Optimization of losless image compression method for GPGPU

This paper presents power and execution time efficient implementation of highly adaptive lossless image compression method based on predictor classification and blending, denoted as CBPC coder. Power efficiency is becoming increasingly important in both: datacenters and consumer electronics. This is why we aimed to target its optimization, as well as throughput, of CBPC coder on a CPU and a GPU, using CUDA for the latter. Tests were conducted using mainstream components in a desktop PC and high end components in a server. The CUDA version proved to be both more efficient and faster than a singlethreaded CPU version, however, further tests should be done comparing even more optimized CUDA version against multithreaded CPU implementation to cover the whole spectrum and to achieve better performance per watt for both consumer desktop and server system running the method. Finally, we demonstrate the benefits of GPGPU approach for compute-intensive, fine grained data-parallel parts of the algorithm, notably predictor classification and blending computations.