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Semantic alignment for multi-item compression


Coding algorithms usually compress independently the images of a collection, in particular when the correlation between them only resides at the semantic level, i.e., 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 data collections. 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.
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Dates and versions

hal-03760650 , version 1 (25-08-2022)


  • HAL Id : hal-03760650 , version 1


Tom Bachard, Anju Jose Tom, Thomas Maugey. Semantic alignment for multi-item compression. ICIP 2022 - IEEE International Conference on Image Processing, Oct 2022, Bordeaux, France. pp.1-5. ⟨hal-03760650⟩
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