An image decoding apparatus obtain pieces of coded data that is included in a bitstream and generated by coding tiles. Tile boundary independence information is further obtained from the bitstream, with the tile boundary independence information indicating whether each of boundaries between the tiles is one of a first boundary or a second boundary. The pieces of coded data are decoded to generate image data of the tiles. Image data of a first tile is generated by decoding a first code string included in first coded data with reference to decoding information of a decoded tile when the tile boundary independence information indicates the first boundary, and by decoding the first code string without referring to the decoding information of the decoded tile when the tile boundary independence information indicates the second boundary.

When encoding a block of data elements in an array of data elements, the data values for data elements in the block are represented and stored in a data packet as truncated data values using a subset of one or more most significant bits of the respective bit sequences for the data values of the data elements. A rounding mode is selected from a plurality of available rounding modes that can be applied when decoding the block of data elements and an indication of the selected rounding mode is provided along with the encoded data packet. The rounding mode is associated with one or more rounding bit sequence(s) that can then be applied to the truncated data values when decoding the data packet to obtain decoded data values for the data elements in the block.

The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.

Residual vectors are compressed in a lossless compression scheme suitable for cloud DVR video content applications. Thus, a cloud DVR service provider can take many copies of the same file stored in the cloud and save storage space by compressing those copies while still maintaining their status as distinct copies, one per user. Vector quantization is used for compressing already-compressed video streams (e.g., MPEG streams). As vector quantization is a lossy compression scheme, the residual vector has to be stored to regenerate the original video stream at the decoding (playback) node. Entropy coding schemes like Arithmetic or Huffman coding can be used to compress the residual vectors. Additional strategies can be implemented to further optimize this residual compression. In some embodiments, the techniques operate to provide a 25-50% improvement in compression. Storage space is thus more efficiently used and video transmission may be faster in some cases.

Methods for lossy and lossless pre-processing of image data. In one embodiment, a method for lossy pre-processing image data, where the method may include, at a computing device: receiving the image data, where the image data includes a model having a mesh, the mesh includes vertices defining a surface, the vertices including attribute vectors, and the attribute vectors including values. The method also including quantizing the values of the attribute vectors to produce modified values, where a precision of the modified values is determined based on a largest power determined using a largest exponent of the values, encoding pairs of the modified values into two corresponding units of information. The method also including, for each pair of the pairs of the modified values, serially storing the two corresponding units of information as a data stream into a buffer, and compressing the data stream in the buffer.

Embodiments described herein provided for an instruction and associated logic to enable a processing resource including a tensor accelerator to perform optimized computation of sparse submatrix operations. One embodiment provides hardware logic to apply a numerical transform to matrix data to increase the sparsity of the data. Increasing the sparsity may result in a higher compression ratio when the matrix data is compressed.

A three-dimensional data encoding method includes: dividing three-dimensional points included in three-dimensional data into three-dimensional point sub-clouds including a first three-dimensional point sub-cloud and a second three-dimensional point sub-cloud; appending first information indicating a space of the first three-dimensional point sub-cloud to a header of the first three-dimensional point sub-cloud, and appending second information indicating a space of the second three-dimensional point sub-cloud to a header of the second three-dimensional point sub-cloud; and encoding the first three-dimensional point sub-cloud and the second three-dimensional point sub-cloud so that the first three-dimensional point sub-cloud and the second three-dimensional point sub-cloud are decodable independently of each other.

A transaction card construction and computer-implemented methods for a transaction card are described. The transaction card has vector formatted visible information lasered onto its surface. In some embodiments, systems and methods are disclosed for enabling the sourcing of visible information using a scalable vector format. The systems and methods may receive a request to add a first plurality of visible information to a transaction card and capture an image of the first plurality of visible information. The systems and methods may also map the image to a bounding box and convert the mapped image into vector format. In addition, the systems and methods may provide the converted image to a laser machine.

A three-dimensional data encoding method includes: dividing three-dimensional points included in three-dimensional data into three-dimensional point sub-clouds including a first three-dimensional point sub-cloud and a second three-dimensional point sub-cloud; appending first information indicating a space of the first three-dimensional point sub-cloud to a header of the first three-dimensional point sub-cloud, and appending second information indicating a space of the second three-dimensional point sub-cloud to a header of the second three-dimensional point sub-cloud; and encoding the first three-dimensional point sub-cloud and the second three-dimensional point sub-cloud so that the first three-dimensional point sub-cloud and the second three-dimensional point sub-cloud are decodable independently of each other.

Provided is a coding method for compressing a complex hologram, in which the coding method includes: (b) creating a complex vector plane divided into unit regions, and giving an index to each unit region of the complex vector plane; (c) projecting the complex hologram to the complex vector plane by regarding the complex hologram as a complex vector, and assigning the index given to the unit region of the projected complex vector plane as a complex index of the complex hologram; and (f) encoding the complex hologram assigned with the complex index. According to the method described above, the full-complex hologram is reconstructed into one piece of index information using the complex vector plane to code the full-complex hologram, such that the hologram can be efficiently compressed while preserving a relationship between a real hologram and an imaginary hologram.