Nokia Technologies Oy

Nokia Corporation

Core Wireless Licensing S.a.r.l.

Microsoft Technology Licensing, LLC

Emre Baris Aksu

Miska Matias Hannuksela

Igor Danilo Diego Curcio

Sujeet Shyamsundar Mate

Francesco Cricri

Kashyap Kammachi Sreedhar

Hamed Rezazadegan Tavakoli

Igor D Curcio

Lukasz Kondrad

Francesco Cricrì

Jani Lainema

Yu You

David Leon

Honglei Zhang

Sebastian Schwarz

Saba Ahsan

Ari Hourunranta

Peter O Fasogbon

Miikka Lundan

Ru-Shang Wang

Kashyap Kammachi-Sreedhar

Ye-Kui Wang

Mikko Johannes Honkala

Ville-Veikko Mattila

Lauri Ilola

Lauri Aleksi Ilola

Viktor Varsa

Xingyang Ni

Nam Le

Rod Walsh

Caglar Aytekin

Alireza Zare

Harsh Mehta

Jonne Juhani Mäkinen

Juha-Pekka Hippeläinen

Marta Karczewicz

Ossi Kalevo

Kimmo Tapio Roimela

Antti Hallapuro

Vinod Kumar Malamal Vadakital

Iraj Saniee

Lixin Fan

Miika Sakari Tupala

Joachim Wabnig

Alireza Aminlou

Keith D Miller

Mika Pesonen

Ville Timonen

Johannes Pystynen

Ramin Ghaznavi Youvalari

Nannan Zou

Igor D D Curcio

Goutham Rangu

Johannes Rajala

Olli Kilpelainen

Jani Kattelus

Igor Danilo Curcio

Peter Oluwanisola Fasogbon

Miika Tupala

Igor DD Curcio

An apparatus comprising: at least one processor; and at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: indicate a mixed network abstraction layer unit type pictures sample group used to merge video base tracks having a subpicture track; indicate a sample group description entry of the mixed network abstraction layer unit type pictures sample group, the sample group description entry indicating a group of pairs of mixed network abstraction layer unit type track reference indices, which reference video subpicture tracks or track groups; and wherein when a video bitstream is resolved from a video base track containing a mixed network abstraction layer unit type sample group with merging pairs of video subpicture tracks signaled in a mixed network abstraction layer unit type pictures sample group entry, then there is mixing of different network abstraction layer unit types.

Coded picture with mixed VCL NAL unit type

Apparatuses, methods, and computer programs for compressing a neural network are disclosed. An apparatus includes at least one processor; and at least one non-transitory memory including computer program code, the memory and the computer program code configured to, with the at least one processor, cause the apparatus to: receive information from a second device, where the information comprises at least one parameter configured to be used for compression of a neural network, where the at least one parameter is in regard to at least one first aspect or task of the neural network; and compress the neural network, where the neural network is compressed based, at least partially, upon the at least one parameter received from the second device. The apparatus may also receive a compressed neural network from the second device, and further compress the compressed neural network based on the information.

High-level syntax for priority signaling in neural network compression

Various embodiments provide an apparatus, a method, and a computer program product. The apparatus includes at least one processor, and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform: define or extend a file format data structure to comprise: a cell size to indicate dimensions of a region, in pixels, comprising a mask value; a mask coding method to indicate a coding method applied to a mask or data of a media; mask coding parameters to indicate additional encoding or decoding parameters needed to process the coded mask data; and a mask configuration comprising the mask related information as a mask configuration structure.

Method and apparatus for signaling of regions and region masks in image file format

The embodiments relate to method for encoding and decoding, wherein the method for encoding comprises receiving an input block of a video frame for encoding; applying at least a learning-based model () for said input block as a processing step for encoding the block; combining () an output of a learning-based model with one or more data sources () by a combination process; encoding block to a bitstream (); using a result of the combination process as additional input for the learning-based model for encoding a subsequent block; and encoding to a bitstream combination information () used in the combination process, said combination information comprising at least one or more combination parameters. The embodiments also relate to technical equipment for implementing the methods.

Method, an apparatus and a computer program product for video encoding and video decoding

The embodiments relate to a method for encoding two or more tensors. The method comprises processing the two or more tensors having respective dimensions so that the dimensions of said two or more sensors have the same number (); identifying which axis of each individual tensor is swappable to result in concatenable tensors around an axis of concatenation (); reshaping the tensors so that the dimensions are modified based on the swapped axis (); concatenating the tensors around the axis of concatenation to result in concatenated tensor (); compressing the concatenated tensor (); generating syntax structures for carrying concatenation and axis swapping information (); and generating a bitstream by combining the syntax structures and the compressed concatenated tensor (). The embodiments also relate to a method for decoding, and to apparatuses for implementing the methods.

Method, an apparatus and a computer program product for neural network compression

A method comprising obtaining a 360-degree video content from a video source; projecting the 360-degree video content onto a 2D image plane; dividing the projected 360-degree video content into a plurality of regions, wherein the regions are partly overlapping and each region covers a region of the 360-degree video content suitable for a viewport presentation; receiving a request for a viewport orientation of the 360-degree video content from a client; and providing the client with an viewport presentation of the region corresponding to the requested viewport orientation.

