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 electronically verifying information on a transaction card. The systems and methods may receive a request to provide a verification status for the transaction card and first visible information comprising a signature of a customer written on a point-of-sale terminal. The systems and methods may also receive second visible information comprising a vector representation of a user signature on a transaction card. In addition, the systems and methods may determine the verification status based on a comparison of the first visible information to the second visible information and send the verification status.

Methods and apparatus for compressing image data are described along with corresponding methods and apparatus for decompressing the compressed image data. A decoder unit samples compressed image data including interleaved blocks of data encoding a first image and blocks of data encoding differences between the first image and a second image, the second image being twice the width and the height of the first image. A difference decoder decodes a fetched encoded sub-block of the differences between the first and second images and output a difference quad and a prediction value for a pixel, and a filter sub-unit generates a reconstruction of the image at a sample position using decoded blocks of the first image, the difference quad and the prediction value.

A lossless decoding unit 52 takes quantization parameters of decoded blocks spatially or temporally adjacent to a block to be decoded, as selection candidates, and extracts, from stream information, difference information indicating difference as to a prediction quantization parameter selected from the selection candidates. A quantization parameter calculating unit 59 calculates, from the prediction quantization parameter and the difference information, a quantization parameter of the block to be decoded. Thus, decoding of the image can be performed correctly by calculating a quantization parameter equal to a quantization parameter used at the time of image encoding.

Conversion of text-based images to vector graphics (VG) is disclosed. The text-based images may include images of equations, custom typefaces, or other types of text that may not be included in a font selection of an optical character recognition (OCR) device or an application stored on a viewing device. A textual image may be converted from a raster graphics (RG) image to a VG image, which may enable resizing and alignment of the VG image with body text. In some aspects, the server may determine a body size of a reference character in the VG image. The server may determine a baseline of the VG image that may be used to align the image with the body text.

Methods and apparatus for compressing image data are described along with corresponding methods and apparatus for decompressing the compressed image data. An encoder unit, which generates the compressed image data, comprises an input arranged to receive a first image and a second image, wherein the second image is twice the width and height of the first image, a prediction generator arranged to generate a prediction texture from the first image using an adaptive interpolator, a difference texture generator arranged to generate a difference texture from the prediction texture and the second image and in encoder unit arranged to encode the difference texture.

Matching processing between pieces of vector data is accelerated. A matching device 100 performs, for a plurality of pieces of vector data each having a plurality of dimensions, a predetermined operation pertaining to each dimension of each piece of vector data. The matching device 100 includes a collective operation unit 150 and an individual operation unit 160. The collective operation unit 150 performs the predetermined operation pertaining to a specific dimension among the plurality of dimensions by a vector operation for different pieces of vector data in the plurality of pieces of vector data. The individual operation unit 160 performs the predetermined operation pertaining to each dimension other than the specific dimension for a piece of vector data that satisfies a predetermined condition among the plurality of pieces of vector data.

An image coding apparatus divides a picture into tiles. The tiles are coded to generate pieces of coded data, each of which corresponds to a different one of the tiles. In this regard, a first tile of the tiles is coded with reference to coding information of an already-coded tile neighboring the first tile when a boundary between the first tile and the already-coded tile is a first boundary. The first tile is coded without reference to the coding information of the already-coded tile when the boundary between the first tile and the already-coded tile is a second boundary. A bitstream including the pieces of coded data is generated. The bitstream includes tile boundary independence information which indicates whether each boundary between the tiles is one of the first boundary and the second boundary.

Techniques are disclosed to compress residual vectors 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 and apparatus for compressing image data are described along with corresponding methods and apparatus for decompressing the compressed image data. A decoder unit samples compressed image data including interleaved blocks of data encoding a first image and blocks of data encoding differences between the first image and a second image, the second image being twice the width and the height of the first image. A difference decoder decodes a fetched encoded sub-block of the differences between the first and second images and output a difference quad and a prediction value for a pixel, and a filter sub-unit generates a reconstruction of the image at a sample position using decoded blocks of the first image, the difference quad and the prediction value.

Systems and methods are provided for encoding a frame of 3D media content. The systems and methods may be configured to access a first frame of 3D media content and generate a data structure for the first frame based on color attributes information of the first frame, wherein each element of the data structure encodes a single color. The systems and methods may be configured to train a machine learning model based on the first frame of 3D media content, wherein the machine learning model is trained to receive as input a coordinate of a voxel of the first frame, and to output an identifier of a particular element in the generated data structure. The systems and methods may be configured to generate encoded data for the first frame based at least in part on weights of the trained machine learning model and the generated data structure.