公开(公告)号：US20190012581A1

公开(公告)日：2019-01-10

申请号：US16017742

申请日：2018-06-25

Applicant: Nokia Technologies Oy

Inventor： Mikko HONKALA ,  Francesco CRICRI ,  Xingyang NI

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06K9/72

Abstract: The invention relates to a method comprising receiving a set of input samples, said set of input images comprising real images and generated images; extracting a set of feature maps from multiple layers of a pre-trained neural network for both the real images and the generated images; determining statistics for each feature map of the set of feature maps; comparing statistics of the feature maps for the real images to statistics of the feature maps for the generated images by using a distance function to obtain a vector of distances; and averaging the distances of the vector of distances to have a value indicating a diversity of the generated images. The invention also relates to technical equipment for implementing the method.

公开(公告)号：US20220141455A1

公开(公告)日：2022-05-05

申请号：US17430893

申请日：2020-01-29

Applicant: Nokia Technologies Oy

Inventor： Francesco CRICRI ,  Caglar AYTEKIN ,  Miska HANNUKSELA ,  Xingyang NI

IPC: H04N19/11 ,  H04N19/85 ,  H04N19/159 ,  H04N19/176 ,  G06N3/08 ,  G06N3/04

Abstract: A method comprising: obtaining a configuration of at least one neural network comprising a plurality of intra-prediction mode agnostic layers and one or more intra-prediction mode specific layers, the one or more intra-prediction mode specific layers corresponding to different intra-prediction modes; obtaining at least one input video frame comprising a plurality of blocks; determining to encode one or more blocks using intra prediction; determining an intra-prediction mode for each of said one or more blocks; grouping blocks having same intra-prediction mode into groups, each group being assigned with a computation path among the plurality of intra-prediction mode agnostic and the one or more intra-prediction mode specific layers; training the plurality of intra-prediction mode agnostic and/or the one or more intra-prediction mode specific layers of the neural networks based on a training loss between an output of the neural networks relating to a group of blocks and ground-truth blocks, wherein the ground-truth blocks are either blocks of the input video frame or reconstructed blocks; and encoding a block using a computation path assigned to an intra-prediction mode for the block.