5268 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.

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.

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.

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.

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.

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.

Methods and apparatus are provided for encoding and subsequent decoding of video data by using a learnt video specific dictionary for implicit neural compression. An implicit neural representation comprising a head layer and a tail layer is used with approximations to the head layer parameters. The approximations are determined with combinations of atoms of a learnt video specific dictionary. In one embodiment, the head layer approximations and tail layer parameters are encoded in a bitstream. The dictionary is learnt at the decoder. In another embodiment, a dictionary is sent in the bitstream. At decoding, a reconstructed image is computed using transmitted INR parameters.

Some embodiments of a method may include: obtaining a feature bitstream; decoding a first feature map from the feature bitstream based on the decoded distribution parameters; obtaining a rate-distortion trade-off parameter; updating the first feature map to obtain a second feature map, wherein updating the first feature map comprises performing an adaptive affine process on the first feature map according to the rate-distortion trade-off parameter; decoding a point cloud from the second feature map; and outputting the point cloud.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. In the method, for a conversion between a current video block of a video and a bitstream of the video, a process is applied to the current video block based on template matching. At least one reference sample of a current template of the current video block is determined based on a block vector (BV) of the current video block during the process. The conversion is performed based on the applying.

A method and an apparatus are disclosed for patch-based mesh coding. In the disclosed embodiments, a mesh decoding device decodes a bitstream to reconstruct patch information and a patch-based base mesh. The mesh decoding device reconstructs base mesh vertices and connectivity by using the patch information and the patch-based base mesh. The mesh decoding device generates predicted vertices and connectivity based on the reconstructed base mesh vertices and connectivity. The mesh decoding device decodes a bitstream to reconstruct a transform-coefficient image, and reconstructs vector differences of vertices by unpacking, inverse quantizing and inverse transforming the transform-coefficient image. The mesh decoding device adds the predicted vertices and the vector differences to reconstruct mesh vertices and connectivity.

A system and method for encoding and decoding the geometry of 3D point clouds using octree-based data structures are disclosed. The method involves encoding and decoding bitstreams containing octree structure information and vertex data, including the presence and position of vertices on cuboid edges corresponding to leaf nodes. The decoding process determines triangles connecting vertices within each cuboid, which are voxelized to reconstruct the 3D point cloud. To enhance voxelization accuracy, triangles may be extended along one or more sides based on a sampling distance parameter (dsampldsampl) or adaptive halo parameters. The encoding process utilizes similar principles to encode the octree structure and vertex information, supporting geometry reconstruction with high fidelity. The system employs the Möller-Trumbore algorithm and barycentric coordinate calculations with constraints based on dsampldsampl for voxelization. Extensions may include fixed or adaptive parameters encoded within the bitstream.

Methods for generating object-based audio programs that are renderable in a personalizable manner and include a speaker channel bed that is renderable without selection of other program content (e.g., to provide a default full-range audio experience). Other embodiments include steps of delivering, decoding, and / or rendering such programs. Rendering of the content of the bed or selected mix of other content of the program can provide an immersive experience. The program can include a plurality of object channels (e.g., object channels indicative of user-selectable and user-configurable objects), a speaker channel bed, and other speaker channels. Another aspect is an audio processing unit (e.g., an encoder or decoder) configured to perform any of the embodiments of the method or that includes a buffer memory storing at least one frame (or other segment) of an object-based audio program (or bitstream thereof) generated according to any of the embodiments of the method.

A method for decoding an image includes receiving information on image partitioning for the image included in a bitstream; obtaining a plurality of image partitions included in the image based on the information on image partitioning; and decoding the plurality of image partitions. Each image partition comprises a plurality of default encoding parts, each default encoding part is selected from a plurality of candidate default encoding parts, each candidate default encoding part is composed of one or more encoding sub-units. The bitstream includes information on rotation of the image.

Provided is an image decoding method performed by a decoding apparatus, the image decoding method including receiving image information including at least one of decoder-side intra mode derivation (DIMD)-related information or template-based intra mode derivation (TIMD)-related information from a bitstream, deriving an intra prediction mode of a current block on the basis of the at least one of the DIMD-related information or the TIMD-related information, generating prediction samples of the current block on the basis of the intra prediction mode, and generating reconstructed samples of the current block on the basis of the prediction samples of the current block. The image information includes matrix-based intra prediction (MIP) flag information, the DIMD-related information includes DIMD flag information indicating whether a DIMD mode is applied to the current block, the TIMD-related information includes TIMD flag information indicating whether a TIMD mode is applied to the current block, and the DIMD flag information or the TIMD flag information is parsed after the MIP flag information.

A method comprising receiving a first input bit stream for a first parametrically coded input audio signal, the first input bit stream including data representing a first input core audio signal and a first set including at least one spatial parameter relating to the first parametrically coded input audio signal. A first covariance matrix of the first parametrically coded audio signal is determined based on the spatial parameter(s) of the first set. A modified set including at least one spatial parameter is determined based on the determined first covariance matrix, wherein the modified set is different from the first set. An output core audio signal is determined, which is based on, or constituted by, the first input core audio signal. An output bit stream for a parametrically coded output audio signal is generated, the output bit stream including data representing the output core audio signal and the modified set.

A device is configured to decode a mesh from a bitstream that includes the encoded mesh data, wherein, as part of decoding the mesh, one or more processors of the device are configured to determine, based on encoded mesh data, a base mesh that includes a set of vertices; apply the entropy decoding to first, second, and third entropy-encoded data comprises using a shared non-bypass context for entropy decoding at least one bin of each of the first truncated unary (TU) data, the second TU data, and the third TU data, where the first, second, and third TU data are included in binarized representations of syntax elements representing first and second residual values of components of normal vectors of vertices and a second residual value of a component of a normal vector of a vertex.

