Abstract: Systems and methods are described for selecting a motion vector (MV) to use in frame-rate up conversion (FRUC) coding of a block of video. In one embodiment, a first set of motion vector candidates is identified for FRUC prediction of the block. A search center is defined based on the first set of motion vector candidates, and a search window is determined, the search window having a selected width and being centered on the search center. A search for a selected MV is performed within the search window. In some embodiments, an initial set of MVs is processed with a clustering algorithm to generate a smaller number of MVs that are used as the first set. The selected MV may be subject to a motion refinement search, which may also be performed over a constrained search range. In additional embodiments, search iterations are constrained to limit complexity.

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: Embodiments contemplate coding video data by generating a video encoded bitstream that may include reference picture set (RPS) extensions for inter-layer reference pictures, and the extensions may include inter-layer delta Picture Order Counts (POCs). Embodiments may also include signaling that lower layer reference pictures may be available in a lower layer decoder picture buffer (DPB), and/or an aggregate DPB, that may be added to the RPS set of a higher layer. The bitstream may include a signal indicating whether the higher layer RPS may be specified by a lower layer RPS, and the lower layer RPS may be temporal, inter-layer prediction (ILP), or both.

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: Systems, methods, and instrumentalities are disclosed relating to intra prediction of a video signal based on mode-dependent subsampling. A block of coefficients associated with a first sub block of a video block, one or more blocks of coefficients associated with one or more remaining sub blocks of the video block, and an indication of a prediction mode for the video block may be received. One or more interpolating techniques, a predicted first sub block, and the predicted sub blocks of the one or more remaining sub blocks may be determined. A reconstructed first sub block and one or more reconstructed remaining sub blocks may be generated. A reconstructed video block may be formed based on the prediction mode, the reconstructed first sub block, and the one or more reconstructed remaining sub blocks.

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: External overlapped block motion compensation (OBMC) may be performed for samples of a coding unit (CU) located along an inter-CU boundary of the CU while internal OBMC may be performed separately for samples located along inter-sub-block boundaries inside the CU. External OBMC may be applied based on substantially similar motion information associated with multiple external blocks neighboring the CU. The external blocks may be treated as a group to provide OBMC for multiple boundary samples together in an external OBMC operation. Internal OBMC may be applied using the same sub-block size used for sub-block level motion derivation. Internal OBMC may be disabled for the CU, for example, if the CU is coded in a spatial-temporal motion vector prediction (STMVP) mode.

Abstract: Systems, methods, and instrumentalities are disclosed for clustering-based quantization for neural network (NN) compression. A distribution of weights in weight tensors in NN layers may be analyzed to identify cluster outliers. Cluster inliers may be coded from cluster outliers, for example, using scalar and/or vector quantization. Weight-rearrangement may rearrange weights for higher dimensional weight tensors into lower dimensional matrices. For example, weight rearrangement may flatten a convolutional kernel into a vector. Correlation between kernels may be preserved, for example, by treating a filter or kernels across a channel as a point. A tensor may be split into multiple subspaces, for example, along an input and/or an output channel. Predictive coding may be performed for a current block of weights or weight matrix based on a reshaped or previously coded block or matrix. Arrangement, inlier, outlier, and/or prediction information may be signaled to a decoder for reconstruction of a compressed NN.

Abstract: Systems, methods, and instrumentalities may be provided for discounting reconstructed samples and/or coding information from spatial neighbors across face discontinuities. Whether a current block is located at a face discontinuity may be determined. The face discontinuity may be a face boundary between two or more adjoining blocks that are not spherical neighbors. The coding availability of a neighboring block of the current block may be determined, e.g., based on whether the neighboring block is on the same side of the face discontinuity as the current block. For example, the neighboring block may be determined to be available for decoding the current block if it is on the same side of the face discontinuity as the current block, and unavailable if it Is not on the same side of the face discontinuity. The neighboring block may be a spatial neighboring block or a temporal neighboring block.

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

Abstract: Sampling grid information may be determined for multi-layer video coding systems. The sampling grid information may be used to align the video layers of a coding system. Sampling grid correction may be performed based on the sampling grid information. The sampling grids may also be detected. In some embodiments, a sampling grid precision may also be detected and/or signaled.

Abstract: Systems, methods, and instrumentalities are disclosed herein that related to video-based point cloud streams in one or more ISO Base Media File Format (ISOBMFF) container files, A container format for point cloud data is provided and the container format indicates at least a relationship between a 3D region of the point cloud and one or more video-based point cloud compression (V-PCC) tracks. The V-PCC tracks may be grouped together and linked to the 3D region to allow spatial access to the 3D region.

Abstract: Systems, methods, and instrumentalities are disclosed for sub-block/block refinement, including sub-block/block boundary refinement, such as block boundary prediction refinement with optical flow (BBPROF). A block comprising a current sub-block may be decoded based on a sample value for a first pixel that is obtained based on, for example, an MV for a current sub-block, an MV for a sub-block adjacent the current sub-block, and a sample value for a second pixel adjacent the first pixel. BBPROF may include determining spatial gradients at pixel(s)/sample location(s). An MV difference may be calculated between a current sub-block and one or more neighboring sub-blocks. An MV offset may be determined at pixel(s)/sample location(s) based on the MV difference. A sample value offset for the pixel in a current sub-block may be determined. The prediction for a reference picture list may be refined by adding the calculated sample value offset to the sub-block prediction.

Abstract: Media content coded using scalable coding techniques may be cached among a group of cache devices. Layered segments of the media content may be pre-loaded onto the cache devices, which may be located throughout a content distribution network, including a home network. The caching location of the media content may be determined based on multiple factors including a content preference associated with the group of cache devices and device capabilities. A cache controller may manage the caching of the media content.

Abstract: A system, method, and computer-readable medium are disclosed for cognitive information processing. The cognitive information processing 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 augmented intelligence governance and assurance operation, the augmented intelligence governance and assurance operation ensuring augmented intelligence performance 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: A system, method, and computer-readable medium are disclosed for cognitive information processing. The cognitive information processing includes receiving data from a plurality of data sources; processing the data from the plurality of data sources 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, the cognitive computing function comprising a cognitive skill, the cognitive skill being composed via a cognitive skill composition platform, the cognitive skill comprising an atomic unit of work, the atomic unit of work being triggered by and input to produce and output; and, using the cognitive skill to generate a cognitive agent, the cognitive skill comprising a distinct unit of functionality of the cognitive agent, the cognitive skill facilitating construction of the cognitive agent.

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.