Improvement of Adaptive Fractal Image Coding Algorithm for GPGPU Systems Using Index Vectors

Summary form only given. Fractal image coding is one of the most prominent compression technologies. It can be also used for industrial applications like image retrieval methods and image indexing methods. In addition, the adaptive approach can achieve the image compression with given quality level by changing the size of range blocks in any location, so the adaptive approach can achieve a high compression rate with less data than the non-adaptive method, but the naive parallel implementation may result in the inefficient parallelization due to the imbalance of work loads. On the other hand, GPGPU (General Purpose computing on Graphics Processing Unit) attracts a great deal of attention, which is used for general-purpose computations like numerical calculations as well as graphics processing. In this paper, we evaluate three parallel programs for the adaptive fractal image coding algorithm on GPUs by using CUDA (Compute Unified Device Architecture) on Nvidia GTX Titan GPU and discuss the effectiveness of parallel programs using index vectors and multithreading. Especially, the third program may enhance the occupancy of GPU computing by distributing the reduction computing over several processing cores.