Abstract: Disclosed is a method that includes detecting a beginning of a movement of a user's gaze by processing gaze-tracking data, collected by a gaze-tracking means; predicting a motion blur in an image which is to be captured by at least one camera during the movement of the user's gaze, using a portion of the gaze-tracking data that corresponds to the beginning of the movement of the user's gaze; and compensating for the predicted motion blur while capturing the image by controlling the at least one camera.

Abstract: A method incorporating spatially-defined video-see-through (VST) modifications. includes receiving VST images (300a-c) of a real-world environment captured using at least one VST camera (208a-b) and depth data corresponding to the VST images; receiving a pose of at least one object present in the environment; analysing the VST images, depth data, and pose of the object(s), for identifying in a given VST image, change(s) in at least one spatial volume surrounding the object(s); determining a range of optical depth values for each pixel of the given VST image, by projecting the spatial volume(s) onto image coordinates; determining whether an optical depth value of a pixel of the given VST image lies within the range of optical depth values determined for the pixel; and applying image processing operation(s) on the pixel, when said determination is true.

Abstract: Disclosed is method for exposure correction. The method includes receiving at least one image; defining a low-resolution brightness map, associated with the at least one image, corresponding to an ambient lighting condition in which the at least one image is captured; and correcting an exposure in the at least one image, to restore at least one inaccurately-illuminated area of the at least one image, by guiding the at least one image based on the low-resolution brightness map, wherein the at least one inaccurately-illuminated area is at least one of: at least one under-illuminated area of the at least one image, at least one over-illuminated area of the at least one image.

Abstract: Disclosed is computer-implemented method including marching first ray and second ray, along first gaze direction and second gaze direction that are estimated using gaze-tracking means, from given viewpoint into depth map, to determine first optical depth and second optical depth corresponding to first eye and second eye, respectively; calculating gaze convergence distance, based on first gaze direction and second gaze direction; detecting whether first optical depth lies within predefined threshold percent from second optical depth; and when it is detected that first optical depth lies within predefined threshold percent from second optical depth, selecting given focus distance as an average of at least two of: first optical depth, second optical depth, gaze convergence distance; and employing given focus distance for capturing given image using at least one variable-focus camera.

Abstract: Disclosed is a video transport method including receiving high dynamic range (HDR) video content comprising HDR images; performing HDR mastering for HDR images according to HDR mastering parameters; transforming an input colour space of each HDR image amongst HDR images into a transport colour space, and transporting HDR images having transport colour space. Herein, transforming an input colour space of HDR images into transport colour space is performed by: plotting colour component values (CCVs) of pixels in a first HDR image amongst a given set of HDR images, across a multi-dimensional colour space; identifying a bounding box corresponding to the first HDR image, wherein the bounding box encloses CCVs of two or more colour components represented in multi-dimensional colour space; determining a transformation to be applied to input colour space of the given set of HDR images; applying transformation to each HDR image in given set of HDR images.

Abstract: Disclosed is a method of optimizing image rendering implemented in at least one display apparatus that includes predicting a pose and a first gaze location of a user for a display target time; rendering an image frame based on the predicted pose and the first gaze location; determining a second gaze location after rendering of the image frame; adjusting the rendered image frame such that an image area of the rendered image frame under the first gaze location and its surrounding region is moved to the second gaze location; and displaying the adjusted image frame on the at least one display apparatus.

Abstract: A computer-implemented method including: capturing at least one image using at least one video-see-through camera of a display apparatus; determining a pose of the at least one VST camera from which the at least one image is captured; identifying image segments in the at least one image that represent different real-world objects in a real-world environment; generating a set of two-dimensional (2D) image masks corresponding to the image segments representing the different real-world objects, wherein a given 2D image mask corresponds to a given real-world object; and digitally projecting the 2D image masks of the set onto a three-dimensional (3D) model of the real-world environment, from a perspective of the pose of the at least one VST camera, to determine at least one of: 3D shapes, 3D locations in the real-world environment, 3D orientations, 3D sizes, of the different real-world objects.

