METHOD AND DATA PROCESSING SYSTEM FOR LOSSY IMAGE OR VIDEO ENCODING, TRANSMISSION AND DECODING

1 - 25 . (canceled)

26 . A method for lossy video encoding, transmission and decoding, the method comprising the steps of:

receiving a plurality of frames of a video at a first computer system;

encoding the plurality of frames using a first trained neural network to produce a plurality of latent representations;

concatenating at least two of the plurality of latent representations to obtain a latent representation subset;

encoding the latent representation subset using a second trained neural network to produce a hyper-latent representation;

performing a quantization process on the latent representation to produce a quantized latent and the hyper-latent representation to produce a quantized hyper-latent;

transmitting the quantized latent and the quantized hyper-latent to a second computer system;

decoding the quantized hyper-latent using a third trained neural network; and decoding the quantized latent using the output of the third trained neural network and a fourth trained neural network to produce a plurality of output frames, wherein the plurality of output frames are an approximation of the plurality of frames of the video.

27 . The method of claim 26 , wherein at least one of the second trained neural network and the third trained neural network comprises a convolution operation performed in at least three dimensions.

28 . The method of claim 27 , wherein the first trained neural network and the fourth trained neural network comprises only convolution operations performed in two dimensions.

29 . The method of any one of claim 26 , wherein optical flow vectors of the at least two latent representations are additionally determined and included in the latent representation subset.

30 . A method for lossy video encoding, transmission and decoding, the method comprising the steps of:

receiving a plurality of frames of a video at a first computer system; concatenating at least two frames of the plurality of frames to obtain a video subset; encoding the video subset using a first trained neural network to produce a latent representation;

performing a quantization process on the latent representation to produce a quantized latent;

transmitting the quantized latent to a second computer system; and

decoding the quantized latent using a second trained neural network to produce an output video subset, wherein the output video subset is an approximation of the video subset.

31 . The method of claim 30 , further comprising the steps of:

encoding the latent representation using a third trained neural network to produce a hyper-latent representation;

performing a quantization process on the hyper-latent representation to produce a quantized hyper-latent;

transmitting the quantized hyper-latent to the second computer system; and decoding the quantized hyper-latent using a fourth trained neural network;

wherein the output of the fourth trained neural network is used during the decoding of the quantized latent.

32 . The method of claim 30 , further comprising the steps of:

encoding at least one further video subset using the first trained neural network to produce at least one further latent representation;

concatenating at least two of the plurality of latent representations to obtain a latent representation subset;

encoding the latent representation subset using a third trained neural network to produce a hyper-latent representation;

performing a quantization process on the hyper-latent representation to produce a quantized hyper-latent;

transmitting the quantized hyper-latent to the second computer system; and decoding the quantized hyper-latent using a fourth trained neural network;

wherein the output of the fourth trained neural network is used during the decoding of the quantized latent.

33 . The method of any one of claim 30 , wherein at least one of the first trained neural network and the second trained neural network comprises a convolution operation performed in at least three dimensions.

34 - 35 . (canceled)

36 . A method for lossy video encoding and transmission, the method comprising the steps of:

receiving a plurality of frames of a video at a first computer system;

encoding the plurality of frames using a first trained neural network to produce a plurality of latent representations;

concatenating at least two of the plurality of latent representations to obtain a latent representation subset;

encoding the latent representation subset using a second trained neural network to produce a hyper-latent representation;

performing a quantization process on the latent representation to produce a quantized latent and the hyper-latent representation to produce a quantized hyper-latent; transmitting the quantized latent and the quantized hyper-latent.

37 . A method for lossy image or video receipt and decoding, the method comprising the steps of:

receiving the quantized latent and the quantized hyper-latent transmitted according to the method of claim 36 at a second computer system;

decoding the quantized hyper-latent using a third trained neural network; and decoding the quantized latent using the output of the third trained neural network and a fourth trained neural network to produce a plurality of output frames, wherein the plurality of output frames are an approximation of the plurality of frames of the video.

38 - 119 . (canceled)