Parallelized video decoding using a neural network
In a method for decoding a data stream by way of an electronic device ( 10 ) including a processor ( 14 ), and a parallelized processing unit ( 16 ) designed to perform a plurality of operations of the same type in parallel at a given time, the data stream includes a first dataset (Fet) and a second dataset (Fnn) representative of audio or video content. The decoding method includes the processor ( 14 ) processing data from the first dataset (Fet), obtaining the audio or video content by processing (E 70 ) data from the second dataset (Fnn) using a process depending at least partially on the data from the first set (Fet) and using an artificial neural network ( 18 ) implemented by the parallelized processing unit ( 16 ).
1 . A method for decoding a data stream by an electronic device including a first processor and a parallelized processor configured to perform in parallel, at a specific time, a plurality of a same type operations, the data stream including a first set of data and a second set of data representative of an audio or video content, the method comprising:
obtaining an identifier or data descriptive of an artificial neural network by processing data from the first set of data by the first processor; and
obtaining the audio or video content by processing data from the second set of data using said artificial neural network implemented by the parallelized processor.
2 . The decoding method according to claim 1 , further comprising configuring the parallelized processor as a function of at least part of the data from the first set of data.
3 . The decoding method according to claim 2 , wherein the first set of data comprises the data descriptive of the artificial neural network, and wherein, in the configuring, the first processor configures the parallelized processor based on said descriptive data.
4 . The decoding method according to claim 2 , wherein:
the electronic device comprises a storage configured to store a plurality of parameter sets respectively defining a plurality of artificial neural networks;
wherein the first set of data comprises the identifier; and
wherein, in the configuring, the first processor configures the processor based on a set of parameters associated with the identifier among the plurality of parameter sets.
5 . The decoding method according to claim 2 , wherein:
the data stream further comprises instructions executable within the electronic device; and
wherein the configuring the parallelized processor is performed due to execution of at least part of said instructions.
6 . The decoding method according to claim 2 , wherein:
the data stream further comprises instructions executable within the electronic device; and
wherein the processing of the first data set is at least partially performed due to execution of at least part of said instructions.
7 . The decoding method according to claim 6 , wherein the configuring of the parallelized processor is performed due to execution of at least part of said instructions.
8 . The decoding method according to claim 2 , further comprising identifying the first set of data and the second set of data within the data stream.
9 . The decoding method according to claim 2 , wherein the first set of data comprises data representative of characteristics of a format of the audio or video content encoded by the data stream.
10 . The decoding method according claim 1 , wherein:
the data stream further comprises instructions executable within the electronic device; and
wherein the processing of the data from the first data set is at least partially performed due to execution of at least part of said instructions.
11 . The decoding method according to claim 1 , wherein the first set of data comprises data representative of characteristics of a format of the audio or video content encoded by the data stream.
12 . The decoding method according to claim 1 , wherein the processing of the data from the second set produces at least one matrix representation of at least a part of an image.
13 . The decoding method according to claim 1 , wherein the artificial neural network receives, as an input, data from the second set of data.
14 . The decoding method according to claim 1 , wherein the artificial neural network receives, as an input, data previously produced as an output of the artificial neural network.
15 . The decoding method according to claim 1 , wherein the first set of data and the second set of data are identifiable individually within the data stream.
16 . An electronic device configured to decode a data stream including a first set of data and a second set of data representative of an audio or video content, the electronic device comprising:
a first processor configured to process data from the first set of data to obtain an identifier or data descriptive of an artificial neural network; and
a parallelized processor configured to perform in parallel, at a specific time, a plurality of same type operations and configured to obtain the audio or video content by processing data from the second set of data using the artificial neural network implemented by the parallelized processor.
17 . The electronic device according to claim 16 , wherein the first processor configures the parallelized processor as a function of at least part of the data from the first set of data.
18 . The electronic device according to claim 17 , further comprising a storage configured to store a plurality of parameter sets respectively defining a plurality of artificial neural networks, wherein:
the first set of data comprises the identifier; and
wherein the first processor configures the parallelized processor based on a set of parameters associated with the identifier among the plurality of parameter sets.
19 . The electronic device according to claim 16 , wherein the parallelized processor is configured to produce at least one matrix representation of at least a part of an image.