Abstract: System, device, and method for improved image encoding that non-iteratively targets and achieves a visual-quality threshold and a compression efficiency threshold. A system receives an input image intended for compression; and applies a pre-trained Convolutional Neural Network (CNN) Engine to predict what would be (i) compression ratio values and (ii) visual-quality score values, for a resulting compressed image that would be encoded or transcoded from said input image, if said input image would be compressed via a particular Image Encoder from a pool of available Image Encoders by using a particular Encoder Settings from a pool of Image Encoder Settings. Based on CNN-based predictions generated by the pre-trained CNN Engine, the system selects a single combination of Image Encoder with Encoder Setting, and performs a single compression of said input image using said single combination of Image Encoder with Encoder Setting to generate an optimally compressed image.

Abstract: A system and method for converting imaging data, for example, medical imaging data, to three-dimensional printer data Imaging data may be received describing for example a three-dimensional volume of a subject or patient. Using printer definition data describing a particular printer, 3D printer input data may be created from the imaging data describing at least part of the three-dimensional volume.

Abstract: A system and method for converting imaging data, for example, medical imaging data, to three-dimensional printer data Imaging data may be received describing for example a three-dimensional volume of a subject or patient. Using printer definition data describing a particular printer, 3D printer input data may be created from the imaging data describing at least part of the three-dimensional volume.

Abstract: A system and method for converting imaging data, for example, medical imaging data, to three-dimensional printer data. Imaging data may be received describing for example a three-dimensional volume of a subject or patient. Using printer definition data describing a particular printer, 3D printer input data may be created from the imaging data describing at least part of the three-dimensional volume.

Abstract: Methods for volume rendering of 3D object data are provided. The methods can include classifying 3D object data and determining a transfer function to use to render the 3D object data. based on the classification; incrementally determining voxels to render from the 3D object data; and/or determining a grid that represents the voxels and rendering based on the voxel grid.

Abstract: Methods for volume rendering of 3D object data are provided. The methods can include classifying 3D object data and determining a transfer function to use to render the 3D object data based on the classification; incrementally determining voxels to render from the 3D object data; and/or determining a grid that represents the voxels and rendering based on the voxel grid.

Abstract: Methods for virtual reality and augmented reality rendering of a 3D object are provided. The methods can include segmenting the 3D object such that portions of the 3D object can be identifiable and distinguishable. The methods can include creating masks, identifying relationships between the masks and marking the masks accordingly.

Abstract: A system and method for converting imaging data, for example, medical imaging data, to three-dimensional printer data. Imaging data may be received describing for example a three-dimensional volume of a subject or patient. Using printer definition data describing a particular printer, 3D printer input data may be created from the imaging data describing at least part of the three-dimensional volume.

Abstract: Methods for volume rendering of 3D object data are provided. The methods can include classifying 3D object data and determining a transfer function to use to render the 3D object data based on the classification; incrementally determining voxels to render from the 3D object data; and/or determining a grid that represents the voxels and rendering based on the voxel grid.

Abstract: Methods for virtual reality and augmented reality rendering of a 3D object are provided. The methods can include segmenting the 3D object such that portions of the 3D object can be identifiable and distinguishable. The methods can include creating masks, identifying relationships between the masks and marking the masks accordingly.