AMENDMENTS TO THE CLAIMS
`
`1. (Currently Amended) An encoder, comprising:
`
`processing circuitry; and
`
`memory, wherein
`
`using the memory, the processing circuitry:
`
`generates a predicted image of an input imagethat is a current image to be
`
`encoded, based on generated data output from a generator network in response to a
`
`reference image being input to the generator network, the generator network being a
`
`neural network;
`
`calculates a prediction error by subtracting the predicted image from the input
`
`image;
`
`generates an encoded image byat least transforming the prediction error;
`
`feeds back, to the generator network, a probability that the predicted image
`
`matchesthe input image by inputting the input image and the predicted image to a
`
`discriminator network, the discriminator network being a neural network and constituting
`
`a generative adversarial network (GAN) with the generator network; and
`
`updates the generator network and the discriminator network to reduce difference
`
`between the input image and the predicted image and increase accuracy of discriminating
`
`between the input image and the predicted image,
`
`wherein the reference imageis a processed image included in a picture, the picture
`
`including the input image, and
`
`wherein in generating the predicted image, the processing circuitry:
`
`generatesa first intra-predicted image byafirst intra prediction based on the
`
`reference image and an intra prediction parameter, the intra prediction parameter being
`
`obtained as the generated data output from the generator network in responseto the
`
`reference image being input to the generator network;
`
`generates a second intra-predicted image of the input image by a second intra
`
`prediction based on the reference image:
`
`selects, as the predicted image, from amongthefirst intra-predicted image and the
`
`secondintra-predicted image: and
`
`
`
`1. (Currently Amended) An encoder, comprising:
`
`processing circuitry; and
`
`memory, wherein
`
`using the memory, the processing circuitry:
`
`generates a predicted image of an input imagethat is a current image to be
`
`encoded, based on generated data output from a generator network in response to a
`
`reference image being input to the generator network, the generator network being a
`
`neural network;
`
`calculates a prediction error by subtracting the predicted image from the input
`
`image;
`
`generates an encoded image byat least transforming the prediction error;
`
`feeds back, to the generator network, a probability that the predicted image
`
`matchesthe input image by inputting the input image and the predicted image to a
`
`discriminator network, the discriminator network being a neural network and constituting
`
`a generative adversarial network (GAN) with the generator network; and
`
`updates the generator network and the discriminator network to reduce difference
`
`between the input image and the predicted image and increase accuracy of discriminating
`
`between the input image and the predicted image,
`
`wherein the reference imageis a processed image included in a picture, the picture
`
`including the input image, and
`
`wherein in generating the predicted image, the processing circuitry:
`
`generatesa first intra-predicted image byafirst intra prediction based on the
`
`reference image and an intra prediction parameter, the intra prediction parameter being
`
`obtained as the generated data output from the generator network in responseto the
`
`reference image being input to the generator network;
`
`generates a second intra-predicted image of the input image by a second intra
`
`prediction based on the reference image:
`
`selects, as the predicted image, from amongthefirst intra-predicted image and the
`
`secondintra-predicted image: and
`
`
`
`when the processing circuitry selects the second intra-predicted image, calculates
`
`the prediction error by subtracting the second intra-predicted image from the input image
`
`in calculating the prediction error.
`
`2 — 10. (Cancelled)
`
`11. (Original) The encoder according to claim 1, wherein
`
`the generator networkis a hierarchical network that includes an input layer, a hidden
`
`layer, and an outputlayer.
