Constant increasing of visual information necessitates most efficient image compression schemes for saving storage space or reducing required transmission bandwidth. In compressing a class of images, such as a fingerprint database, facial images of an organization or MR images of a hospital, overall information redundancy is increased and compression becomes more significant. In this paper, image signal sparse representation and RLS-DLA dictionary design are utilized for compressing whole or part of a facial image database by exploiting the structural similarity of the class members. In the proposed algorithm, images are compressed by multiple overcomplete learned dictionaries which are designed to provide least required bit-rates for different target qualities. To fortify the process, more interested head and shoulders regions of the images are extracted to provide dictionary training sets. A combined edge detection and active contour segmentation method is used for a robust ROI extraction. Simulation results show superior performance of about 0.3 to 1.5 dB quality enhancement in terms of PSNR, for similar compression ratios compared to JPEG2000 standard for the complete image, and a near loss-less compression for restoring the ROI.
Multimedia Tools and Applications – Springer Journals
Published: Jun 4, 2018
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