Long-range context model in neural image compression

An apparatus for decoding a compressed image using a neural image compression network, the apparatus comprising:

• at least one memory configured to store computer program code;
• and
• at least one processor configured to read the computer program code and operate as instructed by the computer program code, the computer program code including:
• generating code configured to cause the at least one processor to generate context parameters associated with a compressed image;
• first determining code configured to cause the at least one processor to determine that a long range global dependency exists between a first latent and a second latent in a long-range global area within the compressed image;
• context feature predicting code configured to cause the at least one processor to predict a plurality of context features using a transformer-based long-range context prediction model, the context predicting code further comprising:
• first predicting code configured to cause the at least one processor to predict first context features using a long-range context prediction model and first context parameters, the first context parameters corresponding to first locations in a first area, and the first context features that are predicted using the first context parameters corresponding to second locations in the first area;
• and
• second predicting code configured to cause the at least one processor to predict second context features using the long-range context prediction model and second context parameters, the second context parameters corresponding to the second locations in the first area, and the second context features that are predicted using the second context parameters corresponding to third locations in the first area;
• and
• reconstructing code configured to cause the at least one processor to reconstruct the compressed image based on the predicted plurality of context features.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor of an apparatus for decoding a compressed image using a neural image compression network, cause the at least one processor to:

• generate context parameters associated with a compressed image;
• determine that a long range global dependency exists between a first latent and a second latent in a long-range global area within the compressed image;
• predict a plurality of context features using a transformer-based long-range context prediction model, the predicting the plurality of context features further comprising:
• predicting first context features using the transformer-based long-range context prediction model and first context parameters, the first context parameters corresponding to first locations in a first area, and the first context features that are predicted using the first context parameters corresponding to second locations in the first area, and
• predicting second context features using the transformer-based long-range context prediction model and second context parameters, the second context parameters corresponding to the second locations in the first area, and the second context features that are predicted using the second context parameters corresponding to third locations in the first area;
• and
• reconstruct the compressed image based on the predicted plurality of context features.