Deep-learning-based compression method using frequency decomposition

Abstract

In one implementation, we propose an end-to-end image video compression method that decomposes the spatial frequencies of the input content into a partitioned latent representation. Decomposed frequencies in the latent space are analyzed and grouped into separate latent representation or separate tensors, each tensor being jointly optimized to be decoded independently one from another. Therefore, the decoder can independently decode the tensors in a scalable manner to progressively reconstruct the input. This method enables quality scalability by progressively transmitting individual latent representations of decomposed frequency data, separated in the produced latent space. Furthermore, the quality scalability of region of interest (ROI) is enabled by which the decoder takes only corresponding latent representations in the enhancement tensors as input together with latent representations already delivered to the decoder.