Abstract: A processor for generating binarized weights for a neural network. The processor comprises a binarization scheme generation module configured to generate, for a group of weights taken from a set of input weights for one or more layers of a neural network, one or more potential binary weight strings representing said group of weights; a binarization scheme selection module configured to select a binary weight string to represent said group of weights, from among the one or more potential binary weight strings, based at least in part on a number of data bits required to represent the one or more potential binary weight strings according to a predetermined encoding method; and a weight generation module configured to output data representing the selected binary weight string for representing the group of weights.

Abstract: A segmented 3D multi-view image generator generates fewer multi-view view images for partitions having less salient features. Saliency values are calculated based on a depth map and image processing of the input image. The saliency values are compared to thresholds to map pixel locations to first, second, and third partitions. First, second, and third segmented images are created from the input image using a partition map. A multi-view generator uses the depth map and viewer eye locations to generates 28 view images from the first segmented image, 14 unique view images from the second segmented image that are replicated to 28 view images, and 7 unique view images from the third segmented image that are replicated to 28 view images. The view images for each segment are interlaced to generated interlaced segmented images that are then integrated together into a single 3D image that drives a 28-view autostereoscopic display.