Embodiments of the present application provide an occupancy information prediction method, an encoder, a decoder, and a storage medium. The occupancy information prediction method comprises: when an encoder encodes geometrical information on the basis of an octree, determining encoding information corresponding to a neighboring node of a node to be predicted, and a distance parameter between a child node of the node to be predicted and the neighboring nodes; wherein the encoding information corresponding to the neighboring node comprises occupancy information; determining an occupancy weight corresponding to the child node of the node to be predicted according to the distance parameter, and the encoding information corresponding to the neighboring node; performing a prediction processing on the child node according to the occupancy weight and a preset occupancy threshold set to obtain a node type corresponding to the child node.

A device configured to compress a tensor including a plurality of cells includes: a quadtree generator configured to generate a quadtree searching for a non-zero cell included in the tensor and extract at least one parameter value from the quadtree; a mode selector configured to determine a compression mode based on the at least one parameter; and a bitstream generator configured to generate a bitstream by compressing the tensor based on the compression mode.

The present disclosure relates to an information processing apparatus and an information processing method that are capable of distributing higher-quality G-PCC streams. When G-PCC streams obtained by encoding Point Cloud data according to G-PCC are generated, there are generated spatial positional information indicating spatial positions of respective pieces of partial Point Cloud data that represent individual parts into which the Point Cloud data is segmented, and grouping information that groups the partial Point Cloud data. The present technology is applicable to a generating apparatus that generates G-PCC streams, for example.

Embodiments of the present application provide an occupancy information prediction method, an encoder, a decoder, and a storage medium. The occupancy information prediction method comprises: when an encoder encodes geometrical information on the basis of an octree, determining encoding information corresponding to a neighboring node of a node to be predicted, and a distance parameter between a child node of the node to be predicted and the neighboring nodes; wherein the encoding information corresponding to the neighboring node comprises occupancy information; determining an occupancy weight corresponding to the child node of the node to be predicted according to the distance parameter, and the encoding information corresponding to the neighboring node; performing a prediction processing on the child node according to the occupancy weight and a preset occupancy threshold set to obtain a node type corresponding to the child node.

Described herein are computer-implemented methods for compressing sparse multidimensional ordered series data. In particular, these methods and apparatuses for performing them (including software) may be particularly well suited to efficiently compressing spectrographic data.

Described herein are computer-implemented methods for compressing sparse multidimensional ordered series data. In particular, these methods and apparatuses for performing them (including software) may be particularly well suited to efficiently compressing spectrographic data.

In some embodiments, a method determines a plurality of classes of bins that are used to determine a context model for entropy coding of a current block in a video. The method calculates a first value for a first class of bins in the plurality of classes of bins and calculates a second value for a second class of bins in the plurality of classes of bins. The first value for the first class of bins is weighted by a first weight to generate a weighted first value and the second value for the second class of bins is weighted by a second weight to generate a weighted second value. The method then selects a context model based on the first weighted value and the second weighted value.

Methods and systems for processing telemetry data that contains multiple data types is disclosed. Optimum multimodal encoding approaches can be used which can achieve data-specific compression performance for heterogeneous datasets by distinguishing data types and their characteristics at real-time and applying most effective compression method to a given data type. Using an optimum encoding diagram for heterogeneous data, a data classification algorithm classifies input data blocks into predefined categories, such as Unicode, telemetry, RCS and IR for telemetry datasets, and a class of unknown which includes non-studied data types, and then assigns them into corresponding compression models.

Systems and methods for the compression and decompression of geospatial locations are disclosed. The compression and decompression are based on a prediction of the geospatial location and a geometrical projection of the Earth.

According to embodiments, point cloud data transmission method may include encoding point cloud data, encapsulating a bitstream that includes the encoded point cloud data and signaling data into a file, and transmitting the file, the bitstream is stored either in a single track or in multiple tracks of the file, the signaling data include at least one parameter set, and the encoded point cloud data include a geometry bitstream containing geometry data and an attribute bitstream containing attribute data.