An Algorithmic Analysis on Optimized Image Compression Model

Abstract

“Image compression is the process of reducing the size of the image without deteriorating the image quality to an intolerable level”. The minimization of file size permits more storage in a given memory space. However, to develop the quality of the images during compression, this paper intends to present an enhanced image compression model, which is dependent on hybrid optimization algorithm by merging Lion Algorithm (LA) and Jaya Algorithm (JA). Here, the input image that has to be compressed is subjected to segmentation using Adaptive Active Contour Model (ACM) that split the image into Region of Interest (ROI) and non-ROI regions. Further, the ROI regions are compressed using JPEG-LS scheme and non-ROI regions are compressed by means of wavelet-oriented lossy compression scheme. After compression process, the decompression of the image is carried out by performing the reverse process of compression. Here, the adaptiveness of the presented scheme is influenced with the aid of proposed Jaya based Cubpool Formation in LA (JCF-LA). In addition, an algorithmic analysis is done for the adopted JCF-LA-based compression model to analyze its performance.