Abstract: A computer vision processing device is provided which comprises memory configured to store data and a processor. The processor is configured to store captured image data in a first buffer and acquire access to the captured image data in the first buffer when the captured image data is available for processing. The processor is also configured to execute a first group of operations in a processing pipeline, each of which processes the captured image data accessed from the first buffer and return the first buffer for storing next captured image data when a last operation of the first group of operations executes.

Abstract: A computer vision processing device is provided which comprises memory configured to store data and a processor. The processor is configured to store captured image data in a first buffer and acquire access to the captured image data in the first buffer when the captured image data is available for processing. The processor is also configured to execute a first group of operations in a processing pipeline, each of which processes the captured image data accessed from the first buffer and return the first buffer for storing next captured image data when a last operation of the first group of operations executes.

Abstract: A host processor, such as a central processing unit (CPU), programmed to execute a software driver that causes the host processor to generate a motion compensation command for a plurality of cores of a massively parallel processor, such as a graphics processing unit (GPU), to provide motion compensation for encoded video. The motion compensation command for the plurality of cores of the massively parallel processor contains executable instructions for processing a plurality of motion vectors grouped by a plurality of prediction modes from a re-ordered motion vector buffer by the plurality of cores of the massively parallel processor.

Abstract: A processing device is provided which comprises memory and a processor. The processor is configured to receive an array of floating point numbers each having a plurality of bits used to represent a probability value. For each floating point number, the processor is configured to replace values in a portion of the bits used to represent the probability value with index values to represent an index corresponding to a location of a corresponding floating point number in the memory. The processor is also configured to process the floating point numbers using SIMD instructions to execute one of an argmax operation and an argmin operation.

Abstract: A neural network is trained at a first precision using a training dataset. The neural network is then calibrated using an augmented calibration dataset that includes a first dataset and one or more second datasets produced by modifying the first dataset. A range of values of activations of nodes in the neural network at the first precision is determined based on inputs to the neural network from the augmented calibration dataset. The activations of the nodes are then quantized to a second precision based on the range of values of the activations of the nodes at the first precision. The first precision is higher than the second precision. For example, in some cases the first precision is a 32-bit floating point precision and the second precision is an 8-bit integer precision.

Abstract: A processing device is provided which comprises memory and a processor. The processor is configured to receive an array of floating point numbers each having a plurality of bits used to represent a probability value. For each floating point number, the processor is configured to replace values in a portion of the bits used to represent the probability value with index values to represent an index corresponding to a location of a corresponding floating point number in the memory. The processor is also configured to process the floating point numbers using SIMD instructions to execute one of an argmax operation and an argmin operation.

Abstract: A host processor, such as a central processing unit (CPU), programmed to execute a software driver that causes the host processor to generate a motion compensation command for a plurality of cores of a massively parallel processor, such as a graphics processing unit (GPU), to provide motion compensation for encoded video. The motion compensation command for the plurality of cores of the massively parallel processor contains executable instructions for processing a plurality of motion vectors grouped by a plurality of prediction modes from a re-ordered motion vector buffer by the plurality of cores of the massively parallel processor.

Abstract: A host processor, such as a central processing unit (CPU), programmed to execute a software driver that causes the host processor to generate a motion compensation command for a plurality of cores of a massively parallel processor, such as a graphics processing unit (GPU), to provide motion compensation for encoded video. The motion compensation command for the plurality of cores of the massively parallel processor contains executable instructions for processing a plurality of motion vectors grouped by a plurality of prediction modes from a re-ordered motion vector buffer by the plurality of cores of the massively parallel processor.

Abstract: A computer vision processing device is provided which comprises memory configured to store data and a processor. The processor is configured to store captured image data in a first buffer and acquire access to the captured image data in the first buffer when the captured image data is available for processing. The processor is also configured to execute a first group of operations in a processing pipeline, each of which processes the captured image data accessed from the first buffer and return the first buffer for storing next captured image data when a last operation of the first group of operations executes.

Abstract: A computer vision processing device is provided which comprises memory configured to store data and a processor. The processor is configured to store captured image data in a first buffer and acquire access to the captured image data in the first buffer when the captured image data is available for processing. The processor is also configured to execute a first group of operations in a processing pipeline, each of which processes the captured image data accessed from the first buffer and return the first buffer for storing next captured image data when a last operation of the first group of operations executes.

