The current document is directed to a dimensional shuffle transform (“DST”) that maps a 3D space to a one-dimensional space that preserves 3D neighborhoods within 1D neighborhoods within an implicit recursive hierarchical structure. The search for points in a 3D subspace is reduced, by the DST, to one or more searches in the transformed 1D space. The search is performed by either recursive decomposition of the 3D region indexed by the transform into subspaces, exploiting the transformed space structure, or by direct indexing into the region of interest. The searches over the subspaces generated by recursive decomposition are independent from one another, providing many opportunities for a variety of parallel, DST-enabled search methods.

Disclosed is an image compression method of a digital pathology system. The image compression method is a method of compressing digital slide images having first to nth original plane images (n is a natural number greater than or equal to 2). The image compression method includes selecting a block having an optimal focal point as an optimal block from each set of blocks positioned at identical positions of the first to nth original plane images; forming one plane image as a virtual optimal plane image by combining only the optimal blocks; generating block descriptors for forming the first to nth original plane images based on the virtual optimal plane image; generating first to nth predictive plane images from the virtual optimal plane image such that the first to nth predictive plane images are the least out of focus by using location information for the blocks and the block descriptors; generating first to nth differential plane images, the first differential plane image corresponding to a difference between the first original plane image and the first predictive plane image and the nth differential plane image corresponding to a difference between the nth original plane image and the nth predictive plane image; and compressing the first to nth differential plane images.

A three-dimensional data creation method includes: creating first three-dimensional data from information detected by a sensor; receiving encoded three-dimensional data that is obtained by encoding second three-dimensional data; decoding the received encoded three-dimensional data to obtain the second three-dimensional data; and merging the first three-dimensional data with the second three-dimensional data to create third three-dimensional data.

A three-dimensional data creation method includes: creating first three-dimensional data from information detected by a sensor; receiving encoded three-dimensional data that is obtained by encoding second three-dimensional data; decoding the received encoded three-dimensional data to obtain the second three-dimensional data; and merging the first three-dimensional data with the second three-dimensional data to create third three-dimensional data.

An apparatus includes a communication interface and a processor operably coupled to the communication interface. The communication interface receives a plurality of tracks in a compressed bitstream. The processor identifies an atlas track corresponding to a point cloud compression (PCC) component and identifies a set of first component tracks that is referenced by the atlas track and at least one second component track, each of the at least one second component track is an alternative version of a first component track of the set of first component tracks. The processor also determines which of the set of first component tracks and the at least one second component track are appropriate versions of the PCC component. The processor further decodes the appropriate version of the PCC component from among the set of first component tracks and the at least one second component track.

An apparatus includes a communication interface and a processor operably coupled to the communication interface. The communication interface receives a plurality of tracks in a compressed bitstream. The processor identifies an atlas track corresponding to a point cloud compression (PCC) component and identifies a set of first component tracks that is referenced by the atlas track and at least one second component track, each of the at least one second component track is an alternative version of a first component track of the set of first component tracks. The processor also determines which of the set of first component tracks and the at least one second component track are appropriate versions of the PCC component. The processor further decodes the appropriate version of the PCC component from among the set of first component tracks and the at least one second component track.

The present invention relates to a technique for encoding and decoding video data, and more particularly, to a method for performing inter-prediction in an effective manner. The present invention combines an inter-prediction method using an AMVP mode and an inter-prediction method using a merge mode so as to propose a method for using the same candidate. The method for encoding video data proposed by the present invention comprises the following steps: receiving mode information on an inter-prediction method of a current block; determining, on the basis of the received mode information, whether the interprediction method to be applied to the current block is an AMVP mode or a merge mode; and selecting a candidate to derive motion information of the current block, wherein the candidate is selected in a left region, top region and corner region of the current block and in the same position block as the current block, and the AMVP mode and the merge mode are applied on the basis of the selected candidate.

Disclosed are methods for encoding information in a graphic image. The information may be encoded so as to have a visual appearance that adopts a particular style, so that the encoded information is visually pleasing in the environment in which it is displayed. An encoder and decoder are trained during an integrated training process, where the encoder is tuned to minimize a loss when its encoded images are decoded. Similarly, the decoder is also trained to minimize loss when decoding the encoded images. Both the encoder and decoder may utilize a convolutional neural network in some aspects to analyze data and/or images. Once data is encoded, a style from a sample image is transferred to the encoded data. When decoding, the decoder may largely ignore the style aspects of the encoded data and decode based on a content portion of the data.

The invention relates to a method for managing image data in a motor vehicle lighting system. The lighting system comprises at least one lighting module intended to project light beams generated on the basis of data relating to the selection of at least one image, each image being defined by a matrix containing a plurality of horizontal or vertical rows of pixels, wherein each pixel is characterized by a numerical value related to a light intensity of the pixel, said method determining whether the analyzed pixel is regarded as a significant inflection point of the image such that said analyzed pixel is transmitted to at least one lighting module so that same can project a resulting image. The invention also relates to a motor vehicle lighting system for carrying out the steps of a method of this kind.

Described herein is a data processing system having a multisample antialiasing compressor coupled to a texture unit and shader execution array. In one embodiment, the data processing system includes a memory device to store a multisample render target, the multisample render target to store color data for a set of sample locations of each pixel in a set of pixels; and general-purpose graphics processor comprising a multisample antialiasing compressor to apply multisample antialiasing compression to color data generated for the set of sample locations of a first pixel in the set of pixels and a multisample render cache to store color data generated for the set of sample locations of the first pixel in the set of pixels, wherein color data evicted from the multisample render cache is to be stored to the multisample render target.