Authors: Tom Bachard, Anju Jose Tom, Thomas Maugey

Title: Semantic Alignment for Multi-Item Compression

Abstract: Coding algorithms usually compress independently the images of a collection, in particular when the correlation be tween them only resides at the semantic level (information related to the high-level image content). In this work, we propose a coding solution able to exploit this semantic redundancy to decrease the storage cost of a data collection. First we introduce the multi-item compression framework. Then we derive a loss term to shape the latent space of a variational auto-encoder so that the latent vectors of semantically identical images can be aligned. Finally, we experimentally demonstrate that this alignment leads to a more compact representation of the data collection.