The present disclosure is directed to techniques for determining a perceptual importance map. The perceptual importance map indicates the relative importance to the human visual system of different portions of an image. The techniques include obtaining cost values for the blocks of an image, where cost values are values used in determining motion vectors. For each block, a confidence value is derived from the cost values. The confidence value indicates the confidence with which the motion vector is believed to be correct. A perceptual importance value is determined based on the confidence value via one or more modifications to the confidence value to better reflect importance to the human visual system. The generated perceptual importance values can be used for various purposes such as allocating bits for encoding, identifying regions of interest, or selectively rendering portions of an image with greater or lesser detail based on relative perceptual importance.

The present disclosure relates to a motion compensation method and module, a chip, an electronic device, and a storage medium, to improve the problem of haloes easily appearing on the edges of moving objects.

Apparatuses, systems, and techniques to interpolate one or more intermediate images from two or more images is disclosed. In at least one embodiment, a processor includes one or more circuits to interpolate one or more intermediate images from two or more images based, at least in part, on one or more inconsistent flow vectors corresponding to the two or more images.

The present disclosure relates to a method of motion segmentation (100) in a video stream. The method comprises the steps of: acquiring (101) a sequence of image frames; dividing (102) a first frame (401) into a plurality of image blocks (403); comparing (103) each image block (403) against a corresponding reference image block (404) and providing a measure of dissimilarity; for image blocks having a measure of dissimilarity less than a threshold: discarding (104a) the image blocks, and for image blocks having a measure of dissimilarity greater than the threshold: keeping (104b) the image blocks and further dividing the image blocks into a new plurality of image blocks (405); repeating the steps of dividing (102) and comparing (103) until a stop condition is met (105a); generating (106) a motion mask (407) indicating areas of movement (408).

The present disclosure relates to a method of motion segmentation (100) in a video stream. The method comprises the steps of: acquiring (101) a sequence of image frames; dividing (102) a first frame (401) into a plurality of image blocks (403); comparing (103) each image block (403) against a corresponding reference image block (404) and providing a measure of dissimilarity; for image blocks having a measure of dissimilarity less than a threshold: discarding (104a) the image blocks, and for image blocks having a measure of dissimilarity greater than the threshold: keeping (104b) the image blocks and further dividing the image blocks into a new plurality of image blocks (405); repeating the steps of dividing (102) and comparing (103) until a stop condition is met (105a); generating (106) a motion mask (407) indicating areas of movement (408).

In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a plurality of images captured by a camera. The processor: accesses visual data associated with an image captured by the camera; determines a tile size parameter for partitioning the visual data into a plurality of tiles; partitions the visual data into the plurality of tiles based on the tile size parameter, wherein the plurality of tiles corresponds to a plurality of regions within the image; compresses the plurality of tiles into a plurality of compressed tiles, wherein each tile is compressed independently; generates a tile-based representation of the image, wherein the tile-based representation comprises an array of the plurality of compressed tiles; and stores the tile-based representation of the image on the storage device.

A method for tracking includes obtaining an infrared image and a visible image from an imaging device supported by a carrier of an unmanned aerial vehicle (UAV), combining the infrared image and the visible image to obtain a combined image, identifying a target in the combined image, and controlling at least one of the UAV, the carrier, or the imaging device to track the identified target. Combing the infrared image and the visible image includes matching the infrared image and the visible image based on matching results of different matching methods.

A method for tracking includes obtaining an infrared image and a visible image from an imaging device supported by a carrier of an unmanned aerial vehicle (UAV), combining the infrared image and the visible image to obtain a combined image, identifying a target in the combined image, and controlling at least one of the UAV, the carrier, or the imaging device to track the identified target. Combing the infrared image and the visible image includes matching the infrared image and the visible image based on matching results of different matching methods.

An image processing device includes a three-dimensional noise reduction (3D NR) circuit, an artificial intelligence noise reduction (AI NR) circuit, a weight determination circuit and an image blending circuit. The 3D NR circuit performs a 3D NR operation on input image data to generate first image data. The AI NR circuit performs an AI NR operation on the input image data to generate second image data. The weight determination circuit outputs a blending weight according to a motion index. The image blending circuit blends the first image data and the second image data according to the blending weight to generate output image data.

An image processing device includes a three-dimensional noise reduction (3D NR) circuit, an artificial intelligence noise reduction (AI NR) circuit, a weight determination circuit and an image blending circuit. The 3D NR circuit performs a 3D NR operation on input image data to generate first image data. The AI NR circuit performs an AI NR operation on the input image data to generate second image data. The weight determination circuit outputs a blending weight according to a motion index. The image blending circuit blends the first image data and the second image data according to the blending weight to generate output image data.