An image dataset is compressed by combining depth values from pixel depth arrays, wherein combining criteria are based on object data and/or depth variations of depth values in the first pixel image value array and generating a modified image dataset wherein a first pixel image value array represented in a received image dataset by the first number of image value array samples is in turn represented in the modified image dataset by a second number of compressed image value array samples with the second number being less than or equal to the first number.

The present invention relates to an improved method and apparatus for image compression and particularly to an improved block coding apparatus and method for compression for use with the JPEG2000 standard, although not limited to this. Methods for coding and decoding blocks and subbands samples derived from still images video frames or related media, involving three bit-streams and the partitioning of samples from the blocking to define groups, is provided. A first bit-stream encodes the significance of whole groups. A second bit-stream encodes the significance of individual samples within each group. The second bit-stream also encodes an unsigned residual value for each significant group. A third bit stream provides a sign bit and any additional magnitude bits required to represent the significant sample values. Exponent predictors are computal using both exponent bounds and the additional magnitude bits associated with previous samples in the block.

A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. A number of contexts associated with the received data is reduced based on reducing a size of an array corresponding to syntax elements for predictive tree-based coding of the point cloud. The data corresponding to the point cloud is decoded based on the reduced number of contexts.

An image dataset is compressed by combining depth values from pixel depth arrays, wherein combining criteria are based on object data and/or depth variations of depth values in the first pixel image value array and generating a modified image dataset wherein a first pixel image value array represented in a received image dataset by the first number of image value array samples is in turn represented in the modified image dataset by a second number of compressed image value array samples with the second number being less than or equal to the first number.

Technologies for dividing work across one or more accelerator devices include a compute device. The compute device is to determine a configuration of each of multiple accelerator devices of the compute device, receive a job to be accelerated from a requester device remote from the compute device, and divide the job into multiple tasks for a parallelization of the multiple tasks among the one or more accelerator devices, as a function of a job analysis of the job and the configuration of each accelerator device. The compute engine is further to schedule the tasks to the one or more accelerator devices based on the job analysis and execute the tasks on the one or more accelerator devices for the parallelization of the multiple tasks to obtain an output of the job.

Various examples are provided for lossless compression of data streams. In one example, a Z-lossless (ZLS) compression method includes generating compacted depth information by condensing information of a depth image and a compressed binary representation of the depth image using histogram compaction and decorrelating the compacted depth information to produce bitplane slicing of residuals by spatial prediction. In another example, an apparatus includes imaging circuitry that can capture one or more depth images and processing circuitry that can generate compacted depth information by condensing information of a captured depth image and a compressed binary representation of the captured depth image using histogram compaction; decorrelate the compacted depth information to produce bitplane slicing of residuals by spatial prediction; and generate an output stream based upon the bitplane slicing.

Methods and devices for encoding a point cloud. A current node associated with a sub-volume is split into further sub-volumes, each further sub-volume corresponding to a child node of the current node, and, at the encoder, an occupancy pattern is determined for the current node based on occupancy status of the child nodes. A probability distribution is selected from among a plurality of probability distributions based on occupancy data for a plurality of nodes neighbouring the current node. The encoder entropy encodes the occupancy pattern based on the selected probability distribution to produce encoded data for the bitstream and updates the selected probability distribution. The decoder makes the same selection based on occupancy data for neighbouring nodes and entropy decodes the bitstream to reconstruct the occupancy pattern.

An image coding method and apparatus considering human visual characteristics are provided. The image coding method comprises (a) modeling image quality distribution of an input image in units of scenes such that the quality of an image input in units of scenes is gradually lowered from a region of interest to a background region, (b) determining a quantization parameter of each region constituting one scene according to the result of modeling of image quality distribution, (c) quantizing image data in accordance with the quantization parameter, and (d) coding entropy of the quantized image data.

A method of video coding using block partitioning process including a binary tree partitioning process is disclosed. The block partitioning process is applied to a block of video data to partition the block into final sub-blocks. Coding process comprising prediction process, transform process or both for the block will be applied at the final sub-blocks level. The binary tree partitioning process can be applied to a given block recursively to generate binary tree leaf nodes until a termination condition is met. In another embodiment, the quadtree partitioning process is applied to a block first. The quadtree leaf nodes are further partitioned using the binary tree partitioning process. The quadtree partitioning process can be applied to a given block recursively to generate quadtree leaf nodes until a termination condition is met.

An encoder is configured to compress point cloud geometry information using an octree geometric compression technique that utilizes a binary arithmetic encoder, a look-ahead table, a cache, and a context selection process, wherein encoding contexts are selected based, at least in part, on neighborhood configurations. In a similar manner, a decoder is configured to decode compressed point cloud geometry information utilizing a binary arithmetic encoder, a look-ahead table, a cache, and a context selection process.