Alberto Presta's PhD defense

Congratulations to Alberto Presta for successfully defending his PhD thesis, titled " Toward sensible learned image compression: closing the gap with standard codecs".

Abstract: This thesis explores limitations of current Learned Image Compression (LIC) models, particularly their inability to support multiple bitrates, progressive coding, and adaptability to unfamiliar domains—challenges not shared by traditional codecs. To address these, we introduce methods such as a parameter-free latent distribution formulation, a graph-based context estimator, and two solutions for variable rate coding: a quantization layer (STanH) and LoRA adapters for transformers. The work also proposes a dual-latent architecture for progressive compression and decoder-based domain-specific modules that enhance performance without retraining, with promising extensions to video compression.

The committee was composed by:

• Giuseppe Valenzise, CNRS Researcher, CentraleSupélec Gif-sur-Yvette, France
• Marco Cagnazzo, Dip. di Ingegneria dell’informazione, Uni Padova
• Gabriella Olmo, Dip Informatica, Polito
• Davide Cavagnino, Dip. Informatica, Unito

PhD Supervisor: Marco Grangetto (University of Turin)