Speeding up the runtime performance for lossless image coding on GPUs with CUDA

With the highly increased capability on parallel processing, computing on graphics processing units (GPUs) have been widely used in applications more than just graphics data processing. In this paper, we apply the compute unified device architecture (CUDA), a parallel computing architecture on GPUs proposed by NVIDIA, for the runtime performance enhancement in a predictively encoded lossless image compression system. For this, a least squares (LS)-adapted predictor, an effective approach for the removal of redundancy around boundaries, is applied. The adaptation process of an LS-based predictor requires multiplications of matrices for the construction of normal equations, which has been known to be the major complexity in LS adaptation process. Fortunately, matrices multiplication is most suitable to be parallel processed, which leads to the idea of speeding up the construction of normal equations with GPUs. With the proposed approach, a noticeable improvement on the runtime performance can be achieved as can be seen in the experiments.