ABSTRACT
Advanced developments in the rise of the Internet of Health Things (IoHT) devices and high-resolution cameras led to the generation of a massive quantity of images that need to be stored or transmitted. In earlier times, distinct image compression algorithms have been discussed and found their applicable nature in different domains. Vector quantization (VQ) acts as a major portion of compressing images and developing a quantization table is a useful task. The effectiveness of the compression technique is solely based on the quantization table and the selection of the quantization table is considered as an optimization issue that can be resolved using bio-inspired algorithms. This chapter presents a new modified cuckoo search (MCS)–based Linde–Buzo–Gray (LBG) algorithm to compress the images generated by IoT devices. The MCS is an extension of the classical cuckoo search algorithm (CSA) by modifying the intensification and diversification models. A brief set of simulation takes place and the results are examined under diverse aspects. The proposed MCS-LBG model has resulted in optimal compression performance over compared methods.
