It turns out it may not always be the case. When training vision models, it is common to resize images to a lower dimension ((224 x 224), (299 x 299), etc.) to allow mini-batch learning and also to keep up the compute limitations. We generally make use of image resizing methods like bilinear interpolation for this step and the resized images do not lose much of their perceptual character to the human eyes. In Learning to Resize Images for Computer Vision Tasks, Talebi et al. show that if we try to optimize the perceptual quality of the images for the vision models rather than the human eyes, their performance can further be improved. They investigate the following question: