An information processing apparatus includes an acquisition unit configured to acquire a plurality of first captured images acquired in a plurality of first image capturing operations based on a first imaging parameter, and motion information for a subject in the plurality of first captured images, a setting unit configured to set a second imaging parameter, an estimation unit configured to estimate, based on the motion information and the second imaging parameter, a motion blur of a subject in a second captured image acquired when a second image capturing is performed based on the second imaging parameter and a notification unit configured to notify of the motion blur. The motion blur is a statistic of a motion blur in each of the plurality of first captured images.

An image processing method includes: downsizing a current frame and a reference frame; dividing the down-sized current frame and the down-sized reference frame into multiple first current blocks and multiple first reference blocks, respectively; performing a first motion estimation to the first current blocks and the first reference blocks to generate multiple first motion vectors; dividing the current picture and the reference picture into multiple second current blocks and multiple second reference blocks, respectively; performing a second motion estimation to the second current blocks and the second reference blocks to generate multiple second motion vectors; and generating a compensated frame between the current frame and the reference frame according to the second motion vectors. The second motion estimation includes: performing a 3D recursive search for each second current block; and adjusting multiple estimation parameters in the 3D recursive search according to the first motion vector.

[Object] To obtain more useful images when images taken using a fisheye lens are used without being remapped. [Solution] Provided is an image processing device including: an image acquisition unit that acquires taken images taken in chronological succession via a fisheye lens; a vector acquisition unit that acquires motion vectors from the taken images; and a point detection unit that detects a point of origin or a point of convergence of the motion vectors.

[Object] To obtain more useful images when images taken using a fisheye lens are used without being remapped. [Solution] Provided is an image processing device including: an image acquisition unit that acquires taken images taken in chronological succession via a fisheye lens; a vector acquisition unit that acquires motion vectors from the taken images; and a point detection unit that detects a point of origin or a point of convergence of the motion vectors.

There is provided a method and apparatus for motion estimation in a sequence of video images. The method comprises a) subdividing each field or frame of a sequence of video images into a plurality of blocks, b) assigning to each block in each video field or frame a respective set of candidate motion vectors, c) determining for each block in a current video field or frame, which of its respective candidate motion vectors produces a best match to a block in a previous video field or frame, d) forming a motion vector field for the current video field or frame using the thus determined best match vectors for each block, and e) forming a further motion vector field by storing a candidate motion vector derived from the best match vector at a block location offset by a distance derived from the candidate motion vector. Finally, steps a) to e) are repeated for a video field or frame following the current video field or frame. The set of candidate motion vectors assigned at step b) to a block in the following video field or frame includes the candidates stored at that block location at step e) during the current video field or frame The method enables a block or tile based motion estimator to improve its accuracy by introducing true motion vector candidates derived from the physical behaviour of real world objects.

There is provided a method and apparatus for motion estimation in a sequence of video images. The method comprises a) subdividing each field or frame of a sequence of video images into a plurality of blocks, b) assigning to each block in each video field or frame a respective set of candidate motion vectors, c) determining for each block in a current video field or frame, which of its respective candidate motion vectors produces a best match to a block in a previous video field or frame, d) forming a motion vector field for the current video field or frame using the thus determined best match vectors for each block, and e) forming a further motion vector field by storing a candidate motion vector derived from the best match vector at a block location offset by a distance derived from the candidate motion vector. Finally, steps a) to e) are repeated for a video field or frame following the current video field or frame. The set of candidate motion vectors assigned at step b) to a block in the following video field or frame includes the candidates stored at that block location at step e) during the current video field or frame The method enables a block or tile based motion estimator to improve its accuracy by introducing true motion vector candidates derived from the physical behaviour of real world objects.

The architecture shown can perform global search, local search and local sub pixel search in a parallel or in a pipelined mode. All operations are in a streaming mode without the requirement of external intermediate data storage.

An apparatus for stabilizing an image includes: an image analyzing unit analyzing characteristics of each block to calculate a weight of each block; a significant block extracting unit extracting, from the blocks, a block having a weight larger than a threshold value, as a significant block; a feature point extracting unit extracting a feature point from the significant block by extracting pixels of the significant block and comparing at least one of color and brightness between the pixels and neighboring pixels of the pixels; a local motion vector calculating unit calculating local motion vectors of blocks corresponding to the feature point by referring a previous frame of the frame and estimating a motion of the at least one block; a global motion vector calculating unit calculating a global motion vector based on the calculated at least one local motion vector.

An apparatus for stabilizing an image includes: an image analyzing unit analyzing characteristics of each block to calculate a weight of each block; a significant block extracting unit extracting, from the blocks, a block having a weight larger than a threshold value, as a significant block; a feature point extracting unit extracting a feature point from the significant block by extracting pixels of the significant block and comparing at least one of color and brightness between the pixels and neighboring pixels of the pixels; a local motion vector calculating unit calculating local motion vectors of blocks corresponding to the feature point by referring a previous frame of the frame and estimating a motion of the at least one block; a global motion vector calculating unit calculating a global motion vector based on the calculated at least one local motion vector.

The architecture shown can perform global search, local search and local sub pixel search in a parallel or in a pipelined mode. All operations are in a streaming mode without the requirement of external intermediate data storage.