Method, an apparatus and a computer program product for video conferencing

There is disclosed an apparatus and a method for spatial computing service session description for volumetric XR conversation. In accordance an embodiment the method comprises receiving volumetric video from a plurality of sources, the volumetric video data comprising color images and depth images representing at least a part of a scene; determining which of the plurality of sources belong to a same group; selecting a group identifier for the group; associating the volumetric video with the group identifier indicative of from which group the volumetric video data is received; associating the volumetric video with a source identifier indicative of from which source of the group the volumetric video data is received; associating the color image of the volumetric video with a color data identifier; and associating the depth image of the volumetric video with a depth data identifier.

Method, an apparatus and a computer program product for spatial computing service session description for volumetric extended reality conversation

In example embodiments, an apparatus, a method, and a computer program product are provided. The apparatus comprises at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform determine a processing order of building blocks to encode or decode a media item; and determine a number of processing steps required to encode or decode the media item; wherein the processing order of building blocks and the number of processing steps are determined based on a content of the media item by using a guided probability model based on a neural network.

Guided probability model for compressed representation of neural networks

An apparatus includes at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: estimate an importance of parameters of a neural network based on a graph diffusion process over at least one layer of the neural network; determine the parameters of the neural network that are suitable for pruning or sparsification; remove neurons of the neural network to prune or sparsify the neural network; and provide at least one syntax element for signaling the pruned or sparsified neural network over a communication channel, wherein the at least one syntax element comprises at least one neural network representation syntax element.

Graph diffusion for structured pruning of neural networks

A method is provided for defining a metadata box of a neural network representation (NNR) item data, wherein the NNR item data comprises an NNR bitstream; and defining an association between the NNR item data and an NNR configuration by using a configuration item property, wherein the NNR configuration item property comprises information about stored NNR item data. Corresponding apparatuses and computer program products are also provided.

Carriage and signaling of neural network representations

A method, apparatus and computer program product are provided to signaling multiple media data types in a container file format, for example, ISOBMFF, in an efficient manner. In the context of a signaling method, media type definitions overloaded in an instance a plurality of media type definitions is present in compatible media type list, wherein a media type definition is associated with a media type. The method then indicates, using the compatible media type list, presence of a plurality of media types in a metadata or multimedia box. Corresponding apparatuses and computer program products are also provided.

Method and apparatus for signaling multiple media data types

There are disclosed various methods, apparatuses and computer program products for video encoding and decoding. In some embodiments the method for video encoding comprises obtaining compressed volumetric video data representing a three-dimensional scene or object (); capsulating the compressed volumetric video data into a data structure (); obtaining data of a two-dimensional projection of at least a part of the three-dimensional scene as seen from a certain viewport (); and including the data of the two-dimensional projection into the data structure ().

Apparatus, a method and a computer program for omnidirectional video

Example embodiments provide a system for training a data coding pipeline including a feature extractor neural network, an encoder neural network, and a decoder neural network configured to reconstruct input data based on encoded features. A plurality of losses corresponding to different tasks may be determined for the coding pipeline. Tasks may be performed based on an output of the coding pipeline. A weight update may be determined for at least a subset of the coding pipeline based on the plurality of losses. The weight update may be configured to reduce a number of iterations for fine-tuning the coding pipeline for one of the tasks. This enables faster adaptation of the coding pipeline for one of the tasks after deployment of the coding pipeline. Apparatuses, methods, and computer programs are disclosed. Apparatuses, methods, and computer programs are disclosed.

Training a data coding system comprising a feature extractor neural network

The embodiments relate to a method comprising establishing a three-dimensional conversational interaction with one or more receivers; generating a pointcloud relating to a user and capturing audio from one or more audio source; generating conversational scene description comprising at least a first dynamic object describing a virtual space for the three-dimensional conversational interaction, wherein the first dynamic object refers to one or more objects specific to the three-dimensional conversational interaction, wherein said one or more objects comprises at least data relating to transformable pointcloud; audio obtained from said one or more audio source and input obtained from one or more connected devices controlling at least the pointcloud, wherein said objects are linked to each other for seamless manipulation; applying the conversational scene description into a metadata, and transmitting the metadata with the respective audio in realtime to said one or more receivers.

A method, apparatus and computer program product are provided to include information within a container that also includes a video bitstream as to whether individual image items corresponding to the video frames of the video bitstream bitstreams are self-decodable or, alternatively, are dependent upon one or more other image items. In an instance in which a respective image item is dependent upon one or more other image items, the method, apparatus and computer program product also include dependence information within the container identifying the other image item(s)upon which the decodability of the respective image item is dependent. As such, the method, apparatus and computer program product permit decoding relationships to be defined in the container between a predictively coded frame and other image item(s)upon which the predictively coded frame is dependent, thereby facilitating the decoding of the frame and, in turn, the video bitstream.

Method and apparatus for storing and signaling predictively coded image items

Growing community of inventors

Tampere, Finland

Emre Baris Aksu