Telefonaktiebolaget Lm Ericsson (publ)

Optis Wireless Technology, LLC

Volodya Grancharov

Sigurdur Sverrisson

Erik Norvell

Sebastian Näslund

Tomas Jansson Toftgård

Harald Pobloth

Jonas Svedberg

Maxim Teslenko

Erlendur Karlsson

Stefan Bruhn

Konstantinos Vandikas

Manish Sonal

José Araújo

Aneta Vulgarakis Feljan

Tomer Amiaz

Alfredo Fanghella

Hadar Gecht

Athanasios Karapantelakis

Mats Folkesson

Willem Bastiaan Kleijn

Lars M Andersson

Elena Fersman

AmirHossein Taher Kouhestani

Alvin Jude Hari Haran

Gunilla Berndtsson

Thalanayar Muthukumar

Charles Kinuthia

Marc Couch

Jiangpeng Tao

Arvind Thimmakkondu Hariraman

Stefano Imoscopi

Tomas Jansson Toftgard

Anders Ekman

Susanna Malm

Quinten Dekker

Steven Combes

A coder and decoder, and methods therein, are provided for coding and decoding of spectral peak positions in audio coding. According to a first aspect, an audio signal segment coding method is provided for coding of spectral peak positions. The method comprises determining which one out of two lossless spectral peak position coding schemes that requires the least number of bits to code the spectral peak positions of an audio signal segment; and selecting the spectral peak position coding scheme that requires the least number of bits to code the spectral peak positions of the audio signal segment. A first one of the two lossless spectral peak position coding schemes is suitable for periodic or semi-periodic spectral peak position distributions; and a second one of two lossless spectral peak position coding schemes is suitable for sparse spectral peak position distributions.

Coding and decoding of spectral peak positions

A method for generating a 3D point cloud of a scene is performed by an image processing device. The method obtains digital images depicting the scene. Each digital image is composed of pixels. The method includes segmenting each of the digital images into digital image segments. The method includes determining a depth vector and a normal vector per each of the digital image segments by applying MVS processing to a subset of the pixels per each digital image segment. The method includes forming a map of depth vectors and normal vectors per each pixel in the digital images by, based on the determined depth and normal vectors per each of the digital image segments, estimating a 3D plane per digital image segment. The method includes generating the 3D point cloud of the scene as a combination of all the maps of depth vectors and normal vectors per each pixel in the digital images.

Generation of a 3D point cloud of a scene

The present disclosure relates to a method of facilitating annotation of an object () in a sequence of images, and a device () performing the method. In a first aspect a method of facilitating annotation of an object () in a sequence of images is provided. The method comprises marking (S) the object () in an image in the sequence of images, performing (S) edge detection on the marked object () in the sequence of images, suppressing (S) visual data () other than the object () being subjected to edge detection in the sequence of images, and adjusting (S) position of a marking () such that the marking is aligned with the object () being subjected to edge detection in subsequent images of the sequence.

Facilitating annotation of objects

A method () for 3D modeling of one or more regions of interest. The method includes obtaining (s) information indicating that a person's gaze is fixed. Advantageously, the method further includes, in response to obtaining the information indicating that the person's gaze is fixed, initiating (s) an active scan of a region of interest (ROI), wherein the ROI is a region in which the person's gaze is directed.

Three-dimensional (3D) modeling

A method () for producing a reduced point cloud comprising an object of interest (OOI) from an original point cloud comprising the OOI. The method includes obtaining (s) the original point cloud; obtaining (s) a set of N images, each of the N images comprising an image of the OOI from a unique position relative to the OOI; for each one of the N images, defining (s) an area of interest in the image that obtaining a set of N images, each of the N images that includes the OOI, thereby defining N areas of interest; for each point included in the original point cloud, determining (s), for each one of the N areas of interest, whether the point is located in the area of interest; for each point included in the original point cloud, determining (s) a first metric for the point based on the total number of the N areas of interest in which the point is determined to be located; and for each point included in the set of points, determining (s) whether or not to include the point in the reduced point cloud based on the first metric for the point.

Three-dimensional modeling

There is provided mechanisms for visual object detection in a sequence of images. A method is performed by a visual object detector (). The method comprises obtaining (S) a sequence of images of a scene. The sequence of images at least comprises a current image of the scene and a previous image of the scene. The method comprises extracting (S) a set of objects from the sequence of images by performing visual object detection in the sequence of images. Performing the visual object detection in at least part of the current image is conditioned on a set of conditions being fulfilled. The set of conditions at least pertains to an image-wise descriptor classification score computed for at least one of the previous image and the current image and pertaining to which type of content the scene comprises, and an image overlapping score pertaining to how much overlap in image area there is between the previous image and the current image. The method comprises constructing (S) an image representation of the scene using the extracted set of objects.

