Acceleration of Evolutive Image Coding Using Highly Parallelized Processing
Details
Evolutive 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, conventional image/video coding methods such as JPEG and H.264/AVC all use fixed (non-dynamic) algorithms without exception. The evolutive coding enables an automatic generation of pixel prediction algorithm. It is a radical departure from conventional "fixed algorithm", "man-made algorithm" and "hand-made programming" toward a new paradigm. In this report, we introduce a GP-based image predictor that is specifically evolved for each input image, which have been evolving day by day. We demonstrate its prediction performance. We also report about speeding up the evolution process using parallelization by a factor of 100-1,500 as well as improving the prediction efficiency by about 2%.
