InterDigital CE Patent Holdings Patente

Some embodiments of a method may include: obtaining a description of services provided by a Content Delivery Network (CDN) according to a Media Presentation Description (MPD) file; obtaining a selection of a first service selected from the description of services provided by the CDN; connecting to a session for the first service; obtaining a plurality of content segments corresponding to a first representation for the first service; providing, to an application for playback, one or more of the plurality of content segments corresponding to the first representation; providing, to the CDN, a report of client energy-related information; obtaining an updated description of services provided by the CDN according to an updated MPD file, wherein the updated description of services provided by the CDN was updated based on the report of client energy-related information; and switching to a second representation for the first service.

A method comprising: applying a correction process for correcting an input SDR picture into an output SDR picture intended to be transformed in a HDR picture using metadata, the correction process using a first LUT for transforming a luma component of the input SDR picture in a luma component of the output SDR picture and a second LUT representing a color correction function, the correction process comprising when applied to a current sample of the SDR input picture. The correction process comprises applying (801) a single LUT combining the first LUT and the second LUT to use less hardware resources and reduce the computation complexity.

Systems and methods for point cloud attribute encoding and decoding. An encoding method includes selecting an orientation of a first micropatch centered on a first point of a first point cloud. For each of a plurality of pixels in the first micropatch, a respective point of the first point cloud is assigned to the respective pixel. For each of the pixels having an assigned point, attribute information of the assigned point is associated with the respective pixel. The attribute information of the pixels of at least the first micropatch is encoded.

Systems, methods, and instrumentalities may be configured for an artificial intelligence (Al) based energy aware bitrate ladder. In examples, a computing device may determine tuples associated with a video, wherein each tuple includes a respective image resolution and a respective bitrate associated with the video. The computing device may predict, using an energy prediction machine learning (ML) model, a respective energy consumption associated with providing the video to a receiving device at the image resolution and the bitrate included in each of the tuples. The computing device may select one or more tuples from the tuples based at least on the respective energy consumptions predicted for the tuples. The computing device may convey information regarding the one or more selected tuples to a video encoder.

Improved storage of neural network models is provided by not storing zeros and non-zero model data is retrieved using various methods. In one embodiment, signal sparsity information is used to retrieve the position of the coefficients. In another embodiment, if a model tensor is sparse, packing information is signaled. In another embodiment, data is read according to sparsity information and packing information. In another embodiment, signaling is used at a higher level whether sparsity information is present, or inferred to be true or false.

A method of encoding a texture segmentation in a three-dimensional mesh topology. The method comprising signaling the three-dimensional mesh topology, a texture mapping, and a label mapping in an encoded bitstream, the label mapping including a mapping of pixel coordinates to one or more labels.

A method and device for decoding an encoded data is based on a hybrid implicit neural representation (INR) network comprising a plurality of INR networks arranged in a plurality of hierarchic levels and latent variables being arranged in a plurality of hierarchic levels. The ground truth images are decomposed using Gaussian pyramid to generate the output images at different resolutions, making it possible to learn one synthesis network per resolution for example. The reconstruction process may be performed in a coarse to fine manner, i.e. from the lowest to the highest resolution.

Some embodiments of a method may include: obtaining Avatar JSON Interchange File (AJIF) data; decoding the AJIF data to generate avatar data and associated neural radiance field properties; obtaining user movement data corresponding to a movement of a user; generating updated avatar data based on the user movement data by using a neural networked configured with the neural radiance field properties; and sending the updated avatar data to a client device. Another method may include: generating AJIF data corresponding to an avatar; sending, to an application server, the AJIF data; obtaining user movement data corresponding to a movement of a user; sending, to the application server; receiving, from the application server, scene update data, wherein the scene update data includes updated avatar data corresponding to the movement of the user; and rendering a scene update based on the scene update data.

Some embodiments of a method may include: obtaining a description of services provided by a Content Delivery Network (CDN) according to a Media Presentation Description (MPD) file; obtaining a selection of a first service selected from the description of services provided by the CDN; connecting to a session for the first service; obtaining a plurality of content segments corresponding to a first representation for the first service; providing, to an application for playback, one or more of the plurality of content segments corresponding to the first representation; providing, to the CDN, a report of client energy-related information; obtaining an updated description of services provided by the CDN according to an updated MPD file, wherein the updated description of services provided by the CDN was updated based on the report of client energy-related information; and switching to a second representation for the first service.

Systems and methods are described for encoding and decoding of 3D volumetric data. Volumetric data is encoded and/or decoded using a syntax that allows for different codec types, such as a point-cloud-based codec type (e.g.. V-PCC), a mesh-based codec type (e.g. V-DMC), and a depth-image-based codec type (e.g. MIV). Example encoding and decoding techniques improve bitrates by allowing for syntax functions that are specific to different codec types, such as syntax functions related to atlas coding, to be tailored to signal only parameters that are relevant to the corresponding codec type that is is used for the volumetric coding.

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …