IMAGE COMPRESSION AND DECODING, VIDEO COMPRESSION AND DECODING: METHODS AND SYSTEMS

1 . A computer-implemented method for lossy image or video receiving and decoding, the method including the steps of:

(i) receiving a first bitstream, a second bitstream, and a third bitstream at a computer system; (ii) the computer system entropy decoding the first bitstream to produce a w latent;

(iii) the computer system processing the w latent using a first trained neural network;

(iv) the computer system entropy decoding the second bitstream using the processed w latent to produce a z latent;

(v) the computer system processing the z latent using a second trained neural network;

(vi) the computer system entropy decoding the third bitstream using the processed z latent to produce a y latent; and

(vii) the computer system using a third trained neural network to produce an output image from the y latent, wherein the output image is an approximation of an input image.

2 . The method of claim 1 , wherein in step (vii) the output image is stored.

3 . The method of claim 1 , wherein processing the z latent, at the computer system, using the second trained neural network comprises obtaining probability distribution parameters of each element of the y latent, wherein the probability distribution of the y latent is assumed to be represented by a probability distribution of each element of the y latent.

4 . The method of claim 3 , wherein in step (vi), entropy decoding the third bitstream comprises using the obtained probability distribution parameters of each element of the y latent.

5 . The method of claim 1 , wherein processing the w latent, at the computer system, using the first trained neural network comprises obtaining probability distribution parameters of each element of the z latent, wherein the probability distribution of the z latent is assumed to be represented by a probability distribution of each element of the z latent.

6 . The method of claim 5 , wherein in step (iv), entropy decoding the second bitstream comprises using the obtained probability distribution parameters of each element of the z latent.

7 . The method of claim 1 , wherein in step (ii) a predefined probability distribution is used for the entropy decoding of the first bitstream to produce the w latent.

8 . The method of claim 1 , wherein in step (ii) the probability distribution characterised by the parameters is used for the entropy decoding the first bitstream to produce the w latent.

9 . A computer-implemented method for lossy image or video compression and transmission, the method including the steps of:

(i) receiving an input image at a computer system;

(ii) encoding the input image using a first trained neural network, using the first computer system, to produce a y latent representation;

(iii) encoding the y latent using a second trained neural network, using the first computer system, to produce a z latent representation;

(iv) encoding the z latent representation, using a third trained neural network, using the first computer system, to produce a w latent representation;

(v) entropy encoding the w latent into a first bitstream, using the first computer system;

(vi) entropy encoding the z latent into a second bitstream, using the first computer system;

(vii) entropy encoding the y latent into a third bitstream, using the first computer system;

(viii) transmitting the first bitstream, the second bitstream and the third bitstream.

10 . The method of claim 9 , comprising quantizing the y latent representation using the computer system to produce a quantized y latent;

11 . The method of claim 10 , wherein quantizing the y latent representation using the computer system to produce a quantized y latent comprises quantizing the y latent representation using the computer system into a discrete set of symbols to produce a quantized y latent.

12 . The method of claim 9 , comprising quantizing the z latent representation using the computer system to produce a quantized z latent.

13 . The method of claim 12 , wherein quantizing the z latent representation using the computer system to produce a quantized z latent comprises quantizing the z latent representation using the computer system into a discrete set of symbols to produce a quantized z latent.

14 . The method of claim 9 , comprising processing the z latent, at the computer system, using a fourth trained neural network to obtain probability distribution parameters of each element of the y latent, wherein the probability distribution of the y latent is assumed to be represented by a probability distribution of each element of the y latent.

15 . The method of claim 14 , wherein in step (vii), entropy encoding the y latent comprises using the obtained probability distribution parameters of each element of the y latent.

16 . The method of claim 9 , comprising processing the w latent, at the computer system, using a fifth trained neural network to obtain probability distribution parameters of each element of the z latent, wherein the probability distribution of the z latent is assumed to be represented by a probability distribution of each element of the z latent.

17 . The method of claim 16 , wherein in step (vi), entropy encoding the z latent comprises using the obtained probability distribution parameters of each element of the z latent.

18 . The method of claim 9 , wherein in step (v) a predefined probability distribution is used for the entropy encoding of the w latent.

19 . A computer system for lossy image or video receiving and decoding, wherein the computer system is configured to:

(i) receive a first bitstream, a second bitstream, and a third bitstream;

(ii) entropy decode the first bitstream to produce a w latent;

(iii) process the w latent using a first trained neural network;

(iv) entropy decode the second bitstream using the processed w latent to produce a z latent;

(v) process the z latent using a second trained neural network;

(vi) entropy decode the third bitstream using the processed z latent to produce a y latent; and

(vii) use a third trained neural network to produce an output image from the y latent, wherein the output image is an approximation of an input image.

20 . A system for lossy image or video compression and transmission, wherein the computer system is configured to:

(i) receive an input image;

(ii) encode the input image using a first trained neural network to produce a y latent representation;

(iii) encode the y latent using a second trained neural network to produce a z latent representation;

(iv) encode the z latent representation, using a third trained neural network to produce a w latent representation;

(v) entropy encode the w latent into a first bitstream;

(vi) entropy encode the z latent into a second bitstream;

(vii) entropy encode the y latent into a third bitstream; and

(viii) transmit the first bitstream, the second bitstream and the third bitstream.