Abstract: A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing an impartiality assessment operation via an impartiality assessment engine, the impartiality assessment operation detecting a presence of bias in an outcome of the cognitive computing function; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

Abstract: Processing video data may include capturing the video data with multiple cameras and stitching the video data together to obtain a 360-degree video. A frame-packed picture may be provided based on the captured and stitched video data. A current sample location may be identified in the frame-packed picture. Whether a neighboring sample location is located outside of a content boundary of the frame-packed picture may be determined. When the neighboring sample location is located outside of the content boundary, a padding sample location may be derived based on at least one circular characteristic of the 360-degree video content and the projection geometry. The 360-degree video content may be processed based on the padding sample location.

Abstract: A cognitive learning method comprising receiving data from a plurality of data sources; processing the data from the plurality of data sources to perform a cognitive machine learning operation, the processing being performed via a cognitive inference and learning system, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive machine learning operation applying the cognitive learning technique via a machine learning algorithm to generate a cognitive learning result; and, updating a destination based upon the learning result.

Abstract: A cognitive learning method comprising: receiving data from a plurality of data sources; processing 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 implementing a cognitive learning technique according to a cognitive learning framework, the cognitive learning operation applying the cognitive learning technique to generate a cognitive learning result; and, updating a destination based upon the learning result.

Abstract: A method, system and computer-readable storage medium for performing a cognitive information processing operation.

Abstract: A video coding system (e.g., an encoder and/or a decoder) may perform face-based sub-block motion compensation for 360-degree video to predict samples (e.g., of a sub-block). The video coding system may receive a 360-degree video content. The 360-degree video content may include a current block. The current block may include a plurality of sub-blocks. The system may determine whether a sub-block mode is used for the current block. The system may predict a sample in the current block based on the sub-block level face association. For a first sub-block in the current block, the system may identify a first location of the first sub-block. The system may associate the first sub-block with a first face based on the identified first location of the first sub-block. The system may predict a first sample in the first sub-block based on the first face that is associated with the first sub-block.

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: A decoding complexity may be used to predict power consumption for receiving, decoding, and/or displaying multimedia content at a wireless transmit/receive unit (WTRU). The decoding complexity may be based on decoding complexity feedback received from a reference device, such as another WTRU. The decoding complexity feedback may be based on measurements performed at the reference device for receiving decoding, and/or displaying the multimedia content. A content providing device may indicate the decoding complexity of requested media content to a WTRU, or another network entity. The decoding complexity may be indicated in a streaming protocol or file associated with the media content. The WTRU, or other network entity, may use the decoding complexity determine its preferences regarding transmission of the media content. The content providing device may determine whether to transmit the media content based on the decoding complexity and/or the preferences of the WTRU or other network entity.

Abstract: Systems and methods are described for video coding using generalized bi-prediction. In an exemplary embodiment, to code a current block of a video in a bitstream, a first reference block is selected from a first reference picture and a second reference block is selected from a second reference picture. Each reference block is associated with a weight, where the weight may be an arbitrary weight ranging, e.g., between 0 and 1. The current block is predicted using a weighted sum of the reference blocks. The weights may be selected from among a plurality of candidate weights. Candidate weights may be signaled in the bitstream or may be derived implicitly based on a template. Candidate weights may be pruned to avoid out-of-range or substantially duplicate candidate weights. Generalized bi-prediction may additionally be used in frame rate up conversion.

Abstract: A device may determine whether to enable or disable bi-directional optical flow (BIO) for a current coding unit (CU) (e.g., block and/or sub-block). Prediction information for the CU may be identified and may include prediction signals associated with a first reference block and a second reference block (e.g., or a first reference sub-block and a second reference sub-block). A prediction difference may be calculated and may be used to determine the similarity between the two prediction signals. The CU may be reconstructed based on the similarity. For example, whether to reconstruct the CU with BIO enabled or BIO disabled may be based on whether the two prediction signals are similar, it may be determined to enable BIO for the CU when the two prediction signals are determined to be dissimilar. For example, the CU may be reconstructed with BIO disabled when the two prediction signals are determined to be similar.

