Oppositional Glowworm Swarm based Vector Quantization Technique for Image Compression in Fiber Optic Communication

Abstract

In recent times, fiber optic communication networks have become commonly applied for commercial as well as military applications. Fiber optic networks have gained popularity owing to the high data rate. At the same time, the generation of huge quantity of data at a faster rate poses a major challenge in the storing and transmission process. To resolve this issue, data compression approaches have been presented to reduce the quantity of transmitted data and thereby minimizes bandwidth utilization and memory. Vector quantization (VQ) is a commonly employed image compression technique and Linde Buzo Gray (LBG) is used to construct an optimum codebook to compress images. With this motivation, this paper presents a new oppositional glowworm swarm optimization based LBG (OGSO-LBG) technique for image compression in fiber optic communication. The OGSO algorithm involves the integration of oppositional based learning (OBL) concept into the GSO algorithm to boost its convergence rate. The OGSO-LBG algorithm produces the codebook at a faster rate with minimal computation complexity. In order to highlight the enhanced compression performance of the OGSO-LBG technique, a series of experiments were carried out and the results are examined under different dimensions.