Abstract: Systems, apparatus, articles, and methods are described below including operations for video tone mapping to convert High Dynamic Range (HDR) content to Standard Dynamic Range (SDR) content.

Abstract: Systems, apparatus, articles, and methods are described below including operations for video tone mapping to convert High Dynamic Range (HDR) content to Standard Dynamic Range (SDR) content.

Abstract: Systems, apparatus, articles, and methods are described below including operations for adaptive control for denoise filtering.

Abstract: A system is provided for generating a three dimensional image. The system may include a processor configured to generate a disparity map from a stereo image, adjust the disparity map to compress or expand a number of depth levels within the disparity map to generate an adjusted disparity map, and render a stereo view of the image based on the adjusted disparity map.

Abstract: A system and method for generating a three-dimensional image using depth layers is provided. A plurality of depth layers may be generated from the image, where each depth layer has a height, a width, and a thickness. Certain regions of the image may be assigned to one of the plurality of depth layers. A depth map may be generated based on the depth layers. Further, a disparity map may be generated based on both the depth map and the depth layers. A stereo view of the image may then be rendered based on the disparity map.

Abstract: A system and method for converting two dimensional video to three dimensional video includes a processor having an input for receiving a two dimensional image data and an output for outputting three dimensional image data to a display. The processor is configured to receive two dimensional image data, segment a specific object in the two dimensional image data based on variations in brightness and sharpness in the two dimensional image data to identify and locate the specific object in the two dimensional image data. The processor is also configured to adjust the depth value of the specific object over the period of time as the size of the specific object changes in each of the two dimensional images or adjust the depth value of the specific object over the period of time as the size of the specific object changes in each of the two dimensional images.

Abstract: In a video processing device, scale of a video image is detected for vertical and horizontal directions based on pixel information, for example, per pixel vertical and horizontal gradients. Gradients are utilized or discarded based on picture format, standard deviation of luma levels and pixel location relative to black border edges, graphics and/or overlaid content. Mosquito noise filters are adapted based on scale and/or noise strength. Median and/or linear filter results are selected based on a weakest, a strongest and/or a blended result. Horizontal and vertical operations are performed separately for edge detection, edge strength determination, filtering and filter correction control. Horizontal and vertical block grid spacing and grid shift are determined. Block noise strength is determined. Block noise filters are configured based on scaling and/or noise strength. Filter corrections are limited based on block noise strength. Noise reduction results may be blended to generate a pixel correction value.

Abstract: A system and method for generating a three-dimensional image using depth layers is provided. A plurality of depth layers may be generated from the image, where each depth layer has a height, a width, and a thickness. Certain regions of the image may be assigned to one of the plurality of depth layers. A depth map may be generated based on the depth layers. Further, a disparity map may be generated based on both the depth map and the depth layers. A stereo view of the image may then be rendered based on the disparity map.

Abstract: A system and method for converting two dimensional video to three dimensional video includes a processor having an input for receiving a two dimensional image data and an output for outputting three dimensional image data to a display. The processor is configured to receive two dimensional image data, segment a specific object in the two dimensional image data based on variations in brightness and sharpness in the two dimensional image data to identify and locate the specific object in the two dimensional image data. The processor is also configured to adjust the depth value of the specific object over the period of time as the size of the specific object changes in each of the two dimensional images or adjust the depth value of the specific object over the period of time as the size of the specific object changes in each of the two dimensional images.

Abstract: A system and method for converting two dimensional video to three dimensional video includes a processor having an input for receiving a two dimensional image data and an output for outputting three dimensional image data to a display. The processor is configured to receive two dimensional image data, segment a specific object in the two dimensional image data based on variations in brightness and sharpness in the two dimensional image data to identify and locate the specific object in the two dimensional image data. The processor is also configured to adjust the depth value of the specific object over the period of time as the size of the specific object changes in each of the two dimensional images or adjust the depth value of the specific object over the period of time as the size of the specific object changes in each of the two dimensional images.

Abstract: A system is provided for generating a three dimensional image. The system may include a processor configured to generate a disparity map from a stereo image, adjust the disparity map to compress or expand a number of depth levels within the disparity map to generate an adjusted disparity map, and render a stereo view of the image based on the adjusted disparity map.

Abstract: In a video processing device, scale of a video image is detected for vertical and horizontal directions based on pixel information, for example, per pixel vertical and horizontal gradients. Gradients are utilized or discarded based on picture format, standard deviation of luma levels and pixel location relative to black border edges, graphics and/or overlaid content. Mosquito noise filters are adapted based on scale and/or noise strength. Median and/or linear filter results are selected based on a weakest, a strongest and/or a blended result. Horizontal and vertical operations are performed separately for edge detection, edge strength determination, filtering and filter correction control. Horizontal and vertical block grid spacing and grid shift are determined. Block noise strength is determined. Block noise filters are configured based on scaling and/or noise strength. Filter corrections are limited based on block noise strength. Noise reduction results may be blended to generate a pixel correction value.