Abstract: Disclosed is method for exposure correction. The method includes receiving at least one image; defining a low-resolution brightness map, associated with the at least one image, corresponding to an ambient lighting condition in which the at least one image is captured; and correcting an exposure in the at least one image, to restore at least one inaccurately-illuminated area of the at least one image, by guiding the at least one image based on the low-resolution brightness map, wherein the at least one inaccurately-illuminated area is at least one of: at least one under-illuminated area of the at least one image, at least one over-illuminated area of the at least one image.

Abstract: Disclosed is a video transport method including receiving high dynamic range (HDR) video content comprising HDR images; performing HDR mastering for HDR images according to HDR mastering parameters; transforming an input colour space of each HDR image amongst HDR images into a transport colour space, and transporting HDR images having transport colour space. Herein, transforming an input colour space of HDR images into transport colour space is performed by: plotting colour component values (CCVs) of pixels in a first HDR image amongst a given set of HDR images, across a multi-dimensional colour space; identifying a bounding box corresponding to the first HDR image, wherein the bounding box encloses CCVs of two or more colour components represented in multi-dimensional colour space; determining a transformation to be applied to input colour space of the given set of HDR images; applying transformation to each HDR image in given set of HDR images.

Abstract: Disclosed is head-mounted display (HMD) apparatus with an ambient light sensor (ALS), tracking camera (TC), video-see-through (VST) camera, and processor(s) configured to: capture average red-green-blue (RGB) illuminance (A) of region of real-world environment (RE) from HMD pose; capture grayscale image (GI) of RE from HMD pose, wherein pixel values indicate intensities (i); calculate average TC intensity (I) for part of FOV of TC that overlaps with FOV of ALS, based on intensities (i); calculate average illuminance (J) for said part of FOV of TC that overlaps with FOV of ALS, based on intensities (i) and pre-determined response (r) of ALS; calculate corrected average RGB illuminance (AC) for region from HMD pose, based on A and ratio of I and J; and determine RGB illuminance for each pixel of GI, based on AC, ratio of intensity (i) of said pixel in GI to I.

Abstract: Disclosed is a method including determining a gaze point and a gaze depth; controlling camera(s) for capturing a real-world image, by adjusting camera settings according to the gaze point and the gaze depth; determining a pose of the camera(s) at a time of capturing the real-world image; identifying region(s) of the real-world environment represented in the real-world image; determining whether a representation of region(s) satisfies quality criteria; when the representation fails to satisfy the quality criteria, capturing a reference real-world image such that the representation fulfills the quality criteria; generating training data comprising reference data and input data wherein reference data comprises reference real-world image, and input data with real-world image and/or previously-captured real-world image; sending training data to a processor to train a first neural network.

Abstract: Disclosed is a method that includes detecting a beginning of a movement of a user's gaze by processing gaze-tracking data, collected by a gaze-tracking means; predicting a motion blur in an image which is to be captured by at least one camera during the movement of the user's gaze, using a portion of the gaze-tracking data that corresponds to the beginning of the movement of the user's gaze; and compensating for the predicted motion blur while capturing the image by controlling the at least one camera.

Abstract: Disclosed is an apparatus, including an image sensor to capture an image with a first number of pixels; execute a sub-sampling during the capture of the image to store a sub-sampled input image comprising a second number of pixels less than the first number of pixels. A processor configured to execute a pre-trained neural network model on the sub-sampled input image to detect one or more memory features in the sub-sampled input image and reconstruct missing or sub-sampled pixels corresponding to the detected one or more memory features in the sub-sampled input image; and generate an output image with enhanced one or more memory features present in a legible form.

