Abstract: Systems and methods are described for adjusting the color spectrum of synthetic objects in augmented reality (AR) displays under varying lighting conditions and a human observers spectral sensitivities, to produce customized color matches for a specific observer. Spectral data may be captured, and color matching functions (CMFs) of the observer may be used by a spectral color workflow that produces color display values, for example coordinates in RGB space. The color rendering may custom-match for multiple observers with different color perceptions under a wide range of environmental (ambient lighting) conditions.

Abstract: A method, system and computer readable medium for generating a cognitive insight comprising: receiving content element data, the content element data representing a content element, the content element comprising an element of a corpus of content; performing a cognitive learning operation on the content element data, the cognitive learning operation identifying descriptive information associated with the content element; associating a cognitive attribute with the content element using the descriptive information associated with the content element.

Abstract: A system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus. The computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data, each knowledge element of the collection of knowledge elements being persisted in its original form.

Abstract: A system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus. The computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data, each knowledge element being structurally defined within the cognitive graph.

Abstract: A system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus. The computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data; and, generating a cognitive insight based upon the collection of knowledge elements stored within the cognitive graph, the generating the cognitive insight using an insight agent to access the collection of knowledge elements.

Abstract: A computer-implementable method for generating an insight comprising: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data; and, generating a cognitive insight based upon the collection of knowledge elements stored within the cognitive graph, the generating the cognitive insight using an insight agent to access the collection of knowledge elements.

Abstract: A computer-implementable method for managing a cognitive graph comprising: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data, each knowledge element of the collection of knowledge elements being persisted in its original form.

Abstract: Systems, methods, and instrumentalities are disclosed for client centric service quality control. A first viewport of a 360 degree video may be determined. The 360 degree video may comprise one or more of an equirectangular, a cube-map, a cylindrical, a pyramidal, and/or a spherical projection mapping. The first viewport may be associated with a spatial region of the 360 degree video. An adjacent area that extends around the spatial region may be determined. A second viewport of the 360 degree video may be determined. A bitstream associated with the 360 degree video may be received. One or more enhanced regions may be included in the bitstream. The one or more enhanced regions may correspond to the first and/or second viewport, A high coding bitrate may be associated with the first viewport and/or the second viewport.

Abstract: Systems, methods, and devices are disclosed for performing adaptive color space conversion and adaptive entropy encoding of LUT parameters. A video bitstream may be received and a first flag may be determined based on the video bitstream. The residual may be converted from a first color space to a second color space in response to the first flag. The residual may be coded in two parts separated by the most significant bits and least significant bits of the residual. The residual may be further coded based on its absolute value.

Abstract: A cognitive information processing system environment comprising a plurality of data sources; a cognitive inference and learning system coupled to receive data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to perform a cognitive learning operation, the cognitive learning operation applying a cognitive learning technique to generate a cognitive learning result; and, a destination, the destination being updated based upon the learning result.

Abstract: Systems, methods, and instrumentalities are disclosed for dynamic picture-in-picture (PIP) by a client. The client may reside on any device. The client may receive video content from a server, and identify an object within the video content using at least one of object recognition or metadata. The metadata may include information that indicates a location of an object within a frame of the video content. The client may receive a selection of the object by a user, and determine positional data of the object across frames of the video content using at least one of object recognition or metadata. The client may display an enlarged and time-delayed version of the object within a PIP window across the frames of the video content. Alternatively or additionally, the location of the PIP window within each frame may be fixed or may be based on the location of the object within each frame.

Abstract: A video coding device may be configured to periodically select the frame packing configuration (e.g., face layout and/or face rotations parameters) associated with a RAS, The device may receive a plurality of pictures, which may each comprise a plurality of faces. The pictures may be grouped Into a plurality of RASs. The device may select a frame packing configuration with the lowest cost for a first RAS. For example, the cost of a frame packing configuration may be determined based on the first picture of the first RAS. The device may select a frame packing configuration for a second RAS. The frame packing configuration for the first RAS may be different than the frame packing configuration for the second RAS. The frame packing configuration for the first RAS and the frame packing configuration for the second RAS may be signaled in the video bitstream.

Abstract: Systems, methods, and Instrumentalities are described herein for calculating local Illumination compensation (LIC) parameters for bi-predicted coding unit (CU). The LIC parameters may be used to generate adjusted samples for the current CU and to address local illumination changes that may exist among temporal neighboring pictures. LIC parameters may be calculated based on bi-predicted reference template samples and template samples for a current CU. Bi-predicted reference template samples may be generated based on reference template samples neighboring temporal reference CUs. For example, the bi-predicted reference template samples may be generated based on averaging the reference template samples. The reference template samples may correspond to template samples for the current CU. A CU may be or may include a coding block and/or a sub-block that may be derived by dividing the coding block.

Abstract: Systems, methods, and instrumentalities are disclosed for discontinuous face boundary filtering for 360-degree video coding. A face discontinuity may be filtered (e.g., to reduce seam artifacts) in whole or in part, for example, using coded samples or padded samples on either side of the face discontinuity. Filtering may be applied, for example, as an in-loop filter or a post-processing step. 2D positional information related to two sides of the face discontinuity may be signaled. In a video bitstream so that filtering may be applied independent of projection formats and/or frame packing techniques.

Abstract: Video data may be palette decoded. Data defining a palette table may be received. The palette table may comprise index values corresponding to respective colors. Palette index prediction data may be received and may comprise data indicating index values for at least a portion of a palette index map mapping pixels of the video data to color indices in the palette table. The palette index prediction data may comprise run value data associating run values with index values for at least a portion of a palette index map. A run value may be associated with an escape color index. The palette index map may be generated from the palette index prediction data at least in part by determining whether to adjust an index value of the palette index prediction data based on a last index value. The video data may be reconstructed in accordance with the palette index map.

Abstract: A cognitive method, system and computer readable media comprising: monitoring a user interaction of a user; generating user interaction data based upon the user interaction; receiving data from a plurality of data sources; processing the user interaction data and the data from the plurality of data sources to perform a cognitive learning operation, the processing being performed via a cognitive inference and learning system, the cognitive learning operation comprising analyzing the user interaction data, the cognitive learning operation generating a cognitive learning result based upon the user interaction data; associating a cognitive profile with the user based on the cognitive learning result; and, performing a cognitive personal assistant operation based upon the cognitive profile, the cognitive personal assistant operation assisting the user by performing a personal assistant task.

Abstract: A method, system and computer readable medium for generating a cognitive insight comprising: receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system; performing a ranked insight machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the ranked insight machine learning operations; and, generating a cognitive insight based upon the cognitive profile generated using the plurality of machine learning operations.

Abstract: A system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus. The computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a data source; processing the data, the processing comprising performing a parsing operation on the data, the processing the data identifying a plurality of knowledge elements based upon the parsing operation, the parsing operation comprising ranking of parse options; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data.

Abstract: A block may be identified. The block may be partitioned into one or more (e.g., two) sibling nodes (e.g., sibling nodes BO and B1). A partition direction and a partition type for the block may be determined. If the partition type for the block is binary tree (BT), one or more (e.g., two) partition parameters may be determined for sibling node BO. A partition parameter (e.g., a first partition parameter) may be determined for sibling node B1. A decoder may determine whether to receive an indication of a second partition parameter for B1 based on, for example, the partition direction for the block, the partition type for the block, and the first partition parameter for B1. The decoder may derive the second partition parameter based on, for example, the partition direction and type for the block, and the first partition parameter for B1.