Every year that I teach the numerical analysis sequence, we end the year with a project on image compression. This year, the students used a technique called Principal Component Analysis (PCA) to sort though large datasets of images, looking for a common structure in the image data. Once they discovered the structure, they could use it to compress the images by only storing some of the pixel data and using what they knew about images to reconstruct the rest. The technique doesn’t work as well as it can on images which are already compressed with JPEG, since that method loses some detail already.
But the results on uncompressed data from a RAW file? Pure genius. Kristen Bach of Treehouse and beautyeveryday and Karen Gerow of Double Helix STEAM Academy donated some of their very excellent photography for the students to try their work on.
After a semi-rigorous set of A/B comparisons between different compressions of various images, the class decided that the best results were due to Fred Hohman (in the 50% compression category, meaning that Fred uses half of the image to predict the other half), Irma Stevens (in the 90% compression category, meaning that Irma used 10% of the image data to predict the rest) and Ke Ma (in the 99% compression category, meaning that Ke used only 1% of the image data to predict the rest).
Here are their results!