This is the first ever digital image. While working as a scientist at the National Bureau of Standards in the 1950’s, Russell Kirsch had the privilege of working on what was then the only programmable computer in the world. One day he took a picture of his infant son and scanned it into the computer. In doing so, he had to invent a way for the computer to represent the image:
Kirsch made that first digital image using an apparatus that transformed his picture into the binary language of computers, a regular grid of zeros and ones. A mere 176 by 176 pixels, that first image was built from roughly one one-thousandth the information in pictures captured with today’s digital cameras. Back then, the computer’s memory capacity limited the image’s size. But today, bits have become so cheap that a person can walk around with thousands of digital baby photos stored on a pocket-sized device that also makes phone calls, browses the Internet and even takes photos.
Yet science is still grappling with the limits set by the square pixel.
“Squares was the logical thing to do,” Kirsch says. “Of course, the logical thing was not the only possibility … but we used squares. It was something very foolish that everyone in the world has been suffering from ever since.”
At the age of 81, Kirsch has decided to do something to improve the way computers represent images. Inspired by mosaic makers, he’s in the process of developing a method for computers to use irregularly-shaped bits to represent an image.
He applied the program to a more recent picture of his son, now 53 years old, which appears with Kirsch’s analysis in the May/June issue of the Journal of Research of the National Institute of Standards and Technology.
“Finally,” he says, “at my advanced age of 81, I decided that instead of just complaining about what I did, I ought to do something about it.”
Kirsch’s method assesses a square-pixel picture with masks that are 6 by 6 pixels each and looks for the best way to divide this larger pixel cleanly into two areas of the greatest contrast. The program tries two different masks over each area — in one, a seam divides the mask into two rough triangles, and in the other a seam creates two rough rectangles. Each mask is then rotated until the program finds the configuration that splits the 6-by-6 area into sections that contrast the most. Then, similar pixels on either side of the seam are fused.