Paper Info
Title
An Evolutionary Lion Optimization Algorithm-Based Image Compression Technique For Biomedical Applications
Abstract
Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde-Buzo-Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2-LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search-based LBG (CS-LBG), Firefly-based LBG (FF-LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2-LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS-LBG, FA-LBG, and JPEG2000 methods. The experimental values revealed that the L2-LBG process yielded effective compression performance with a better-quality reconstructed image.
Year
DOI
Venue
2021
10.1111/exsy.12508
EXPERT SYSTEMS
Keywords
DocType
Volume
evolutionary algorithms, Lion optimization algorithm, medical data, LZMA, vector quantization, biomedical applications
Journal
38
Issue
ISSN
Citations 
1
0266-4720
2
PageRank 
References 
Authors
0.36
0
6
Name
Order
Citations
PageRank
Karuppaiah Geetha120.36
Veerasamy Anitha220.36
mohamed elhoseny358349.57
K. Shankar49513.88
Pourya Shamsolmoali520.36
Mahmoud M. Selim620.36