Multicore Architecture Optimization Using Novel Smart Parallel Algorithms for Steganography and Image Feature Extraction

Abstract

Applications of Steganography and Image Feature Extraction are widely used and adopted by many organizations in the image processing industry. Many of these applications involve computeintensive tasks which demand for full processor power. This research work intends to intervene on the performance problems that are experienced by algorithms in both steganography and Image Feature Extraction over Multicore Architectures systems. The main goal of this research is to provide Novel and Smart parallel algorithms in Steganography and Image Feature Extraction that are optimized to obtain enhanced performance in Multicore Architectures. The objective is to design, optimize and implement Novel Smart Parallel Algorithmic Models that will fully utilize Multicore Architectures. The results show high performance throughput as compared to ordinary algorithms which can only span about 433 samples when in execution. We have successfully developed algorithms which can span about 32,642 samples in execution. From these observations we conclude that Multicore Architectures Optimization is a necessity for the scaled performance of Steganography and Image Feature Extraction algorithms.