Quantifying the effect of image compression on supervised learning applications in optical microscopy

no code implementations • 26 Sep 2020 • Enrico Pomarico, Cédric Schmidt, Florian Chays, David Nguyen, Arielle Planchette, Audrey Tissot, Adrien Roux, Stéphane Pagès, Laura Batti, Christoph Clausen, Theo Lasser, Aleksandra Radenovic, Bruno Sanguinetti, Jérôme Extermann

We propose an experimental method to evaluate the tolerability of image compression distortions in 2D and 3D cell segmentation SL tasks: predictions on compressed data are compared to the raw predictive uncertainty, which is numerically estimated from the raw noise statistics measured through sensor calibration.

Cell Segmentation Data Compression +1