Decoding a current block includes receiving a compressed bitstream. A transform block of transform coefficients is decoded from the compressed bitstream. The transform coefficients are in a transform domain. The transform block is input to a machine-learning model to obtain a residual block that is in a pixel domain. The residual block is used to reconstruct the current block. Encoding a current block includes receiving a current residual block. The current residual block and a specified rate-distortion parameter are input to a machine-learning model to obtain a quantized transform block. The quantized transform block is entropy encoded into a compressed bitstream.

A method for decoding a 360-degree image includes: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; combining the generated prediction image with a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format. Here, generating the prediction image includes: checking, from the syntax information, prediction mode accuracy for a current block to be decoded; determining whether the checked prediction mode accuracy corresponds to most probable mode (MPM) information obtained from the syntax information; and when the checked prediction mode accuracy does not correspond to the MPM information, reconfiguring the MPM information according to the prediction mode accuracy for the current block.

In an example point cloud decoding method, at least a portion of a bitstream is entropy decoded to obtain an ordered sequence of transform coefficients. The transform coefficients are arranged such that a first set of the transform coefficients for a first block in a point cloud is followed consecutively by a second set of the transform coefficients for a second block in the point cloud; the transform coefficients in the first set are arranged in order of increasing frequency; and the transform coefficients in the second set are arranged in order of decreasing frequency. The first and second blocks in the point cloud are reconstructed using the entropy decoded transform coefficients.

A system for encoding and compressing images and textures to a bitstream as an executable program of computer instructions operable on pixel space, permitting the Encoder to arbitrarily determine the order, location and method used for reconstructing the components of an image at the decoder. The bitstream is typically further compressed by an Adaptive Entropy Compressor. Bitstream execution reconstructs the image directly to pixel space. Memory management and parallel processing controls may be embedded in the bitstream. The system is optimised for AI processing of images with a universally interpretable construction format that enables machines and humans to understand precisely how the image was created and can be reconstructed. Dual bitstreams may be combined at the decoder for securely embedding customised content (e.g., advertising) at the edge.

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 unit of a video and a bitstream of the video, information regarding applying a sample blending scheme to a boundary of the current video unit based on a type or a characteristic of the boundary; and performing the conversion based on the information.

Systems and methods for video coding and decoding using region packing are provided. At an encoder, a region detection module receives a video frame for encoding, identifies regions of interest in the video frame, and generates a bounding box for each region of interest. A region extractor module obtains the pixels within the bounding box from the video frame. A region packing module receives the identified regions of interest and arranges the bounding boxes within a packed frame substantially reducing the data to be encoded outside the identified regions of interest. A video encoder receives the packed frame and generates an encoded bitstream therefrom. At the decoder, the encoded bitstream is decoded and parameters sufficient to place the regions within a reconstructed frame are extracted. A reconstructed frame is generated which substantially maintains the spatial relationship and size of regions of interest in the original video frame.

An electronic apparatus performs a method of decoding video data. The method comprises: receiving, from bitstream, a plurality of syntax elements associated with a coding unit, wherein the plurality of syntax elements indicate a size of the coding unit and a coding tree type of the coding unit; determining a minimum palette mode block size for the coding unit in accordance with the coding tree type of the coding unit; in accordance with a determination that the size of the coding unit is greater than the minimum palette mode block size: receiving, from the bitstream, a palette mode enable flag associated with the coding unit; and decoding, from the bitstream, the coding unit in accordance with the palette mode enable flag.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

Embodiments of the present disclosure provide a solution for visual data processing. A method for visual data processing is proposed. The method comprises: determining, for a conversion between visual data and one or more bitstreams of the visual data with a neural network (NN)-based model, a target reconstruction of a first component of the visual data based on a first candidate reconstruction and a second candidate reconstruction of the first component, wherein the first candidate reconstruction is generated based on a first filtering process, and the second candidate reconstruction is generated based on a second filtering process different from the first filtering process; and performing the conversion based on the target reconstruction.

Encoding using adaptive transform type sets based on frame level statistics includes obtaining an encoded bitstream by encoding a current block of a current frame of a current sequence of frames of an input video stream using adaptive transform type sets based on frame level statistics and outputting the encoded bitstream. Encoding the current block includes obtaining transform type statistics for previously reconstructed reference frames from the current sequence of frames, the previously reconstructed reference frames including at least one previously reconstructed reference frame, determining, in accordance with the transform type statistics, a current subset of transform types from a set of available transform types, generating encoded block data for the current block using a current transform type from the current subset of transform types, and including the encoded block data in the encoded bitstream.

Embodiments of the present disclosure provide a solution for visual data processing. In the method, for a conversion between a current visual unit of visual data and a bitstream of the visual data, a plurality of threads for coding residual information of the current visual unit is determined. The conversion is performed based on the plurality of threads.

In one implementation, we propose a scalable framework where a base layer uses NN-based methods to compress the content for computer vision machine tasks and enhancement layer(s) use traditional predictive coding for human viewing. Typically, the based layer performs NN-based analysis to generate a latent tensor, which is entropy coded to produce the base layer bitstream. By performing synthesis on the latent tensor, an inter-layer predictor can be obtained for the enhancement layer(s). Since many machine tasks are not required to be performed for each frame, the base layer may skip analysis for some frames. The synthesis may be performed at the base layer or the enhancement layer(s). In one example, the base layer compresses features optimized for a machine task and the enhancement layer(s) rely on predictive coding. In another example, the enhancement layer(s) can use traditional scalable video compression methods.