Telecom Italia S.p.a.

Ateme

Attilio Fiandrotti

Gianluca Francini

Thomas Guionnet

Skjalg Lepsoy

Mohsen Abdoli

Anthony Nasrallah

Marco Cagnazzo

Enzo Tartaglione

Skjalg LEPSØY

A method includes providing a neural network having a set of weights. The neural network receives an input data structure for generating a corresponding output array according to values of the set of weights. The neural network is trained to obtain a trained neural network. The training includes setting values of the set of weights with a gradient descent algorithm which exploits a cost function including a loss term and a regularization term. The trained neural network is deployed on a device through a communication network, and used by the device. The regularization term is based on a rate of change of elements of the output array caused by variations of the set of weights values.

Neural networks having reduced number of parameters

A method of processing a first image in a first plurality of images, wherein the first image is divided into a plurality of pixel blocks, is proposed, which comprises, for a current block of the first image: selecting, in a set of a plurality of predefined interpolation filters, an interpolation filter based on a prediction of an interpolation filter determined by a supervised learning algorithm to which data related to the current block is input; and using the selected interpolation filter for calculating fractional pixel values in a second image of the plurality of images for a temporal prediction of pixels of the current block based on a reference block correlated to the current block in the second image, wherein the second image is distinct from the first image and was previously encoded according to an image encoding sequence for encoding the images of the plurality of images.

Method for image processing and apparatus for implementing the same

Growing community of inventors

Turin, Italy

Attilio Fiandrotti