Visual object detection in a sequence of images

A frame error concealment method based on frames including transform coefficient vectors including the following steps: It tracks sign changes between corresponding transform coefficients of predetermined sub-vectors of consecutive good stationary frames. It accumulates the number of sign changes in corresponding sub-vectors of a predetermined number of consecutive good stationary frames. It reconstructs an erroneous frame with the latest good stationary frame, but with reversed signs of transform coefficients in sub-vectors having an accumulated number of sign changes that exceeds a predetermined threshold.

Frame error concealment

A mobile communications device for performing localization and mapping is disclosed, comprising a sensor operative to capture sensor data of its local environment, and operative to derive point-cloud data from the sensor data, which represents structural features of the local environment, determine a pose of the mobile communications device relative to a point-cloud map representing the local environment, derive a concealed representation of the point-cloud data, by applying a current linear transformation to the point-cloud data, and transmit the concealed representation of the point-cloud data to a map server. The current linear transformation is a shared secret, or is derivable from a shared secret, which shared secret is available to a group of mobile communications devices comprising the mobile communications device. Also disclosed are the map sever and a trusted server.

Localization and mapping by a group of mobile communications devices

A method for partitioning of input vectors for coding is presented. The method comprises obtaining of an input vector. The input vector is segmented, in a non-recursive manner, into an integer number, N, of input vector segments. A representation of a respective relative energy difference between parts of the input vector on each side of each boundary between the input vector segments is determined, in a recursive manner. The input vector segments and the representations of the relative energy differences are provided for individual coding. Partitioning units and computer programs for partitioning of input vectors for coding, as well as positional encoders, are presented.

Methods and devices for vector segmentation for coding

An audio signal, having first and second regions of frequency spectrum, is coded. Spectral peaks in the first region are encoded by a first coding method. For a segment of the audio signal, a relation between energy of bands in the first and second regions is determined. A relation between the energy of the band in the second region and energy of neighboring bands in the second region is determined. A determination is made whether available bits are sufficient for encoding at least one non-peak segment of the first region and the band in the second region. Responsive to first and second relations fulfilling a respective predetermined criterion and a sufficient number of bits, encoding the band in the second region using a second coding method different from the first coding method, and otherwise, subjecting the band in the second region to BandWidth Extension BWE or noise fill.

Audio coding method and apparatus

Object tracking for augmented reality involves determining a location of an object in a current frame () of a video stream based on object feature representations extracted from different locations in the current frame () and object feature representations belonging to a model of the object. The model comprises at least one object feature representation extracted from at least one previous frame () of the video stream and at least one object feature representation from a feature set comprising at least one server-generated object feature representation of objects belonging to a same object class as the object.

Object tracking and detection in augmented reality applications

There is provided mechanisms for joint visual object detection and object mapping to a 3D model. A method is performed by an image processing device. The method includes obtaining a first sequence of digital images of a scene as captured by a first image capturing unit, and obtaining a second sequence of digital images of the scene as captured by a second image capturing unit. The second sequence of digital images is time-wise synchronized with the first sequence of digital images by being captured time-wise in parallel with the first sequence of digital images. The first image capturing unit has a narrower field of view than the field of view of the second image capturing unit. The first image capturing unit and the second image capturing unit have a known spatial relation. The method includes performing joint visual object detection and object mapping to the 3D model.

Joint visual object detection and object mapping to a 3D model

A gain adjustment apparatus for use in decoding of audio that has been encoded with separate gain and shape representations includes an accuracy meter configured to estimate an accuracy measure of the shape representation, and to determine a gain correction based on the estimated accuracy measure. An envelope adjuster further included in the apparatus is configured to adjust the gain representation based on the determined gain correction.

Post-quantization gain correction in audio coding

There is provided mechanisms for identifying decreasing object detectability for a video stream. A method is performed by a controller. The method includes receiving the video stream over a communication channel. The method includes receiving first visual object detection (VOD) data of the video stream. The first VOD data has been obtained from the video stream before is sent over the communication channel towards the controller. The method includes obtaining second VOD data of the video stream. The second VOD data is obtained from the video stream after is received by the controller. The method includes determining, by comparing the first VOD data to the second VOD data, whether any object present in the first VOD data has a decreased object detectability in the second VOD data, thereby identifying the decreasing object detectability for the video stream. The method includes issuing a notification when the decreasing object detectability has been identified.

Identification of decreased object detectability for a video stream

An encoder for encoding a parametric spectral representation (f) of auto-regressive coefficients that partially represent an audio signal. The encoder includes a low-frequency encoder configured to quantize elements of a part of the parametric spectral representation that correspond to a low-frequency part of the audio signal. It also includes a high-frequency encoder configured to encode a high-frequency part (f) of the parametric spectral representation (f) by weighted averaging based on the quantized elements ({circumflex over (f)}) flipped around a quantized mirroring frequency ({circumflex over (f)}), which separates the low-frequency part from the high-frequency part, and a frequency grid determined from a frequency grid codebook in a closed-loop search procedure. Described are also a corresponding decoder, corresponding encoding/decoding methods and UEs including such an encoder/decoder.

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Solna, Sweden

Volodya Grancharov