It was over 50 years ago when Russell Kirsch scanned a photograph of his infant son and digitized it into a mere 176×176 pixel matrix, setting forth what became one of the unshakable foundations of computer graphics: Square Pixels. Chosen merely for its simplicity, he has forever regretted the decision that has limited innovation and visualization ever since. In the May/June issue of “Journal Of Research of the National Institute of Standards and Technology” (also knows as NIST), he proposes a new algorithm that can take these simple square pixels and recreate something far more useful and accurate.
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.
His algorithm already shows promise in fields like medical imaging due to its ability to improve MRI scans. Also, the algorithms nature to find edges rather than similar regions allows it to compress megabytes of pixels into mere kilobytes of data, making it great for long-distance transmission (eg. Satellite imagery).
