Training method, image encoding method, image decoding method and apparatuses thereof

1. A training device for an image processing apparatus, in which an image encoder and an image decoder are trained by using a training image, the training device comprises:

a memory to store a plurality of instructions; and

a processor coupled to the memory and configured to:

acquire a latent variable obtained by the image encoder by encoding input training image data;

acquire first restored image data obtained by the image decoder by decoding the latent variable and second restored image data obtained by the image decoder by decoding a sum of the latent variable and a noise; and

train the image encoder and the image decoder according to a cost function, the cost function being related to a deviation between the input training image data and the first restored image data and a deviation between the first restored image data and the second restored image data.

2. An image encoding apparatus, comprising:

an image encoder configured to encode input image data to obtain a latent variable, the image encoder encoding the input image data according to training by the training device as claimed in claim 1 ;

a quantizer configured to perform quantizing processing on the latent variable according to a quantization operation to generate a quantized latent variable; and

an entropy encoder configured to perform entropy coding on the quantized latent variable by using an entropy model to form a bit stream.

3. The image encoding apparatus according to claim 2 , wherein the image encoding apparatus further comprises:

a quantization adjuster configured to adjust the quantization operation to adjust a bit rate of the bit stream.

4. The image encoding apparatus according to claim 2 , wherein,

the quantizing processing of the quantizer is non-uniform quantizing processing.

5. The image encoding apparatus according to claim 4 , wherein,

the non-uniform quantizing processing comprises:

taking a latent variable to which a probability distribution peak value of the latent variable corresponds as a zero point, a latent variable of a first range containing the zero point corresponding to a first quantized latent variable; and

for other quantized latent variables than the first quantized latent variables, the other quantized latent variables corresponding to latent variables of a second range, the second range being less than the first range.

6. The image encoding apparatus according to claim 5 , wherein,

the probability distribution peak value of the latent variable is obtained based on the entropy model.

7. An image decoding apparatus, comprising:

an entropy decoder configured to perform entropy decoding on a bit stream by using an entropy model to form a quantized latent variable;

a de-quantizer configured to perform de-quantizing processing on the quantized latent variable according to a quantization operation to generate a reconstructed latent variable; and

an image decoder configured to perform decoding processing on the reconstructed latent variable to obtain restored image data, the image decoder performing the decoding processing according to training by the training device as claimed in claim 1 .

8. The image decoding apparatus according to claim 7 , wherein,

the de-quantizer performs the de-quantizing processing according to the quantization operation.

9. The image decoding apparatus according to claim 7 , wherein the image decoding apparatus further comprises:

a quantization adjuster configured to adjust the quantization operation.