A novel face images compression scheme using sparse signal representation and RLS_DLA dictionary learning algorithm

Due to the rapid growth of information technology and exponential increasing of information the need for more and more storage capacity and efficiency has increased. Image compression is an important tool to reduce the redundancy of images data in order to be able to store or transmit them in an efficient manner. When images are limited to a specific and limited family of images like MRI databases of a hospital or facial image database of a university or an organization or fingerprint image databases, this limitation increases the total spatial redundancy. Thus, efficient storage of such images is beneficial, and their compression becomes an appealing application, and this urges algorithms specially tailored for the task of content base image compression to surpass general purpose compression algorithms. The facial images, due to their wide application as the most common images in the organizations and companies are more considerable for image compression. In this paper a new image compression scheme using sparse coding and RLS-DLA redundant dictionary learning is proposed that can be used for compressing of face image databases. In the proposed method, several dictionaries are exploited adaptively based on the required image quality to enhance the overall rate-distortion. The simulation results show that this scheme outperforms the state-of-art algorithms like JPEG2000 by about 0.5 to 1.2 dB for reconstructed images PSNR.