Abstract: Systems, methods, and devices for generating an image frame for display to a user. Brain activity sensor data correlated with movement of a user is received. A predicted field of view of the user is determined based on the brain activity sensor data. An image frame is generated based on the predicted field of view. The image frame is transmitted to a display for display to a user. Some implementations provide for receiving and displaying a foveated image frame based on a predicted field of view of a user. Brain activity information of a user is captured. The brain activity information is communicated to a transceiver. The brain activity information is transmitted to a rendering device using the transceiver to generate a foveated image frame based on a predicted field of view of the user. The foveated image frame is received from the rendering device, decoded, and displayed to the user.

Abstract: Systems, methods, and devices for generating an image frame for display to a user. Brain activity sensor data correlated with movement of a user is received. A predicted field of view of the user is determined based on the brain activity sensor data. An image frame is generated based on the predicted field of view. The image frame is transmitted to a display for display to a user. Some implementations provide for receiving and displaying a foveated image frame based on a predicted field of view of a user. Brain activity information of a user is captured. The brain activity information is communicated to a transceiver. The brain activity information is transmitted to a rendering device using the transceiver to generate a foveated image frame based on a predicted field of view of the user. The foveated image frame is received from the rendering device, decoded, and displayed to the user.

Abstract: A processing device is provided which includes memory configured to store data and a processor. The processor is configured to receive a plurality of panoramic video images representing views around a point in a three dimensional (3D) space and warp the plurality of panoramic video images, using a panoramic format, into a plurality of formatted warped images. The processor is also configured to store, in the memory, the plurality of formatted warped images and perform a motion search around each co-located pixel block of a reference panoramic frame by limiting the motion searches in a vertical direction around the co-located pixel blocks.

Abstract: A method and apparatus of precomputing includes capturing a first image by a first image capturing device. An image space for the first image is defined and pixels in the image space are analyzed for validity. Valid pixels are stored as valid pixel groups and the valid pixel groups are processed.

Abstract: Methods and apparatus for video processing are disclosed. In one embodiment the work of processing of different types of video frames is allocated between a plurality of computing resources. For example, different computing resources for can be used for I, P and B frames, where an I frame is an intra-frame encoded with no other frames as a reference; a P frame is encoded with one previous I or P frame as a reference and a B frame is encoded with one previous and one future frame as references. In one example, a central processing unit (CPU) performs encoding of I frames and P frames of a video and a graphics processing unit (GPU) performs initial encoding of B frames of the video in connection with a fixed function video encoder configured to perform entropy encoding of the B frames.

Abstract: A method and apparatus of seam finding includes determining an overlap area between a first image and a second image. The first image is captured by a first image capturing device and the second image is captured by a second image capturing device. A plurality of seam paths for stitching the first image with the second image is computed and a cost is computed for each seam path. A seam is selected to stitch the first image to the second image based upon the cost for the seam path for that seam being less than a cost for all other computed seam paths, that seam is maintained as the selected seam for stitching based upon a predefined criteria.

Abstract: A processing device is provided which includes memory configured to store data and a processor. The processor is configured to receive a plurality of panoramic video images representing views around a point in a three dimensional (3D) space and warp the plurality of panoramic video images, using a panoramic format, into a plurality of formatted warped images. The processor is also configured to store, in the memory, the plurality of formatted warped images and perform a motion search around each co-located pixel block of a reference panoramic frame by limiting the motion searches in a vertical direction around the co-located pixel blocks.

Abstract: A computer vision processing device is provided which comprises memory configured to store data and a processor. The processor is configured to store captured image data in a first buffer and acquire access to the captured image data in the first buffer when the captured image data is available for processing. The processor is also configured to execute a first group of operations in a processing pipeline, each of which processes the captured image data accessed from the first buffer and return the first buffer for storing next captured image data when a last operation of the first group of operations executes.

Abstract: Described herein is a method and apparatus for using cube mapping and mapping metadata with encoders. Video data, such as 360° video data, is sent by a capturing device to an application, such as video editing software, which generates cube mapped video data and mapping metadata from the 360° video data. An encoder then applies the mapping metadata to the cube mapped video data to minimize or eliminate search regions when performing motion estimation, minimize or eliminate neighbor regions when performing intra coding prediction and assign zero weights to edges having no relational meaning.

Abstract: A method and apparatus of precomputing includes capturing a first image by a first image capturing device. An image space for the first image is defined and pixels in the image space are analyzed for validity. Valid pixels are stored as valid pixel groups and the valid pixel groups are processed.