BACKGROUND

Standard groups and companies have been actively searching for potential needs for standardization of future video coding technology. These standard groups and companies have focused on artificial intelligence (AI)-based end-to-end neural image compression (NIC) using deep neural networks (DNNs). The success of this approach has brought more and more industrial interest in advanced neural image and video compression methodologies.

Flexible bitrate control remains a challenging issue for previous NIC methods. Conventionally, it may include training multiple model instances targeting each desired trade-off between a rate and a distortion (a quality of compressed images) individually. All these multiple model instances may need to be stored and deployed on a decoder side to reconstruct images from different bitrates. This may be prohibitively expensive for many applications with limited storage and computing resources.

SUMMARY

According to embodiments, a method of multi-rate neural image compression with stackable nested model structures is performed by at least one processor and includes iteratively stacking, on a first prior set of weights of a first neural network corresponding to a prior hyperparameter, a first plurality of sets of weights of a first plurality of stackable neural networks corresponding to a current hyperparameter, wherein the first prior set of weights of the first neural network remains unchanged, encoding an input image to obtain an encoded representation, using the first prior set of weights of the first neural network on which the first plurality of sets of weights of the first plurality of stackable neural networks is stacked, and encoding the obtained encoded representation to determine a compressed representation.

According to embodiments, an apparatus for multi-rate neural image compression with stackable nested model structures, includes 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 includes first stacking code configured to cause the at least one processor to iteratively stack, on a first prior set of weights of a first neural network corresponding to a prior hyperparameter, a first plurality of sets of weights of a first plurality of stackable neural networks corresponding to a current hyperparameter, wherein the first prior set of weights of the first neural network remains unchanged, first encoding code configured to cause the at least one processor to encode an input image to obtain an encoded representation, using the first prior set of weights of the first neural network on which the first plurality of sets of weights of the first plurality of stackable neural networks is stacked, and second encoding code configured to cause the at least one processor to encode the obtained encoded representation to determine a compressed representation.

According to embodiments, a non-transitory computer-readable medium stores instructions that, when executed by at least one processor for multi-rate neural image compression with stackable nested model structures, cause the at least one processor to iteratively stack, on a first prior set of weights of a first neural network corresponding to a prior hyperparameter, a first plurality of sets of weights of a first plurality of stackable neural networks corresponding to a current hyperparameter, wherein the first prior set of weights of the first neural network remains unchanged, encode an input image to obtain an encoded representation, using the first prior set of weights of the first neural network on which the first plurality of sets of weights of the first plurality of stackable neural networks is stacked, and encode the obtained encoded representation to determine a compressed representation.

