encoded bitstream comprising the independently encoded subpicture, the second track comprises the second encoded bitstream comprising the independently encoded subpictures; and wherein generating the encapsulated file comprises including following information in the encapsulated file: a picture-in-picture relationship between the first track and second track; and data units independently encoded subpictures in the first encoded bitstream that are to be replaced by data units of the independently encoded subpictures of the second encoded bitstream.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, for a conversion between a current video block of a video and a bitstream of the video, a sign prediction of a block vector difference (BVD) of the current video block; determining the BVD at least based on the sign prediction of the BVD; and performing the conversion based on the BVD.

Computer-implemented methods and systems for processing geometry replacements are disclosed. The methods include decoding / encoding a syntax element associated with a coding mode from / into a bitstream associated with geometry displacements; and reconstructing / converting, based on a coefficient configuration associated with the coding mode, a plurality of quantized transform coefficients from / to a plurality of zero-run length codes.

An image encoding / decoding method and device according to the present disclosure can induce an intra-prediction mode of the current block on the basis of an induction mode, determine a reference sample of the current block, and generate a prediction sample of the current block on the basis of the intra-prediction mode and the reference sample.

To provide a video coding mechanism.SOLUTION: A video coding mechanism includes receiving a bitstream comprising an output layer set (OLS) and a video parameter set (VPS). The OLS includes one or more layers of coded pictures and the VPS includes an OLS mode identification code (ols_mode_idc) specifying that for each OLS, all layers in each OLS are output layers. The output layers are determined on the basis of the ols_mode_idc in the VPS. The coded picture from the output layers is decoded to produce a decoded picture. The decoded picture is forwarded for display as a part of a decoded video sequence.SELECTED DRAWING: Figure 7

In one implementation, point cloud data for a point cloud is decoded. The decoder obtains features representing voxels in a tree structure, where feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed. The decoder then determines an occupancy probability of the current voxel based on the feature, and decodes occupancy information of voxels in the tree structure, where whether a current voxel is occupied or not is decoded based on the occupancy probability for the current voxel. The point cloud can be reconstructed based on the occupancy information. On the encoder side, the feature for the current voxel is obtained from the voxels that are still to be encoded and encoded into a bitstream.

A device comprises a plurality of hardware, HW, modules including a first HW module comprising a Physically Un-clonable Function, PUF, the first HW module or the plurality of HW modules being communicated with a requester. The device receives (1006), from the requester, a request to transmit an output to the requester. It activates (1008) the PUF, reads a response from the PUF, and adds the response to an output. The device transmits (1012) the output to the requester, and it receives (1002) one or more of a firmware, FW, component, a software, SW, component, or a bitstream, BS, component before receiving (1006) the request from the requester.

A method of processing video data. The method includes selecting an in-loop filter from a plurality of neural network (NN) filter model candidates, wherein the plurality of NN filter model candidates are based on a reconstructed quality level of a video unit, and performing a conversion between a video media file comprising the video unit and a bitstream based on the in-loop filter selected. A corresponding video coding apparatus and non-transitory computer readable medium are also disclosed.

Innovations in systems, methods, and software for features of a neural image or video codec are described herein. For example, a neural video encoder can receive a current video frame, encode the current video frame to produce encoded data, and output the encoded data as part of a bitstream. As part of the encoding, the encoder can determine a current latent representation for the current video frame, and encode the current latent representation using an entropy model network that includes one or more convolutional layers. As part of the encoding the current latent representation, the encoder can estimate statistical characteristics of a quantized version of the current latent representation based at least in part on a previous latent representation for a previous video frame, and entropy code the quantized version of the current latent representation based at least in part on the estimated statistical characteristics.

An encoder projects a scene represented by 3D gaussian splats into 2D representation(s), where the 2D representation(s) store different parameters of the 3D gaussian splats, along with associated metadata describing transformation from 3D space into 2D representations. The encoder forms the 2D representation(s) and the associated metadata into bitstream(s). The encoder outputs the bitstreams. The decoder receives the bitstream(s) encoding 2D representation(s) and associated metadata. The decoder applies decoding to corresponding individual bitstreams to form the 2D representation(s) and the associated metadata. The decoder reprojects, as described by the associated metadata, the 2D representations) into the scene represented by the 3D gaussian splats. The decoder outputs the scene for rendering, or renders the scene, to a viewer.

Some embodiments of a method may include: decoding a motion feature by accessing a motion bitstream; predicting a predicted feature based on the motion feature and one or more reference point cloud frames; decoding a first feature representing an occupancy status of a child level voxel; predicting a second feature based on the first feature and the predicted feature; and decoding a tree voxel occupancy status of the child level voxel via the second feature.

Certain aspects of the present disclosure generally relate to techniques for selecting a base graph to be used for wireless communications. Selection can be based on a variety of factors. A base graph can be used to derive a low-density parity-check (LDPC) code used for encoding a retransmission of an original transmission. An exemplary method generally includes selecting, based on a modulation and coding scheme (MCS) and a resource allocation (RA) for transmitting a codeword, a base graph (BG), from which to derive a low density parity check (LDPC) code for use in encoding data bits in the codeword (e.g., encoding data bits of a bitstream such that some redundant bits are included in the codeword), encoding the data bits to generate the codeword using the LDPC code derived from the selected BG, and transmitting the codeword using the MCS via resources of the RA.