Abstract: Disclosed is head-mounted display (HMD) apparatus with an ambient light sensor (ALS), tracking camera (TC), video-see-through (VST) camera, and processor(s) configured to: capture average red-green-blue (RGB) illuminance (A) of region of real-world environment (RE) from HMD pose; capture grayscale image (GI) of RE from HMD pose, wherein pixel values indicate intensities (i); calculate average TC intensity (I) for part of FOV of TC that overlaps with FOV of ALS, based on intensities (i); calculate average illuminance (J) for said part of FOV of TC that overlaps with FOV of ALS, based on intensities (i) and pre-determined response (r) of ALS; calculate corrected average RGB illuminance (AC) for region from HMD pose, based on A and ratio of I and J; and determine RGB illuminance for each pixel of GI, based on AC, ratio of intensity (i) of said pixel in GI to I.

Abstract: Disclosed is computer-implemented method including marching first ray and second ray, along first gaze direction and second gaze direction that are estimated using gaze-tracking means, from given viewpoint into depth map, to determine first optical depth and second optical depth corresponding to first eye and second eye, respectively; calculating gaze convergence distance, based on first gaze direction and second gaze direction; detecting whether first optical depth lies within predefined threshold percent from second optical depth; and when it is detected that first optical depth lies within predefined threshold percent from second optical depth, selecting given focus distance as an average of at least two of: first optical depth, second optical depth, gaze convergence distance; and employing given focus distance for capturing given image using at least one variable-focus camera.

Abstract: A method including: receiving visible-light images captured using camera(s) and depth data corresponding to said images; identifying image segments of visible-light image that represent objects or their parts belonging to different material categories; detecting whether at least two adjacent image segments in visible-light image pertain to at least two different material categories related to same object category; and when it is detected that at least two adjacent image segments pertain to at least two different material categories related to same object category, identifying at least two adjacent depth segments of depth data corresponding to at least two adjacent image segments; and correcting errors in optical depths represented in at least one of at least two adjacent depth segments, based on optical depths represented in remaining of at least two adjacent depth segments.

Abstract: A method including: receiving visible-light images captured using camera(s) and depth data corresponding to said images; identifying image segments of visible-light image that represent objects or their parts belonging to different material categories; detecting whether at least two adjacent image segments in visible-light image pertain to at least two different material categories related to same object category; and when it is detected that at least two adjacent image segments pertain to at least two different material categories related to same object category, identifying at least two adjacent depth segments of depth data corresponding to at least two adjacent image segments; and correcting errors in optical depths represented in at least one of at least two adjacent depth segments, based on optical depths represented in remaining of at least two adjacent depth segments.

Abstract: A method including: receiving visible-light images captured using camera(s) and depth data corresponding to said images; identifying image segments of visible-light image that represent objects or their parts belonging to different material categories; detecting whether at least two adjacent image segments in visible-light image pertain to at least two different material categories related to same object category; and when it is detected that at least two adjacent image segments pertain to at least two different material categories related to same object category, identifying at least two adjacent depth segments of depth data corresponding to at least two adjacent image segments; and correcting errors in optical depths represented in at least one of at least two adjacent depth segments, based on optical depths represented in remaining of at least two adjacent depth segments.

Abstract: Disclosed is a computer-implemented method comprising: tracking positions and orientations of devices (104, 106, 204a-204f, A-F, 402, 404) within real-world environment (300), each device comprising active sensor(s) (108, 110, 206a-206f); classifying devices into groups, based on positions and orientations of devices within real-world environment, wherein a group has devices whose active sensors are likely to interfere with each other; and controlling active sensors of devices in the group to operate by employing multiplexing.

Abstract: An imaging system including visible-light camera(s), depth sensor(s), pose-tracking means, and server(s) configured to: control visible-light camera(s) and depth sensor(s) to capture visible-light images and depth images of real-world environment, respectively, whilst processing pose-tracking data to determine poses of visible-light camera(s) and depth sensor(s); reconstruct three-dimensional lighting model of real-world environment representative of lighting in different regions of real-world environment; receive, from client application, request message comprising information indicative of location in real-world environment where virtual object(s) is to be placed; utilise three-dimensional lighting model to create sample lighting data for said location, wherein sample lighting data is representative of lighting at given location in real-world environment; and provide client application with sample lighting data.