Method and apparatus for rate-adaptive neural image compression with adversarial generators

An apparatus for rate-adaptive neural image compression with adversarial generators, the apparatus comprising:

• at least one memory configured to store program code;
• and
• at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:
• first obtaining code configured to cause the at least one processor to obtain a first feature of an input image, using a first portion of a first neural network;
• first generating code configured to cause the at least one processor to generate a first substitutional feature, based on the obtained first feature, using a second neural network;
• first encoding code configured to cause the at least one processor to encode the generated first substitutional feature, using a second portion of the first neural network, to generate a first encoded representation;
• compressing code configured to cause the at least one processor to compress the generated first encoded representation;
• decompressing code configured to cause the at least one processor to decompress the compressed representation;
• and
• first decoding code configured to cause the at least one processor to decode the decompressed representation, using a third neural network, to reconstruct a first output image.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor for rate-adaptive neural image compression with adversarial generators, cause the at least one processor to:

• obtain a first feature of an input image, using a first portion of a first neural network;
• generate a first substitutional feature, based on the obtained first feature, using a second neural network;
• encode the generated first substitutional feature, using a second portion of the first neural network, to generate a first encoded representation;
• compress the generated first encoded representation;
• decompress the compressed representation;
• and
• decode the decompressed representation, using a third neural network, to reconstruct a first output image.