Nokia Technologies Oy

Alireza Zare

Miska Matias Hannuksela

Jani Lainema

Francesco Cricri

Emre Baris Aksu

Alireza Aminlou

Yat Hong Lam

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 and a decoding, and apparatuses for the same. The method for encoding comprises receiving a block of a video frame for encoding (); making a decision on whether or not a learning-based model is to be applied as a processing step for encoding the block (); applying the learning-based model for said input block according to the decision, where the learning-based model has been selectively fine-tuned according to information relating to activation of the learning-based model of previously-decoded blocks (); encoding a signal corresponding to the decision on usage of the learning-based model into a bitstream (); and encoding the block into a bitstream with an information whether the block is to be used for finetuning ().

Optimization of a neural network, for example in a video codec at the decoder side, may be guided to limit overfitting. The encoder may encode video(s) with different qualities for different frames in the video. Low-quality frames may be used as both input and ground-truth during optimization. High-quality frames may be used to optimize the neural network so that higher-quality versions of lower-quality inputs may be predicted. The neural network may be trained to make such predictions by making a prediction based on a constructed low-quality input for which the corresponding high-quality version is known, comparing the prediction to the high-quality version, and fine-tuning the neural network to improve its ability to predict a high-quality version of a low-quality input. To limit overfitting, the neural network may be concurrently or in an alternating fashion trained with low-quality input for which a higher-quality version of the low-quality input is known.

Guiding decoder-side optimization of neural network filter

A method comprising obtaining a full-picture track or bitstream including a motion-constrained tile set; and constructing a full-picture-compliant tile set track or bitstream on the basis of the motion-constrained tile set or generating instructions to construct a full-picture-compliant tile set track or bitstream on the basis of the motion-constrained tile set.

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Tampere, Finland

Alireza Zare