This paper is interested with consistent rendering of 16-bits SENTINEL-2 images into 8-bits ones in order to produce visually sound global products (useful for basemap for example). Though the colorization of a single 16-bits data can efficiency be done with histogram stretching techniques for example, applying a unique transformation able to generate consistent data with enough details/contrasts whatever the content of the original image remains challenging. We propose here to train a neural network on a wide variety of images that have been manually enhanced to derive a unique model able to consistently recolor images and generate sound global products without mosaic effects.