Content adaptive optimization for neural data compression

A method for use by a data processing system including a computing platform having a hardware processor and a system memory storing a data compression software code, a trained neural encoder and a trained neural decoder, the trained neural encoder and the trained neural decoder each including parameters of a latent space probability model determined during training using a neural network, the method comprising:

• receiving, by the data compression software code executed by the hardware processor, a plurality of compression input data;
• encoding, using the trained neural encoder by the data compression software code executed by the hardware processor, a first compression input data of the plurality of compression input data to a latent space representation of the first compression input data;
• decoding, using the trained neural decoder by the data compression software code executed by the hardware processor, the latent space representation of the first compression input data to produce an input space representation of the first compression input data corresponding to the latent space representation of the first compression input data;
• generating, by the data compression software code executed by the hardware processor and using additive noise, first compression input data refined latent values, based on a comparison of the first compression input data with the input space representation;
• re-encoding, using the trained neural encoder by the data compression software code executed by the hardware processor, the first compression input data using the first compression input data refined latent values to produce a first compressed data corresponding to the first compression input data;
• transmitting the first compressed data to a remote trained neural decoder having the parameters of the latent space probability model;
• wherein encoding, decoding and generating the first compression input data refined latent values do not change any of the parameters of the latent space probability model of each of the trained neural encoder and the trained neural decoder, thereby not requiring any change to any parameter of the latent space probability model of the remote trained neural decoder.

A data processing system comprising:

• a computing platform including a hardware processor and a system memory storing a data compression software code, a trained neural encoder and a trained neural decoder, wherein the trained neural encoder and the trained neural decoder each includes parameters of a latent space probability model determined during training using a neural network;
• the hardware processor configured to execute the data compression software code to:
• receive a plurality of compression input data;
• encode, using the trained neural encoder, a first compression input data of the plurality of compression input data to a latent space representation of the first compression input data;
• decode, using the trained neural decoder, the latent space representation of the first compression input data to produce an input space representation of the first compression input data corresponding to the latent space representation of the first compression input data;
• generate, using additive noise, first compression input data refined latent values based on a comparison of the first compression input data with the input space representation;
• and
• re-encode, using the trained neural encoder, the first compression input data using the first compression input data refined latent values to produce a first compressed data corresponding to the first compression input data;
• wherein encoding, decoding and generating the first compression input data refined latent values do not change any of the parameters of the latent space probability model of each of the trained neural encoder and the trained neural decoder.