Entropy estimation method and apparatus for image compression

An entropy estimation apparatus for image compression, comprising:

• an obtaining module, configured to obtain a to-be-processed first feature matrix, wherein a shape of the first feature matrix is [Cin, hin, win], Cin indicates a quantity of channels of the first feature matrix, hin indicates a height of the first feature matrix, and win indicates a width of the first feature matrix;
• and
• a processing module, configured to input the first feature matrix to an entropy estimation network to obtain a second feature matrix, wherein a shape of the second feature matrix is [Cout, hout, wout], Cout indicates a quantity of channels of the second feature matrix, hout indicates a height of the second feature matrix, and wout indicates a width of the second feature matrix;
• the second feature matrix and the first feature matrix meet the following conditions:
• Cout=Cin, hout=s·hin, and wout=s·win, wherein s is an integer greater than 1;
• and the entropy estimation network comprises a first convolutional layer and a pointwise operation, wherein the first convolutional layer comprises two types of convolution processing, one of the two types of convolution processing is two-dimensional group convolution, and an input of the pointwise operation is respective outputs of the two types of convolution processing.

A computer-readable storage medium, comprising instructions, wherein when the instructions are run on a computer, the computer is enabled to:

• obtain a to-be-processed first feature matrix, wherein a shape of the first feature matrix is [Cin, hin, win], Cin indicates a quantity of channels of the first feature matrix, hin indicates a height of the first feature matrix, and win indicates a width of the first feature matrix;
• and
• input the first feature matrix to an entropy estimation network to obtain a second feature matrix, wherein a shape of the second feature matrix is [Cout, hout, wout], Cout indicates a quantity of channels of the second feature matrix, hout indicates a height of the second feature matrix, and wout indicates a width of the second feature matrix;
• the second feature matrix and the first feature matrix meet the following conditions:
• Cout=Cin, hout=s·hin, and wout=s·win, wherein sis an integer greater than 1;
• and the entropy estimation network comprises a first convolutional layer and a pointwise operation, wherein the first convolutional layer comprises two types of convolution processing, one of the two types of convolution processing is two-dimensional group convolution, and an input of the pointwise operation is respective outputs of the two types of convolution processing.