Accelerating pixel predictor evolution using edge-based class separation

Evolutionary methods based on genetic programming (GP) enable dynamic algorithm generation, and have been successfully applied to many areas such as plant control, robot control, and stock market prediction. However, one of the challenges of this approach is its high computational complexity. Conventional image/video coding methods such as JPEG and H.264 all use fixed (non-dynamic) algorithms without exception. However, one of the challenges of this approach is its high computational complexity. In this article, we introduce a GP-based image predictor that is specifically evolved for each input image, as well as local image properties such as edge direction. Via the simulation, proposed method demonstrated ~180 times faster evolution speed and 0.02-0.1 bit/pel lower bit rate than previous method.