Methods, systems, and bitstream syntax are described for a scalable 3D scene representation. A general framework presents a dual-layer architecture where a base layer provides a baseline scene representation, and an enhancement layer provides enhancement information under a variety of scalability criteria. The enhancement information is coded using a trained neural field. Example systems are provided using a PSNR criterion and a baseline multi-plane image (MPI) representation. Examples of bitstream syntax for metadata information are also provided.

In some embodiments, a mesh encoder encodes a dynamic mesh with efficient geometry coding. The encoder normalizes and integerizes the coordinates of vertices of a mesh frame. The encoder segments the integerized coordinates for the vertices into 3D sub-blocks, each 3D sub-block containing at least one vertex and local coordinates of vertices in each 3D sub-block having a value range fitting into a video bit depth. For each 3D sub-block, the encoder converts coordinates of a vertex inside the 3D sub-block to a local coordinate system of the 3D sub-block and maps each vertex inside the 3D sub-block to a corresponding 2D patch in a geometry component image that represents the mesh frame. The encoder compresses the geometry component image to generate a geometry component bitstream and further generates the coded mesh bitstream for the dynamic mesh by including the geometry component bitstream.

The invention discloses a user non-inductive resource efficient scheduling method and device for a VR application and a storage medium, and belongs to the field of VR application. The method comprises the following steps: respectively extracting track time sequence features and video content features from user track data and image content data; respectively calculating attention weights of track time sequence features and video content features through a cross attention mechanism, adaptively and dynamically adjusting the attention weights of the features, and generating fusion feature information; inputting the fused feature information into a viewport prediction model, and outputting a viewport position prediction result in a future time window; dynamically selecting a multi-stream data source to perform resource scheduling by taking the viewport position prediction result and the current network state as resource scheduling conditions; and allocating a first code stream channel and network resources meeting the set QOS standard to a high-priority region concerned by the user in the viewport position prediction result. The dynamic change and scene switching of user behaviors are effectively handled, and the user experience quality is ensured.

Various embodiments provide methods, apparatuses, and computer program products. An example apparatus includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: receiving a file; determining a single layer and a multi-layer data from the file; and extracting data and related parameters sets of the single layer and the multi-layer.

The present invention describes an encoding scheme for arithmetically encoding a sequence of information values into an arithmetically coded bitstream, using providing entry point information to the bitstream, thereby allowing arithmetic decoding of the bitstream to be resumed forward from a predetermined entry point. The invention also provides a corresponding decoding scheme. These encoding and decoding schemes provide a more efficient encoding concept in terms of decoding speed.

The application provides a signal transmission method, a video encoder and a computer readable storage medium. A computer-implemented signal transmission method performed by an encoder comprises the following steps: a processor transmits a bitstream comprising weight information for a prediction coding unit (CU) to a video decoder, the weight information indicating that if weighted prediction is enabled for a bi-prediction mode of the CU, weighted averaging for the bi-prediction mode is disabled, and the weight information indicating that if weighted prediction is enabled for at least one of a luma component and a chroma component of a reference picture of the CU, weighted averaging for the bi-prediction mode is disabled, wherein the bitstream comprises a flag indicating whether weighted prediction is enabled for at least one of the luma component and the chroma component of the reference picture, and the flag comprises a flag luma_weight_lx_flag[i] transmitted for an i-th reference picture in a reference picture list Lx, wherein x is 0 or 1.

This disclosure provides an image encoding / decoding method and device and recording medium storing a bitstream. The image decoding method may comprise: constructing a candidate list related to prediction of a current block; generating a prediction block of the current block based on a candidate selected from the candidate list; and generating a reconstructed block of the current block based on the prediction block, wherein the constructing of the candidate list includes adding candidates to the candidate list in an order of a first candidate serving as a spatially neighboring block, a second candidate serving as a temporally neighboring block, and a third candidate serving as a non-neighboring block.

A method and apparatus comprising computer code for video processing, the method including setting a first value of a text description purpose parameter in a bitstream, the first value of the text description purpose parameter indicating a type of information included in a text description information string in the bitstream; setting artificial intelligence (AI) marking information associated with one or more pictures in the bitstream as the text description information string when the first value indicates that the type of information included in the text description information string comprises marking information associated with one or more artificial intelligence processes used; signaling the text description purpose parameter in the bitstream; and signaling the text description information string.

A device for decoding encoded dynamic mesh data determines a set of quantized integer coefficient values for displacement vectors of the encoded dynamic mesh data; determines a quantization parameter based on one or more syntax elements included in a displacement sub-bitstream of the encoded dynamic mesh data; inverse quantizes, based on the quantization parameter, the set of quantized integer coefficient values to determine a set of fixed-point dequantized coefficient values; determines a set of fixed-point transformed coefficient values based on the set of fixed-point dequantized coefficient values; converts the set of fixed-point transformed coefficient values to a set of floating-point transformed coefficient values; inverse transforms the set of floating-point transformed coefficient values to determine a set of reconstructed displacement vectors; and determines a reconstructed deformed mesh based on the set of reconstructed displacement vectors.