Samsung Electronics Co., Ltd.

Hunsop Hong

Yeong-Taeg Kim

Seongnam Oh

Berkay Kanberoglu

One embodiment provides a method comprising generating, via an edge detection algorithm, a first edge map based on a first frame of an input content comprising a sequence of frames. The method further comprises generating, via the edge detection algorithm, a second edge map based on a second frame of the input content. The first frame precedes the second frame in the sequence of frames. The method further comprises determining a difference between the first edge map and the second edge map, determining a metric indicative of an estimated amount of judder present in the input content based on the difference, and dynamically adjusting a frame rate of the input content based on the metric. The input content is displayed on a display device at the adjusted frame rate.

Judder detection for dynamic frame rate conversion

One embodiment provides a method comprising acquiring 3D content comprising a 3D object in 3D space. The 3D object has object information indicative of a location of the 3D object in the 3D space. The method further comprises projecting the 3D object to a 2D object in 2D space based on the object information. The 2D object has one or more 2D vertices indicative of a location of the 2D object in the 2D space. The method further comprises determining one or more latent variables in the 2D space based on the object information and the one or more 2D vertices, and generating a 2D feature database including the one or more latent variables.

Two-dimensional (2D) feature database generation

One embodiment provides a method comprising segmenting an input video into a first video chunk and one or more subsequent video chunks. The method further comprises, for each subsequent video chunk, generating a corresponding window of overlapped frames by selecting a subsequence of frames from a different video chunk immediately preceding the subsequent video chunk. The method further comprises generating metadata corresponding to each video chunk by processing each video chunk in parallel. Each subsequent video chunk is processed based in part on a corresponding window of overlapped frames. The method further comprises, for each subsequent video chunk, discarding a portion of metadata corresponding to the subsequent video chunk, where the portion discarded is specific to a corresponding window of overlapped frames. The method further comprises merging each video chunk into a single output video. Each video chunk merged is associated with any remaining corresponding metadata.

Parallel metadata generation based on a window of overlapped frames

A method, apparatus, and computer readable medium for frame rate conversion based on object tracking. The method includes determining whether a block of a first frame of an input video stream corresponds to a detected object region or a non-object region. The method further includes selecting a motion estimation operation from a group of operations comprising an object tracking operation based at least on the determining of whether the block of the first frame corresponds to the detected object region or the non-object region. The method further includes determining a motion vector of the block by performing the selected motion estimation operation and determining an interpolated frame based at least on the determined motion vector and the first frame and a second frame of the input video stream.

Frame rate conversion based on object tracking

A method of operation of a depth estimation system includes: calculating focus measures for positions on a two-dimensional image; generating a depth map for the two-dimensional image based on fitting the focus measure through a Gaussian function; generating a three-dimensional image from the depth map and the two-dimensional image; and processing the three-dimensional image on a storage unit for displaying on a device.

Depth estimation system for two-dimensional images and method of operation thereof

A method of operation of a display system includes: calculating a focus measure for an original image; calculating a segment mean based on the focus measure for a segment; generating an ordered segment based on the segment mean; generating a segment depth based on the ordered segment; and generating a three-dimensional image with the segment depth for displaying on a device.

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Irvine, CA, United States of America

Hunsop Hong