Abstract: A block of video data is split using one or more of several possible partition operations by using the partitioning choices obtained through use of a deep learning-based image partitioning. In at least one embodiment, the block is split in one or more splitting operations using a convolutional neural network. In another embodiment, inputs to the convolutional neural network come from pixels along the block's causal borders. In another embodiment, boundary information, such as the location of partitions in spatially neighboring blocks, is used by the convolutional neural network. Methods, apparatus, and signal embodiments are provided for encoding.

Abstract: A block of video data is split using one or more of several possible partition operations by using the partitioning choices obtained through use of a texture-based image partitioning. In at least one embodiment, the block is split in one or more splitting operations using a convolutional neural network. In another embodiment, inputs to the convolutional neural network come from pixels along the block's causal borders. In another embodiment, boundary information, such as the location of partitions in spatially neighboring blocks, is used by the texture analysis. Methods, apparatus, and signal embodiments are provided for encoding.

Abstract: A block of video data is split using one or more of several possible partition operations by using the partitioning choices obtained through use of a texture-based image partitioning. In at least one embodiment, the block is split in one or more splitting operations using a convolutional neural network. In another embodiment, inputs to the convolutional neural network come from pixels along the block's causal borders. In another embodiment, boundary information, such as the location of partitions in spatially neighboring blocks, is used by the texture analysis. Methods, apparatus, and signal embodiments are provided for encoding.

Abstract: A method for encoding a block is disclosed. To this aim, a split mode is determined based on a rate-distortion optimization using a texture-based split prediction set obtained for the block. As an example, the split mode is determined by adapting the texture-based split prediction set according to at least one of a binary or triple split mode non-redundancy constraint or a heuristic-based split mode set pruning. The block is finally encoded using the determined split mode.

Abstract: A method for encoding a block is disclosed. To this aim, a split mode is determined based on a rate-distortion optimization using a texture-based split prediction set obtained for the block. As an example, the split mode is determined by adapting the texture-based split prediction set according to at least one of a binary or triple split mode non-redundancy constraint or a heuristic-based split mode set pruning. The block is finally encoded using the determined split mode.

Abstract: A method for encoding a block is disclosed. To this aim, a split mode is determined based on a rate-distortion optimization using a texture-based split prediction set obtained for the block. As an example, the split mode is determined by adapting the texture-based split prediction set according to at least one of a binary or triple split mode non-redundancy constraint or a heuristic-based split mode set pruning. The block is finally encoded using the determined split mode.

Abstract: A block of video data is split using one or more of several possible partition operations by using the partitioning choices obtained through use of a texture-based image partitioning. In at least one embodiment, the block is split in one or more splitting operations using a convolutional neural network. In another embodiment, inputs to the convolutional neural network come from pixels along the block's causal borders. In another embodiment, boundary information, such as the location of partitions in spatially neighboring blocks, is used by the texture analysis. Methods, apparatus, and signal embodiments are provided for encoding.

Abstract: A method for encoding a block is disclosed. To this aim, a split mode is determined based on a rate-distortion optimization using a texture-based split prediction set obtained for the block. As an example, the split mode is determined by adapting the texture-based split prediction set according to at least one of a binary or triple split mode non-redundancy constraint or a heuristic-based split mode set pruning. The block is finally encoded using the determined split mode.

Abstract: A block of video data is split using one or more of several possible partition operations by using the partitioning choices obtained through use of a texture-based image partitioning. In at least one embodiment, the block is split in one or more splitting operations using a convolutional neural network. In another embodiment, inputs to the convolutional neural network come from pixels along the block's causal borders. In another embodiment, boundary information, such as the location of partitions in spatially neighboring blocks, is used by the texture analysis. Methods, apparatus, and signal embodiments are provided for encoding.

Abstract: A block of video data is split using one or more of several possible partition operations by using the partitioning choices obtained through use of a texture-based image partitioning. In at least one embodiment, the block is split in one or more splitting operations using a convolutional neural network. In another embodiment, inputs to the convolutional neural network come from pixels along the block's causal borders. In another embodiment, boundary information, such as the location of partitions in spatially neighboring blocks, is used by the texture analysis. Methods, apparatus, and signal embodiments are provided for encoding.

Abstract: A method for encoding a block is disclosed. To this aim, a split mode is determined based on a rate-distortion optimization using a texture-based split prediction set obtained for the block. As an example, the split mode is determined by adapting the texture-based split prediction set according to at least one of a binary or triple split mode non-redundancy constraint or a heuristic-based split mode set pruning. The block is finally encoded using the determined split mode.

Abstract: A block of video data is split using one or more of several possible partition operations by using the partitioning choices obtained through use of a deep learning-based image partitioning. In at least one embodiment, the block is split in one or more splitting operations using a convolutional neural network. In another embodiment, inputs to the convolutional neural network come from pixels along the block's causal borders. In another embodiment, boundary information, such as the location of partitions in spatially neighboring blocks, is used by the convolutional neural network. Methods, apparatus, and signal embodiments are provided for encoding.

Abstract: The present invention relates to a pharmaceutical composition comprising fingolimod and a weak acid cation exchange resin in the form of an ion-exchange complex and pharmaceutically acceptable 5 excipients.

Abstract: The present invention relates to a pharmaceutical composition comprising fingolimod and a weak acid cation exchange resin in the form of an ion-exchange complex and pharmaceutically acceptable 5 excipients.

Abstract: The present invention relates to a pharmaceutical composition comprising fingolimod and a weak acid cation exchange resin in the form of an ion-exchange complex and pharmaceutically acceptable excipients.

Abstract: The present invention relates to a pharmaceutical composition comprising fingolimod and a weak acid cation exchange resin in the form of an ion-exchange complex and pharmaceutically acceptable excipients.