Abstract: Systems, devices, and methods are described herein for symmetric merge mode motion vector coding. Symmetric bi-prediction (bi-pred) motion vectors (MVs) may be constructed from available candidates in a merge candidate list for regular inter prediction merge mode and/or affine prediction merge mode. Available MV merge candidates may be symmetrically extended or mapped in either direction (e.g., between reference pictures before and after a current picture), for example, when coding a picture that allows bi-directional motion compensation prediction (MCP). A symmetric bi-pred merge candidate may be selected among merge candidates for predicting the motion information of a current prediction unit (PU). The symmetric mapping construction may be repeated by a decoder (e.g., based on a coded index of the MV merge candidate list), for example, to obtain the same merge candidates and coded MV at an encoder.

Abstract: A device may control a video communication via transcoding and/or traffic shaping. The device may include a multipoint control unit (MCU) and/or a server. The device may receive one or more video streams from one or more devices. The device may analyze a received video stream to determine a viewing parameter. The viewing parameter may include a user viewing parameter, a device viewing parameter, and/or a content viewing parameter. The device may modify a video stream based on the viewing parameter. Modifying the video stream may include re-encoding the video stream, adjusting an orientation, removing a video detail, and/or adjusting a bit rate. The device may send the modified video stream to another device. The device may determine a hit rate for the video stream based on the viewing parameter. The device may indicate the bit rate by sending a feedback message and/or by signaling a bandwidth limit.

Abstract: Inter-layer motion mapping information may be used to enable temporal motion vector prediction (TMVP) of an enhancement layer of a bitstream. For example, a reference picture and a motion vector (MV) of an inter-layer video block may be determined. The reference picture may be determined based on a collocated base layer video block. For example, the reference picture may be a collocated inter-layer reference picture of the reference picture of the collocated base layer video block. The MV may be determined based on a MV of the collocated base layer video block. For example, the MV may be determined by determining the MV of the collocated base layer video block and scaling the MV of the collocated base layer video block according to a spatial ratio between the base layer and the enhancement layer. TMVP may be performed on the enhancement layer picture using the MV of the inter-layer video block.

Abstract: Intra sub-partitions (ISP) may be enabled for a current block, for example, based on an ISP indication. The block may be partitioned into multiple sub-partitions, and a sub-partition may belong to a prediction unit (PU). A sub-partition width for the current block and a minimum prediction block width may be obtained. A PU corresponding to a current sub-partition may be determined based on the sub-partition width and the minimum prediction block width. For example, when the sub-partition width is less than the minimum prediction block width, the PU may include multiple sub-partitions. In examples, the minimum prediction block width may be four samples. Reference samples may be determined, and the PU may be predicted using the reference samples.

Abstract: Bi-directional optical flow (BDOF) may be bypassed, for a current coding block, based on whether symmetric motion vector difference (8MVD) is used in motion vector coding for the current coding block, A coding device (e.g., an encoder or a decoder) may determine whether to bypass BDOF for the current coding block based at least in part on an SMVD indication for the current coding block, The coding device may obtain the SMVD indication that indicates whether SMVD is used in motion vector coding for the current coding block. If SMVD Indication indicates that SMVD is used in the motion vector coding for the current coding block, the coding device may bypass BDOF for the current coding block. The coding device may reconstruct, the current coding block without performing BDOF if it determines to bypass BDOF for the current coding block.

Abstract: Systems, methods, and instrumentalities are disclosed for obtaining coded video data comprising quantized transform coefficients for a plurality of blocks, obtaining a first precision factor associated with a first block for performing at least one decoding function on the first block, obtaining a second precision factor associated with a second block for performing the at least one decoding function on the second block, and performing the at least one decoding function on the quantized transform coefficients for the first block using the first precision factor and on the quantized transform coefficients for the second block using the second precision factor.

Abstract: Video coding methods are described for reducing latency in template-based inter coding. In some embodiments, a method is provided for coding a video that includes a current picture and at least one reference picture. For at least a current block in the current picture, a respective predicted value is generated (e.g. using motion compensated prediction) for each sample in a template region adjacent to the current block. Once the predicted values are generated for each sample in the template region, a process is invoked to determine a template-based inter prediction parameter by using predicted values in the template region and sample values the reference picture. This process can be invoked without waiting for reconstructed sample values in the template region. Template-based inter prediction of the current block is then performed using the determined template-based inter prediction parameter.

Abstract: A method, system and computer readable medium for performing a cognitive search operation comprising: receiving training data, the training data comprising information based upon user interaction with cognitive attributes; performing a machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the machine learning operation; and, performing a cognitive search operation on a corpus of content based upon the cognitive profile, the cognitive search operation returning cognitive results specific to the cognitive profile of the user.

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 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 being structurally defined within the cognitive graph.