`
`12. (Currently Amended) A decoder, comprising:
`
`processing circuitry; and
`
`memory, wherein
`
`using the memory, the processing circuitry:
`
`generates a decoding prediction error by performing at least inverse transform on
`
`an encoded imagethat is a current image to be decoded;
`
`generates a predicted image of the encoded image, based on generated data output
`
`from a generator network in response to a reference imagebeing input to the generator
`
`network, the generator network being a neural network;
`
`generates a decoded image by adding the decoding prediction error to the
`
`predicted image;
`
`feeds back, to the generator network, a probability that the predicted image
`
`matches the decoded image by inputting the decoded image and the predicted image to a
`
`discriminator network, the discriminator network being a neural network and constituting
`
`a generative adversarial network (GAN) with the generator network; and
`
`updates the generator network and the discriminator network to reduce difference
`
`between the decoded image and the predicted image and increase accuracy of
`
`discriminating between the decoded imageand the predicted image,
`
`wherein the reference imageis a processed image included in a picture, the picture
`
`including the encoded image, and
`
`wherein in generating the predicted image, the processing circuitry:
`
`
`
`generatesa first intra-predicted image by a first intra prediction based on the
`
`reference image and an intra prediction parameter, the intra prediction parameter being
`
`obtained as the generated data output from the generator network in responseto the
`
`reference image being input to the generator network;
`
`generates a second intra-predicted image of the encoded image by a second intra
`
`prediction based on the reference image:
`
`selects, as the predicted image, from amongthefirst intra-predicted image and the
`
`secondintra-predicted image: and
`
`when the processing circuitry selects the second intra-predicted image, generates
`
`the decoded image by adding the decoding prediction error to the second intra-predicted
`
`image in generating the decoded image.
`
`13 — 21. (Cancelled)
`
`22. (Original) The decoder according to claim 12, wherein
`
`the generator networkis a hierarchical network that includes an input layer, a hidden
`
`layer, and an outputlayer.
`
`23. (Currently Amended) An encoding method, comprising:
`
`generating a predicted image of an input imagethatis a current imageto be encoded,
`
`based on generated data output from a generator network in response to a reference image being
`
`input to the generator network, the generator network being a neural network;
`
`calculating a prediction error by subtracting the predicted image from the input image;
`
`generating an encoded imagebyat least transforming the prediction error;
`
`feeding back, to the generator network, a probability that the predicted image matches the
`
`input image by inputting the input image andthe predicted imageto a discriminator network, the
`
`discriminator network being a neural network and constituting a generative adversarial network
`
`(GAN)with the generator network; and
`
`updating the generator network and the discriminator network to reduce difference
`
`between the input image and the predicted image and increase accuracy of discriminating
`
`between the input image and the predicted image,
`
`
`
`wherein the reference imageis a processed image includedin a picture, the picture
`
`including the input image, and
`
`wherein the generating the predicted imageincludes:
`
`generating a first intra-predicted image bya first intra prediction based on the
`
`reference image and an intra prediction parameter, the intra prediction parameter being
`
`obtained as the generated data output from the generator network in responseto the
`
`reference image being input to the generator network;
`
`generating a second intra-predicted image of the input image by a secondintra
`
`prediction based on the reference image:
`
`selecting, as the predicted image, from amongthefirst intra-predicted image and
`
`the secondintra-predicted image: and
`
`whenthe second intra-predicted imageis selected _as the predicted image,
`
`calculating the prediction error by subtracting the second intra-predicted image from the
`
`input image.
`
`24. (Currently Amended) A decoding method, comprising:
`
`generating a decoding prediction error by performingat least inverse transform on an
`
`encoded imagethat is a current image to be decoded;
`
`generating a predicted image of the encoded image, based on generated data output from
`
`a generator network in response to a reference image being input to the generator network, the
`
`generator network being a neural network;
`
`generating a decoded imageby adding the decoding prediction error to the predicted
`
`image;
`
`feeding back, to the generator network, a probability that the predicted image matches the
`
`decoded imageby inputting the decoded image and the predicted imageto a discriminator
`
`network, the discriminator network being a neural network and constituting a generative
`
`adversarial network (GAN) with the generator network; and
`
`updating the generator network and the discriminator network to reduce difference
`
`between the decoded image and the predicted image and increase accuracy of
`
`discriminating between the decoded imageandthe predicted image,
`
`
`
`wherein the reference imageis a processed image includedin a picture, the picture
`
`including the encoded image, and
`
`wherein the generating the predicted imageincludes:
`
`generating a first intra-predicted image bya first intra prediction based on the
`
`reference image and an intra prediction parameter, the intra prediction parameter being
`
`obtained as the generated data output from the generator network in responseto the
`
`reference image being input to the generator network;
`
`generating a second intra-predicted image of the encoded image by a second intra
`
`prediction based on the reference image:
`
`selecting, as the predicted image, from amongthefirst intra-predicted image and
`
`the secondintra-predicted image: and
`
`whenthe second intra-predicted imageis selected _as the predicted image,
`
`generating the decoded image by adding the decoding prediction error to the second
`
`intra-predicted image.
`
`
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