Earticle

Image compression is the process to remove the redundant information from the image so that only essential information can be stored to reduce the storage size and transmission time. Advancement in still image compression becomes essential for various applications like medical imaging and multimedia applications. The task of image compression generally refers to deriving a near approximated compressed image for the given input image. Methods for tackling this problem have to do a delicate balancing act of suppressing the unwanted effects in visualizing without losing features of interest. Different techniques were already proposed in this area which is not sufficient to maintain good quality of the compressed image without sacrificing the compression ratio. Thus, new image compression algorithm based on order reduction using sub-image formation is proposed for lossy compression scheme. Obviously, the tradeoff between compression ratio and picture quality is an important issue in image compression. The importance of running time of compression was further investigated as this process can be used in high speed data transmission applications. The simulation process is carried out determining the computation time using MATLAB. The algorithm tested for still images of different size too.

Subspace learning is an important direction in computer vision research. In this paper, a new method of face recognition based on uncorrelated multilinear principal component analysis (UMPCA) and linear discriminant analysis (LDA) is proposed. First, instead of transforming matrices into vectors for principal component analysis (PCA), UMPCA seeks a tensor-to-vector projection that captures most of the variation in the original tensorial input while producing uncorrelated features through successive variance maximization. A subset of features is extracted and the classical LDA is then applied to find the best subspaces. Finally, the comprehensive experiments are provided on AT&T databases and the experiment results show its superiority through the comparison with other PCA plus LDA based algorithms.

Exact closed-form outage probability (OP) expressions are presented for multiple-mobile relay-based mobile-to-mobile (M2M) systems with threshold digital relaying (TDR) and relay selection over N-Nakagami fading channels. Numerical simulation is used to verify the accuracy of the analytic results. The effect of the fading coefficient, the number of cascaded components, the relative geometric gain, and the power-allocation on the OP is examined.