Abstract: Techniques for partitioning data flow operations between execution on a compute instance and an attached accelerator instance are described. A set of operations supported by the accelerator is obtained. A set of operations associated with the data flow is obtained. An operation in the set of operations associated with the data flow is identified based on the set of operations supported by the accelerator. The accelerator executes the first operation.

Abstract: An example apparatus includes a kernel size estimator to estimate a blur kernel size of a captured image. The example apparatus also further includes a frame checker to discard the captured image if the estimated blur kernel size of the captured images exceeds a threshold kernel size. The captured image is to be used in a set if the estimated blur kernel size does not exceed the threshold kernel size. The example apparatus further includes a composite generator to generate a composite image based on the set of images.

Abstract: An example apparatus includes a kernel size estimator to estimate a blur kernel size of a captured image. The example apparatus also further includes a frame checker to discard the captured image if the estimated blur kernel size of the captured images exceeds a threshold kernel size. The captured image is to be used in a set if the estimated blur kernel size does not exceed the threshold kernel size. The example apparatus further includes a composite generator to generate a composite image based on the set of images.

Abstract: Video stabilization is described using content-aware camera motion estimation. In some versions a luminance target frame and a luminance source frame of a sequence of video frames of a scene are received. Motion is extracted from the received luminance target and source frames and the motion is represented as a motion vector field and weights. The weights are divided into a first set of zeros weights for motion in the motion vector field that is near zero motion and a second set of peak weights for motion in the motion field that is not near zero. The zeros weights are compared to a threshold to determine whether there is motion in the scene and if the zeros weights exceed the threshold then selecting a zero motion motion model. A frame of the video sequence is adjusted corresponding to the target frame based on the selected motion model.

Abstract: Video stabilization is described using content-aware camera motion estimation. In some versions a luminance target frame and a luminance source frame of a sequence of video frames of a scene are received. Motion is extracted from the received luminance target and source frames and the motion is represented as a motion vector field and weights. The weights are divided into a first set of zeros weights for motion in the motion vector field that is near zero motion and a second set of peak weights for motion in the motion field that is not near zero. The zeros weights are compared to a threshold to determine whether there is motion in the scene and if the zeros weights exceed the threshold then selecting a zero motion motion model. A frame of the video sequence is adjusted corresponding to the target frame based on the selected motion model.

Abstract: Methods, apparatus, systems and articles of manufacture to perform block-based static region detection for video processing are disclosed. Disclosed example video processing methods include segmenting pixels in a first frame of a video sequence into a first plurality of pixel blocks. Such example methods can also include processing the first plurality of pixel blocks and a second plurality of pixel blocks corresponding to a prior second frame of the video sequence to create, based on a first criterion, a map identifying one or more static pixel blocks in the first plurality of pixel blocks. Such example methods can further include identifying, based on the map, a static region in the first frame of the video sequence.

Abstract: Techniques related to managing the use of motion estimation in video processing are discussed. Such techniques may include determining dividing two video frames each into corresponding regions, generating phase plane correlations for the corresponding regions, determining whether the video frames are motion estimation correlated based on the phase plane correlations, and providing a video frame prediction mode indicator based on the determination.

Abstract: Methods, apparatus, systems and articles of manufacture to perform block-based static region detection for video processing are disclosed. Disclosed example video processing methods include segmenting pixels in a first frame of a video sequence into a first plurality of pixel blocks. Such example methods can also include processing the first plurality of pixel blocks and a second plurality of pixel blocks corresponding to a prior second frame of the video sequence to create, based on a first criterion, a map identifying one or more static pixel blocks in the first plurality of pixel blocks. Such example methods can further include identifying, based on the map, a static region in the first frame of the video sequence.

Abstract: Techniques related to managing the use of motion estimation in video processing are discussed. Such techniques may include determining dividing two video frames each into corresponding regions, generating phase plane correlations for the corresponding regions, determining whether the video frames are motion estimation correlated based on the phase plane correlations, and providing a video frame prediction mode indicator based on the determination.

Abstract: Systems, apparatus, articles, and methods are described related to motion estimation using hierarchical phase plane correlation and block matching.